Closing the gap: How to transform GenAI PoCs into production success

Every day, new GenAI models are released by providers, each promising more sophisticated responses and improved logic. Despite this rapid evolution, Gartner predicts that by the end of 2025, at least 30% of GenAI projects will be abandoned after the proof-of-concept (PoC) stage. In fact, AI project failure rates have been reported as high as 80%, according to the-Harvard Business Review, reflecting the challenges organizations face in achieving intended outcomes. 

Many organizations continue to struggle with transitioning from a PoC to full-scale implementation. Despite the advancements of models, there is a gap in execution that companies across different sectors are encountering, which prevents them from moving GenAI projects into production. 

In the previous blog post, “Effective stakeholder management: The key to advancing your GenAI PoC and realizing its benefits,” April Weihe emphasized the importance of early stakeholder engagement in GenAI projects. Building on that, this piece explores how clear business goals and trusted teams or partners can help move GenAI efforts from proof of concept to successful production.  
 
Focus on execution details, not sophisticated models  
For many GenAI use cases, success doesn’t depend on choosing the most sophisticated model, as they rely on standard configurations. Let’s call these Standard/Foundational Use Cases. In these scenarios, models from different providers perform similarly, with execution being the key differentiator. Here are some examples: 

• Text summarization: Different models can yield comparable results.
• Customer support automation: Generating responses to common queries requires a solid integration with customer support systems and a well-curated instruction dataset. The model choice becomes less critical than the implementation details.
• Language translation: For widely spoken language pairs, several large language models achieve high-quality translations. The key lies in how you integrate the model into your workflow.
• Automated report generation: Creating client reports or internal documents depends on structuring data, designing effective prompts and formatting outputs. Here, the implementation's role overshadows the choice of model. 

On the other hand, for complex use cases — such as analyzing lengthy legal documents — model choice is critical. These tasks may require larger context windows or domain-specific training, making pre-deployment testing essential. While this post focuses on foundational use cases, specialized tasks demand careful model selection and tuning. 
 
Smooth your journey from pilot to production 
Choosing the right GenAI provider is key to moving from pilot to production. While many models offer similar capabilities, factors like existing vendor relationships, platform familiarity and trust are often more important than marginal performance differences. Consider your existing relationships with vendors or cloud service providers. Trust and familiarity can be significant assets, especially when dealing with sensitive data or proprietary processes. 

Working with a trusted provider who understands your IT infrastructure, security and compliance needs can streamline integration and reduce risk. They are more likely to adhere to your company's IT policies and can ensure data integrity and confidentiality. Familiarity with their platform also helps your engineering team work more efficiently, speeding up development and deployment. 

As GenAI evolves, sticking with a reliable provider ensures stability, security, and faster execution—making trust and alignment more valuable than chasing the newest model. 
 
Assemble your GenAI implementation team 
When starting your GenAI project, we advise collaborating with your existing technology team along with a trusted partner versus engaging an entirely new team. This approach offers several distinct advantages, ensuring a smoother and more successful project outcome. Below are the key reasons why this strategy proves more effective: 

• Business fit: Your current technology team or trusted partners possess a deep understanding of your organization, enabling them to develop solutions that are tailored to your specific needs and aligned with your strategic goals.
• Flexibility: Since your existing teams and partners are already aligned with your operational processes, they can adapt to changes seamlessly, maintaining project momentum even as requirements evolve.
• Ongoing support: Your existing team’s involvement ensures the solution remains operational and continues to evolve post-deployment, providing sustained value and long-term reliability.
• Lower risk: Familiarity with your organization’s regulations and policies allows your team and partners to mitigate the risk of compliance issues, safeguarding the project from potential legal or operational setbacks. 

Instead of undertaking a complete team restructuring, consider integrating one or two GenAI specialists into your current team. This approach allows you to acquire the necessary expertise while maintaining cohesion and trust within your team. This helps to foster a collaborative environment that enhances overall project success. Note that decisions about the team responsible for implementing your GenAI project should ideally be made during the initial pilot phase.  
 
Understand the business process  
With the GenAI provider chosen and a trusted team in place, the next step is to take a deep dive into how work gets done today. Engineers, domain experts and project sponsors need a single, detailed map of the current workflow – i.e., pain points, edge cases and desired outputs. This blueprint drives prompt design, data preparation and integration, ensuring the solution fits naturally into existing processes instead of forcing people to adapt. 

For instance, in customer support automation, missing the nuances of query-handling could result in a GenAI tool that disrupts the support team’s flow, leading to inefficiencies or pushbacks. Likewise, in automated report generation, lacking insight into data sources and stakeholder needs might produce irrelevant reports, undermining the project’s impact. 

How your systems interact with the GenAI model is equally vital. Seamless integration with existing tools and platforms boosts efficiency and user adoption. When the solution fits naturally into established processes, it minimizes friction and drives productivity. 
 
Take the next step 
Transforming GenAI proofs of concept into production successes demands more than cutting-edge models – it requires a strategic focus on execution. With AI project failure rates reaching 80%, success lies in partnering with trusted, experienced teams, aligning stakeholders with clear business logic, ensuring seamless system integration and nailing details like prompt engineering and data preprocessing. By emphasizing these execution factors, organizations can sidestep common pitfalls and turn promising GenAI ideas into thriving solutions that deliver real, lasting business value. 

Learn more about how to successfully navigate the myths, risks and opportunities around GenAI by visiting our Artificial Intelligence page.