With generative AI, that future is already here.

This technology is laying the groundwork for entirely novel business ventures and strategic approaches and has been heralded as a generational technological innovation. 

The global generative AI market is growing exponentially. Currently standing at an impressive $44.89 billion, it’s on track for significant expansion across multiple industries, including retail, banking, insurance, and marketing teams. 

The question now becomes, is the industrial revolution of our time? Or is it another blip in the business product life cycle? This article looks at how AI has become the future of business transformation and why it’s here to stay.

What is Generative AI?

We’ll let you in on a little secret. The introduction to this article was written entirely by generative AI (don’t worry, the rest of this article is 100% written the good old-fashioned way). 

Generative AI is most widely known by the public at large for its ability to create the written word and the production of stunning images from a mere text prompt. Types of generative AI like ChatGPT and Gemini have become household names. 

Just like ‘Google’ changed how we search online, or how ‘Facebook’ redefined the way we market our products, they’re changing the very face of business itself.

But beyond writing a few lazy articles for college students or creating interesting artwork, what exactly is generative AI?

Generative AI refers to a type of Artificial Intelligence that can generate completely new and unique content, from texts and images to music and even code. Generative AI learns how to do this after being trained on a set of human-generated data in a process known as ‘Machine Learning,’ or ‘ML’ for short. 

ML and deep learning allow AI to learn patterns and features from data, enabling it to generate new content based on what it has learned from human datasets. 

Once fully trained, it can ‘generate’ new content without explicit human input.

Why It’s Important

For businesses, the generative AI market translates into turning the cogs of creativity without constant human oversight. Picture the freedom to design processes that run themselves while your team focuses on strategy over routine tasks. 

It means allocating your resources with laser precision and making informed decisions based on foresight rather than hunches.

Generative AI is catapulting companies into a future brimming with uncharted potential, offering tools not only for survival but also for reimagining what’s possible in commerce and service offerings.

For business, embracing generative AI is like opening doors to rooms unknown: vital if staying ahead of the curve isn’t merely an aspiration but a business imperative.

How Does Generative AI Work?

Generative AI technology uses numerous sophisticated neural network architectures and algorithms to learn from truly massive amounts of human-generated data and create new and unique content. 

These architectures work side by side and generally include Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), transformers, and foundation models. For those interested in how they work together, we’ll explore them in more detail below.

Generative Adversarial Networks (GANs)

Generative Adversarial Networks, or “GANs,” are like a high-stakes game between two rival chess players. One player, the generator, develops new data points and creates fresh pieces to put into play. 

The other player is the discriminator. It has an eagle eye for detail and judges whether those newly minted data points look authentic when squared against true data.

The two go back and forth, refining moves until what they’re producing could pass for genuine at first glance. This dynamic duo has found its niche in business operations where creating something new yet convincingly real can be quite valuable.

Variational Autoencoders (VAEs)

Think of VAEs as diligent librarians. They excel at organizing information into more manageable, compact forms while ensuring that nothing critical is lost in the process. 

When you need them to find or reconstruct particulars from this condensed library to create something new (perhaps a document that’s never been written before), they’re on it with remarkable accuracy. 

Here, you can see why their potential in fields where every detail matters, such as finance and healthcare, is so high.

Transformers and Foundation Models

Transformers pay close attention not just to the words spoken but also how they relate to each other for a full grasp of meaning. That level of discernment is invaluable for machines meant to understand human language nuances and translate or respond accordingly.

Meanwhile, foundation models are like multitasking prodigies, initially nurtured on diverse data diets. These adaptable systems readily shift gears across various jobs without missing a beat. 

Crafting eloquent chatbot responses, translating languages seamlessly, and generating articles and reports with finesse all streamline customer interactions perfectly.

Real-World Applications of Generative AI in Business

Practical use cases for these models stretch across multiple industries and offer tangible benefits. 

• GANs play a significant role in making virtual worlds within video games and VR spaces more captivating, elevating the user’s enjoyment. 
• VAEs contribute to e-commerce by refining how online shops suggest items that shoppers might like. 

And when it comes to transformers, they’re behind the scenes of those smart chatbots replying swiftly and helpfully on many customer service platforms.

When a business brings these sophisticated generative AI tools into its fold, They have the potential to completely change and refine existing processes for the better. 

Businesses typically witness marked improvements in efficiency. Products and services feel more tailored to individual needs, and there’s a noticeable leap forward in creativity across different sectors of their business.

Benefits of Generative AI

Generative AI is reshaping the way industries operate, introducing levels of efficiency and scalability previously out of reach. Picture your business not just keeping pace but setting the tempo in a rapidly shifting market.

Let’s consider what these systems can do for you. They refine workflows to such an extent that “streamlined” seems like an understatement—they’re practically redefining process optimization. As your company expands, so does the prowess of generative AI. It adapts seamlessly, ensuring growth doesn’t become a bottleneck.

But here’s where it gets truly interesting: Customization.

Generative AI grants businesses the golden opportunity to craft products and services with precision, meeting each customer on their terms. In a day and age where customers rate personalization above even the products themselves, isn’t that exactly where any successful enterprise wants to be?

Impact on Industries

Let’s talk about how generative AI is making a mark across various sectors:

In healthcare, this technology acts like a turbo-charged assistant in drug research. It zips through the data on molecular interactions that used to take an age to process. For patients, think of it as having a guardian angel who develops treatment plans suited just for them while boosting recovery and health outcomes.

Switch gears to retail, where shopping becomes less about sifting through endless aisles and more tailored-to-you showcases. Generative AI sifts through heaps of customer data quicker than any human could, predicting what buyers might want next before they even click ‘search.’

Over in the manufacturing industry, imagine designing new products not over months but days with the help of 3D models churned out by AI—getting these innovations onto shelves swiftly while also keeping warehouses smartly stocked by anticipating demand curves with minimal manual meddling.

The entertainment and media realm keeps viewers glued because generative AI learns what tickles their fancy. It’s a matchmaker between audience preferences and content offerings, which means stickier subscription numbers. Industry giants like Netflix have been implementing this tech for quite some time now with incredible results.

Lastly, fintech businesses find a sidekick in AI. Tedious tasks like punching numbers are automated, while risk calculations get sharper. Plus, these models act almost clairvoyant when peeking into market trends so firms can make smarter investment calls.

Business Value Contribution

Generative AI is shaping the operational dynamics of businesses, serving as a catalyst for creativity and efficiency.

Imagine the possibilities when time-consuming creative tasks are streamlined by smart systems that learn and improve. Suddenly, your team can fast-track new products from concept to customer’s hands without breaking a sweat.

Take design, for example. Now, powerful software tools infused with AI capabilities let you whip up designs at lightning speed. You get instant feedback, you adapt immediately, and what used to be an endurance race against competitors becomes more like a sprint.

The brilliance of this technology extends beyond speed: Think about diving into oceans of data that would otherwise drown mere mortals in analysis paralysis. 

With generative AI, insights come not just faster but sharper, too, enabling you to stay ahead of market trends and really ‘get’ what your customers yearn for before they’ve fully articulated it themselves.

By 2030, forecasts suggest that AI-augmented decision support won’t just be a ‘should have.’ It will define business strategies across industries, with nearly half of all derived value hailing from these smarter systems, helping humans make bolder moves confidently.

Common Generative AI Use Cases

The effects of generative AI have already been significant across key industries, transforming traditional business operations with its innovative applications.

Here are a few of the common practical applications:

Content Creation

Perhaps no industry has felt the impact of Generative AI as much as content creation. Here, AI streamlines crafting written pieces, images, and various multimedia content. It’s a boon for marketing teams aiming to churn out captivating material with greater efficiency.

Customer Support

Whether they know it or not, around 88% of customers have already interacted with an AI-powered chatbot. Advanced chatbots and virtual assistants powered by this technology can field customer queries round-the-clock. This enhancement in service comes with the diminished need for human intervention. 

Security

The realm of cybersecurity benefits from simulated attack scenarios concocted by generative AI, serving as drills that bolster protective measures. It’s cheaper than penetration testing and unlike its human counterpart, the simulated attacks are relentless.

Predictive Maintenance

Foreseeing equipment failures before they occur is now possible thanks to AI capabilities. A tool for preventing unnecessary expenses and disruptions in industrial environments.

Cutting-Edge Diagnostic Systems

In healthcare, professionals can make more accurate diagnoses by analyzing patterns extracted from data collections, significantly advancing patient care. Perhaps one of the more well-known examples is the Cyto-AiSCAN AI, which is used to detect cancer. Interestingly, its original application was identifying pastries in a Japanese bakery.

Automated Financial Advisory

Personalized financial guidance is now available through AI technology. Clients receive advice based on thorough data analysis that can enhance investment strategies for both individual and corporate portfolios.

Choosing Your Tech Partner To Integrate Generative AI Into Your Business

Selecting the ideal technology collaborator is pivotal for a smooth and successful integration of generative AI into your enterprise. Intellectsoft distinguishes itself as an authoritative provider of robust AI development services.

For more than a decade, Intellectsoft has steered clients through the intricate process of digital revolution. Recognizing the essential role that AI plays in bolstering business profits is second nature to us. 

Our knowledgeable developers are at your service, offering bespoke solutions meticulously developed to meet your unique needs.

Explore our services and discover the transformative impact generative AI can have on your operations: Intellectsoft AI Development Services.

Final Thoughts

Tech trends can be fleeting, yet there’s a sense that generative AI has staked its claim in the digital terrain with more permanence than Google+ or the Facebook phone ever managed. 

Weaving this technology into your business framework might just unlock innovative strategies and ensure your offerings maintain their edge in an ever-competitive market.

Generative AI development stands at the forefront of change, regardless of industry boundaries. Consider it an industrial revolution 2.0—one that promises to redefine how we approach our businesses and markets.

As adoption grows, expect to witness new industries emerging from this technological boon. Here at Intellectsoft, we’re not waiting for tomorrow. We’ve already embraced what feels like the next chapter.

Is your enterprise prepared for what’s unfolding? Reach out to our specialists today and discover how seamless integration can be under expert guidance.

The post Generative AI Explained: What It Means for Your Business appeared first on Intellectsoft Blog.

Zendesk, a pioneer in customer service solutions, simply states that these systems become indispensable as organizations develop: 

“Given enough time and growth, most organizations will need an ERP and a CRM.“

But what do these 3 letter acronyms actually mean? How do they ensure the success of your business?

There’s little disagreement regarding CRM and ERP use in the business world. Approximately three-quarters of all businesses employ an ERP system to interact with customers in real-time, while two-thirds adopt a CRM within their early years.

These numbers mean that both technologies are widely adopted and also signal recognition of the strategic value inherent in such systems.

Each addresses different aspects of operations; yet when used together, they create powerful leverage for any company’s progression in terms of efficiency, scalability, and competitive edge.

What is a CRM?

CRM, short for Customer Relationship Management, refers to the systems designed to keep track of a company’s engagements with current and future customers. CRM systems act as a central hub where all customer-related information is stored, making it easier for businesses to nurture their relationships with clients.

With CRM software in place, repetitive tasks are automated, which allows employees to focus on providing higher-quality service. This enhances the efficiency of processes from marketing campaigns right through to sales activities and after-sales support.

By keeping detailed records of customer interactions, CRMs provide valuable insights that help tailor communication and services precisely matched to client needs. The ultimate aim is to foster stronger connections with customers by being relevant and responsive – attributes known for driving loyalty and repeat business.

Effectively employed CRM tools enable organizations not just to manage contacts but also to make strategic decisions based on solid data analysis. Such informed decision-making can lead directly towards more successful outcomes both internally within operations management as well as externally regarding market presence.

The Benefits of a CRM System

Implementing a CRM offers several key advantages for business:

• Elevated Customer Support: CRM tools act as an aggregate for customer information from both outside and inside the organization. Information is consolidated into one accessible platform, which allows for a more personalized service experience. 
• Boosted Sales Performance: Analytics information provided by the CRM means sales teams can identify promising leads and trends with far greater precision. When focusing on these prospects, they greatly increase the chances of turning inquiries into profitable outcomes.
• Improved Customer Retention: Access to trends analysis performed by the CRM provides valuable insights into consumer behavior. A business may utilize this knowledge to retain its client base, and also increase brand loyalty.

What is an ERP?

Enterprise Resource Planning, better known as ERP, is a comprehensive software platform that helps businesses manage and connect the many facets of their operations. 

Think of it like the central nervous system for a company’s data and processes. It brings together finance, supply chain management, operations, reporting functions, manufacturing capabilities, and human resource tasks under one roof.

The key aim of having an ERP system is to break down silos between departments. By allowing free-flowing communication within an organization, and with its business partners, ERP ensures everyone has access to up-to-date information they need when they need it.

This kind of coordination can significantly boost decision-making abilities as you have all your actionable data in one accessible spot. 

Additionally, by streamlining workflows and automating certain administrative tasks through this centralized approach, companies often find marked improvements in efficiency and productivity.

