More and more businesses all over the globe are investing in the development and implementation of generative AI software, which is capable of content summarization, analysis, and generation. It is no surprise, as corporate leaders increasingly view GenAI as a critical means of enhancing diverse business workflows, from routine content creation and customer communication to more complex processes involving decision-making and problem-solving, such as strategic planning or budgeting.
However, a significant proportion of GenAI projects never reach production or scale to enterprise-wide deployment. For instance, according to the estimations from MIT’s GenAI Divide: State of AI in Business 2025 report, only 5% of enterprise-grade GenAI systems reach production. In this context, adopting proven software development practices derived from the real-world experiences – both successes and failures – of early GenAI adopters is critical. These practices can help your company avoid common pitfalls in generative AI development and increase the likelihood of project success.
In this article, generative AI developers from Itransition share several practical practices to help you build and implement GenAI software efficiently.
Adopting a modular software architecture
Developing a generative AI solution as a monolithic application, in which all components (AI models, interfaces, database, APIs, etc.) are tightly coupled, may seem easier and faster than developing an app based on a modular software architecture. However, this approach can become a significant bottleneck over time.
First, GenAI models typically require vast computing resources (CPU, memory, etc.) for inference, while many other application components can consume far fewer resources. But even if only one AI component requires more resources, the entire application must be scaled to handle the increased demand, which can be both costly and technically complex.
Second, corporate generative AI software should be continuously updated to ensure its outputs remain accurate, secure, and relevant to ever-changing business and market conditions, which can require fine-tuning AI models embedded into the software or switching them to newer, more efficient models. However, since the entire GenAI application is built and deployed as a single unit, modernizing AI models can involve updating the entire system, which makes upgrading a challenging and risky process.
For these reasons, modular architectures, such as those based on microservices, can be more effective for generative AI solutions. Instead of scaling the entire application, a microservices architecture allows IT teams to scale individual AI components based on their specific resource requirements, facilitating software maintenance. Similarly, AI models can be updated or replaced independently, without refactoring the entire application.
Keeping human in the loop during testing
While automated testing tools are essential for optimizing many repetitive testing tasks in GenAI development, they cannot always sufficiently evaluate GenAI outputs due to the lack of deep contextual understanding and nuanced human-level judgment. Therefore, it is essential that companies use hybrid testing strategies that balance test automation with human-in-the-loop validation.
Particularly, it is best to use automated tools for the following tasks:
- Generating test cases based on AI model requirements
- Detecting missing or incorrect values in training datasets
- Evaluating the semantic meaning of an AI model’s response against the expected outcome
- Calculating and tracking key metrics such as AI model accuracy and precision scores during training iterations
Human-in-the-loop validation, in turn, can be used for:
- Evaluating AI model responses’ relevance, creativity, and tone
- Detecting discriminatory language patterns in AI model outputs
- Fact-checking AI-generated information and confirming whether it is valid
Implementing an AI governance framework
Without continuous and systematized governance, even reliable generative AI software can introduce operational, reputational, and financial risks to businesses. For example, insufficient access controls can result in the overexposure of sensitive information, potentially leading to violations of data protection laws. Similarly, without proper monitoring, the performance of AI models can degrade over time, leading to inaccurate, biased, or misleading outputs and wrong business decisions at the operational and strategic levels.
To mitigate these risks, companies investing in generative AI development should necessarily build and implement AI governance frameworks that define policies, standards, and oversight mechanisms for the use of GenAI technology. It can be done in accordance with the following algorithm:
- To start, it is advisable for a company to establish a multidisciplinary AI council to systematically oversee GenAI development and implementation.
- Next, a company should create a registry of corporate GenAI tools that covers their purposes, associated data sources, and potential risks.
- After that, it is worth defining standards, policies, and protocols for governing GenAI software throughout its lifecycle, and clearly outlining accountability for implementing and overseeing them.
- It is also vital to complement a framework with a user training plan to ensure that employees have the required knowledge and skills to use GenAI properly.
- Last but not least, a company should establish a continuous monitoring process to automatically audit GenAI system performance and timely detect and mitigate hallucination, drift, or bias in AI model performance.
Final thoughts
While many modern companies are investing in developing and implementing generative AI solutions, only some of them successfully move beyond pilot projects and scale initiatives enterprise-wide. Lack of future-proof software architecture, human-in-the-loop validation, and AI governance are some of the common pitfalls leading to GenAI development failures. Three practices listed in this article can help your company avoid these pitfalls and therefore ensure smoother project execution.
Also, if you feel that your in-house team lacks the skills required to jump-start a project and handle all its aspects, you can consider partnering with an experienced developer team. Third-party developers can augment your internal teams with the necessary AI expertise to help you ensure seamless project delivery or handle the development project end-to-end and deliver a scalable, secure, and high-performance GenAI solution.
