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.

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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!

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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.

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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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Thriving with Innovation and Dedication

“This honor is a true reflection of our team’s tireless efforts and expertise,” says Alex Radchenko, CEO of Intellectsoft. “We consistently strive to deliver innovative solutions and exceptional value to our clients, and this recognition motivates us to push even further.”

Inclusion on the Global Outsourcing 100 list involves a rigorous evaluation based on size, growth, customer references, best practices, and industry awards. Our dedication to these areas shines through, exemplified by recent achievements like being named a Top Firm in Web Dev and Design by 50Pros and making it to the list of the Top Software Developers by Clutch.

Growth Strategy at the Core

Olga Kavunenko, Chief of Growth at Intellectsoft, adds, “Being recognized by IAOP validates our growth strategy and reinforces our commitment to building long-term partnerships with our clients. We believe in a collaborative approach, leveraging innovative software solutions to help them achieve their business goals.”

This achievement marks another exciting milestone in our journey, and we’re excited to continue delivering exceptional experiences for our clients.

Join Us on Our Voyage of Innovation!

Are you looking for a trusted partner to help you navigate the ever-evolving tech landscape? Look no further than Intellectsoft. With our award-winning expertise and collaborative approach, we’re here to propel your business forward. Contact us today, and let’s start shaping your digital future together!

The post Intellectsoft Soars as a Rising Star on IAOP’s Global Outsourcing 100 List! appeared first on Intellectsoft Blog.

According to McKinsey, 45% of IT projects exceed their budget, and 7% exceed planned time constraints. This signifies poor strategy development at the initial stages.

Intellectsoft’s expertise in conducting the Discovery Phase for system engineering and project management helps you avoid costly mistakes and achieve project success. Learn more about the benefits and deliverables in the article below.

What is the Discovery Phase?

Before learning the intricacies of the project Discovery Phase, you need to make sure you know which “neighborhood” you’re in. When looking for answers online, you can come across a range of various definitions that have nothing to do with the Discovery Phase concept that you really need to know. 

Let’s just clarify that, in this article, we are going to talk about the Discovery Phase as part of the system architecture conducted by your software engineering partner, Intellectsoft. We will also look at the alternative definitions from the Project Management perspective so that you know the difference. 

In the first case, the Discovery Phase encompasses the primary step of the Solution Architecture lifecycle (Lovatt, 2021) that creates a foundation for a successful solution. In this context, it typically includes three major types of activities:

• Fact-checking. Thoroughly studying all architectural inputs that are relevant to the problem area.
• Engaging stakeholders. Identifying people relevant to the solution scope and their responsibilities. 
• Establishing Success Criteria. You’ll never know if the solution turned out well if you don’t set expectations, assessments, and success criteria beforehand.

Inputs to these activities: architectural artifacts, stakeholders register, business case. If some critical information is unavailable, the Discovery Phase has to be extended to cover investigation and modeling. Existing artifacts like business requirements are also subject to reformulation and modification if key stakeholders deem those insufficient.

Deliverables: technical approach outline, system design outline, project planning roadmap, wireframes, and prototypes.

Important Differences

In our customers’ internal business processes, the Discovery Phase in project management involves identifying the business value of some idea. Thus, the major deliverable is the business case. More often than not, we cover the system architecture concept of the Discovery Phase because customers prefer to create their business cases internally (since it depends on many organization-specific and sensitive data).

The primary role of the business idea justification is defining a dominant business objective. This could be a market demand, organizational need, customers’ request, technologically competitive edge, social responsibility, or legal requirements. Whatever the purpose, a project needs some rationale to begin with.

Deliverables: Business case/plan, current vs. future state, return on investment; list of prequalified vendors, predetermined clients, preexisting contracts, capital expenses (CapEx), and operational expenses (OpEx) plans.

In this scenario, the main deliverable (business case) outlines the scope, audience, and purpose of the proposed work and establishes the value it could bring. A well-researched business case can show that a project is worth the investment. It is a strategic document.

Consequently, here Intellectsoft steps in on the next phase (which traditionally could be called the Initiation phase), where we define success criteria, preliminary scope and requirements, high-level risks and assumptions, summary milestones, summary budget, and key technical stakeholders, create a stakeholder register, develop a responsibility assignment matrix, establish communication channels, begin records management, review existing artifacts, determine a solution design, define access requirements for project work, obtain charter sign-off, and perform kickoff.

How to Plan a Discovery Phase of a Project?

Discovery Phase planning at Intellectsoft is adjusted to every individual request. Instead of proposing cookie-cutter solutions, we emphasize quality and usefulness, even if it’s a preliminary system design outline. That’s why, after the initial internal discussion, we allocate resources to cover the full-scope Discovery Phase. If we are working with the existing client, the planning phase can be very fast because we already know the artifacts and the current state. However, if we are working with a new customer, the planning process may take up to a few weeks. 

Of course, there is also a standard process at play; for example, our project Discovery Phase normally starts with activities like:

• Preliminary meetings with a customer,
• Interviews with stakeholders,
• Contextual research,
• Documentation study,
• Review of preexisting processes.

