Artificial intelligence (AI) and Machine learning (ML) have become one of the most talked-about technology advancements, often called the key to development. BairesDev’s survey of software engineers revealed that 72% use generative AI capabilities in their development processes, while 48% work with these tools daily. The markets respond as the global AI industry is expected to grow by 37% from 2024 to 2030.
Not all companies actively use AI in their processes, but those that do gain significant benefits. Automation of routine tasks, personalized user experience, and more accurate analytics for decision-making – just to name a few. These are essential for custom development, too.
Businesses often face difficulties due to a lack of a clear AI implementation strategy, mismatched technologies, or insufficient team expertise. AI integration is a complex process that requires technical competence and resources. Still, by choosing the right vendor, you will get a high-quality solution that optimizes costs, reduces risks, and delivers fast results.
This article focuses on real-world examples of using AI in custom software development. Through examples and insights, we will look at how companies can overcome the hype and unlock the practical benefits of AI in their software projects.
It's hard to deny that AI has a bright tomorrow. However, developers shouldn’t fear that AI will soon replace them. According to a GitHub survey, 92% already use AI tools at work or in their time. However, integrating AI into development presents challenges, requiring organizations to weigh the benefits against potential risks.
AI rapidly transforms software development, enhancing the Software Development Life Cycle (SDLC) through tools like Cursor or GitHub Copilot. As developers move from manual coding to "orchestrating AI-driven ecosystems," the role of AI in boosting efficiency becomes clear.
But then, why is AI still treated as an experimental technology? One of the main reasons for this attitude is misconceptions:
AI is only for large corporations: Many assume that AI solutions require enormous resources and are exclusive to tech giants like Google or Amazon. In reality, businesses of all sizes can harness AI through affordable, scalable solutions.
AI is prohibitively expensive: While custom AI models can be costly, leveraging off-the-shelf APIs like OpenAI significantly reduces implementation barriers.
If there is a need to integrate AI into development, you will have to choose between two equal approaches:
Off-the-Shelf APIs: Readily available APIs like OpenAI's suite allow developers to integrate powerful AI capabilities like natural language processing (NLP) and image recognition without starting from scratch. These APIs are cost-effective, user-friendly, and ideal for common use cases.
Custom AI Models: Building custom AI models offers tailored solutions for unique, complex challenges. Although this approach is more resource-intensive, it ensures that AI aligns with business goals.
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At Integrio, we understand the transformative power of AI. Here's how enhancing custom software with AI can be tailored for organizations.
AI solutions are key to making the user experience seamless and engaging. Virtual assistants, for example, offer real-time support by answering queries and automating repetitive tasks. Smart recommendations also demonstrate the potential of AI to provide personalized content, products, or services.
Examples of such implementations include AI-powered customer support solutions that solve problems quickly and efficiently and personalized dashboards that dynamically adapt to individual user needs by offering relevant information and intuitive navigation. For example, Switcher implemented Help Scout on their website and provided constant user support.
RPA (robotic process automation) lets digital workers execute business processes quickly and in high volume. It is best suited for relatively simple and linear processes. It can also be extremely useful in legacy systems where API integration is impossible. Conversely, AI is a technology that can 'think' for itself and thus perform more complex processes autonomously.
Integrating RPA and AI offers an efficient and simple automation strategy. RPA can handle repetitive tasks, while AI can bring analytical capabilities to decision-making. A Deloitte survey reveals that 53% of businesses actively explore RPA solutions.
Predictive analytics uses historical and real-time data to predict trends, identify potential risks, and understand user behavior. Businesses can draw useful insights that help them make proactive decisions and drive strategic growth.
For example, AI-powered CRM tools, like Salesforce CRM (with Einstein), can predict customer churn by analyzing patterns such as declining engagement or changes in buying behavior. Similarly, sales trends can be predicted by evaluating seasonal data, market conditions, and historical performance, which helps businesses plan inventory, allocate resources, and optimize marketing campaigns.
Natural language processing (NLP) helps convert unstructured data in text form into actionable insights, assisting businesses in getting critical information from large volumes of textual data.
The most common example is sentiment analysis, which estimates customer opinions by analyzing reviews or social media activity. Another use case is document summarisation, where NLP models condense long reports or articles into succinct summaries.
AI improves software quality assurance by automating time-consuming tasks and increasing code reliability. AI-powered QA tools can perform automated code reviews, scanning for errors, inefficiencies, or potential vulnerabilities much faster than manual processes. These tools detect bugs and suggest fixes, helping developers solve problems proactively.
In addition, AI models help with code refactoring, performance optimization, and maintainability. For example, some tools analyze code bases to recommend cleaner and more efficient structures or identify redundancies.
Learn more about how code reviews with AI impact on quality assurance.
