How to Measure AI ROI and Build Rapid Prototypes

The Core Argument

Guided by host Sanjay Saini, Jagatveer argued that enterprise AI adoption is stalling not because the technology is flawed, but because prototyping efforts lack executive alignment and robust data governance. Jagatveer outlined a strategic playbook: to de-risk AI investments, organizations should leverage cloud provider funding to subsidize proofs of concept. However, these PoCs must be ruthlessly evaluated through ROI matrices before writing a single line of code. Sanjay and Jagatveer concluded that building a successful pilot is easy, but scaling it requires treating AI as an integrated capability platform, rather than an isolated tool.

“If a decision maker is not doing the PoC, it's better not to do the PoC anyway.” Jagatveer Singh (19:35)

Detailed Answers

1. Could you share a brief overview of your professional background?

Jagatveer manages the AWS alliance for AI and Data at Amplify. His primary role involves guiding organizations from the initial assessment of AI technologies all the way through to go-to-market motions. He actively connects businesses with cloud provider funding to subsidize prototyping. Personally, he runs an independent team focused on strategic AI investments, operating with extreme calendar rigidity with the ultimate goal of buying back all of his own time within five years.

2. What is the process for assessing and selecting AI use cases for PoCs?

Jagatveer's team uses strict technical and business questionnaires to evaluate potential AI use cases. They plot these cases on a matrix measuring the speed of delivery against the maximum anticipated business value. Out of 10 potential ideas, they might select only the top three contenders that guarantee rapid, measurable ROI. The remaining use cases are deliberately sequenced based on dependencies, ensuring maximum extraction of available cloud funding across the roadmap.

Scoring use cases on speed against business value, before anyone writes code, is the assessment discipline our Artificial Intelligence training program teaches teams to apply to their own backlogs.

3. What are the top challenges when moving an AI use case from PoC to production?

Data integrity is the absolute bottleneck. Jagatveer noted that people generally interact with consumer AI tools without thinking about where the data lives. However, when taking an enterprise PoC to production, architecture shifts from a simple tool to a capability platform. This requires solving massive organizational hurdles regarding data governance, identifying the single source of truth, and defining access roles in a regulated environment.

4. How is leadership reacting to these AI PoCs and use cases?

Skepticism is rapidly turning into strategic curiosity. Jagatveer explained that historically, PoC conversion rates were abysmal. Today, because implementation partners strictly focus on ROI-positive use cases, executive leadership is seeing tangible value. The conversation has shifted away from outright blocking the technology toward genuine questions regarding process optimization, governance integration, and identifying exactly where executives need to engage.

5. Why do some organizations struggle to scale successful AI pilots?

Sanjay highlighted that everyone wants a successful pilot, but taking it to real users becomes a nightmare. Jagatveer revealed the core issue: a lack of executive sponsorship. Often, the individuals driving the PoC are not the actual decision-makers who hold the budget for scaling. To prevent scaling failures, his team insists on helping engineers build localized experiments specifically designed to pitch the overarching business value directly to those decision-makers.

6. What criteria do cloud providers look for before funding an AI initiative?

When seeking funding from a major cloud provider, the primary factor is actually the competency of the implementation partner. The partner must prove they have successfully delivered significant revenue growth for clients. Once competency is established, providers look for committed executive sponsorship, a history of strong enterprise execution, and proof that the organization has the financial backing to eventually take the subsidized prototype into full production.

“Instead of waiting for the bubble to burst, get into it, get your work done, and build on top.” Jagatveer Singh (30:36)

7. Is the current AI boom just a bubble waiting to burst?

Jagatveer advised a highly pragmatic approach: do not sit on the sidelines waiting for a collapse. Referencing early tech booms, he noted that even if bad practices are eventually regulated or a market contraction occurs, the underlying foundational value remains. The best strategy is to rapidly exploit current capabilities, get your products to market, and build value immediately. If the bubble bursts, you retain the value created; if it continues expanding, you hold a significant competitive advantage.

8. Where do you see untapped opportunities or gaps in the current AI market?

Jagatveer identified hardware and manufacturing compliance as a largely untapped sector. He noted that high-quality manufacturers, particularly in Asia, face global distribution blocks due to fears of data theft. He proposed that an AI-driven startup could purchase this hardware, strip the native software, and use AI to rapidly generate completely localized, strictly governed, and compliant operating software. This would open blocked hardware to lucrative Western markets.