More and more companies are investing in the development of AI agents, GenAI tools, intelligent RPA bots, and other AI-enabled solutions, using them to improve business process efficiency and customer satisfaction. However, a portion of AI projects fail, with many of them not moving past the proof-of-concept stage into full production. For instance, in 2025, MIT reported that only 5% of custom enterprise GenAI tools reach production.
While project failures can be attributed to many factors, they often stem from the common, recurring issues that unsuccessful adopters fail to overcome. The need to define high-value and feasible use cases, maintain high data quality, and build skilled cross-functional teams are top examples of challenges AI adopters are likely to face.
In this article, artificial intelligence experts from Itransition highlight the common challenges companies face during AI development projects and offer recommendations to address them.
Identifying optimal AI use cases
AI development can provide higher ROI when the solution is implemented to solve an existing pain point in a company’s processes, rather than being implemented as a nice-to-have generic business tool. But correctly identifying the bottleneck, defining the appropriate AI capabilities to fix it, and translating all this information into a targeted and actionable AI use case can be difficult, especially for large companies due to the volume and complexity of their work processes.
Recommendations:
- To efficiently locate pain points in your processes, use intelligent process mining tools, which can provide a holistic, multi-dimensional view of your operations and pinpoint specific bottlenecks within them. Focus on processes that already have bottlenecks or issues, such as high turnaround time or high error rates, instead of mapping every single process, which can be too time-consuming.
- After detecting operational pain points and bottlenecks, identify those causing the most significant operational inefficiencies. After validating that AI can be an efficient solution to these issues, define all possible scenarios of AI application for solving them.
- After creating AI use cases, carefully prioritize them by using the impact-effort matrix, the ICE scoring model, or other relevant techniques. Focus the development project around the most suitable use case based on your long-term business objectives.
Ensuring data quality and availability
The lack of high-quality data is a factor that can compromise development projects in both pilot and production phases. For instance, an AI model that is trained with poor-quality or insufficient data can fail to ensure the necessary accuracy for real-world application, which can reduce stakeholder trust in the project. In turn, when AI tools working in the production environment are fed with low-quality data, they provide low-quality outputs to end-users, which can reduce their trust in AI and the solution’s adoption. However, many companies overlook this factor, which often leads to AI project failures.
Recommendations:
- Before the project starts, establish clear, measurable data quality metrics (accuracy, completeness, consistency, etc.) that are aligned with your business objectives. Assign data stewards and document procedures that they should follow to monitor the data throughout the development lifecycle against these quality metrics.
- Carefully preprocess training data and clean it from duplicates and inconsistencies to ensure efficient AI model training. Use automated data cleansing tools to accelerate this process and preprocess even large datasets more quickly and with less effort.
- Implement ETL/ELT tools equipped with built-in quality checks to set up data pipelines that can automatically flag poor-quality data or even reject and divert it to a separate storage area, therefore preventing it from reaching your AI solutions.
Integrating AI tools with legacy systems
A significant number of companies still heavily rely on legacy IT systems that can be challenging to integrate with newly-built AI solutions. For instance, legacy systems can lack native support of standard API protocols, such as REST or GraphQL, which can undermine effective data exchange with the AI solution. Legacy systems also can lack software and hardware capabilities required to support AI algorithms, and equipping these systems with AI features can lead to system crashes and performance bottlenecks.
Recommendations:
- Use middleware to connect legacy systems and AI solutions that use different communication protocols. These tools can efficiently convert legacy protocols, such as SOAP and FTP, into modern, AI-friendly protocols like GraphQL or REST API, and vice versa, enabling seamless data flow between systems.
- If your legacy systems are hosted on-premises, consider migrating them to the cloud. Cloud platforms can provide legacy systems with the necessary computing resources (CPU, memory, storage, etc.) to handle the demands of AI algorithms, which can improve their performance and ability to integrate with AI tools.
- After integrating an outdated system with an AI solution, conduct rigorous integration tests to guarantee that the communication between them is stable and secure, while ensuring that a legacy system can operate with an AI solution without crashes or severe slowdowns.
Final thoughts
If you plan to build an AI solution, you need to be aware in advance of potential challenges related to such a project and be prepared to properly address them. Such a proactive approach will help you avoid common development pitfalls and eventually maximize the chances that your AI project succeeds.
Also, if you realize that your in-house team lacks expertise to handle the project, consider hiring external AI developers. They can either join your existing team and help them with specific project activities or handle the AI development project end-to-end, from outlining your software requirements and creating AI solution design to engineering the solution and deploying it to production.