Once we have a general understanding of the scope of work, we are ready to dive deeper and work on discovering appropriate technological solutions to the project in question.

Sometimes, when technical stakeholders say planning, they also mean all the activities of the actual, ongoing Discovery Phase of a project. So, just to clarify, all the processes of brainstorming and creating supporting documentation throughout the already initiated phase can be referred to as Planning.

What Are the Discovery Phase Deliverables?

Below are examples of Discovery Phase deliverables that might be associated with software development projects. Note that it greatly depends on the customer and the particular objectives. For larger projects and sometimes for venture series startups, more paperwork is involved.

Existing Artifacts Review

Artifacts are tangible items, like documents or products, that were previously made and are available at the time the project Discovery Phase starts. For example, we might ask to review the business case, current project documentation or previous designs, and so on. Without understanding what you already have, we won’t be able to make informed conclusions and outline of what needs to be done next.

If we are to build a complex system, we might ask to clarify your relationship with current or future clients, vendors, etc. Preexisting contracts might affect how the system needs to be shaped, e.g., what business processes and logic to include. 

You might supply such artifacts as a master service agreement (MSA), statement of work (SOW), or terms of reference (TOR).

Capital Expenses vs. Operational Expenses Assessment

Budget allocation is usually the customer’s call, but when we see room for optimization, we offer guidance. For instance, in heavy equipment facilities, projects often involve both operational expenditure (OpEx) for software, which doesn’t appreciate or depreciate, and capital expenditure (CapEx) for durable assets like equipment, which are taxed and appreciate/depreciate over time. In such cases, we suggest ways to leverage technology to increase the value of those durable assets and lower OpEx, ultimately boosting ROI.

Example

A client needs software for monitoring ice melting that might result in spring flood damage to a city at the foot of the mountain. The problem is that the visualization quality is poor, so the client is considering a multi-million dollar investment into cloud-based data processing architecture that improves quality with AI. We analyze their situation and point out that improving the physical layer (OSI Layer 1) by ensuring a clear line of sight between transceivers, along with minimum loss of local connectivity on Layers 2 and 3, could achieve similar, even better results faster and at a lower cost. 

Therefore, we’d advise enabling VLANs to isolate network traffic throughput and introducing QoS routing. Then, we could suggest lightweight software specifically designed for handling interference, phase delay, and signal loss issues to avoid operational lags. We would also ask questions about how satisfied the client is with the current data processing algorithms because GIS is known to work with huge amounts of data, so before doctoring that with AI, it is necessary to make sure that we do not work with erroneous and poorly pre-processed data in the first place (which can be achieved by well-known mathematical algorithms and quickly done using existing libraries). This software might focus on key data points and visualization techniques optimized for the available bandwidth, delivering clear visuals more reliably.

Note: The low-level software solution will depend on various factors like the facility’s specific needs, the type of wireless system, and the desired data representation. This example aims to illustrate the principle of offering software alternatives that capitalize on improved hardware conditions instead of relying solely on “fashionable” cloud solutions that don’t necessarily address the core problem.

Because capital assets will benefit the organization for years after the project ends, they may be sourced from the organization’s budget, not the project’s. That is often a point that needs to be escalated to a higher level of negotiation.

Current State vs. Future State / Gap Analysis

Here, the analyst will create a contrast-and-comparison table of the current state of project solutions vs. the desired improvement. While these statements should be clear and concise, they should not focus on either the technical or implementation aspects of the issues.

For example, instead of mentioning the future state as “Sales team is migrated to This CRM Brand,” it is better to write “Sales leads are increased by 30%.” Particular solutions to the future state will be designed at a later stage of the project Discovery Phase.

This document can focus on gaps, bottlenecks, and weaknesses in current processes, such as lack of automation or missed revenue caused by outdated software. Growth of business operations might also go here, as it is an opportunity for improvement.

Interface Inventory, IT Asset Inventory

Naturally, any solution has multiple components at different levels of interaction and communication with each other. By creating an interface inventory, we are clarifying our understanding of the solution architecture and, therefore, can refer to it when deciding on the implementation roadmap priorities.

We document details like:

• Name of the interface
• Source
• Destination
• Rules and other events
• items exchanged, including any supporting information
• Workflows, itineraries
• Pre- or post-conditions.

Same goes for IT assets. We want to itemize all the physical and non-physical digital entities such as networks, servers, workstations, databases, and so on.

Project Charter

Usually developed by the PM during the Discovery Phase of project management, this document is the cornerstone of the project and its main point of reference. It defines the reporting structure for decision-making, as well as the project’s purpose, goals, objectives, requirements, high-level assumptions and constraints, high-level risks, success criteria, a summary milestone schedule, key stakeholders, a summary budget, and an initial key stakeholder register. The project success criteria, which serve as a benchmark for the quality of the produced outcome, are also included (Heldman, 2022).

Risk Assessment

Discovery Phase of a project also highlights possible issues and high-level solutions that are possible to suggest based on existing knowledge.

