Proof of Concept (PoC) projects are the testing ground for new technology, and Generative AI (GenAI) is no exception. What does success really mean for a GenAI PoC? Simply put, a successful PoC is one that seamlessly transitions into production. The problem is, due to the newness of the technology and its rapid evolution, most GenAI PoCs are primarily focused on technical feasibility and metrics such as accuracy and recall. This narrow focus is one of the primary reasons for why PoCs fail. A McKinsey survey found that while one-quarter of respondents were concerned about accuracy, many struggled just as much with security, explainability, intellectual property (IP) management, and regulatory compliance. Add in common issues like poor data quality, scalability limits, and integration headaches, and it’s easy to see why so many GenAI PoCs fail to move forward.
Beyond the Hype: The Reality of GenAI PoCs
GenAI adoption is clearly on the rise, but the true success rate of PoCs remains unclear. Reports offer varying statistics:
- Gartner predicts that by the end of 2025, at least 30% of GenAI projects will be abandoned after the PoC stage, implying that 70% could move into production.
- A study by Avanade (cited in RTInsights) found that 41% of GenAI projects remain stuck in PoC.
- Deloitte’s January 2025 The State of GenAI in the Enterprise report estimates that only 10-30% of PoCs will scale to production.
- A research by IDC (cited in CIO.com) found that, on average, only 5 out of 37 PoCs (13%) make it to production.
With estimates ranging from 10% to 70%, the actual success rate is likely closer to the lower end. This highlights that many organizations struggle to design PoCs with a clear path to scaling. The low success rate can drain resources, dampen enthusiasm, and stall innovation, leading to what’s often called “PoC fatigue,” where teams feel stuck running pilots that never make it to production.
Moving Beyond Wasted Efforts
GenAI is still in the early stages of its adoption cycle, much like cloud computing and traditional AI before it. Cloud computing took 15-18 years to reach widespread adoption, while traditional AI needed 8-10 years and is still growing. Historically, AI adoption has followed a boom-bust cycle in which the initial excitement leads to overinflated expectations, followed by a slowdown when challenges emerge, before eventually stabilizing into mainstream use. If history is any guide, GenAI adoption will have its own ups and downs.
To navigate this cycle effectively, organizations must ensure that every PoC is designed with scalability in mind, avoiding common pitfalls that lead to wasted efforts. Recognizing these challenges, leading technology and consulting firms have developed structured frameworks to help organizations move beyond experimentation and scale their GenAI initiatives successfully.
The goal of this article is to complement these frameworks and strategic efforts by outlining practical, tactical steps that can significantly increase the likelihood of a GenAI PoC moving from testing to real-world impact.
Key Tactical Steps for a Successful GenAI PoC
1. Select a use case with production in mind
First and foremost, choose a use case with a clear path to production. This does not mean conducting a comprehensive, enterprise-wide GenAI Readiness assessment. Instead, assess each use case individually based on factors like data quality, scalability, and integration requirements, and prioritize those with the highest likelihood of reaching production.
A few more key questions to consider while selecting the right use case:
- Does my PoC align with long-term business goals?
- Can the required data be accessed and used legally?
- Are there clear risks that will prevent scaling?
2. Define and align on success metrics before kickoff
One of the biggest reasons PoCs stall is the lack of well-defined metrics for measuring success. Without a strong alignment on goals and ROI expectations, even technically sound PoCs may struggle to gain buy-in for production. Estimating ROI is not easy but here are some recommendations:
- Devise or adopt a framework such as this one.
- Use cost calculators, like this OpenAI API pricing tool and cloud provider calculators to estimate expenses.
- Instead of a single target, develop a range-based ROI estimate with probabilities to account for uncertainty.
Here’s an example of how Uber’s QueryGPT team estimated the potential impact of their text-to-SQL GenAI tool.
3. Enable rapid experimentation
Building GenAI apps is all about experimentation requiring constant iteration. When selecting your tech stack, architecture, team, and processes, ensure they support this iterative approach. The choices should enable seamless experimentation, from generating hypotheses and running tests to collecting data, analyzing results, learning and refining.