ERP also plays an important role in helping organizations keep pace with compliance requirements set by various regulatory bodies. Tasks are made simpler when all relevant info is easily reachable within a unified system.

The Benefits of an ERP System

ERP systems unify multiple aspects of a business into a single interface, which simplifies many day-to-day operations:

• Cost Efficiency: Streamlining processes and reducing the costs of administration and operations with an ERP leads to significant savings.
• Improved Productivity: Naturally, when all tools used by the business are integrated into one single system, efficiency and productivity are greatly enhanced. 
• Better Decision Making: An ERP system provides operational data in real-time, meaning strategic planning and decision-making can be made based on accurate and consolidated information.

CRM vs ERP: The Key Differences

The major difference between CRM and ERP systems lies within their core function in the business:

• CRM focuses primarily on the “front-end” or “front-office”. It manages the interactions with customers and sales prospects across various communication channels.
• ERP, in contrast, focuses on integrating “back-end”, or internal information across the entire business.

This CRM vs ERP table illustrates the difference between ERP and CRM more clearly, and compares the typical use cases for each system:

ERP vs CRM: Exploring the Similarities 

At their core, ERP and CRM systems serve as pivotal instruments for organizational achievement due to a shared foundation in several key areas:

Centralized Data 

CRM and ERP stand out for their ability to bring data under one roof. This centralization provides data for decision-makers who need current and uniform information across all business units.

Efficiency in Operations

Streamlining internal processes with ERP and CRM systems creates an environment where time-consuming tasks are automated, teams work cohesively, and the overall machinery of your company operates without unnecessary friction.

Insightful Reporting 

In terms of analytics with ERP vs CRM, both types of systems provide powerful reporting utilities that peel back layers on operational dynamics and customer engagements. These insights are fuel for developing forward-thinking strategies that align with both market trends and resource allocation.

Productivity Enhancement 

At their essence, ERP and CRM tools increase customer engagement, but ERP platforms orchestrate resource management. Both paths lead toward elevating organizational productivity, not incrementally but significantly.

Integrating CRM and ERP

Combining your CRM and ERP systems has the potential to revolutionize your business operations by bringing together customer interaction and internal processes. 

To ensure a successful integration, it is essential to follow steps that will align activities, data, and processes throughout your company with the two separate systems.

1. Prepare for CRM and ERP Integration  

Begin by establishing a plan that clearly outlines your objectives. Set the boundaries for integration and determine beforehand the specific data to be exchanged between the two platforms. Develop an analysis detailing the effects of the transition, on existing procedures.

2. Keep Data in Sync  

All data between the two platforms needs to be synchronized. Use specifically designed tools and middleware that can automate and check this ongoing process and reduce data duplication during the ERP and CRM integration process. 

3. Align Workflows  

Creating an integration between CRM and ERP systems requires outlining procedures that cover areas in both platforms. For example, order fulfillment and customer service. 

The aim is to establish operations that eliminate the need for input, in transferring data or finishing assignments.

4. Testing and Validation 

Before the launch, make sure to examine the integrated systems. Confirm that data moves smoothly between CRM and ERP systems and that all processes are working correctly. Testing in real-life scenarios is crucial at this point in order to identify any integration issues before the integration goes to production.

5. User Training and Support  

Once the two systems are successfully integrated, synced, and functional with the business, it’s time to train your existing users and staff in the operation of the new platform.

Continuous support is crucial at this stage to help staff adapt to the integrated ERP and CRM systems.

6. Review and Iterate 

After the integration goes live, a post-implementation monitoring of the system is required. This includes soliciting user feedback on a regular and ongoing basis. 

This feedback should be used to make adjustments and tweak the environment, which will improve the integration over time. These regular adjustments will reduce in frequency over time.

ERP vs CRM: What Should You Choose?

When deciding between a CRM or ERP system, it’s important to consider your business needs. If you’re interested in building customer connections, enhancing service quality, and driving up sales numbers, a CRM is the way to go. 

It’s tailored to cater to your customers, improving interactions supporting marketing campaigns, and streamlining services—all of which can lead to increased sales.

On the other hand, if your company is looking for an all-in-one solution that integrates functional areas such as HR, logistics, or finance, then an ERP system might be more suitable. 

ERP excels at bringing together data and processes in one place. They are crucial for businesses aiming to improve efficiency while reducing expenses. 

Regardless of choice, a CRM or ERP system handles management tasks more efficiently. These systems simplify intricate operations across your organization and enhance collaboration among different departments.

Choosing a Trusted Custom CRM Development Partner

Should you wish to experience enhanced employee efficiency, improved customer satisfaction, and consistent increases in sales performance, consider integrating a CRM platform into your business strategy.

Intellectsoft stands as a comprehensive software development company enriched by years of crafting CRM solutions tailored for businesses across the spectrum. 

Whether you’re migrating to a brand new platform, or requiring a CRM built from the ground up, our experts at Intellectsoft are equipped with the knowledge, expertise, and experience to support you through the entire process.

Summing Up

When deciding between a CRM or ERP system, it’s important to consider your company’s requirements and long-term objectives. 

Whether you aim to enhance customer interactions or optimize workflows, grasping the differences between CRM and ERP is crucial for making a well-informed choice.

The post CRM vs ERP: Choosing the Right Solution for Your Business appeared first on Intellectsoft Blog.

Companies that use customer relationship management (CRM) systems have experienced a 47% increase in customer retention rate, a 21% improvement in productivity, 16% higher customer retention, and a 17% boost in lead conversions. So if you are a business owner looking to improve customer relationships and streamline internal processes, this article is for you. In this comprehensive guide, we will explore the various aspects of custom CRM implementation, from the process to its benefits.

While it takes 13 months to see an ROI from this tech investment, the $45 per dollar spent is well worth the wait.

Advantages of a Custom-Built CRM

Because every business is unique, custom solutions offer tailored features that can create the best experience for their specific customers. Unlike ready-made CRMs, which come with unnecessary features, choosing custom solutions allows you to build the exact functionalities your teams need, saving money in the process.

Big brands have already adopted custom-built CRMs in favor of pre-made ones.

For example, Apple and Uber use custom-built CRMs to drive sales and customer engagement.

Amazon’s own CRM captures customer data at the point of purchase, instantly customizing the user experience and providing a full view of the customer journey. This helps reduce returns and cart abandonment.

Similarly, Tesla started using its own CRM in 2019. Today, the company boasts the highest customer satisfaction rate in the car manufacturing industry according to Consumer Reports. Ninety-one percent of Tesla owners are more likely to rave about their vehicles than other brands, partly due to their in-house developed sales software.

Overcoming Challenges with Tech Partner

While custom-built CRMs can present challenges such as higher costs, longer development times, and maintenance complexities, these can be effectively managed with the right approach. To circumvent these challenges smoothly, partner with a software provider specializing in custom software, such as Intellectsoft. 

What is a CRM Implementation?

Beyond just setting up software, CRM implementation is a strategic move towards better understanding and managing your interactions with current and future customers. It’s a holistic process that entails meticulously configuring the software to align with your unique business needs, ensuring that every customer interaction is captured, analyzed, and leveraged to foster stronger relationships. Your CRM implementation plan shall begin with a deep dive into your business-specific requirements and goals.

Choosing your CRM development partner is next, and it’s a decision that should not be taken lightly. This is where expertise meets execution. Partnering with a company that not only understands the intricacies of CRM systems but also appreciates the unique challenges and opportunities within your industry can make a significant difference.

Before hiring a developer, make sure they are capable of tailoring the CRM to fit your unique business requirements. A one-size-fits-all approach simply does not apply to CRM systems. Your development partner must also bring the training aspect to the table. Onboarding your team to effectively utilize the CRM system according to their respective roles is a pivotal part of the implementation.

And the final step, which is not an endpoint but a commitment to continuous evolution. Regular monitoring and evaluation of the CRM system’s performance against your objectives allow for timely adjustments and optimizations.

Let’s explore the details of the implementation steps in the next section.

CRM Implementation Process: 6 Steps to Success

CRM implementation is a multifaceted undertaking that necessitates a roadmap marked with critical milestones to ensure a smooth transition and alignment with your business goals. This roadmap is not just a path but a strategic blueprint for transforming customer relationship management within your organization.

Below are the six essential steps to navigate this journey successfully:

Step 1: Identify CRM Goals

Delineate what you aim to achieve, whether it’s enhancing customer engagement, improving customer interactions, optimizing sales processes, or refining marketing strategies. Having crystal-clear goals sets a targeted direction for the implementation process.

This stage is crucial as it establishes the foundation for the entire process. Setting objectives early on guides the entire procedure so that every decision aligns with your business aspirations.

Step 2: Consider Required CRM Integrations

Assessing your current operational workflows and technology stack is imperative before introducing a new CRM system. Evaluating the current ecosystem shows potential problems and chances to streamline. It confirms the new CRM system can blend into it.

This is where you pinpoint the applications your CRM needs to synchronize with, such as email platforms, social media channels, and customer service tools. The goal is to establish seamless data flow and functionality across all operational areas. After all, a CRM is supposed to enhance efficiency and provide a unified view of customer interactions.

When you have created a cohesive ecosystem where data flows freely, you will be able to provide your team with the insights they need to make informed decisions.

Step 3: Choose Your CRM Development Partner

The right partner not only brings technical prowess but also possesses a deep understanding and extensive experience in the following:

Data Management

A good CRM software vendor delivers cutting-edge techniques like advanced data management, predictive analytics, and smart automation. They must be knowledgeable in tapping into the latest technology trends, such as Big Data, AI, Machine Learning, and Blockchain, to stay at the forefront.

Software Integration

Choose a company that can enhance your technological foundations through the development of robust microservices, dependable API structures, and seamless data integration techniques. You can propel your strategic objectives forward if they are capable of creating comprehensive enterprise applications or leveraging enterprise app integration solutions to streamline your operations and foster innovation.

Legacy Application Modernization

Outdated software not only poses significant security risks but also demands high maintenance costs. Engage leading experts to conduct comprehensive reviews of your systems and upgrade them with cutting-edge tools and technologies for enhanced performance and security.

Step 4: Customize for Tailored Solutions

Customizing a CRM system involves adjusting features, functionalities, and interfaces to align with your workflows, user needs, and overall business strategy. It requires a detailed checklist that enables you to create a configured design that delivers on its promise to enhance your business operations and customer relationships.

Instituting a governance framework will also provide guidelines on how the CRM system should be used and maintained. This typically includes standards and procedures for data management, user access, customization, and system updates that guarantee your CRM initiative’s long-term sustainability.

Step 5: Empower Teams with Training

The success of a CRM implementation heavily relies on user adoption. It’s not just about knowing which buttons to click but also understanding how to leverage the system to enhance customer interactions and drive business growth.

Providing comprehensive training sessions ensures your team is well-equipped to unleash the full potential of the system. This empowerment not only enhances efficiency but also fosters a culture of continuous improvement.

Step 6: Ongoing Assessment and Adaptation

No enterprise is static, so your CRM system shouldn’t be either. Establish an iterative process that ensures it remains agile, responsive, and aligned with your evolving business needs. For example, conduct weekly performance reviews of your CRM system to track its efficiency. Also, consider utilizing CRM analytics tools should be utilized to gauge user adoption and the system’s effectiveness in achieving business goals.

Always be open to incorporating new functionalities as the CRM vendor releases updates or your business needs shift. Regular employee feedback can also be invaluable in spotting areas of the CRM system that need tweaking for better performance.

Navigate these steps with precision and strategic foresight, and you will pave the way for a CRM implementation that not only meets but exceeds your expectations and drives your business toward enhanced customer-centricity and operational excellence.

Remember, effective CRM implementation is not just about technology but also about strategically aligning such technology with your business goals and customer needs.

Importance of CRM Implementation

In essence, CRM implementation is an investment in a strategic framework that transcends mere technological adoption by representing a strategic pivot toward a more engaged and insightful business model. It empowers your business to:

• be more responsive, agile, and customer-focused
• deepen the understanding of customer needs, preferences, and behaviors
• anticipate customer desires and tailor interactions to individual expectations
• build enduring relationships that drive loyalty and growth
• commit to operational excellence
• enable teams across sales, marketing, and customer service to work more coherently and efficiently

A well-implemented CRM system provides a unified platform that fosters collaboration and enhances the ability to act on data-driven insights. Consequently, you will achieve improved internal workflows, better customer experience, and a competitive edge in the marketplace.

Benefits of CRM Software Implementation

While implementing a CRM system is considered an operational upgrade, it’s actually a strategic investment that yields a high return over time in terms of catalyzing business growth and customer rapport improvement.

These are the changes your organization can expect post-implementation:

Precision in Sales Forecasting

CRM systems offer a granular view of sales trends and customer behaviors. This predictive prowess enables your business to craft strategies that are both responsive and anticipatory. As a result, you are staying a step ahead of market dynamics.