The basic version of the document is usually an executive summary that provides a top-level synopsis of the problem, the proposed solution, the justification, and the expected benefit, but it skips the analysis behind these decisions. That analysis, along with the estimates of financials, would be included in the more in-depth version of the risk assessment. Depending on the size of the project, it could include the following elements:

• Problem statement: Description of the issue.
• Analysis: A root cause analysis or any other analysis that predicts the outcome of the stated issue.
• Recommended solution: The solution that was analyzed as most likely to succeed
• Proposed solutions: Alternative approaches to the recommended solution, their strengths and limitations.

The initial risk assessment documents are typically reviewed, refined, and formalized in the project charter.

Preliminary Scope Statement

Outlines the tasks to be done and specifies what’s not part of the project. It’s often called the big-picture scope.

A PM looks at the charter’s requirements and makes a detailed scope statement during planning. This statement gets added to the scope baseline and lists the individual tasks needed for the project. However, the charter’s scope is preliminary until more analysis is done.

Usually, it describes the project, what needs to be delivered, how it will be accepted, important points in the timeline, and what’s not included in the project.

Solution Design Outline

Solution architecture is often the most anticipated part of the project Discovery Phase. Everyone wants to know the architect’s vision of how to build the solution to estimate what it would take to make it a reality.

Determining which solutions are needed and how they will interact with existing infrastructure is what our architects want to show. We also want to plan for the future, making this solution maintainable and scalable to support the business’s growth.

The solution design process has two main outputs: a high-level conceptual design and a low-level technical roadmap. 

Technical Roadmap

Our tech team, including the architect, developers, UX designers, and more, are the main stakeholders for this document. They want to outline the low-level tech details, such that are specific enough to put into sprints and iterations. 

Importance of the Discovery Phase

The Discovery Phase activities target a primary goal: developing a conceptual design for part or all of the project. The proposed solutions must align with our customer’s business requirements, therefore addressing business needs and opportunities.

It might be one of the most challenging parts of the solution design pipeline because we need to showcase the value of our proposed design without going into too much detail. It is important to keep an open mind and stay away from specifics because doing so might result in focusing on logical conclusions rather than premises. On the flip, more investigation and flexibility down the pipeline typically result in better working systems.

Importantly, the conceptual solution outline ideally holds the core logical reasoning, which can be extended into and connected to all further levels, down to the physical. Due to the nature of modern system architectures, the level of technological volatility might be high, so making a high-level solution outline ensures that we will not make premature decisions that will have to be redone further down the line. It is essential to acknowledge that the solution architecture life cycle exhibits a high degree of parallelism and iterative refinement based on accumulating knowledge.

Discovery Phase Best Practices

Adhering to industry-wide standards such as ISO/IEC/IEEE is considered the best practice in the project Discovery Phase. Some of the activities described in these guidelines include the following.

Gathering Architecture Inputs

A number of factors determine whether useful architecture artifacts are available at the start of the project Discovery Phase:

• Architecture maturity: whether architecture practice is established and working well in the organization or enterprise and if there is an efficient repository of artifacts that can be easily accessed and in which it is easy to find relevant artifacts.
• Overlap with current problem area: if the new problem to be solved is in an area or operational domain that has never been addressed before, there will be fewer relevant artifacts.
• Clarity of understanding of the current problem: if the focus of the problem is too broad or ill-defined, it is harder to distinguish relevant artifacts, and too many irrelevant ones may be included as inputs.

The main product of this activity is a catalog of relevant architecture artifacts for the operational domain of the solution vision. At this stage, it is not necessary to examine the artifacts in detail, but it is important to identify them and know they are available when needed.

If the artifacts are organized in a repository, then more details will be available, such as:

• Date of production and modification
• Version number
• Owner
• Business area
• Related artifacts that would be impacted by change, and any dependencies.

It is also useful to record the reason for selecting this artifact by linking it to an element or concept in the problem domain.

Defining Stakeholder Engagement

According to ISO/IEC 42010:2011 and TOGAF definitions, stakeholders are individuals, groups, or organizations having an interest or concern in a solution. They can be internal or external.

Beyond just listing stakeholders, we can also create a stakeholder communication plan that expands on the level of involvement in the solution architecture process, responsibility matrix, methods of communication, deliverables, etc. Usually, that level of detail is more important in enterprise projects and can be omitted in startups and SMBs or ongoing agile processes.

Refining Business Requirements

Project success criteria are not possible to draw without prior documentation of business requirements. The logic here is simple: if all the requirements are met – the project deliverable is considered successful. Alternatively, the success can also be judged from the perspective of project constraints like time and resources – in this case, their optimization would imply high-quality results.

Further down the line, business requirements also become a basis for us to create a solution design proposal and a roadmap outline. Keep in mind that if we are adopting an Agile methodology, business requirements might come and go as the project evolves.

The documenting process is sometimes more complex than writing something like “Ok so my idea for a project is this…” If we are taking the business value seriously, our business analysts need to go through several stages:

• Capturing: listing requirements, plus simple yet actionable explanations.
• Validating: checking for completeness and correctness.
• Verifying: testing requirements to see if they are the best option, checking for conflicts and overlaps.

The resulting requirements catalog might include:

• Name of the business requirement;
• ID;
• description;
• Justification;
• Category;
• Status;
• Change control details.