- Consider hiring small and medium sized services vendors to accelerate experimentation.
- Choose benchmarks, evals and evaluation frameworks at the outset ensuring that they align with your use case and objectives.
- Use techniques like LLM-as-a-judge or LLM-as-Juries to automate (semi-automate) evaluation.
4. Aim for low-friction solutions
A low-friction solution requires fewer approvals and therefore, faces fewer or no objections to adoption and scaling. The rapid growth of GenAI has led to an explosion of tools, frameworks, and platforms designed to accelerate PoCs and production deployments. However, many of these solutions operate as black boxes requiring rigorous scrutiny from IT, legal, security, and risk management teams. To address these challenges and streamline the process, consider the following recommendations for building a low-friction solution:
- Create a dedicated roadmap for approvals: Consider creating a dedicated roadmap for addressing partner-team concerns and obtaining approvals.
- Use pre-approved tech stacks: Whenever possible, use tech stacks that are already approved and in use to avoid delays in approval and integration.
- Focus on essential tools: Early PoCs typically don’t require model fine-tuning, automated feedback loops, or extensive observability/SRE. Instead, prioritize tools for core tasks like vectorization, embeddings, knowledge retrieval, guardrails, and UI development.
- Use low-code/no-code tools with caution: While these tools can accelerate timelines, their black-box nature limits customization and integration capabilities. Use them with caution and consider their long-term implications.
- Address security concerns early: Implement techniques such as synthetic data generation, PII data masking, and encryption to address security concerns proactively.
5. Assemble a lean, entrepreneurial team
As with any project, having the right team with the essential skills is critical to success. Beyond technical expertise, your team must also be nimble and entrepreneurial.
- Consider including product managers and subject matter experts (SMEs) to ensure that you are solving the right problem.
- Ensure that you have both full-stack developers and machine learning engineers on the team.
- Avoid hiring specifically for the PoC or borrowing internal resources from higher-priority, long-term projects. Instead, consider hiring small and medium-sized service vendors who can bring in the right talent quickly.
- Embed partners from legal and security from day 1.
6. Prioritize non-functional requirements too
For a successful PoC, it's crucial to establish clear problem boundaries and a fixed set of functional requirements. However, non-functional requirements should not be overlooked. While the PoC should remain focused within problem boundaries, its architecture must be designed for high performance. More specifically, achieving millisecond latency may not be an immediate necessity, however, the PoC should be capable of seamlessly scaling as beta users expand. Opt for a modular architecture that remains flexible and agnostic to tools.
7. Devise a plan to handle hallucinations
Hallucinations are inevitable with language models. Therefore, guardrails are critical for scaling GenAI solutions responsibly. However, evaluate whether automated guardrails are necessary during the PoC stage and to what extent. Instead of ignoring or over-engineering guardrails, detect when your models hallucinate and flag them to the PoC users.
8. Adopt product and project management best practices
This XKCD illustration applies to PoCs just as it does to production. There is no one-size-fits-all playbook. However, adopting best practices from project and product management can help streamline and achieve progress.
- Use kanban or agile methods for tactical planning and execution.
- Document everything.
- Hold scrum-of-scrums to collaborate effectively with partner teams.
- Keep your stakeholders and leadership informed on progress.
Conclusion
Running a successful GenAI PoC is not just about proving technical feasibility, it’s about evaluating the foundational choices for the long term. By carefully selecting the right use case, aligning on success metrics, enabling rapid experimentation, minimizing friction, assembling the right team, addressing both functional and non-functional requirements, and planning for challenges like hallucinations, organizations can dramatically improve their chances of moving from PoC to production.
That said, the steps outlined above are not exhaustive, and not every recommendation will apply to every use case. Each PoC is unique, and the key to success is adapting these best practices to fit your specific business objectives, technical constraints, and regulatory landscape.
A strong vision and strategy are essential for GenAI adoption, but without the right tactical steps, even the best-laid plans can stall at the PoC stage. Execution is where great ideas either succeed or fail, and having a clear, structured approach ensures that innovation translates into real-world impact.
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