Enhancement of Customer Service

With a comprehensive CRM, customer interactions are no longer siloed but part of a continuum. Hence, support teams are able to deliver personalized and contextually relevant assistance. Doing so not only boosts customer satisfaction but also nurtures loyalty and retention.

Efficiency in Communication

Yet another advantage is breaking down barriers between departments, which streamlines internal communication and collaboration. Such a unified approach ensures that all team members are on the same page, marching cohesively towards common business goals.

Automation of Repetitive Tasks

Liberating your team from the mundane and routine everyday tasks allows them to focus on initiatives that drive business growth. This shift optimizes resource allocation while boosting employee morale as they engage in more meaningful and impactful work.

Highly Targeted Marketing Campaigns

Marketing efforts based on robust data analysis resonate with the intended audience, thus maximizing impact and return on investment.

As you can see, CRM software implementation touches every aspect of your business, from sales and customer service to internal workflows and marketing. This indispensable investment simultaneously propels operational excellence and promotes a customer-centric organization.

Wrapping Up

This guide has walked you through the essential steps in the planning, execution, and deployment of a CRM implementation strategy, underscoring the importance and benefits of adopting a CRM system tailored to your business needs.

Adding this software to your current tech stack guarantees to enhance customer relationships and elevate operational efficiencies. However, the system’s success greatly depends on the willingness of your team to adopt it. Statistics show that while 65% of businesses use CRM within the first five years of operations, less than 40% achieve full adoption. That’s why you should commission a vendor that is capable of training users from various departments of your company who perform different roles.

Partnering with a seasoned expert like Intellectsoft ensures not only a smooth transition but also an optimized integration of CRM into your business landscape. Our expertise in enterprise software development covers the full life cycle of application design, integration, and management.

We are experienced in deploying this technology through the construction, healthcare, dental, insurance, FinTech, logistics and automotive, travel and hospitality, and retail and e-commerce industries. Discover how our custom CRM software implementation helped a dental clinic scale new heights in this case study. For a deeper dive into how Intellectsoft can future-proof your business operations, visit our services page.

The post A Comprehensive CRM Implementation Guide in 2024 appeared first on Intellectsoft Blog.

Why Your Company Needs CRM Software?

The best way to retain customers and grow sales is by prioritizing your customer experience (CX). This means investing in customer relationship management (CRM) software. A CRM lets you broaden your sales pipeline, learn more about your customers, and deliver more value to them over the entire customer life cycle. From sales and marketing to customer service, CRM software is an essential tool that will benefit several departments in your organization. 

The global CRM market is also projected to experience a compound annual growth rate (CAGR) of 12% between 2023 and 2030, according to a report from Fortune Business Insights. With so many companies taking advantage of the benefits offered by CRM software, you don’t want to get left behind.

Investing in CRM software is money well spent. Your return on investment (ROI) after adopting a CRM software will more than justify the money you spent on the adoption. According to a Nucleus Research report, the average ROI after implementing CRM software is $8.71 for every dollar spent! That’s nearly nine times the amount of returns compared to the initial investment, which is a strong argument in favor of using CRM software for your company. 

If you haven’t yet found CRM software for your business, then this article is the perfect starting point. We will make a case for why you should get CRM software, cover the steps to build a custom CRM, and look at some of the challenges you might face when adopting new CRM software. 

Let’s dive right in.

Building Your Own CRM: Worth It?

Now that you’re convinced about the need for a CRM platform at your business, the next step is deciding what kind of CRM software you want to work with. While many businesses choose to build custom CRMs, it’s not absolutely necessary to do so. You can also shop for an off-the-shelf solution to help manage customer relationships. 

Building a custom CRM platform requires time, money, patience, skill, and knowledge. Unless you have plenty of these resources to spare, it’s not advisable to take on the challenge of building a custom CRM for your business. 

Instead, it would be much wiser to evaluate the prepackaged software solutions to see which most closely matches your business needs. It will save your company the expense of designing the software while still bringing you the most desirable benefits of using CRM platforms. 

However, if you have an extremely specific list of requirements, investing in a custom CRM software for the long term would be better.

Let’s look at the options available for companies looking to implement a CRM platform. There are three main types of CRM software available today, and we shall examine each in turn.

1. Ready-Made CRM Platform

The first option is a ready-made, off-the-shelf CRM platform. There are dozens of these on the market, including extremely popular and widely used ones like Salesforce, Zoho CRM, or Freshsales Suite. 

The reason why these CRM software are so popular is because they are extremely convenient to begin using. You simply choose a pricing plan that works for you and train your team members to use the software. In just two simple steps, you’re ready to launch your new CRM and start reaping the benefits.

These ready-made CRM platforms come with a fixed list of features and a fixed amount of storage space. So unless the features list covers every single one of your business requirements, it’s not a good idea to go for a ready-made CRM software. They lack flexibility in terms of features and functionality, and the limited storage space makes scaling up operations tricky. 

Another drawback of ready-made CRM software is that they can often be unjustifiably expensive if you have a large team, because they usually charge per user license. 

Those who have found an effective CRM solution with ready-made platforms are lucky, but the rest of us need to keep looking for a more appropriate solution.

2. Custom CRM System

If you want to have more flexibility in terms of features and a greater degree of control over the functioning of your CRM platform, then you can opt to create a CRM that is customized to your specific needs. This is the smart option if you have a large team and complex requirements to meet with your CRM.

When you build a CRM from scratch, you can be sure that it will seamlessly integrate with your other business tools to deliver maximum value from its position. Other benefits of opting for a custom CRM system include being able to enforce more robust security measures, and being allowed to add more users at no extra cost. 

If you have the appetite to create custom-built software, you can address every single need you have, without spending money on extraneous features you never use. For many companies, regardless of size, a custom CRM system is the best fit.

3. Customizable CRM Software

Some companies are trapped in between ready-made CRM solutions that don’t have enough versatility and custom-built CRM platforms that take a long time to develop, What these companies need is a happy middle ground in the CRM market, where the available software is as easy to set up as its ready-made alternatives, but offers a level of flexibility closer to custom-built softwares. They will find what they are looking for is customizable CRM software. 

Despite the similar sounding names, there is a clear difference between custom-built and customizable CRM software. While the former is built entirely from scratch, the latter allows users to add or remove modules and workflows to the pre-existing CRM platform’s structure according to their needs. With custom-built software, no two solutions are identical, but with customizable software, the base product remains the same and only certain additional features and cosmetic elements change. 

If your company is looking for a level of customization in its CRM platform, but doesn’t want to invest the time and money that goes into developing a new software, customizable CRM solutions are your best bet.

Seven Steps in Creating a CRM from Scratch

If you’ve scanned all the CRM software available in the market and not found one that matches your needs, there’s no need to fret. You can build a custom CRM software for your business, no matter the size of your company. Many small businesses have turned to CRM solutions to improve their performance, with a Grand View Research market report saying that 91% of all businesses with 10 or more employees have already invested in CRM software. 

If you approach the project with a well-planned strategy, then building a CRM doesn’t have to be a daunting task that drains your company’s resources. Instead of a burden, your custom CRM software can empower your employees to perform at a higher level. 

Here’s how to approach the task of creating a CRM that is tailor-made for your company, laid out step-by-step.

1. Pre-Operational Stage

The first stage of building your own CRM platform is the most important, even though it might seem like it’s the stage where the least work gets done. This is the point where you need to clearly state the purpose of your CRM platform. That means listing the goals you wish to achieve by using the CRM platform, the users who will benefit from its adoption, and the specific benefits it will grant your business.

For example, some pre operational goals you can set for your custom-built CRM platform include:

• Streamlining sales processes and boosting sales numbers
• Increasing customer retention rate by identifying reasons for customer churn
• More productive lead generation and more efficient lead management
• Improving CX and raising the overall customer satisfaction rating of your company

As you can see, CRM systems are very versatile tools that can be put to various uses. Depending on the use case for your CRM platform, you will then be able to decide what type of CRM software you wish to implement. There are three main categories of CRM software, each with its own dedicated purpose. Let’s look at each type of CRM platform, and how they can be used.

• Collaborative CRM System: Improves teamwork and communication within your company by promoting clear and transparent data exchanges. These types of CRM platforms have features like collective communication channels, file sharing capabilities, and activity streaming functions. 
• Analytical CRM System: As the name implies, these types of CRMs help with making plans and drawing insights based on customer and sales data. These types of CRM platforms must have analytical processing capabilities and data mining functions.
• Operational CRM system: This type of CRM platform is designed to make business processes run smoothly. They increase employees’ work efficiency by automating important but time-consuming marketing and customer service tasks, like lead generation or sending follow-up emails to customers.

Each type of CRM platform serves a different purpose. Still, it’s important to remember that it’s possible for a single platform to cover all these bases when you are building a CRM from scratch. You can mix-and-match different features and create a comprehensive CRM solution for your business.

2. Choosing a Development Partner

Once you have a clear vision of what you want your custom CRM software to look like, you can move on to the next step. This involves finding the right partners that will develop the software with you. At this stage, there are two options available to you. Either you go for in-house development, or you decide to rely on outsourcing the software development task to a qualified third-party.

In-house development can be tricky, especially if you don’t already have the right developers and software engineers on your payroll. While you get full control over the software development life cycle (SDLC), it comes at a high cost, which can also possibly include the cost of hiring new developers.

On the other hand, outsourced development grants you access to a pool of specialists who are well-qualified to help you design a company CRM platform. You can scale up the size of the team if you need to without having to worry about making new hires while outsourcing the CRM platform’s development. From their turnaround time to the quality of their work, third-party developers can add great value to your CRM solution’s SDLC.

If you’re convinced about hiring the services of an outsourced, third-party software developer for your custom CRM platform, then ensure you choose the right one. The development partner you decide to work with should have previous experience in building CRM software for your industry. Look at their past projects and go over reviews from earlier customers to get an idea of the quality of work they create.

3. Choosing Your CRM’s Features

Now that you have your development partner by your side, your custom-built CRM software will start to take real shape. This is the point where you meet with your software developer, and finalize the list of features to be included in the final product. The features list doesn’t have to be exhaustive. Make sure to review different combinations of features to see which ones will lead to a minimum viable product (MVP) that satisfies your company’s needs. 

Your final list of CRM software features depends on several factors: the nature of your industry, the size of your company, and the tools that are already in your technology stack. Because of these variables, no two custom-built CRM platforms are exactly the same. However, here is a short list of some commonly found features that are often included when building your own CRM software:

• Contact management
• Interaction tracker
• Email marketing
• Scheduled reminders
• Sales automation

This list is not a complete features list by any means. Every CRM platform has up to three different modules, each with its own suite of features. The three main modules of a CRM platform are the sales, marketing, and customer support modules. When building your own CRM solution, you can pick and choose which modules you would like to include in your software, and to what extent.

4. Calculating Costs

Once you’ve created a shortlist of features and modules you want in your CRM platform, calculating the total cost of the project becomes much simpler. With clear deliverables and a framework to operate within, your software development partner will be able to provide a transparent quote with no hidden costs. 

Depending on how far you are willing to stretch your budget, you may even be able to include a few additional features at this stage. Alternatively, you might find the costs are rising too high, and will have to decide which features to forgo while developing the CRM software.

5. Designing UI/UX

There’s no point in spending money on creating a comprehensive, bespoke CRM solution for your company if your employees are unable to use it. Choosing a list of features especially because they will help employees fulfill their roles is only the start. You also need to house those features in a user-friendly CRM software. 

Your CRM software’s user interface and user experience (UI/UX) are a critical component of its ultimate success. A poorly designed UI/UX will drive users away, preventing them from accessing the many benefits of your CRM software. While functionality is a top priority when building a CRM from scratch, aesthetic appeal and ease-of-use should never be overlooked either.

6. Testing and Quality Assurance

Now that your CRM software’s features and design have been finalized, the only thing left to do is make sure the software is working as intended. Before you launch your CRM solution, you need to conduct strict testing to identify bugs and glitches in the code. Software testers play an important role in any development team, and the ones working on your CRM platform will be the ones who ensure every feature is functioning the way it should.

7. Launch and Support

After completing every step mentioned above, you’re finally ready to launch your new CRM. This is a landmark moment for your company, and to ensure your launch goes well, you must work with your development partner and create a clear strategy for migrating data from existing systems into the new CRM. 

Remember, your CRM platform is software, and software tends to become outdated within a few months. That’s why, like every other software product owner, you will now have to provide ongoing support to your CRM platform’s users. This includes regular security updates to protect data stored on the CRM platform, new features that users have been asking for, or simply routine updates to improve the quality of UX on the platform. As long as your CRM platform is in use, you will have to keep supporting the users with regular software patches.

By following the seven steps listed above, you will see that creating your own CRM system isn’t as complicated as it seemed when you first considered the notion.

Challenges in Developing a CRM Platform

It’s extremely clear that adopting a CRM platform can do wonders for your business in many key areas like sales figures and customer satisfaction. These are good enough reasons to create your own CRM. While the end result will be beneficial for your organization, it’s quite likely that you will encounter some challenges along the way.

