AI PoCs: Friend, Foe, or the First Step Forward?

PoC Fatigue is REAL!

Working with multiple customers on AI adoption, I’ve seen a common trend: PoC after PoC, experimenting over and over to find the right solution for user problems.

I get it; AI is still a relatively new frontier. It comes with its own unique set of challenges, such as:

• Data privacy and compliance requirements
• Readiness of data and infrastructure
• Organizational culture and adoption hurdles

But at its core, AI integration is just an enabler. It’s a tool, albeit a powerful one, to solve user problems in a better, often more efficient way. It’s important to remember that AI’s ultimate goal is to deliver value: improving efficiency, enabling personalization, and enhancing overall user experience.

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What is an AI POC?‍

Let’s start with the basics: Proof of Concepts (PoCs) are small-scale, experimental implementations designed to validate the feasibility of an idea or technology in a controlled setting. They allow organizations to test whether a solution will work as intended without committing to a full-scale deployment. 

Essentially, PoCs are a validation mechanics, and as every validation can either fail or succeed.

Some interesting statistics about PoCs is that only 53% of AI PoCs successfully transition to production, and that is actually good about them. Because it can act as a cost-saving mechanism before a bigger investment is made that could be without a return or can yield positive results. ‍

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Why PoCs Are a Must?

PoCs are absolutely critical to the success of any AI initiative. They’re not just important—they’re a must for a variety of reasons:

• Validating the Hypothesis: Especially in early-stage projects or venture building, PoCs help confirm whether the proposed solution is feasible and worthwhile.
• Assessing Data Readiness: Many organizations discover through PoCs that their data quality or availability isn’t where it needs to be, which allows them to address gaps early.
• Risk Mitigation: By experimenting in a controlled environment, PoCs uncover potential challenges before committing to full-scale implementation. 
• Building Stakeholder Confidence: Demonstrating tangible results helps secure buy-in from leadership and other key stakeholders.
• Highlighting Business Value: PoCs showcase how AI can deliver measurable benefits, from improved efficiency to cost savings and enhanced user experiences.

When to Move Beyond PoCs?

The big question is: when do we stop? AI capabilities evolve rapidly, and concerns about performance or feasibility can often be addressed as the technology matures. Additionally, the cost of AI adoption is steadily decreasing.

Once the hypothesis has been validated and the PoC demonstrates proven value, it’s time to take the next step. This is where the Agile approach becomes essential.

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Agile is the Way Forward

Agile principles focus on incremental delivery, enabling teams to:

• Introduce smaller AI capabilities into production incrementally.
• Gather real-world user feedback quickly.
• Make informed decisions on what to build next based on actual data.
• Continuously deliver value to end users.
• Cost Savings from Incremental Delivery: Companies that adopt Agile delivery models report a 25-30% reduction in overall project costs compared to traditional waterfall methods. (Source: McKinsey)

The Agile approach brings us back to the roots of product development: delivering incremental value to real users. By deploying AI features in smaller, manageable chunks, you’re able to test and iterate faster, ensuring that every step forward is a step in the right direction. This reduces the risk of over-investing in solutions that may not resonate with users or provide tangible benefits.

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The Role of Incremental Value in AI Adoption

In a recent project, we started small by integrating an AI chatbot to assist Sales Managers with competitor analysis. Instead of building an entire interface and flow upfront, we introduced incremental pieces, like the chatbot, that not only increased efficiency but also familiarized users with AI capabilities in their daily operations. This approach helped create demand for additional features and tools while delivering immediate value.

AI should never feel like an all-or-nothing gamble. Instead, it should be a journey of gradual integration, where small wins pave the way for larger transformations. For example, starting with an AI feature that automates a single repetitive task can provide immediate efficiency gains while setting the stage for more complex capabilities like predictive analytics or personalized user experiences.

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AI PoCs at the intersection of validation and agility

PoC fatigue is a real challenge, but it doesn’t have to be a roadblock. By using PoCs strategically and adopting an Agile approach, organizations can overcome common hurdles and transition from experimentation to delivering real-world value. The key lies in balancing the need for validation with the agility to move forward and deliver incremental benefits to users.

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About the author:

Ioanna has 8+ years of experience in product management and has evolved through roles ranging from a Scrum Master to a Technical Program Manager. Her expertise and knowledge entail a deep understanding of what it truly takes to devise and implement strategies that meet and exceed customer expectations.

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