Measuring AI by Business Results Instead of Adoption With Domino Data Lab

How should a business measure AI success when employee adoption tells leaders very little about revenue, savings, risk, or better decisions?

In this episode of Tech Talks Daily, I speak with Thomas Robinson, better known as T-Rob, who recently moved from Chief Operating Officer to CEO of Domino Data Lab. After ten years inside the company, he has seen enterprise AI move through several phases, from specialist data science projects to generative AI tools available across the workforce.

T-Rob argues that businesses have become too focused on the technology itself. Generative AI has attracted attention because almost anyone can use it, but an individual productivity tool is very different from an AI system making decisions about mortgages, clinical trials, financial markets, or defense operations.

As the potential value of a decision rises, so does the financial, regulatory, and operational risk. That is why T-Rob believes governance should be built alongside AI development rather than added after a system has been completed.

He compares the process with constructing a building. Engineers do not wait until the work is finished before checking whether it has been designed and assembled correctly. Reviews happen throughout construction. Domino applies the same principle to AI through policy controls, production monitoring, tracing, and continued human oversight.

We also discuss why companies should avoid beginning with a fashionable tool and searching for somewhere to use it. T-Rob recommends starting with the company’s primary business measures and working backward. A pharmaceutical business may examine the number of promising therapies entering its pipeline, revenue, and risk. The appropriate AI system can then be designed around those outcomes.

That system may combine large language models with computer vision, statistical models, rules, and company data. T-Rob believes the assumption that every business problem requires the latest frontier model can waste money and produce weaker results.

People remain a major part of the equation. T-Rob has seen companies reduce headcount in anticipation of AI replacing employees before the technology was ready. He argues that domain experts become more valuable because they understand the business history, operating environment, exceptions, and consequences that a model may miss.

The conversation also considers model independence and AI sovereignty. Many enterprises became dependent on a single cloud provider by building their own technology on proprietary services. T-Rob believes businesses should avoid repeating that decision with AI models. Open systems can allow companies to replace models as prices, capabilities, regulations, and operational needs change.

For organizations handling sensitive intellectual property, sovereignty also raises questions about what information leaves the business when employees prompt external models. T-Rob describes the risk of enterprise knowledge being absorbed into future model development, even when information has been anonymized.

Perhaps his strongest argument concerns measurement. He calls consumption and adoption terrible measures of success because they mainly reveal cost. Giving every employee an AI tool does not mean the entire company becomes proportionally more productive. Real return comes from improving the business processes that generate revenue, reduce expense, control risk, or support better decisions.

Are businesses ready to stop measuring AI by logins and start measuring what it changes inside the company?

Listen to the episode and share your thoughts.