Here are four major obstacles to achieving runaway success with your new CRM software.

1. Difficult to Implement

CRM software is a relatively new entrant in the field of business tools. As such, there is a prevalent resistance toward its adoption among professionals, particularly salespeople. A HubSpot survey found that 50% of all sales managers say a CRM is too difficult to implement in their organization.

2. Partial Adoption

Carrying on from the previous point, incomplete adoption of CRM platforms across the company can also lead to poor results. All the stakeholders need to be using the CRM for it to deliver maximum value. If a portion of the employees do not use the CRM platform, the other half who do will find it extremely difficult to communicate and share important information.

3. Manual Data Entry

The number one issue with CRM platforms is that manual data entry is extremely painstaking and has a high chance of errors. However, today’s generation of smart, artificial intelligence- (AI-) enhanced CRM softwares are able to automate those tasks, bypassing the loudest complaint against CRM platforms.

4. Calculating the Success of a CRM

Because a custom-built CRM platform is built at great expense, it’s only natural that you want to be able to measure its performance. This can be complicated, since certain aspects of a CRM’s performance are subjective. Also, your ability to measure the CRM platform’s success is tied to how clearly you stated your goals at the beginning of the SDLC. Only when you have a clearly marked frame of reference will you be able to judge the performance of your CRM system. 

Another important prerequisite to calculating the success of your CRM platform is defining user personas and mapping their journeys during the pre-operational stage. This allows you to compare the actual user experience to your theoretical user journey and identify areas where your CRM platform’s performance can be improved.

By working closely with your development partner and encouraging wholehearted participation from every other stakeholder, you can overcome most of the common challenges in developing a CRM platform.

How Much Does it Cost to Build a CRM System?

A custom CRM system is an investment in your company’s future. It’s a solution that can scale with your company as it grows, enabling you to deliver a high level of customer service and employee productivity through your operations. 

With the high ROI rates seen with CRM platforms, you can be sure you will get your money’s worth from your custom-built CRM system. This raises an important question— how much will it cost you to build your own CRM platform?

At the risk of sounding vague, there is no definitive answer for that question. 

The total cost of building your own CRM software can range from anything between $50,000 to $500,000. There are a number of factors at play that influence the cost of your CRM software. These include:

• Number of core, standard, and extra features in your CRM platform
• Number of roles who will access the CRM platform
• Size of your development team
• Expertise level of your development team
• Additional expenses beyond development, like project management and quality assurance

When working with third-party developers, you will get a more accurate quote after arriving at an MVP for your CRM software.

Wrapping Up

If you’re ready to enjoy greater employee productivity, higher customer satisfaction, and steady sales growth, then it’s time to invest in a CRM platform for your business. Intellectsoft is an end-to-end software development company with years of experience designing CRM solutions for companies of all sizes. Whether you are simply migrating to a new platform or developing a CRM system from scratch, the experts at Intellectsoft will be able to guide you every step of the way.

The post How to Build a CRM: A Step-by-Step Guide appeared first on Intellectsoft Blog.

Enterprise software plays a big part in finding success in a digital world. If your business is looking for software solutions to make operations smoother, you will need to keep abreast of emerging technologies in software development—and assemble a great development team.

Why Your Business Needs a Software Development Team?

Software developers and engineers have an inside joke: “Building software is like building cathedrals; first you build it, then you pray!” However, getting together a software development team with the right technical know-how can remove the uncertainties of the process. This makes it crucial to work with seasoned professionals with experience in products that are in line with the latest software development trends.

Software development is what transforms ideas into tangible digital products. It’s a process that includes ideation, creation, design, and launch of software, along with providing ongoing technical support for the software’s users. Naturally, such technical responsibilities need to be fulfilled by IT experts, which is why businesses need to assemble a qualified team that is up to date with software industry trends.

Across the world, software development trends indicate that companies spend more than $1 trillion on enterprise software every year. These software solutions help companies become more efficient by reducing costs and streamlining business processes.

Whether it’s a customer-facing product or a tool for internal use, you should contact experts to help design software solutions. According to TechReport, 84.7% of software companies specialize in enterprise application development, so you will have plenty of options when looking to recruit your software development team.

What are the Major Factors That Influence the Software Development Team Structure?

Every software development project is unique. That’s why you should tailor the structure of your software development team according to each project’s stated goals and objectives and the trending technologies in software development. When assembling your software development team, there are four main factors you should consider.

Let’s take a closer look at each one of these.

Project Type and its Complexity

It’s counterproductive to assemble a software development team before the nature of the project is made clear. 

Before finding team members, ask yourself what kind of software product you want. If you are experimenting with a new idea and only want software as a proof-of-concept, a smaller team of five or fewer members will be more than enough. However, if your product is much more complex, like a market-ready e-commerce app with third-party integrations, you will need a much larger team.

As the scope of a software development team increases, so does the required team size. Software developers come in many stripes, from generalists to specialists, with hybrid developers in between. A niche software product with very specific functions will require specialists on the team, whereas a more generic product can be created by generalist and hybrid developers.

Development Budget

Good software costs money. It’s a simple fact, and the sooner you accept it, the easier it will be for you to get the most out of your software development budget. With the funds available, you can either hire full-time developers directly or contract the services of third-party software developers. It’s up to you to decide which is more cost-effective for your company. 

Once a software development project has an allocated budget, you need to find ways to maximize its usefulness. Consider focusing on high-priority features first or limiting the size of your team. When you try to pack in too many features or hire a large team without planning out each member’s roles, your expenses will go up. 

It is, therefore, advisable to ensure that the project’s budget, scope, and team composition are all aligned perfectly before starting the software development process.

Time to Develop a Project

Rome wasn’t built in a day, and neither is good software. Rushing a software development project is a surefire way to end up with a buggy product that doesn’t perform how it should. Looming deadlines are a reality that every software development team deals with on a regular basis, but effective time management makes the process of delivering the product much less stressful.

You should carefully consider the timeline of your project when assembling a team. If you have a small software development team, you should account for the project taking longer. Larger development teams might be able to complete a project in less time, but such teams will also need to be managed closely to ensure cohesion. 

Sometimes, if you have a very tight deadline for an urgent project, you will have to bring senior specialists on board to help achieve quick results.

Size of the Project

In software development, project size refers to the number of features and functionalities that need to be added to the product. The more feature-rich a software product is, the bigger the project becomes. This means you must hire more team members to complete it successfully. 

Every additional feature added to a software product could require a specialist developer on the team to ensure it works properly. If your team is made up entirely of generalist developers, it might be smarter to limit the size of the project. 

There are two ways to account for project size during software development. In the first approach, you list out the features you want to see in the finished product and then hire a team to bring those ideas to life. On the other hand, you can assemble a team first and then work out what features will be included according to the team’s composition, specializations, and capabilities.

All of the factors mentioned above are essential when putting together a software development team. You should begin hiring software developers for a project only after its scope, size, budget, and timeline have been finalized.

Modern Software Development Team Structure

Once you have a clear understanding of all the factors that will influence your software development team’s composition, you’re almost ready to begin. 

But first, you need to decide on what approach you will follow for software development. The two most common approaches are the traditional method, which is sometimes also called the waterfall method, and the second is agile development.

The approach you choose will add clarity to exactly what roles your team will need to fill. From the coders who prepare the foundational structure of the software to the testers who comb the finished product for bugs—there are many roles in a modern software development team that need to be filled with competent professionals.

Product Owner

In software development teams, product owners are a vital link between engineers, developers, and clients. They work closely with the client to understand their business needs and define the project’s overall scope. The other important responsibility product owners have is to monitor the development team’s progress. 

The product owner’s job is to schedule tasks for developers, ensure timelines are being met, and also ensure that the promised deliverables are all being met. As the key point of contact for clients and stakeholders, product owners need to be well-informed about the customer’s product, market, and business needs. 

Business Analyst

The business analyst’s role appears to be superficially similar to that of the product owner because they both need to work with the client and development team. However, the key difference between the two is that business analysts focus much more on the technical aspect of software development, while product owners have to focus on customer satisfaction.

A software development business analyst’s main job is to work with the client to understand their business needs and then identify areas where software development can make a positive difference for them. They also need to be aware of software technology trends to create a list of requirements to take to developers, which eventually become the list of features and functions in the finished software product.

Project Manager

Software development is a team-based activity, and every team needs a captain to energize their efforts. Whether you’re using a traditional or agile approach, that captaincy role can be filled by a project manager. Project managers are the driving force behind the software development team and oversee the entire development process.

The project manager is responsible for assigning tasks to team members based on their strengths, enabling communication between them and other key stakeholders. It’s also their job to ensure that a project proceeds as planned, according to budget, and on schedule with all the promised deliverables.

Software Architect

Designing software is complicated, with many moving parts and unforeseen complications. Every team needs a software architect to navigate the complexities of software development. This will usually be the most senior software engineer on the team, and they will oversee the rest of the development team’s work and make executive decisions regarding software design. 

The software architect is the person who selects which tools and platforms the team will use to complete the project. One of their key responsibilities while overseeing the software’s architecture and components is ensuring the product is secure, meets all of the client’s technical and functional requirements, and is relevant to the current trends in software development.

Software Developer

The main body of your team will be made up of software developers. These are the team members whose output helps create the software product from the ground up. Whether you are building an entirely new software solution or making changes to an existing one, you will need to hire experienced software developers to do the job.

Every team will have a mix of developers. There are front-end developers who work on the user-facing portions of a product. There are also back-end developers who work on the inner workings of an application by writing code and devising algorithms. Then, you have full-stack developers who can work in any capacity for software development. 

Whatever the type, a successful software developer needs to be technically proficient and well-versed in a variety of computer programming languages.

Software Testing Engineer

It’s essential that your software development team includes testers if you want to create a product that users value. Software testing engineers act as your quality assurance (QA) team during development. In traditional development approaches, the testing engineers conduct tests toward the end of the software development life cycle (SDLC). 

However, when following the agile approach, testing is conducted alongside other development approaches in short sprints.

As QA testers, these engineers have to ensure the software performs according to the client’s requirements. They identify both functional and non-functional defects in the software, reporting on their findings to the developers and helping refine the product over multiple iterations.

UI/UX Designer

The UI/UX designer is a vital member of every software development team because they are responsible for curating the user experience. While front-end developers focus on the functional aspects of user-facing features, the UI/UX designer has control over how users will interact with those features.

A UI/UX designer creates every software’s visual interface. While developers have to ensure the product is functional, UI/UX designers are more interested in making the product appealing and engaging for end users. They need to have an eye for design, attention to detail, and be well-versed in using design tools.

DevOps Engineer

No software development team is complete without DevOps specialists. The DevOps engineer ensures collaboration between the developmental and operational arms of the team. They need to be well-versed in both the technical requirements of software development and the business needs of the client.

DevOps engineers are the ones responsible for the ongoing maintenance of a software product, even after the end of a development cycle. They are the ones who set up pipelines for continuous integration and continuous delivery after software has been finished. If a software product starts to display marked improvements with each successive iteration, that’s a sign that their DevOps engineer is doing a good job.

Software Development Teams vs. Development Approaches

Due to the multifaceted nature of software development, it’s worthwhile to consider different approaches to find which one works best for your needs. Projects that are smaller in scope can benefit from a sequential approach, but if there’s a chance of scope creep or the project has an extensive scope to begin with, then an agile methodology will work best.

There are several key differences between traditional (waterfall) methods and agile methods. 

Let’s take a closer look.

Now that you’re familiar with the key differences between waterfall and agile development, you can make a well-informed decision based on your approach before assembling a software development team.

How to Build a Successful Software Development Team?

Once you’ve assembled a team and chosen a development approach, all that remains is to give the team the best possible environment to accomplish their goals. Successfully leading a software development team involves careful planning, smart recruitment, and cross-functional collaboration. To create the conditions for success in software development, you should follow these best practices:

• Encourage transparent, effective, and productive communication between team members.
• Establish clear goals and expectations and make them clear to every stakeholder on the project.
• Empower team members to make decisions that support the team’s objectives.
• Hire the right mix of developers, including generalists and specialists, so that every member is comfortable with their workload and caught up with the latest trends in software development.
• Promote team cohesion, build trust between team members, and foster an attitude of shared success.

Summing Up

Software development teams perform a critical function, designing the digital tools used by consumers to connect with companies. Investing in enterprise software is a sound business decision, especially for a company that wants to enable a digital transformation in its operations. Putting together a software development team staffed by experts to fulfill each role helps create products that benefit both businesses and their customers.

It takes technical expertise to create a well-made, popular software application. If you wish to develop software for your business, you should work with a company known for excellent end-to-end software development like Intellectsoft. 

From web and mobile applications to customized software solutions, Intellectsoft has over 15 years of experience in helping companies achieve their goals with software solutions. Regardless of your industry, Intellectsoft can help you develop software technology that boosts your business’ performance and delights your customers.