The requirements catalog is useful for tracing how business requirements become system or data requirements. It can also be used to check if the business needs to consider additional operational measures such as new SLAs.

To document business requirements, we might interview stakeholders, review existing artifacts, or even make new ones (such as use cases, user stories, data models, etc.)

Creating Solution Proposal

At this stage of the project Discovery Phase, we are approaching the most interesting part – describing and illustrating our vision for the solution. We need to focus on core models without swinging away into specifics that could potentially lead us astray. So here we are showing the essence of the design, not constrained by jumping to conclusions or step-by-step plans yet.

If there are several good alternatives, we might propose more than one solution for a customer to choose from. It is also important to come to an agreement between all the stakeholders at this stage.

Visuals

Complex tech solutions really come alive in stakeholders’ imaginations once they are presented visually. For that, we can use a variety of mediums:

• Diagrams, charts, graphs, maps
• Wireframes
• Design mockups
• And more!

Our objectives here are to abstract high enough from technical buzzwords, convey meaning, and, ultimately, obtain stakeholders’ approval.

Supporting Artifacts

Nice visuals are fun, but stakeholders want to understand how the solution addresses their area of business. For that, we dive deeper into supporting artifacts.

For example, a sample high-level architecture definition:

• Outlines the infrastructure layer that an application will be built on top of
• Depicts main components together and highlights proposed building blocks
• Specifies relations and interactions between subsystems
• Defines security boundaries and considerations
• Declares data types and their storage approaches
• Determines application resiliency and failover processes
• Highlights the insights of the middleware layer
• Illustrates the principles of scaling and load balancing of the system and its components.
• Indicates observability functionalities and toolset
• Marks out external interfaces and integration capabilities and approaches

Project discovery phase might result not only in the collection of information but in the true discovery of requirements that stakeholders didn’t know before. That is why, to clarify our solution ingenuity, it’s useful to maintain artifacts like traceability/cross-reference grids where we show the links between the initial idea and its evolution path, providing explanations for the variations.

Outlining the Step-byStep Plan

At this stage, we collect all the artifacts we obtained or produced before and go down the rabbit hole of the specifics. We are scoping the solution, analyzing interfaces and risks for smaller deliverables (like features), looking at the tech stack options, and offering a high-level step-by-step plan.

Scope Definition

After choosing a solution, we formalize its scope by detailing changes in each business area, specifying their nature (new, existing modification/removal/replacement), and linking them to relevant architecture documentation. This ensures clarity and avoids conflicting efforts throughout the solution’s lifecycle.

Building Block Analysis

Building block analysis helps identify reusable components within the organization and assess their suitability for the solution. It creates a solution-building block model detailing each component’s name, category, ownership, current state, and required modifications.

KPI Documentation

Before initiating the project, we need to formalize our acceptance criteria with stakeholders. What is considered successful? What is considered done? What are the key metrics that we are striving for? What is the logic behind deciding what’s good and what isn’t? All that needs to be described at the stage of documenting KPIs.

Most often, the KPI documentation types will depend on methodologies and stakeholders involved.

Examples of KPI documentation:

• Budget (cost performance, cost variance, planned value, etc.) 
• Performance (schedule performance/value, resource capacity/velocity, stories in development, tasks reopened, changes accepted, burndown, cumulative flow, code coverage, etc.)

Wrapping Up

Discovery Phase is one of the most exciting stages in software development. It is the time to be creative and to attempt to save the world by visualizing how your solutions will positively impact the global landscape. Whether you are an aspiring startup or an international enterprise, Intellectsoft offers a variety of Discovery Phase packages to suit your needs.

We’ve built software for train passengers, enabled multilingual communications, streamlined investment decision-making, and even helped to build cities. With a diverse experience under our belt, we confidently scale ambitious businesses that want to embrace digital evolution in all of its beauty and power.

The post Guide to the Project Discovery Phase in 2024 appeared first on Intellectsoft Blog.

If you’re looking to make your mark in this dynamic industry, you’re on the right page. Read on to better understand the idea of app design cost.

Having an app that can either entertain, help or educate your audiences is likely to keep profits skyrocketing. Data shows a steady increase in mobile app downloads, with predictions of 181 billion downloads by 2026.

With such projections, researchers predicted the total revenue of the app development market to reach around $400 billion in 2024.

Source: Statista

With a total of 26.3 million developers in the world just last year, startups are faced with the problem of how to choose a partner designer.

Adding up to that are the factors and processes that drive the total app design cost. From the conception of your ideas down to software updates, each step affects your balance sheets. It’s crucial to know how to estimate the cost of a mobile app design.

Let’s get started.

What Factors Determine the Cost to Design an App?

Whether you’re running a seasoned app company or a startup, developers have to make efforts to ensure the following: 

• A reasonable timeframe for development and launching
• Zero bugs, errors, and any other issues
• Users are willing to pay for your app, in-app purchases, and/or a subscription

Each application software has a unique purpose, set of features, and target audiences. That’s why the total mobile app design cost also varies. While there are quite a few factors that affect your expenses, there are also things that you can do to manage both these determinants and your expectations better.

