The Proof of Concept (POC) project is a test site for new technologies, and the generated AI (Genai) is no exception. What does success really mean for Genai POC? In short, successful POC is a kind of POC that seamlessly transitions to production. The problem is that due to the novelty of technology and its rapid development, most Genai POCs are mainly focused on indicators such as technical feasibility and accuracy and recall. This narrow focus is one of the main reasons for POC failure. one McKinsey Investigation It was found that while a quarter of respondents were concerned about accuracy, many experienced a lot of efforts in security, interpretability, intellectual property (IP) management and regulatory compliance. Add to common problems such as poor data quality, scalability limitations, and integration headaches, and it’s easy to understand why so many Genai Pocs can’t move forward.
Beyond the Hype: The Reality of Genai Poc
Genai adopts It is obviously rising, but the true success rate of POC is not yet clear. The report provides different statistics:
- Gartner It is predicted that by the end of 2025, at least 30% of Genai projects will be abandoned after the POC phase, which means 70% can enter production.
- Avanade’s research (citation rinsights) found that 41% of Genai projects are still stuck in POC.
- Deloitte’s January 2025 Genai status in the enterprise The report estimates that only 10-30% of POC will be expanded to production.
- IDC research (citation cio.com) found that on average only 5 of 37 POCs (13%) entered production.
The estimated value is between 10% and 70%, and the actual success rate may be closer to the lower end. This highlights the efforts of many organizations to design POCs with clear expansion pathways. Lower success rates can consume resources, attenuate enthusiasm and booth innovation, resulting in what is commonly known as “POC fatigue”, where the team feels running pilots fall into trouble and never enter production.
Go beyond waste
Genai is still in the early stages of the adoption cycle, just like before cloud computing and traditional AI. It took 15-18 years to achieve widespread adoption, while traditional AI took 8-10 years and is still growing. Historically, AI adoption rates followed a cycle of obstacles to prosperity in which initial excitement leads to over-expectations, then slows down when challenges arise, and then eventually stabilizes in mainstream use. If history is any guide, Genai adoption will have its own ups and downs.
To effectively browse this cycle, organizations must ensure that each POC is designed with scalability in mind, avoiding the common pitfalls that lead to wasteful efforts. Recognizing these challenges, leading technology and consulting firms have developed structured frameworks to help organizations move beyond experiments and successfully expand their Genai program.
The purpose of this article is to complement these frameworks and strategic efforts by outlining practical tactical steps that can significantly increase the likelihood of Genai POC’s transition from testing to real-world impact.
Key tactical steps to successful Genai POC
1. Choose a use case with production
First, choose a use case with a clear production pathway. This does not mean a comprehensive enterprise-wide Genai ready assessment. Instead, each use case is evaluated separately based on factors such as data quality, scalability, and integration requirements, and prioritizes the factors that reach the highest production likelihood.
More key issues to consider when choosing the right use case:
- Is my POC aligned with long-term business goals?
- Can the required data be legally accessed and used?
- Are there obvious risks to prevent expansion?
2. Define and align success metrics
One of the biggest reasons for POC stalls is the lack of well-defined metrics to measure success. Even technically reasonable POCs may be difficult to obtain a production buy without strong targets and expectations of ROI. It is estimated that ROI is not easy, but here are some suggestions:
- Design or adopt such a framework one.
- Use a cost calculator, e.g. This OpenAI API pricing tool and cloud provider calculator to estimate costs.
- Rather than a single target, scope-based ROI estimation is developed and has probabilities to explain uncertainty.
Here is an example Uber’s Querygpt The team estimates the potential impact of its text-to-SQL Genai tool.
3. Enable Quick Experiment
Building a Genai application is about experiments that require continuous iteration. When selecting your technology stack, architecture, team, and processes, make sure they support this iterative approach. These choices should enable seamless experiments, from generating hypotheses and running tests to collecting data, analyzing results, learning and refining oil.