The post Explaining the Software Development Team Structure appeared first on Intellectsoft Blog.

In this article, we will discuss the best software development trends to follow in 2024. Staying updated on the latest software development trends allows industry players and developers to maintain a competitive edge and deliver fresh and cutting-edge solutions every time.

The Evolving Landscape of Software Development

An interesting shift happened globally post-pandemic in 2021, during which the IT sector reentered a growth trajectory after having suffered numerous project delays, cutbacks, and retrenchments during the pandemic. The post-pandemic world saw enterprises big and small recognizing the need to adopt a digital culture. After being caught unprepared when the world went into lockdown, businesses now know better than to stick to the old ways in order to keep the lights on, come what may.

Gartner reports that 91% of businesses have now launched digital transformation initiatives, with 87% of more senior business leaders highlighting that digitalization is a top priority for them. Additionally, 89% of companies have also reportedly adopted a digital-first business strategy moving forward. 

In terms of software development, Statista projects that the market is likely to garner revenues reaching $738.96 billion by 2025. Enterprise software is set to dominate the market with a projected market volume of $312.80 billion in 2025, followed by application development software ($193.30 billion), system infrastructure software ($151.20 billion), and productivity software ($81.66 billion).

Statista also predicts that the global software developer population will hit 28.7 million this year. In terms of working arrangements, it appears that 41% of developers have continued to work remotely despite the IT industry prioritizing a return to office culture overall. 

What’s Trending in the Software Development Industry in 2024?

Among the top software industry trends in 2024 are technologies like artificial intelligence, machine learning, and cloud computing. Development, Security, and Operations (DevSecOps) practices, as well as microservices architecture are also among the latest trends in software development.

Overall, the current trends in software development are driven by previous changes in the IT sector. As mentioned, the shift to digital has catalyzed a lot of change within the industry and has since transformed the way software is developed, deployed, and maintained.

Additionally, Agile development methodologies have gained wider acceptance, allowing for shorter development cycles amid hybrid and remote work. Just a decade ago, much of this would not have been possible without the increased collaboration among teams and departments facilitated by modern technology today.

As such, many of these software engineering trends are tied to the drive toward increasingly optimized current workflows, facilitating the shift to digital, and continuing to adopt and develop emerging technologies in software development. 

Let’s take a closer look at some trending technologies in software development.

1. Increased AI and ML Integration

On top of the list of the latest trends in software development is the recent uptick in the adoption and integration of artificial intelligence (AI) and machine learning (ML) across industries. Both technologies are now widely used by software development companies across the globe to enable data-driven decision-making in businesses as well as to build predictive analytics algorithms.

Regardless of business size, we’ve seen a surge in AI and ML integration all the way from small digital marketplaces to tech giants. This integration is projected to stay on a steady growth course in the coming years. AI, in particular, is expected to surpass half a trillion dollars this year. With a CAGR of 38.1% from 2022 to 2030, the AI market is projected to become a $1.87 trillion-dollar industry by 2030.

In addition to ML, among the top AI development software technology trends include:

• Automated Machine Learning: AutoML will be incredibly useful in simplifying incredibly complex data sets across industries
• Generative AI: Data generated by businesses will soon be used to create original content, including videos, images, and sounds
• Natural Language Processing (NLP): NLP is set to enrich communication between algorithms and users with more advanced natural language understanding
• Ethical AI: Designed to bridge the gap in trust between users and technology  by reinforcing cybersecurity measures, boosting confidence in automated systems
• AI-as-a-Service (AIaaS): Companies will begin leveraging cloud-based platforms for a variety of AI services.

2. Application of Blockchain Technology Beyond FinTech

Blockchain technology itself is not new — but this time, tech experts see blockchain disrupting the software development sector in 2024 as it goes beyond fintech.

Businesses from various industries are beginning to understand the usability and value of the technology and are starting to implement it — especially since blockchain offers incredible security thanks to decentralization. 

The rise of decentralized systems in different software development sectors like the media, publishing, supply chain tracking, and healthcare is expected to continue on its trajectory moving forward. This is because blockchain’s decentralized nature makes it transparent, virtually unhackable, and incorruptible.

Developers are continuously finding new ways to create custom software development around blockchain technology, driving the blockchain market toward a CAGR of 58.90% with a projected value of $20 billion by the end of 2024.

3. Increased Investment in Progressive Web Apps

Like blockchain technology, progressive Web Apps (PWAs) are also not new — they’ve been around since their introduction in 2015. However, it is now gaining widespread adoption and acceptance, with a CAGR of 31.9% and a projected market value of up to $10.44 billion by 2027.

It also seems that the tech industry has caught up to PWA technology, especially given the exponential increase in mobile usage over the past decade. PWAs offer an opportunity for website owners to improve their website’s user experience and aesthetics by giving them a more app-like feel.

The development scene is seeing more site owners investing in PWAs. Previously, converting to PWAs wasn’t so common, which is why Pinterest made headlines for converting. Following an analysis of areas that caused poor site performance and retention, Pinterest went through an overhaul by rebuilding the website using React and converting it into a PWA. As a result, the site enjoyed a 40% increase in time spent on the platform and 44% more revenue. This was back in 2017.

4. More Companies are Embracing Low-Code/No-Code Development

It’s no secret that developing software can be a lengthy and tedious process. Low-code and no-code development offer developers a much faster and easier way to create apps with minimum coding, allowing teams to ship in record time.

Gartner has previously projected that companies of all sizes will begin to embrace low-code development, with the adoption rate set to exceed 50% through 2026. Experts suggest that the key drivers that will accelerate the adoption of low-code and no-code development are:

• Increase in the number of business technologists
• Growing levels of enterprise-level hyper automation
• Composable business initiatives

“Organizations are increasingly turning to low-code development technologies to fulfill growing demands for speed application delivery and highly customized automation workflows,” shares Varsha Mehta, Senior Market Research Specialist at Gartner. 

Basically, these development types allow teams to develop apps with a limited budget and much shorter timeframes. Low code platforms function similarly to purpose-built Integrated Development Environments (IDEs) or platforms that are specifically designed to address varying needs and challenges. Some popular low-code platforms include Mendix, Appian, and Visual LANSA.

Some examples of low-code use cases are:

• Leveraging an existing SaaS infrastructure with customized features according to what a specific business needs
• Transitioning to a cloud-based relational database from traditional spreadsheets
• Converting legacy applications into modern versions

5. The Emergence of FinOps and GreenOps

The cloud computing landscape is currently ushering in the emergence of the FinOps and GreenOps movements, counting them among the latest software development trends. Given the fairly recent upheavals with regard to the way things work, more companies are recognizing how important it is to streamline operations and processes across the board.

After the pandemic, no one wants to get caught unprepared to adapt and keep up with customers’ and the market’s changing demands. Businesses are also putting more effort into sustainability while driving savings and efficiency at the core of business efforts.

In a nutshell, FinOps seeks to drive efficiency across various financial processes, whereas GreenOps focuses on implementing eco-friendly practices in line with long-term value creation. By integrating FinOps and GreenOps, businesses can work to increase their bottom line, comply with regulations, support sustainability efforts, and improve their brand reputation.

6. Serverless Computing

Serverless computing, also called Function as a Service (FaaS), allows developers to create applications and services without having to manage infrastructure, leading to better efficiency and cost savings. 

Among the leading providers are Azure Functions, AWS Lambda, and Google Cloud Functions. For example, streaming provider Netflix has started using AWS Lambda for certain functions within the platform. 

Lambda takes care of tasks such as user authentication,  encoding videos, and backend processes. This allows Netflix to scale its resources based on demand without needing to manage servers while providing a smooth user experience and cutting costs.

Spotify, on the other hand, uses Google Cloud Functions to handle tasks such as user authentication, processing user-generated content, and backend tasks for music recommendation algorithms.

7. Greater Reliance on Distributed Computing

Distributed computing is another software development trend that is gaining more popularity in 2024. Distributed computing is a way of data sharing by way of different computer servers connected as a group. 

As such, large volumes of data that may be too much work for just one server can be spread across multiple servers and thus be processed much more quickly. Distributed computing allows applications to scale accordingly by just adding servers as needed. 

Overall, distributed computing improves performance while minimizing costs. Best of all, a system failure in one server will not affect others — allowing for continuity of operations should something arise.

8. Increased Focus on Cybersecurity with DevSecOps

Development + Security + Operations is DevSecOps. In a nutshell, it’s an automation, platform design, and culture approach. It also integrates security as a crucial and shared responsibility throughout the IT lifecycle. 

Many more companies are beefing up their cybersecurity programs with DevSecOps, as it focuses on built-in security over security measures built around data and apps. DevSecOps prioritizes early detection of potential risks and fixing these vulnerabilities before they become more significant issues.

DevSecOps breaks down silos between different teams and puts security at the forefront of all commit and pull requests. Compliance is also of utmost importance in the DevSecOps pipeline, and as such, compliance and security checks are always integrated.

9. Ushering the AR/VR Era

AR and VR are no longer confined to just video games and entertainment. For clarity, whereas AR uses real-life elements supplemented with computer-generated content to create an interactive and dynamic user experience, VR is entirely a virtual, immersive simulation. 

More and more businesses are seeing the value of these technologies in 2024 and are using them to create real-world simulations for customers and stakeholders, as well as interactive training programs for employees. 

For example, large pieces of tech or machinery that cannot realistically be brought into a trade show fair can be rendered via VR or AR, allowing customers and stakeholders to “experience” what it’s like. The same applies to prototypes that may not yet be ready but already have a defined blueprint.

Also, training programs can be enriched with demonstrative and hands-on training through VR and AR, allowing staff to perfect muscle memory without even having to operate the real thing right away (like heavy machinery and other equipment).

10. Microservice Architecture

Microservices architecture in software development refers to a different approach to software development. Microservices focus on creating single-function modules with specifically defined interfaces and operations.

This is in line with Agile and DevOps and has really picked up in recent years. Microservice architecture is small in scale and independent, allowing smaller development teams to write and maintain services much quicker and more efficiently. 

In such an architecture, each layer of service is separated from the codebase, making it easy to manage. This allows developers to update and manage an app or service without needing to rebuild or redeploy the whole thing.

11. AI in Healthcare

AI offers to the medical sector what a Deloitte study revealed to be among the top three goals of AI in healthcare: Improved efficiency, lower costs, and enhanced products and services.

One of the practical ways AI can help support these goals is by developing AI software that can improve symptom analysis through patient data, automatically detect medication errors, and automate administrative tasks overall. 

Venture capitalists have also reportedly invested over $8.5 billion in the top 50 healthcare AI firms, highlighting the current importance of AI in healthcare.

12. NFT Tokens in Cryptocurrency

NFTs, or Non-Fungible Tokens, are digital elements connected to the Blockchain. They are called such because they are non-fungible, meaning that they’re completely unique and not interchangeable. 

No two NFTs are similar, and this concept has pushed it to become a significant trend, especially concerning Blockchain-based payments in recent times. It is already one of the most common payment methods in software development outsourcing, possibly because it allows for fast, secure, reliable, and transparent payment between people. 

NFTs are projected to grow exponentially and reach $211.7 billion by 2030, with a growth rate of 34.2%. NFTs are built on smart contracts or self-executing lines of code, allowing for speedy and secure transactions.

13. React Native Continues to Thrive

The programming React Native has emerged at the top of the hybrid development market. This is because it allows developers to create high-quality cross-platform applications using a single codebase.

This allows teams to work much faster and more efficiently, cementing its status as an important tool for businesses looking to maximize their reach and streamline workflows. 

14. The Switch to 5G Technology

Alongside the rapid 5G rollout — experts predict that over 1.4 billion devices, or 15% of the global total, will be connected to this network by 2025. 

This is because 5G is more efficient than 4G. It is a new network architecture that seeks to redefine how people communicate and connect digitally, leading to enhanced user experiences. The 5G architecture is also expected to accelerate the growth of innovative technologies like the Internet of things and Artificial Intelligence (AI).

15. The Popularity of IoT is on the Rise

The widespread adoption and growth of smart devices calls for more IoT solutions, ushering in an era of more connectivity — allowing people to bridge the gap between the physical and digital. 

According to research, the global IoT market is projected to grow to $650.5 billion by 2026 and at a CAGR of 16.7%. This is because IoT has found several applications due to the rise of related technologies like AI, ML, automation through edge computing, and more.

City governments exhibit another welcome use of IoT solutions — from enhancing security to traffic congestion reduction, the goal is to streamline urban living and optimize city infrastructure with technology. Healthcare IoT gadgets are also facilitating early disease detection, providing people with better chances of recovery and healing.

16. Python Developer Demand Expected to Rise

Machine learning and data science continue to dominate several industries, causing an increased demand for experienced Python app developers. Python is often considered the ultimate Swiss knife of programming languages, as it is capable of performing numerous tasks and handling complex jobs.