Complexity

Logically, a more complicated app will lead to a higher app design cost. 

With a basic app that has limited features and functionality, developers need less time. Complex applications often contain more features, advanced algorithms, and powerful integrations. 

Actionable insight: Consider whether your app needs features like live chat, payment gateways, third-party app integrations, or customized add-ons, as these tend to ramp up overall expenses.

Platforms

Your chosen platforms – iOS, Android, or both – will considerably shape your app design cost. Designing for only one platform is considerably cheaper; however, targeting users of both platforms ensures you have a bigger customer base and larger profits. 

Here, the main reason for a higher mobile app design price is the programming languages for these two operating systems. This means designing for both platforms simultaneously may require separate teams or developers with cross-platform skills, which can inflate costs further. 

The choice between native or web apps also impacts the budget. Since native apps – where mobile apps fall under – are defined as those built for certain device types, they’re more expensive to develop. 

Actionable insight: Take time to analyze your target audience. For example, if your intended users would benefit from an on-the-go app, opt for mobile-only software. You can justify cross-platform development later should you think your audience tends to use your software across all devices.

Design Elements and User Experience

The cost of app design also depends on how recognizable your brand is within the app. Your app should be consistent with your brand’s look and feel. This includes logos, colors, and fonts. 

One key factor here is the user experience (UX), which is the overall flow and ease of navigation. Part of UX can also be impactful visual aesthetics, too. These include the user interface (UI), animations, and in-app interactions, which developers may meticulously design.

Actionable insight: Your app should be well-structured, meaning users can simply navigate. Doing so provides users with a positive experience. While giving them such ease, you should also focus on staying true to your brand. Balancing this may incur costs, but satisfaction and user retention can make it worthwhile.

Team Type

Whether you hire freelancers, an in-house team, or outsource to an app development agency will affect the overall cost. Here’s a quick list of these team types’ cost levels, pros, and cons for you to weigh your choices:

• Freelancers 
  • Cost: Low
  • Pros: This tier has the most flexible rates for startups with less complex app needs. Note that there are freelancers who have diverse skill sets that can help with sudden changes in development. 
  • Cons: Due to the lack of supervision, quality can be unpredictable. Moreover, potential timezone problems may cause challenging communication and updates.
• In-house Team 
  • Cost: Mid to High
  • Pros: Since this will be your team, there’s the potential for excellent collaboration and more control over the project. 
  • Cons: The cost of hiring and maintaining an in-house team can be steep due to overhead expenses such as office space, equipment, and training.
• Outsourced Agency 
  • Cost: Mid to High
  • Pros: Here, you’ll access a company’s broad professional skills and resources. They can even offer an all-in-one solution, from idea conception to post-launch updates.
  • Cons: Can be more expensive than hiring your team. Timezone complications might also occur when working with overseas agencies.

Actionable insight: You should define your desired work quality of work and the level of control you wish to maintain over the project. Regardless of who you hire, don’t forget to check portfolios and reviews to see whether you’re speaking with the right professionals.

Team Location

As mentioned, geography can also affect the overall app design cost due to time zones. More often than not, developers in North America and Western Europe charge higher compared to those in Asia. 

These discrepancies are often due to differing costs of living and market standards. 

Actionable insight: Choose a partner developer that’s able to align with your time zone. Remember, while an agency or freelancer may be overseas, they can bring fresh perspectives to your projects. Ensure you clarify some information about your target audiences, including their potential expectations, needs, and values.

Your average mobile app design cost depends on these factors – but that’s just the tip of the iceberg. It’s still the steps of the process that set the base prices.   

App Design Cost Breakdown

While various components make up the actual mobile app design cost, understanding them can be easier as you see the price of each element. Take these – all while considering the factors mentioned above – to get at least a rough estimate of how much your total spend will be.

Pre-Design Costs: Market Research and Concept Development

Market research lets you have a view of your demographics and some information about them. These bits of info, in turn, solidify your knowledge of your target audience’s wants, needs, and behavior, which you can later use as the foundation of your app’s concepts.

As shown in the table below, there are around 800,000 market research analysts in the U.S., with the population growing at a fast rate.

Source: U.S. Bureau of Labor Statistics

According to the U.S. Bureau of Labor Statistics, most industries make use of market research, highlighting the potential increase in demand and median pay in the following years.

This usually takes 6–7 weeks, depending on how large the sample population is. If you expect one researcher to work on your project for at least 40 hours per week, the average could start at around $1,500/researcher for one entire research process. However, keep in mind that not every software development contractor conducts this type of research, so you might want to consider services of a specialized market research vendor.

Once you know about your audience, you can now conceptualize your app. This includes drafting its primary features and user journey. This is when you want to start working with software development specialists. In most methodologies, this stage is called the Discovery Phase. It includes close interaction with business stakeholders for understanding the business logic and offering the best solution in terms of the app experience.

Overall pre-design costs: Market research starts at $1,500 per researcher, Discovery Phase – $10,000-$15,000.

Core Design Phases: Wireframing, Prototyping, UI/UX Design

This is where the concepts are brought to life. When researching the idea of app design cost, you’ll see the use of terms like wireframing, prototypes, and mockups. The core design phase starts with wireframing, similar to blueprints of architectural structures. 