- Consider hiring small and medium-sized service providers to speed up the experiment.
- choose BenchmarkEvals and evaluation frameworks at the beginning ensure they are aligned with your use cases and goals.
- Use similar techniques llm-as-aaa gudge or llm-as-juries Automated (semi-automatic) evaluation.
4. Aiming at low friction solutions
Low friction solutions require fewer approvals and therefore face less adoption and expansion or no objection. Genai’s rapid growth has led to an explosion of tools, frameworks and platforms designed to accelerate POC and production deployment. However, many of these solutions are black boxes that require rigorous scrutiny of their, legal, security and risk management teams. To address these challenges and simplify the process, consider the following suggestions for establishing a low-friction solution:
- Create a dedicated roadmap for approval: Consider creating a dedicated roadmap that addresses issues with partner teams and gets approval.
- Use a pre-approved technology stack: Use an approved technology stack whenever possible and use it to avoid latency in approval and integration.
- Focus on basic tools: Early POCs usually do not require fine-tuning of models, automatic feedback loops, or extensive observability/SRE. Instead, tools that prioritize core tasks such as vectorization, embedded, knowledge retrieval, guardrails, and UI development.
- Use caution with low-code/no-code tools: While these tools can speed up schedules, their black boxes naturally limit customization and integration capabilities. Use them with caution and consider their long-term effects.
- Solve security issues early: Implement technologies such as synthetic data generation, PII data masking and encryption to actively solve security issues.
5. Form a Lean, Entrepreneur Team
As with any project, having the right team with basic skills is essential to success. In addition to technical expertise, your team must be agile and entrepreneurial.
- Consider including product managers and subject matter experts (SMEs) to make sure you have solved the right problem.
- Ensure that the team has both full-stack developers and machine learning engineers.
- Avoid borrowing internal resources specifically for POCs or from higher priority long-term projects. Instead, consider hiring small and medium-sized service providers that can quickly bring in the right talent.
- Embed legal and security partners from day 1.
6. Also determine non-functional requirements
For successful POCs, it is crucial to establish clear problem boundaries and a fixed set of functional requirements. However, non-functional requirements should not be ignored. While POCs should remain focused on problem boundaries, their architecture must be used for high performance. More specifically, reaching millisecond delay may not be an immediate necessity, but as Beta users expand, POCs should be able to scale seamlessly. Choose a modular architecture that maintains flexibility and agnosticity of tools.
7. Make plans to deal with hallucinations
Language models are inevitably hallucinations. Therefore, guardrails are crucial to responsibly scaling the Genai solution. However, the assessment is made during the POC phase and to what extent automatic guardrails are required. Instead of ignoring or overengineering the guardrail, detect When your model is illusion and mark it as a POC user.
8. Adopt product and project management best practices
this XKCD Illustrations are just as suitable for POC as production. There is no script of size. However, adopting best practices in project and product management can help simplify and make progress.
- Use Kanban or agile methods for tactical planning and execution.
- Record everything.
- Hold Scrum of-Scrum and work effectively with the partner team.
- Keep your stakeholders and leadership informed of progress.
in conclusion
Running the Genai POC successfully is not only a demonstration of technical feasibility, but also involves evaluating the basic options for long-term evaluation. By carefully selecting the right use cases, aligning the success metrics, enabling rapid experimentation, minimizing friction, assembling the right team, meeting functional and non-functional requirements, and planning for challenges such as hallucinations, organizations can greatly improve their chances of moving from POC to production.
That is, the steps outlined above are not exhaustive and not all suggestions apply to every use case. Each POC is unique and the key to success is to adapt these best practices to suit your specific business goals, technical limitations, and regulatory environment.
A strong vision and strategy is crucial to Genai’s adoption, but even the best-planned plan can stagnate during the POC phase without the right tactical steps. Execution is where great ideas succeed or fail, and taking a clear structured approach ensures innovation translates into real-world impact.