Python also continues to remain versatile and relevant. From web design and app development to machine learning models, the programming language allows developers to complete projects efficiently. Among the key areas in which Python may experience an uptick in usage are game development, AI and ML applications, IoT, web and mobile apps, and automation and robotization. 

17. Web 3.0 Continues to Evolve

It is said that Web 3.0 holds the future of the Internet, as it is continuously being propelled by the latest technologies and software development processes. 

The three basic IT architecture tiers of Web 3.0 are:

• Interaction: This refers to the content, hardware, and software that users communicate with
• Computation: This makes up the logic to enable the interaction between users and the above elements
• Information: Refers to the data and structure required for computing functions to be done accurately and efficiently.

Web 3.0 apps are characterized by complexity and interactivity and go a step beyond the static pages we got used to in previous web versions. JavaScript development is used to create Web 3.0 apps, making them both dynamic and responsive. 

Currently, Web 3.0 applications are already available on desktop browsers and some high-end mobile devices like smartphones with OLED panels. However, tech experts anticipated a much higher volume of Web 3.0 apps and compatible devices moving forward.

18. Cloud-Native Technologies Continue to Evolve

Cloud-native technologies and cloud-based architectures allow one to design, construct, and operate cloud-developed workloads, taking full advantage of the cloud computing model. Cloud-native prioritizes agility and better speed, allowing organizations to create strategic solutions that can support their business growth.

In 2024, cloud-native technologies will continue to evolve and increase in usage as cloud-native implementation allows developers to develop and execute scalable applications. Cloud-native apps work across modern and dynamic work environments, such as private, public, and hybrid clouds. This also covers microservices, containers,  immutable infrastructure, service meshes, and declarative APIs.

Additionally, with cloud-native stacks like DevOps and GitOps, developers can easily use continuous innovation and continuous delivery (CI/CD) to test applications while simultaneously pushing new code into production.

By leveraging cloud-native development, organizations can bank on built-in scalability and match any demand profile without investing in added infrastructure design or provisioning. Cloud-native development also allows developers to choose from various tools instead of being limited to in-the-box options.

19. The Advent of IoB (Internet of Behavior)

Personalization is an important driver of success in any business. The more effective your service is, the more your users will adapt their approach to your infrastructure. The Internet of Behavior (IoB) brings to the table important data like user behaviors, interests, and preferences from Big Data,  BI (Business Intelligence), and CDPs. 

Pulling from wearable devices, smart household electrical gadgets, and individual internet activity, IoB reveals valuable information on user behavior and their interests. IoB facilitates easier tracking, merging, and evaluating large volumes of data from various online activities and personal behaviors.

This includes things like social media habits and even commercial transactions. IoB is a little behind IoT in terms of momentum and adaptation. However, as consumers continue to gravitate toward personalization and businesses seek ways to give users a customized (yet still on-brand experience), it is highly likely that IoB will gain greater momentum. 

IoB is already being used by companies and startups. YouTube, for example, has begun to provide viewers with a more customized feed, much like Facebook and other social media platforms. Overall, IoB can help businesses resolve challenges that may hinder them from closing sales and acts much like consumer surveys without needing to expressly ask consumers about their preferences.

20. Edge Computing

Edge computing is called such because it processes data closer to the “edge” of the network or where it’s created—in an effort to greatly reduce latency and bandwidth use. According to research, the global edge computing market is expected to grow at a CAGR of 37.4% by 2027.  Fortune Business Insights projects that the global edge computing market will grow from $15.96 billion in 2023 to $139.58 billion by 2030. 

Edge computing is particularly crucial for IoT devices because they generate huge amounts of data. It can also improve privacy and security as data is kept locally. This is in opposition to centralized cloud platforms, which are not the closest to data sources. 

An increasing number of businesses have started to shift their analytics and decision-making from centralized cloud platforms to the edge.

This trend is largely driven by the observation that data captured at the edge often requires processing, storage, and analysis on-site, and it can’t always be done via cloud-based applications due to cost implications, uptime constraints, and latency issues.

Edge computing is an evolution of on-premise computing. It is technically an IT architecture that processes client data at the network’s edge, closest to the site of capture. By bringing computing closer to such data sources, edge computing is able to reduce latency and enhance real-time data processing capabilities.

Edge computing is particularly useful for applications that require immediate data analysis, such as autonomous vehicles and smart city technologies. In the field of healthcare, edge computing also ensures utmost data privacy and allows for AI-based real-time monitoring and close analysis of the patient’s condition. 

When applied in practice, this technology can transform traffic management by regulating traffic lanes, optimizing bus schedules, and even guiding autonomous vehicle flows.

21. Quantum Computing

The principles of superposition and entanglement are among the most fundamental quantum-mechanical phenomena used in quantum computing for data operations. Quantum computing is an even more advanced form of computing that uses quantum bits (called qubits) instead of classical bits.

The capacity to execute specific calculations much faster than classical computers is one of the primary advantages of quantum computing. As such, it has the potential to greatly revolutionize how machines solve intricate computational tasks, including cryptography, optimization, machine learning, and molecular simulation.

Quantum computing can be particularly useful in sectors like energy, finance, healthcare, and logistics, especially to address computational challenges that may be deemed too complex (even insurmountable) for classical computers. This includes simulating complex system behavior, decryption of cryptographic codes, and optimizing logistics networks that are incredibly large in scale.

That said, the development of quantum computing technology is still in its early stages. Researchers are currently actively engaged in building quantum computers and refining quantum algorithms, as well as software tailored for their operation. Leading companies are currently developing software to make quantum computing more accessible to a wider audience. 

These include tech industry leaders like IBM, Google, Microsoft, D-Wave, and Amazon. IBM is ahead of its peers with Quantum System One and Two, which uses powerful processors of up to 127 qubits. Microsoft, on the other hand, is focusing on topological qubits and integrating them into the already-up Azure cloud platform for wider accessibility.

Conclusion

Current software development trends are driven by the massive shift to digital, as well as the convergence between AI and new technologies that seek to improve our ways of working and living. 

Significant leaps are being made in the field of groundbreaking technologies like the Internet of Things, Internet of Behavior, and Web 3.0, as well as quantum computing — all of which can catalyze even more advancements especially when applied to city traffic, healthcare, finance, and other sectors.

Additionally, technologies that have been around for a while are now finding a firmer footing and are enjoying wider adoption. These include blockchain technology, NFTs, and PWAs, as companies see more use cases and security advantages.

Trusty programming languages like Python and JavaScript remain at the forefront of software development despite these trends. Still, newer ways of doing things — like serverless computing, edge computing, DevSecOps, FinOps, and GreenOps — are coming to the fore as workplaces continue to evolve and create new demands that developers need to address.

Your Partner in Development

Intellectsoft prides itself on a comprehensive approach that includes consulting, engineering, and support services. As your software solutions provider, we can offer custom AI, Blockchain, IoT, mobile, and other solutions depending on your needs. 

Our team of experienced developers can handle projects of any type and difficulty. From simple web and mobile apps to cloud-based enterprise-level products, we’ll take your vision from the idea board and make it a reality. Get in touch with us today to consult with an expert!

The post Best Software Development Trends to Follow in 2024 appeared first on Intellectsoft Blog.

The game development industry even birthed a new movement known as Creative Coding. While some sources define it solely as creating digital art through code, the gaming world expands this concept significantly. Creative Coding is actively practiced at game development conventions and incorporated into the curriculum of software engineering schools. It represents a unique blend of art, design, and scientific principles. This approach transcends conventional thinking, fostering the creation of extraordinary experiences where the physical and digital realms converge.

Game Dev coders endeavor to bridge the gap between the digital and physical worlds by enriching digital elements with the ability to interact with real-world environments. This allows digital information to become “embodied,” responding to the viewer, dynamically altering the context, and establishing intricate forms of interaction and behavior. Let’s see how AI and its disruptive tech counterparts like VR are currently transforming the gaming industry.

So, What is AI in Gaming?

Artificial intelligence (AI) in games extends beyond the boundaries of traditional interactive experiences. These systems learn about players’ behavior during gameplay, going beyond pre-programmed responses, which allows them to dynamically adapt and provide an optimal gaming experience tailored to each individual.

Role of Artificial Intelligence in the Gaming Industry

The likely reason why AI in the gaming industry strives, particularly in gaming, might be attributed not only to the fact that it is a lucrative market worth billions of dollars. The roots of this interest might stem from the deeply human nature of wanting to gain an advantage in a competitive field to achieve goals. Remember the Game Theory? It is a mathematical study that drives strategic decision-making among individuals and bigger entities like companies or even entire societies. 

Despite the games being traditionally related to entertainment, scientists are working on some serious applications of AI by training their models in the gaming setting. Possible real-world scenarios include military training, education, driving training, medical training, and mental health therapy. 

What’s more, game dev is at the forefront of software development innovation at scale. For instance, IEEE’s annual conference is a platform for presenting research papers. The latest findings are highlighted by the gaming giant EA:

A case study [for automated testing] is Battlefield V, which requires testing of 601 different features amounting to around 0.5M hours of testing if done manually. This corresponds to ~300 work years.

In response to these challenges, researchers are training Reinforcement Learning (RL) and other models, thus striving to rise beyond the limitations of scripted bots (while manual testing becomes increasingly obsolete). Additionally, developers spend thousands of hours on routine tasks like programming characters’ gestures, which could be automated with AI, freeing their time for more interesting tasks.

In this light, integrating AI in gaming and testing appears not just desirable but essential.

How is AI Used in Gaming?

Gaming industry seems to be leading the race in AI creation and evolution. The market has flourished with AI algorithms that are actually working in cloud gaming platforms and scaling on an ongoing basis.

Non-Player Character (NPC) Behavior

Older-generation NPCs are known for their unnatural body language, which results from their lack of responsiveness. AI engineers have figured out multiple ways of enhancing NPC behavior to make games more enjoyable. For example, smart NPCs were implemented in Unreal Engine. According to the original paper behind the research, scientists developed an algorithm based on the use of finite state machines and fuzzy state machines. Finite state machines adjust the NPC behavior based on a set of states (patrolling, chasing, etc.) that are triggered by events (seeing the player, hearing a noise). Fuzzy state machines allow for smooth and nuanced transitions between states based on fuzzy logic (spectrum of variables as opposed to binary logic). Additional models observed in smart NPCs are artificial neural networks that let them learn from experiences and genetic algorithms that let the behaviors evolve over time.

Procedural Content Generation

Machine learning algorithms can generate new levels, items, and quests. This can help keep games fresh and interesting for players, who will always encounter new content. Procedural simply means that developers do not let AIs go haywire while only letting them generate new content based on the predetermined set of rules. This helps them avoid unethical content generation and propagation. Generative adversarial networks work as an improvement mechanism where a generator and a discriminator work “against” each other, which results in more realistic and creative content.

Cheating Detection

AI can be used to detect cheating players. This can help to keep games fair for everyone. They do this by employing anomaly detection to identify and isolate deviating patterns. Supervised machine learning algorithms can be trained on cheating data and non-cheating data so that AI can understand the difference.

Dynamic Difficulty Adjustment

Similar to dynamic online exams where the questions adjust to the learner’s knowledge level, AI in the gaming industry can adjust a game’s difficulty based on a player’s skill level. This can help ensure that the game is challenging enough to keep it interesting, yet at the same time not frustrating. First of all, key metrics to track are identified, and tracking mechanisms are activated. For example, these could be time to complete a level, number of enemies killed, weapons used, etc. Then, the model of the player’s skill level is created. This model can then be used to predict how the player will perform on future tasks and adjust the difficulty accordingly.

Personalized Gaming Experience

Content recommendations that are familiar to us from most of the video and music content apps have also been implemented in cloud gaming. When a user logs into a game, they might be recommended items, heroes, and quests that they are more likely to enjoy. Furthermore, the whole game world can adjust based on the user’s preferences, including the landscape, quests, and AI companions. For example, No Man’s Sky introduced the possibility of creating a whopping 18 quintillion open worlds that are modified based on the player’s experiences.

Enhanced Graphics with AI Upscaling

Cloud gaming lets you play high-end games on devices that wouldn’t normally be powerful enough. However, the data stream from the powerful remote servers can be hefty, requiring good internet bandwidth. Here’s where AI upscaling comes in, offering a win-win situation for both cloud gaming services and players. The remote server renders the game at a lower resolution, reducing the data required for transmission, while the AI model analyzes the incoming lower-resolution image and predicts missing details that would be needed to get a high-resolution experience. Finally, the missing details are generated, and the upscaled imagery is delivered right to your laptop or mobile device.

Major Trends in AI Gaming

Below, we will describe in more detail the key AI trends in the gaming industry.

Generative AI

One of the most ubiquitous, this type of AI in gaming is much more than just text-based chatting. Yes, it can elevate dialogues, but it can also adapt gestures and tone of speech to what the character is saying. This can be used to create more realistic and believable game worlds and characters. When developing open worlds and personalized storylines, game creators rely on generative AI in procedural content generation and speech-to-gesture animation, which frees developers from the overwhelming amount of tedious routine jobs.