In wireframing, you lay out the structure of the app and determine the functionalities for each part. This usually comes as the easiest core design phase, which doesn’t require heavy designing. All it usually takes to create a wireframe is the following: 

• Content blocks, such as placements for logos, artworks, or text descriptions
• Navigation menus
• Buttons

After finalizing these on a piece of paper or sketching app, developers then put them into prototypes, which turn the blueprints of navigation into actual app interactions. You can usually do the wireframes on your own, but they often come in a package with prototyping and mockups. 

While prototypes have the actual interactions, mockups are where the UI/UX design shines. Here, designers discuss with you the colors, fonts, and images of the app, showing its complete visuals. To make sure that your users get the best experience, you might need to start from working with a business analyst who helps you create requirements for UI/UX.

Wireframes can cost you anywhere from $2000 – this also depends on whether the freelancer or agency you hire charges per hour. For example, you might need to hire both the designer and the business analyst for 20 hours per week each. Prototypes start at $4,000, while mockup designs range from $3,000 to $15,000. 

Overall core design costs: Starts at ~$7,000

Post-Design Costs: Testing, Revisions, and Final Touches

Regardless of whether you outsource only UI/UX design or the app development and deployment altogether, you need to perform the quality assurance phase. Developers typically test apps for bugs, compatibility issues, and performance loopholes. These issues cause apps to lag, one of the top reasons people uninstall their apps. So, while visuals and the intended experience might be appealing, the most important consideration is for all your ideas to perform seamlessly before the app gets out there on the market.

You need to ensure that your app undergoes all kinds of tests, such as:

• Performance and function
• Security
• Installation
• Device compatibility
• Usability

That’s why testing costs can start at $5,000. The only test not within the scope of developers is usability, which real test users should take. However, agencies sometimes handle this part as well.

To reduce the costs of testing an app, you can ask the agency to start testing during the development’s early stages. Sometimes professionals call it “shifting left”.

Making necessary revisions and fine-tuning to ensure optimal user experience can require an additional $1,000 to $2,000. Wrapping up with final touches to perfect your app might entail a further $500 to $1,000.

Overall post-design costs: $5,000+ depending on complexity

Maintenance and Updates

Though not traditionally part of the design process, maintaining the app and rolling out timely updates keeps the app functional and relevant. This can incur a recurring cost of about 20% of the total app design cost annually.

Custom Features & Design

Additional features such as push notifications, geo-location tracking, or third-party integrations will increase the price of the app. Of course, you need those to fit well into the overall UI/UX, and if you need custom design, this will increase the cost as well. 

Pricing for these vary widely depending on the nature of the feature and the complexity involved in its integration. It could range anywhere from $1,000 to $30,000 and higher, depending on the complexity, whether or not you need business analysis, the number of features, and so on.

To give you a quick look, here’s a quick app design cost calculation if we were to base it on a really basic app: 

• $1,500 (Pre-design) + $7,000 (Core design) + $6,500 (Post-design) = $15,000
• $15,000 (Design) + $7,500 (Post-design) + 8*$3,000 (eight custom features) = $46,500

Remember, these figures are mere estimations and can fluctuate based on various factors such as project scope, timelines, and the expertise of your design and/or development team.

How Does the Design Process Affect an App Design Price?

As previously mentioned, this is the longest part of the core design process, which means it also affects the overall cost to design an app.

There are two primary cost-driving factors here: UX and UI, the cornerstones of an exceptional app design.

UX design focuses on the interactive component of the design process. This is the main reason you do your market research, hence its effect on the mobile app design price. 

With knowledge about the users and their needs in mind, developers then draft wireframes and make prototypes. The mockup is then tested by real users who provide feedback about your app. 

On the other hand, UI design gets into the detailed visual and interactive aspects of the app. This phase is where the prototypes and mockups come to life – all with the help of a style guide established by you and your partner developer. 

It’s the overall design, including the setup of buttons and icons, that makes UI quite a more expensive part of the process. Whether you hire animators for better transitions, buy stock photos, or have your creative team provide you with content, it’s going to cost you money. 

Both UX and UI design play off each other, forming a cohesive aesthetic and functional identity for your app. That’s why it’s only vital that you understand how these two affect your total mobile application design cost.

Tips on How to Save on App Design Costs

There’s no need to be intimidated by the cost to design an app. With a few simple tips, you can save a significant amount of money and avoid unnecessary costs.

• Prioritize the key features; you can build on them later.
• A well-researched project can save redesigning costs and ensure clarity as you relay information to your partner developer. 
• UX matters, so Invest in good interactive design from the beginning. It pays off by reducing troubleshooting costs later.
• Cheaper isn’t always better.
• Opt for agile development, which lets you test features, fix bugs, and make improvements more affordable over time.
• Optimize for maintenance. The better the initial design and development are, the lesser you need for pricey updates and bug fixes later. 
• Evaluate your in-house capabilities. For some, in-house design may save costs, but outsourcing can offer superior quality and speed.
• Use generic and reusable elements in design where uniqueness is not required. This can reduce the graphic design time and cost.
• Keep the design and functionality intuitive and straightforward. Complex features can drive up costs without necessarily increasing user engagement.

