AI POCs—the Pathway from Innovation to ROI
A proof-of-concept (POC) is a realization of a technical (AI) capability that demonstrates its potential for value and viability.
Pas(t)imes in the Computer Lab by Hanna Barakat & Cambridge Diversity Fund
From my experience, the power of a POC comes from its ability to start with well-defined scope and success criteria.
Good POCs facilitate lessons in understanding the benefits, constraints, risks, and guardrails, relative to the business objectives.
I’ve seen organizations gain traction around AI via incremental realizations of value; POCs are a great way to test and iteratively improve functionality until they're robust enough to scale.
The business case should tie to substantial ROI, even if it starts small.
AI POCs typically involve:
• Identifying the use case to opportunity mapping.
• Securing funding, resources, and project support.
• Characterizing the user profile and business problem, to determine the success metrics.
• Designing the UX to solve for pain points.
• Profiling, cleaning, and curating the data as required for enablement.
• Designing system architecture and capabilities in the responsible AI framework.
• Training, fine-tuning, and evaluating AI model output; followed by integration with the rest of the system.
• Deploying the POC and measuring its performance.
• Determining whether to scale and govern the solution.
Well-defined goals and exit criteria build trust with business partners.
AI use cases that demonstrate a clear need with sufficient data sources, and a roadmap are ripe for innovation to value.
Start with goal-setting: from there, it’s paramount to develop a comprehensive AI strategy (as mentioned in my previous posts).
Your team members and stakeholders all have valuable contributions to make to the plan.
Having a pre-disposition about how customers feel about the solution relative to the problem statement can help OKR development, and provide critical insights on how the solution manifests in a phased approach.