AR/VR Technology

This can be used to create more immersive gaming experiences. For example, AR could be used to overlay game elements onto the real world, or VR could be used to create a completely virtual world for players to explore. Technology-wise, hardware quality has skyrocketed over the last few years. Major brands are offering truly immersive experiences with photorealistic image quality, eye motion tracking, body motion tracking, spatial audio, and even sensors that enable tactile sensations and feedback. 

BCI-Enabled Games

Speaking of body signal tracking, some EEG headset manufacturers claim that they implemented neural interfaces for AI in gaming that allow users to play with the power of their minds. Big players like Valve (the owner of Steam) are actively researching the niche, but the progress could be faster. Meanwhile, famous Twitch streamers are trying to play games with their DIY BCI headsets, while critics think it’s a joke because EEG headsets can’t tell meditation state from the hustle of a busy convention space. 

Multiplayer Games

Cloud-based gaming revamped the whole industry, allowing users to play multiple games on a single platform regardless of their device’s processing power. Additionally, it responds to a growing trend of multiplayer games. With an estimated 1.86 billion PC gamers worldwide, the ability to come together in a game is what fuels the industry’s growth. To spice it up a little, developers have something more to offer.

AI Opponents

Smart NPCs are fun, but how about smart opponents? Of course, we are all familiar with playing “against the computer,” which has been historically challenging. However, AI in gaming aims to make this experience more adaptive and exciting. For example, case-based reasoning systems store past experiences and use them to make decisions in new situations. In a game, a case-based reasoning system could be used to allow an AI opponent to learn from its mistakes and adapt its behavior accordingly.

How to Use AI in Game Development?

AI in Game Programming

Sometimes boilerplate code is what you need. If the unit of writing is destined to be reused without any changes to it, then why write it every time by hand? AI can automate the creation of such basic backend code for common functionalities like basic enemy movement or environmental interactions. This frees up developers to focus on more complex backend logic specific to the game.

AI in Game Design

AI in game development is still evolving, but it holds promise for assisting in the development process, not necessarily replacing it entirely. Multiple use cases require continuous optimization and automation. Luckily, AI is specifically good at that kind of stuff.

For example, you want to optimize server performance. You can employ AI algorithms that analyze player behavior and server load to suggest resource allocation and server infrastructure adjustments. This can help maintain smooth gameplay and scalability for a large player base.

Furthermore, AI in gaming can help you monitor server activity and predict potential issues like outages or bottlenecks. This allows developers to take preventative measures and ensure a stable gaming experience.

AI in Game Testing and Debugging

Machine learning algorithms can analyze vast amounts of gameplay data to identify potential bugs and glitches, allowing programmers to address them before release. As discussed above, AI can potentially mitigate the issue of the overwhelming amount of testing scenarios, even in an automated setting.

QA engineers strive to deploy AI that can control virtual testers to explore the game world, identify areas where players might get stuck, and uncover potential balance issues. This reduces the manual effort required for thorough testing.

After AI playtesting, algorithms can employ data-driven analysis to review the collected data and pinpoint problematic areas or unbalanced mechanics, allowing designers to refine the game.

Benefits and Disadvantages of Using AI in Games

AI in gaming offers a double-edged sword. On the bright side, it can create sprawling open worlds, enrich interactions with NPCs, and render stunning visuals. It also proves to be valuable in AI developers’ pipelines, automating simple repetitive tasks and performing QA on massive amounts of gaming material.

However, content generation might need help to rise beyond simple tasks. Procedural content, though vast, can be unpredictable and repetitive. For example, open worlds might offer generic, repetitive patterns, while human characters might have three hands or two heads.

Training AI in game development requires significant computational resources, potentially increasing gaming prices and amplifying the unnecessary carbon footprint. Additionally, ethical concerns linger as AI may struggle to distinguish between intended and unintended actions. For instance, in content moderation, a chat joke might be interpreted as hate, and the user will be banned, while a player who is displaying toxic behavior during the quest will stay in the game.

The Future of AI in the Gaming Industry

Future applications of AI in gaming involve building on new hardware capabilities. With the rise of edge AI devices, this technology is being brought closer to the sources of collected data (like blood pressure, eye motion, etc.). If that is complemented with VR/AR, brain-computer interfaces, and quantum computing, the games will probably become even better than real life. 

The increased opportunities for open worlds and character development in virtual realities will allow for social interactions and creative exploration. However, ethical concerns about such an escape from the real world are rising proportionally to AI gaming’s popularity.

Summing Up

Overall, AI in the gaming industry enables enhanced player experiences and software scalability at an unprecedented pace, so it seems like we will see it becoming mainstream soon. Some applications of AI in gaming are purely practical (like AI testing in QA), while others are quite far-fetched (like BCI). One thing we know for sure is that the implementation of AI requires a lot of research and experimentation. An iterative approach can fuel this technology’s evolution because, as they say, “A journey of a thousand miles begins with a single step.”

Intellectsoft collaborates with various companies exploring AI, from early-stage startups to giants like Sony. You can talk to our experts and obtain insights about your idea or an ongoing project absolutely free of charge — just hit the button below to schedule your spot.

The post AI in Gaming: Transforming and Reshaping the Industry appeared first on Intellectsoft Blog.

Building AI software is about designing human-centered solutions. This article guides you through a thoughtful and real approach that breaks the mold of boilerplate, formulaic methods.

Having been navigating the wild world of IT since 2007, we’ve seen the good, the bad, and the downright misleading when it comes to building AI. This guide aims to cut through the noise and dive into the practical trenches of crafting real-world AI solutions that solve problems, not chase trends.

How to Create an AI Software: Major Steps

Planning Your AI Software (Defining Business Goals)

All in all, building AI is about mimicking human intelligence, i.e., learning, logical reasoning, making decisions, and, ultimately, solving problems. Machine Learning is usually a starting point for many businesses in the AI journey because they want to learn from vast amounts of data and build optimal solutions that go beyond the limitations of human error and cognitive capacity. Then, if ML models begin to learn and self-optimize effectively without human facilitation, there is a possibility of them becoming true AI.

This perspective sounds a little intimidating, but it is considered a North Star for the majority of businesses today, regardless of whether IT is a part of their value proposition or not. McKinsey reports that over 40% of respondents will increase AI investments. Peter H. Diamandis, a future-focused serial entrepreneur, puts it very clear: “In 2023, more than 1 in 4 dollars invested by VCs in US startups went to an AI-related company.”

Let’s say you decided it’s time to roll up your sleeves and start the AI project. As we mentioned, you can’t go wrong with problem-solving. Identify areas where other AIs currently struggle and work on engineering solutions. You might also consider making custom AI on demand (for example, in healthcare or construction). That would be smart because of the specificity of datasets and relationships between hyperparameters. However, this approach is based on scarcity (create something exclusive that no one else will have), – which is extremely hard to scale. On the other hand, if you aim at creating some core algorithm that gracefully resolves complexities that everyone else is having, you might achieve the true North Star of your business sooner than you know. 

Alternatively, consider the reverse psychology principle if you don’t like to go with the flow. For example, if everyone is working on AI, what impact does it have on our use of data and electrical signals? Maybe we can make data storage or transmission more efficient. You need an idea that will truly stand out to get your foot in the door of venture investments. Sometimes, instead of pursuing a vague mission of making the world a better place, choosing a niche and sticking with value creation for that particular AI niche is more beneficial.

To put it simply, imagine AI companies like different floors in a giant building, each adding value in a unique way.

• The common area (Applications) is filled with companies creating finished products like apps. It can be tough for them to stand out from the crowd, just like competitors selling similar products in a store.
• The middle floor (Infrastructure) holds the tools that help AI engineers build these applications, kind of like the workshop where tools are made for different projects. Can you create a library or a framework? Go for it!
• The boiler room (Models) is where you boldly go, while no man has ever been there before. These are the special ingredients (parameters) and secret recipes (formulas) that make AI tools work their wonders. The primary focus is on advanced mathematical operations and complex logic between them.
• Foundation, pillars, ceiling (Hardware) This level includes the powerful machinery that runs everything, similar to the power plant that keeps the whole building functioning. While important, working on this floor can be challenging, just like building and maintaining a power plant requires a lot of resources.

Once you decide on the right way to go, it’s time to move on to the next step.

Data Collection and Preparation

A lot of novice AI makers are tempted by vast amounts of public data that they can get absolutely free. However, it might be useful to look beyond those common sources. Instead, what if you collect niche industry data? This strategy might result in more accurate predictions because your model will not be distracted by noise, and you won’t have to spend so much resources on cleaning your data. Try to diversify your data sources: smart devices, citizen science initiatives, and likewise pools of information with a wealth of real-world scenarios might be more valuable than just downloading public data that everyone else is likely to use for building their models. If you have no idea where to take this data, aim at partnering with subject matter experts, researchers, and organizations that are collecting this data and are interested in enhancing their insights.

Implement a pipeline that preserves its initial state and tracks modification to ensure data integrity. This might be based on the blockchain of version control systems. This will help you eliminate bias and errors down the line.

Finally, you need to make sure that the data you collect is interpretable. Use techniques like feature importance analysis and counterfactual explanations to understand how data points to influence your model’s decisions. This can help identify potential biases and ensure responsible AI development.

AI Model Selection and Development

The selection of an AI model depends on the business value that you decide to pursue. After crafting a business case and project charter, your software engineering stakeholders will proceed with outlining a network architecture, overarching logic of the solution, technical roadmap, and tech stack. 

Finding experienced software folks who code in specialized AI-oriented libraries might be challenging. If you are a novice startup, you might not possess that experience yourself. If you need to move fast, the viable solution here is to gather brilliant software developers and embark on a learning journey. 

To get started with coding, you need to decide which of the available AI architectures you are going to use to achieve your business objectives. 

If you have never programmed neural networks before, keep calm! Just think of them as colleagues in your company:

Convolutional Neural Networks (CNNs)

• Imagine these as image processing experts. They’re like programs that break down images into smaller pieces using multiple “filters.” By analyzing these pieces, they can identify objects, classify images, and even segment different parts within an image.

Recurrent Neural Networks (RNNs)

• Think of these as business intelligence specialists. They excel at handling data that comes in order, like text or time series. They have a “memory” that allows them to remember past information and use it to understand the current data point better. This makes them perfect for tasks like analyzing text sentiment, predicting future patterns, or understanding trends over time.

Generative Adversarial Networks (GANs)

• Imagine these as a developer and product owner. They consist of two parts: a “generator” and a “discriminator.” The generator tries to create new data based on existing knowledge, while the discriminator tries to distinguish real data from the generated one and give feedback on what is acceptable and what is not. This review process pushes the generator to become better at creating data.

Autoencoders

• Think of these as storage optimization experts. They’re like programs that learn to represent complex data in a simpler way. They achieve this by compressing the data into a smaller form (a “latent representation”) while still capturing the important features. This compressed data can then be used for other tasks like extracting informative features for further analysis or detecting anomalies in datasets.

Transformers

• These are like customer support experts in the world of AI, particularly for tasks involving text. They use a special mechanism called “attention” that allows them to focus on specific parts of a text sequence, just like you might focus on certain keywords in a sentence. This helps them understand complex relationships between words, making them powerful for tasks like machine translation, analyzing emotions in text, and summarizing large amounts of text data.

By understanding particular AI roles in simple terms, you can manipulate their functionality more easily without getting lost in their mathematical complexity.

Training and Evaluation

AI training is not a one-off activity. In fact, it requires multiple iterations of fine-tuning the algorithm. This doesn’t mean that the algorithm you built was wrong or buggy. On the contrary, fine-tuning is quite beneficial, provided that it gives valuable improvements with every iteration. 

To boot, some interesting findings are only discoverable in the process of experimentation. You wouldn’t have known them otherwise. If additional robustness is needed, use augmentation to increase dataset diversity. Examples of fine-tuning-worthy parameters include: Regularization Strength, batch size, learning rate/schedule, decay rate/schedule, number of hidden layers, and dropout rate. 

Testing the Model

While algorithms consistently outperform humans in tasks like predicting employee success and optimizing supply chains, people struggle to trust them due to a phenomenon called “algorithm aversion.” This lack of trust stems from our inability to understand the inner workings of algorithms and learn from their mistakes, unlike human advisors. 

Studies show that simply demonstrating an algorithm’s ability to learn through its past performance significantly increases user trust and preference for the algorithm compared to humans, even when both have the same success rate. Additionally, even implying an algorithm’s potential for future learning with terms like “machine learning” can boost user acceptance.

On a technical level, by employing a comprehensive testing strategy and embracing XAI techniques, QA specialists can ensure the development of reliable, trustworthy, and user-friendly AI software.