Each of these strategies can help reduce upfront and long-term costs. They can grant your developer more time to prioritize important tasks, including crafting better UX. 

How to Choose the Right Partner for App Designing?

Selecting the right partner developer influences three things: how much it can cost to design an app, the length of the process, and the resulting product quality.

To choose the right partner for your app design journey, you can start by looking at the track record of whom they’ve worked with. Doing so lets you see what they’ve done for their clients so far – all transparently presented in their portfolio.

A versatile mobile app development partner can work on every step of the design process. At Intellectsoft, we use the Agile approach to software development. This means you enjoy more transparency and on-time delivery from start to finish. 

If you have other software needs, we can help with those, too. Be it hybrid TV apps, software on wearable devices, web apps, or even IT consulting, you can rely on Intellectsoft. 

We take pride in having serviced companies from a diverse set of industries, including the following: 

• Construction
• Fintech
• Mobile Health
• Travel and Hospitality
• Retail and Ecommerce
• Insurance

Remember, a successful app is not simply functional; it must delight users with its aesthetic appeal and seamless operation. Intellectsoft has been recognized for consistently achieving this delicate balance, making us a great choice for your app design partner.

The post How Much Does It Cost to Design an App? appeared first on Intellectsoft Blog.

ChatGPT has gained a reputation as a transformative tool that reshapes certain aspects of our development processes. IT professionals are using it to automate and streamline specific tasks, such as assisting in writing test cases or providing quick solutions to common coding problems. On the business side, ChatGPT assists in rapid prototyping and ideation phases, where its ability to generate coherent, context-aware text can significantly speed up the initial stages of development. However, its effectiveness is constrained by the scope of its training and its lack of deep, project-specific context. It can suggest but not validate, nor can it understand the nuanced requirements of complex, custom software solutions.

Intellectsoft’s Center of Excellence (CoE) is an innovation hub where our technical teams conduct experiments and test new approaches. 

This interview covers our expert’s perspective on the use of AI, specifically focusing on GPT (Generative Pre-trained Transformer) models and their applications, limitations, and impacts on software development. 

Let’s dive right in! What are your plans for contributing to the TechBridge project?

Yevhen Kulinichenko: My plans for contributing to the TechBridge project are strategically focused on leveraging our technological expertise to maximize the project’s success while ensuring alignment with our broader business objectives. Firstly, we intend to deploy a cross-functional team of our best engineers and developers, who will work closely with the TechBridge project stakeholders. This team will not only bring in-depth technical knowledge but also a profound understanding of agile methodologies to ensure rapid and efficient project development.

A key aspect of our contribution will be the integration of advanced technologies like AI and cloud computing to enhance the project’s capabilities. For instance, we plan to utilize AI algorithms to analyze and optimize data flow, ensuring efficient processing and storage of data. Cloud computing resources will be leveraged for scalable infrastructure, ensuring the project can adapt to changing demands seamlessly.

Moreover, we are committed to fostering an environment of innovation and collaboration. We’ll encourage our team to engage in creative problem-solving sessions and think-tank workshops with other TechBridge participants. This collaborative approach is designed to not only contribute our ideas but also to learn from the diverse perspectives and expertise within the TechBridge community.

Lastly, we recognize the importance of sustainability in technology. Therefore, our involvement will also focus on developing sustainable and environmentally friendly technology solutions. We’ll explore and implement green computing practices to minimize the environmental impact of our technological contributions.

In summary, we plan to contribute a blend of technical excellence, innovative thinking, collaborative engagement, security-conscious practices, and a commitment to sustainability to the TechBridge program. We aim to help set a foundation for its long-term success and growth.

You mentioned AI implementation as a focus area. Can you expand on that?

Yevhen Kulinichenko: We want to build on the experience that we already have. In 2023, we conducted experiments on improving software development processes by implementing generative AI assistance. Results are still being finalized, but we’ve identified dozens of scenarios where ChatGPT can be safely utilized to streamline delivery and facilitate problem-solving. 

As we delve deeper into the utilization of ChatGPT in software development, we’re uncovering its profound potential in augmenting our coding processes. For instance, when dealing with boilerplate code, ChatGPT can swiftly generate templates for commonly used code structures in various programming languages like Python or JavaScript, significantly reducing repetitive tasks. We’ve experimented with using it to draft initial versions of RESTful API endpoints, where it considers standard practices and even suggests appropriate HTTP methods and response codes. 

We’ve also started leveraging ChatGPT for debugging assistance. By describing a bug in natural language, the model can suggest potential causes and solutions, drawing from a wide range of similar issues it has learned from. This doesn’t replace a developer’s expertise but acts as an additional tool to accelerate problem-solving. Thus, while ChatGPT is a game-changer in certain aspects of software development, it works best as a complement to human skills, enhancing productivity and quickly highlighting some basic blind spots or standard reusable patterns. However, it still cannot handle the complex logic, style, or requirements.

Can ChatGPT handle tasks requiring senior-level expertise?

