Don’t let your organization be among those that launch a generative AI proof of concept only to see the project wither and die.
Generative AI (GenAI) is the hottest topic in technology today, with companies across industries scrambling to build their own GenAI proof of concepts (POCs). Organizations are trying out use cases, with larger enterprises running multiple POCs. This widespread experimentation indicates a universal eagerness to harness GenAI’s potential for the enterprise.
Building a GenAI POC is not difficult. It’s a simple three-step process: choose a cloud provider (e.g. Azure, AWS, Google), pick a popular large language model (LLM) from the cloud provider, and develop a POC using the popular retrieval-augmented generation (RAG) pattern to optimize the output.
However, many POCs end up gathering dust in the “POC graveyard,” failing to progress to production.
In our experience building numerous GenAI proof of concepts, we have identified the most common pitfalls that lead to POC failure and how to overcome them to ensure GenAI experiments thrive.
Beyond the Hype: Picking the Right Use Case
One key reason POCs falter is that they are based on a poorly chosen use case. Don’t get caught up in the hype by building a POC for the sake of creating one. Select a business-led use case with a clear return on investment (ROI) and align it to your core business performance metrics. Focus on operational efficiency, cost savings, new revenue streams, or fostering innovation. Use a strategic, long-term lens rather than a short-term sizzle. Focus on “clear win” scenarios that offer high value and are achievable within your resource constraints (budget, data, skills, time).
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