A testing routine could look something like this:

1. Test preparation:
   • Identify the desired user experience.
   • Establish quantifiable metrics.
2. Data preparation:
   • Evaluate data quality and diversity.
   • Test for data poisoning and manipulation.
   • Implement data augmentation techniques.
3. Running functionality testing:
   • Test core functionalities.
   • Run scenario testing.
   • Stress test the system.
4. Ensuring explainability:
   • Utilize explainable AI (XAI) techniques.
   • Test for fairness and bias.
5. Testing integration and security:
   • Test integration with other systems.
   • Perform security testing.
6. User testing:
   • Involve real users to gather feedback.
   • Conduct A/B testing against humans and/or non-AI software.

Integration and Deployment

Relying on cloud infrastructure might be efficient, but to go even further, consider deploying lightweight AI models on edge devices closer to data sources. As a result, you can communicate a lot of interesting value props, like reducing latency, improving data privacy by keeping sensitive data localized, and increasing system resilience in case of network disruptions.

Before fully deploying the AI, you can also try to experiment by running it in a “shadow mode” alongside existing systems. This allows you to observe its performance in real-world scenarios, compare its outputs to human decisions, and refine the model or decision-making logic based on insights gleaned from this shadow phase.

Last but not least, ensure ethical AI deployment. Go beyond the technical level by creating policies that address possible bias and unethical usage. Propose detection and mitigation strategies for unacceptable content. For example, implement human-in-the-loop to monitor the output. You might also include environmental considerations like designing efficient architectures that minimize computational requirements and memory footprint.

Best Practices to Develop AI Software from Scratch

If you’ve gone this far in this blog post, you already know that ensuring efficient and reliable AI software development requires a multifaceted approach. To ensure you can keep track of multiple initiatives when you build AI software, adopting version control systems is a good idea. This allows for tracking different model architectures, hyperparameter configurations, and training runs, simultaneously ensuring that ethical concerns are met. 

Secondly, containerization with tools like Docker is a valuable strategy. It packages your code and dependencies into a self-contained unit, streamlining deployment across various environments and guaranteeing consistent execution. It is also easier to revert to more successful images or to go back and see why less successful experiments didn’t work.

Finally, proactive problem identification is key. A robust logging system captures information about model training, inference, and system errors. This data proves invaluable for debugging, performance analysis, and pinpointing potential issues before they escalate in production. 

Common Challenges in Developing AI Software

Scarce or Biased Data

This consideration is like a double-edged sword. If you feed your model with too much diverse data, it might be difficult for it to learn, which will lead to errors. On the contrary, if you have too little data or biased data, the model will make errors, too. Unfortunately, there is no uniform solution when you decide how to build AI software. You need to experiment and fine-tune your algorithms for your particular use cases.

Lack of Model Explainability

To effectively debug the model, you need to be able to trace the reason for problems that arise. However, many AI models come to their conclusions in mysterious ways. That is why developers strive to create Explainable AI (XAI), where it will be possible to understand how the model came to a particular outcome.

High Costs of Training and Deployment

Training an AI model might require you to go wild with those CPU cores. As a result, infrastructure and computational costs skyrocket, and it becomes difficult to achieve a substantial ROI. When designing a technological roadmap, consider the future need for optimizing resource allocation.

After-Release Fears

Integrating AI models into production environments holds a certain degree of risk when building AI software. The erroneous output might be offensive to certain groups of people or even plainly wrong. That is why AI developers can’t afford just to let AI go wherever it wants to go. Instead, they need to set up continuous monitoring, logging, and incident response routines. 

AI Software Solutions: Success Stories

While AI models are still regarded as a technology of the distant future, some businesses have already embraced their power and are now enjoying benefits.

JP Morgan Chase

One of the world’s oldest chain of banks, recently started using an anti-fraud AI model. OmniAI works as a helper of human data scientists, helping them extract valuable insights from vast varieties of unstructured information. The result is quite promising: the company claims to have reduced fraudulent transaction attempts by 80%.

Duolingo

Duolingo Max uses ChatGPT 4 to personalize the learning experience. They also host English language proficiency exams that automatically adapt to the knowledge level of the student while taking the exam. While using the app, learners can ask Duo to explain why the previous answer was wrong in the chat window. Additionally, they can practice simulated real-world conversations by chatting with AI.

Future Trends in AI Software Development

While generative AI is on top right now, emerging trends bring those models closer to edge devices. As intelligence gets closer to the data source, it becomes easier to create AI software for correct decision-making. Such models reduce latency and dependence on third-party cloud processing, thereby enhancing user experience. Some examples include developing AI for autonomous vehicles or industrial process control.

Development tools like low-code and no-code platforms are emerging as well, empowering software developers with a broad range of skill sets to contribute to creating and training AI models. This trend makes AI more accessible and democratizes the market.

Wrapping Up

Building AI software is currently a top trend in the IT industry. Companies that offer proprietary insights receive generous funding and explore ways of improving AI output. However, there are still some substantial risks to be addressed when it comes to collecting relevant data, maintaining efficient learning, mitigating ethical concerns, and optimizing infrastructure costs. 

Intellectsoft offers 15+ years of experience in custom software development, system architecture, and team augmentation. Talk to our experts today to discover how you can improve your AI development pipelines, conduct efficient project management, and meet your business goals.

The post How to Build an AI Software: A Comprehensive Guide appeared first on Intellectsoft Blog.

Nowadays, IT teams want to find the right balance between classical and agile software development methodologies. While any of them might sound like a perfect solution theoretically, the practice might show otherwise. Agile projects grow into relentless scope creep without any direction, while heavily documented processes end up being heavily bureaucratic and inefficient.

If you want to find out more about what software development methodologies are out there and how we use them at Intellectsoft, check out our webinar recording from BrightTalk. 

In this article, we explore how the Stage Gate Process is beneficial for IT teams. It emerged as a risk-reducing tactic for product development in other industries and now starts to gain traction in the digital realm.

What is the Stage Gate Process?

Stage Gate process is a project management technique that breaks down projects into distinct stages with decision points (“gates”) between them. Companies can use it in software development, but it’s not the most common approach. 

The Stage Gate methodology has its roots in the 1960s within the New Product Development (NPD) practices of major pharmaceutical and aerospace companies. These industries dealt with complex projects requiring significant resources, risk management, and regulatory hurdles. The Stage Gate process template provided a way to break down these projects into manageable phases with clear instructions for go/no-go decisions.

How is the Stage Gate Process Used in SDLC?

In software development, the Stage Gate Process is reminiscent of hybrid methodologies like the Spiral Model and Rational Unified Process that connect the best of both polar opposites: Waterfall and Agile. 

Stage Gate Process is rather a simplified alternative because the so-called “gates” in the sense of milestones are basically present in every other software development methodology out there. However, it does include ideation and business case creation, which are often left out in common SDLC workflows. In this regard, the Stage Gate Process offers a more holistic approach to product development where the actual production is a logical outcome of the business needs.

So, Which Projects is It Good For?

• Large, complex projects: For major software development initiatives, Stage Gate can provide structure and control.
• Highly regulated industries: Some industries have strict compliance requirements that benefit from the structured approach of Stage Gate.
• Risk-averse companies: For companies prioritizing risk mitigation, the decision points of Stage Gate can offer reassurance.

Alternatives to Stage Gate Process

• Agile methodologies: More iterative and adaptable, better suited for rapid changes and uncertain requirements.
• Lean development: Focuses on minimizing waste and delivering value quickly through feedback loops and continuous improvement.

Ultimately, the best approach depends on the specific project and the company’s needs. Keep in mind that unless you work in a heavily regulated industry, you don’t necessarily have to do everything by the Stage Gate book. Opt for optimizing if you like some parts of this approach and dislike others. Your team can hybridize the Stage Gate Process to have some understandable structure to follow while keeping the flexibility options, for example. It is better to follow what works best for you rather than adopting Scrum and then having robotic stand-ups where people are involved just formally.

5 Phases of the Stage Gate Process

Go! 🏇

You’re good to launch! The tech works, the market wants it, and the numbers add up. Time to hit the gas and execute the plan.

Kill It 🙈

Not every idea wins. Maybe tech hurdles are insurmountable, the market shifted, or costs outweigh benefits. Shutting down saves resources and lets you focus on better bets.

Hold On ✋

Hit a temporary stop sign. Maybe you’re waiting for key partners, the market needs time to evolve, or your team is busy elsewhere. Define clear conditions for restarting when things change.

Rework It 👷

The design needs tweaks, user feedback demands changes, or finances need fixing. Refine your plan, address issues, and re-enter the race with a stronger engine.

Go With Conditions 🚸

Proceed with caution! You get the green light, but specific milestones need hitting, uncertainties resolved, or external approvals secured. Prove you’re on track before going full speed.

Key 6 Gates of the Stage Gate Process

Just like any project milestones, Stage Gates represent some structures that you have to pass. In order to be approved for the passage, you need to study what has been done up until this point and meet some predetermined criteria.

Let’s review those six gates in more detail.

To understand Stage Gates in a less boring way, let’s imagine a software project as your favorite RPG. Think of the code delivery as a grand quest and Stage Gate as a series of key milestones. Each phase brings unique challenges and rewards, shaping your journey to success.

Gate 1: Ideation (The Tavern)

This is where your initial spark ignites! Brainstorm ideas, explore possibilities, and gather your team. Define your quest’s core problem to solve and its value (impact/reward).

Gate 2: Scoping (The Cartographer’s Tent)

Map your journey in detail. What features are essential (main quest)? What can wait (side quests)? Define the tech stack (your tools) and estimate the resources needed (gold and supplies).

Gate 3: Business Case (The New Quest)

Convince the stakeholders(NPCs). Research the market (terrain), identify your target audience (allies), and estimate potential rewards (profit). Show your project is worth the risk (stage gates).

Gate 4: Development (The Forge)

Time to build your software (hero)! Use your chosen tech stack (tools) to craft features and functionalities (armor and weapons). Your team works through sprints (quests) to progress.

Gate 5: Testing & Validation (The Arena)

Put your hero to the test! Users (battle opponents) provide feedback, revealing strengths and weaknesses. Iterate and refine (train and level up) before the final launch (battle).

Gate 6: Launch & Beyond (The Victory Celebration)

Release your hero into the wild! Support users (allies), gather feedback (loot) and address issues (monsters). Remember, the quest never truly ends – adapt and evolve (new quests) to conquer future challenges!

Stage Gate Process Example

Imagine you’re a PO working on a mobile app for one of Intellectsoft’s clients in the construction industry. Key business stakeholders want to upgrade the app to introduce a better UX and enhanced functionality. They give you the task of researching what features to add, what bugs to fix, and how to increase the overall experience. After the validation of the presented ideas, your team will develop all the updates. While the scope is yet to be discovered, one requirement is already crystal clear: you need to use the Stage Gate process for managing this project.

Phase 1: The Brainstorming

Your team gathers in the office kitchen, fueled by coffee from the machine, and discusses the next Stage Gate for the construction app. Ideas fly: “What if we could track materials in real-time, saving time and money?” You define the core problem – inefficient material tracking, and the added value – increased productivity and cost savings.

Phase 2: The Blueprint

The project backlog is enriched with new items. Features like GPS tags, inventory management, and progress reports take shape. You assess the tech stack (cloud platforms, APIs) and estimate resources (development time, budget).

Phase 3: The Investor Pitch

Time to convince the stakeholders. Your BA mates present market research with a beautiful presentation on industry trends and competitor analysis, showcasing how your feature solves real pain points and generates profit in the form of increased subscriptions and reduced project delays.

Phase 4: The Coding

The coding commences! Your development team builds the feature using chosen tools. Sprint after sprint, you see new features like GPS tracking and inventory management come alive.

Phase 5: The Beta Build

Real construction sites become your testing grounds. Beta testers are real sector employees like foremen and site workers. They provide feedback. As a result, bugs are squashed, and features are refined. For example, you decided to add alerts for low material stock. The fresh new features undergo user acceptance testing before launch.

Phase 6: The App Store Celebration

Your features go live! Users download the app upgrade, construction projects run smoother, and positive reviews flow in. But the journey continues. You gather feedback, continue fixing bugs, and plan future enhancements to keep your app at the top of the construction app charts.

So, will your real-time material tracking system revolutionize construction? With well-navigated Stage Gate project management, your app could be the next big thing!

What Are the Benefits of the Stage Gate Process?

Transparency

The defined stages and gate reviews promote clear communication and collaboration among stakeholders, ensuring everyone is aligned with project goals and decisions.

Flexibility

The ability to hold or recycle projects allows teams to adapt to changing market conditions or unexpected challenges, ensuring the project remains relevant and feasible.

Better Outcomes

Early identification and termination of unviable projects minimize wasted resources and financial losses associated with failed projects.

Wrapping Up

Overall, the higher project success rate that is associated with employing Stage Gate is most likely associated with a clear structure and quick elimination of inefficiencies. Streamlined decision-making helps to allocate resources with maximum efficiency while reducing risks and waste.

Intellectsoft offers a variety of project management techniques that are tailored uniquely to the business and technical needs of our customers. For instance, we designed a mobile solution for Ernst & Young that empowered investors with a customizable financial forecast interface.

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