Yevhen Kulinichenko: ChatGPT is limited in creating novel solutions. It works by learning from vast amounts of text data to generate plausible word combinations. However, it neither invents nor actually comprehends ideas. It is still a mathematical algorithm that arranges words according to numeric probabilities. There is no independent consciousness that is inherent to human intelligence. ChatGPT’s utility is rather in quick recommendations based on existing knowledge.

How does ChatGPT compare with development environment tools like PyCharm?

Yevhen Kulinichenko: That’s an interesting question. Indeed, Integrated Development Environments (IDE) like PyCharm offer tools like code completion, debugging, and context-aware refactoring options that consider the entire project structure. What’s more, It can adapt to the style and requirements of the codebase it’s working with.

Meanwhile, ChatGPT’s understanding of software languages and programming concepts is broad but can be superficial. It lacks the ability to understand the deeper context of a software project, such as project-specific architectures, dependencies, or the state of the current codebase. It can assist in generating code snippets, providing documentation references, or even suggesting solutions to coding problems. Still, it’s limited to the data that it’s trained on and doesn’t attend to the entire code structure and its state in real time like IDEs.

Are there any concerns about the misuse of ChatGPT?

Yevhen Kulinichenko: Yes, there’s a risk of misuse, especially if ChatGPT provides information based on public data that could be harmful. The potential for misuse of technologies like ChatGPT forms a crucial part of our risk assessment and management strategy. There’s always a possibility of inappropriate information being fed into AI models with malicious intent.

For example, in the software development realm, we can talk about the potential for intellectual property theft or inadvertent leakage. ChatGPT, when fed with sensitive data, could incorporate this information into its outputs, leading to data breaches. As a result, proprietary code or business logic could be exposed. So, for now, my recommendation is to refrain from trying to fix the code in ChatGPT or copy-pasting any information that might be considered sensitive. And since chatGPT 4 can now crawl the web, make sure your websites and social media accounts are properly protected as well.

Moreover, there’s the risk of psychological dependency on AI. Over-reliance of IT professionals on ChatGPT for code generation or problem-solving could lead to a degradation of fundamental skills. I think it’s vital to use such tools to augment, not replace, human expertise, ensuring that core development skills are retained and nurtured within the team.

Finally, what’s your perspective on the future of ChatGPT and AI in collaborative programs like TechBridge?

Yevhen Kulinichenko: Looking to the future, I anticipate significant advancements in tools like ChatGPT, particularly in their integration with more and more traditional software development tools and environments. We’re likely to see more sophisticated versions that offer better contextual understanding and can interact dynamically with existing codebases, potentially offering more nuanced and targeted suggestions, which will help us quickly scale and efficiently maintain global digital infrastructures. 

These advancements could lead to models that not only assist in code generation but also play a role in code review processes, offering intelligent insights based on coding best practices and even specific project history. Furthermore, as AI models become more adept at understanding and generating human languages, they will become invaluable in bridging communication gaps between technical and non-technical stakeholders, facilitating a clearer understanding of project goals and requirements. This evolution will require a shift in how we perceive these tools, not just as assistants but as integral components of our software development ecosystems, enhancing both efficiency and creativity.

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In her capacity as Chief of Growth, Olga will lead the charge in fortifying Intellectsoft’s growth initiatives, with a keen focus on identifying novel business opportunities, nurturing client relationships, and amplifying our technological footprint. Prospective clients can expect a heightened commitment to innovation and client success under Olga’s leadership.

Bringing a wealth of experience from her illustrious career in the technology and digital transformation sector, Olga is well-positioned to enhance Intellectsoft’s standing in the industry. Her industry insights will undoubtedly contribute to our mission of providing cutting-edge digital solutions.

“We are excited to welcome Olga Kavunenko to Intellectsoft as our Chief of Growth. Her leadership will be pivotal in steering our growth strategy, aligning perfectly with our commitment to delivering innovative solutions,” expressed Alex Radchenko, CEO of Intellectsoft. “Olga’s proven track record and unique skill set make her an invaluable asset to our team.”

Before joining Intellectsoft, Olga held key leadership roles in renowned technology companies, consistently achieving significant milestones. Her proficiency in navigating intricate markets and fostering innovation aligns seamlessly with Intellectsoft’s dedication to empowering businesses through digital transformation.

“I am eager to be part of the Intellectsoft team and contribute to its evolution journey,” said Olga Kavunenko, Intellectsoft’s Chief of Growth. “Intellectsoft has a remarkable reputation for delivering innovative digital solutions, and I look forward to collaborating with the team to accelerate growth and make a meaningful impact in the industry.”

For future and present customers, Olga’s appointment signals a renewed focus on delivering tailored solutions and unmatched expertise. With her at the helm, Intellectsoft aims to provide clients with not only innovative technology solutions but also strategic insights and a collaborative approach to ensure the success of their digital transformation endeavors.

As we welcome Olga Kavunenko to this pivotal role, we invite enterprises to anticipate an elevated standard of service, innovation, and synergistic collaborations from Intellectsoft. Together, we are ready to achieve new heights in the realm of digital transformation.

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