Proof of Concept (PoC) projects are the testing ground for brand spanking new technology, and Generative AI (GenAI) isn’t any exception. What does success really mean for a GenAI PoC? Simply put, a successful PoC is one which seamlessly transitions into production. The issue is, on account of the novelty of the technology and its rapid evolution, most GenAI PoCs are primarily focused on technical feasibility and metrics comparable to accuracy and recall. This narrow focus is one in every of the first 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, mental 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 maneuver forward.
Beyond the Hype: The Reality of GenAI PoCs
GenAI adoption is clearly on the rise, however the true success rate of PoCs stays unclear. Reports offer various statistics:
- Gartner predicts that by the top of 2025, at the least 30% of GenAI projects will probably 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 within 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 starting from 10% to 70%, the actual success rate is probably going closer to the lower end. This highlights that many organizations struggle to design PoCs with a transparent path to scaling. The low success rate can drain resources, dampen enthusiasm, and stall innovation, resulting in what’s often called “PoC fatigue,” where teams feel stuck running pilots that never make it to production.
Moving Beyond Wasted Efforts
GenAI continues to be within the early stages of its adoption cycle, very like cloud computing and traditional AI before it. Cloud computing took 15-18 years to achieve widespread adoption, while traditional AI needed 8-10 years and continues to be growing. Historically, AI adoption has followed a boom-bust cycle during which the initial excitement results in overinflated expectations, followed by a slowdown when challenges emerge, before eventually stabilizing into mainstream use. If history is any guide, GenAI adoption could have its own ups and downs.
To navigate this cycle effectively, organizations must be sure that every PoC is designed with scalability in mind, avoiding common pitfalls that result in wasted efforts. Recognizing these challenges, leading technology and consulting firms have developed structured frameworks to assist organizations move beyond experimentation and scale their GenAI initiatives successfully.
The goal of this text is to enhance these frameworks and strategic efforts by outlining practical, tactical steps that may 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
Initially, select a use case with a transparent path to production. This doesn’t mean conducting a comprehensive, enterprise-wide GenAI Readiness assessment. As an alternative, assess each use case individually based on aspects like data quality, scalability, and integration requirements, and prioritize those with the very best likelihood of reaching production.
A couple of more key questions to contemplate while choosing the suitable 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 may prevent scaling?
2. Define and align on success metrics before kickoff
Certainly one of the most important reasons PoCs stall is the shortage of well-defined metrics for measuring success. With no strong alignment on goals and ROI expectations, even technically sound PoCs may struggle to realize buy-in for production. Estimating ROI isn’t easy but listed here are some recommendations:
- Devise or adopt a framework comparable to this one.
- Use cost calculators, like this OpenAI API pricing tool and cloud provider calculators to estimate expenses.
- As an alternative of a single goal, 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
Constructing GenAI apps is all about experimentation requiring constant iteration. When choosing your tech stack, architecture, team, and processes, ensure they support this iterative approach. The alternatives 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 speed up experimentation.
- Select benchmarks, evals and evaluation frameworks on the outset ensuring that they align together 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 due to this fact, 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 speed up PoCs and production deployments. Nevertheless, a lot of these solutions operate as black boxes requiring rigorous scrutiny from IT, legal, security, and risk management teams. To handle these challenges and streamline the method, consider the next recommendations for constructing a low-friction solution:
- Create a dedicated roadmap for approvals: Consider making a dedicated roadmap for addressing partner-team concerns and obtaining approvals.
- Use pre-approved tech stacks: Every time possible, use tech stacks which are already approved and in use to avoid delays in approval and integration.
- Deal with essential tools: Early PoCs typically don’t require model fine-tuning, automated feedback loops, or extensive observability/SRE. As an alternative, 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 speed up 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 comparable to synthetic data generation, PII data masking, and encryption to handle security concerns proactively.
5. Assemble a lean, entrepreneurial team
As with every project, having the suitable team with the essential skills is critical to success. Beyond technical expertise, your team must even be nimble and entrepreneurial.
- Consider including product managers and material experts (SMEs) to be sure that you’re solving the suitable problem.
- Be certain that you’ve gotten each 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. As an alternative, consider hiring small and medium-sized service vendors who can herald the suitable 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 ascertain clear problem boundaries and a set set of functional requirements. Nevertheless, non-functional requirements shouldn’t be ignored. While the PoC should remain focused inside problem boundaries, its architecture should be designed for top performance. More specifically, achieving millisecond latency will not be a right away necessity, nonetheless, the PoC must be able to seamlessly scaling as beta users expand. Go for a modular architecture that continues to be flexible and agnostic to tools.
7. Devise a plan to handle hallucinations
Hallucinations are inevitable with language models. Due to this fact, guardrails are critical for scaling GenAI solutions responsibly. Nevertheless, evaluate whether automated guardrails are needed in the course of the PoC stage and to what extent. As an alternative 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 because it does to production. There is no such thing as a one-size-fits-all playbook. Nevertheless, adopting best practices from project and product management will help streamline and achieve progress.
- Use kanban or agile methods for tactical planning and execution.
- Document all the things.
- Hold scrum-of-scrums to collaborate effectively with partner teams.
- Keep your stakeholders and leadership informed on progress.
Conclusion
Running a successful GenAI PoC isn’t nearly proving technical feasibility, it’s about evaluating the foundational selections for the long run. By rigorously choosing the suitable use case, aligning on success metrics, enabling rapid experimentation, minimizing friction, assembling the suitable team, addressing each functional and non-functional requirements, and planning for challenges like hallucinations, organizations can dramatically improve their possibilities of moving from PoC to production.
That said, the steps outlined above will not be exhaustive, and never every suggestion will apply to each use case. Each PoC is exclusive, and the important thing to success is adapting these best practices to suit your specific business objectives, technical constraints, and regulatory landscape.
A powerful vision and strategy are essential for GenAI adoption, but without the suitable tactical steps, even the best-laid plans can stall on the PoC stage. Execution is where great ideas either succeed or fail, and having a transparent, structured approach ensures that innovation translates into real-world impact.