While you may only be on the way to using AI in software development, other companies are already showing tangible results from such interaction. Here are some of the best examples to check out
Zillow leveraged AI to address the challenge of predicting property prices, moving beyond traditional valuation methods that lacked flexibility in the dynamic real estate market. Their flagship product, ‘Zestimate,’ evolved through AI-driven improvements, including the introduction of the Neural Zestimate model in 2021. This modernization enhanced the accuracy of property valuations, although challenges like the miscalculation of property values in the iBuying model emphasized the importance of precision in AI applications.
AI integration at Zillow extended beyond valuation to transform the entire real estate experience. Additionally, AI tools like coding assistants streamlined internal workflows, showcasing the company’s commitment to enhancing operational efficiency and user experience.
Key to Zillow’s success was its robust data infrastructure, continuous training and learning from past challenges, and cross-functional collaboration. This approach not only modernized its core products but also emphasized the importance of human-AI synergy and ethical AI practices in driving innovation and improving real estate transactions.
Tchibo, a German coffee roaster and retailer, offers an extensive and dynamic range of around 3,000 products, including clothing, furniture, household items, and electronics. Managing supply and demand for such a diverse portfolio poses logistical challenges.
Tchibo collaborated with Google Cloud to develop an AI-driven forecasting service to address this. Leveraging Google's Vertex AI, they created a DEMON system, which processes over three years of data spanning products, marketing, sales, and logistics. DEMON utilizes a temporal fusion transformer model—similar to the architectures behind large language models (LLMs)—to predict product demand up to 84 days in advance. This predictive capability improved warehouse management, reduced logistical workload, and identified products worth restocking due to potential popularity.
Mobiry, a SaaS company based in the United States, specializes in improving marketing and increasing customer loyalty for omnichannel retailers. Founded in 2018, the company started with the vision of using ML to increase customer loyalty. In partnership with Integrio Systems, Mobiry has turned its ambitious idea into a robust product designed to address the diverse challenges of modern retail.
The Mobiry solution integrates with retailers' ERP, POS, and data systems to synchronize customer data, inventory, pricing, and sales transactions. Integrio Systems has developed a flexible integration framework that supports direct connections, e-commerce platforms, data lakes, and batch exchanges to accommodate different retailer security protocols and operational practices. Advanced AI and ML techniques have also been used to create an intelligent engine that allows clients to analyze POS data, predict consumer behavior, and run personalized promotions based on customer preferences.
Using advanced technologies such as Java, Python, Docker, and Kubernetes, Mobiry successfully launched its innovative SaaS product on the global stage. This solution has improved how promotions are managed, and consumers are engaged, ultimately driving sales and profitability.
Integrio has experience implementing AI in custom software in different areas. Explore more about how AI enhances predictive maintenance in heavy industries.
While AI offers transformational potential, its implementation in custom software projects often comes with challenges. Below are some of the most common obstacles and practical solutions to overcome them.
Integrating AI into development can be challenging, especially if you're dealing with legacy infrastructure.
Start small by implementing scalable AI tools and interfaces. Platforms such as OpenAI provide ready-to-use APIs that reduce the complexity of building AI-powered features from scratch, enabling step-by-step integration.
AI often relies on sensitive data, which raises concerns about compliance with data protection regulations such as GDPR and CCPA.
Ensure compliance with these standards by using encrypted data storage and implementing strong access controls. Prioritise transparency with users about data usage policies to build trust.
Many companies dive into AI expecting an overnight transformation, only to be disappointed.
Focus on realistic, incremental improvements. Start by solving specific problems and scaling solutions as results are confirmed, aligning AI projects with measurable business goals.
Lack of in-house expertise in AI technologies can delay or lead to ineffective implementation.
Collaborate with AI experts to streamline AI deployments by minimizing the need for extensive technical expertise while providing effective results using available platforms such as OpenAI. These solutions simplify AI deployments by reducing the need for in-depth technical knowledge while delivering effective results.
The focus of AI is not to replace the developer but to transform the way of building, testing, and deploying the software. With AI tools becoming increasingly available, organizations from every sector can begin to utilize their capacity to improve efficiency, inform smarter choices, and provide a more tailored user experience. However, AI is not a silver-bullet solution —its successful application depends on a team that can implement it in your software, with experience and knowledge of all the pitfalls.
Looking ahead, the key to unlocking AI's true potential is integrating it with existing systems and workflows. Integrio and our professional team can modernize existing platforms based on AI and ML, integrate APIs with AI services, and create custom solutions based on IT staff augmentation.
Ultimately, AI is not just a tool of the moment; it is the foundation for the future of custom software development. If you're ready to keep up with the times and aren't afraid of modern software approaches, contact Integrio to get help in implementing AI in yout custom solution.
Focus on business goals, data quality, scalability, compliance with regulations, and integration compatibility with existing systems.
Assess the project's goals, complexity, and available data. Use off-the-shelf APIs for general tasks and custom models for unique challenges.
Generative AI, advanced NLP, explainable AI, and AI-driven automation are transforming software development and enhancing its efficiency and user experience.
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