Beyond Token Caps: How Enterprises Actually Measure AI Impact

Most enterprise AI conversations get stuck on the wrong number: how many tokens an employee should be allowed to burn. Tomer Elias argues the cap is the least interesting question in the room. The hard part is attribution, knowing whether any of that spend moved a business metric at all. We compare notes on what enterprises are actually doing right now, why AI keeps exposing organizational problems that predate it, and what has to be in place before "AI impact" means anything.

About the Guest

Tomer Elias is a product executive with more than 15 years leading startups from zero to one and one to 100, through unicorn and IPO stages. His focus has been AI and data throughout. He was part of the first AI lab in Israel, helped build a cybersecurity unicorn, and served on a committee defining agentic identity standards alongside OpenAI, AWS, and Cloudflare. He is currently mapping how enterprises adopt AI and where they sit on the maturity curve.

Chapters
00:00 Why this conversation
00:54 Tomer's background: AI labs, a cyber unicorn, agentic identity standards
02:26 What actually changed with gen AI: ambiguity
02:50 Human operating systems and agent operating systems
06:17 Setting a token cap, and why the number isn't the point
07:19 Measuring outcomes was broken long before AI
08:43 Activity, output, outcome, impact: what the work really looks like
10:55 AI surfaces the organizational DNA you never fixed
11:19 The factory lens: an industrial engineer's view of the enterprise
14:31 Goldratt and the constraint: where AI actually pays
15:56 Two levels of observability: spec conformance vs. value
19:00 A worked example: meeting transcripts as context
20:33 Kill criteria and staged funding for AI experiments
22:41 Build vs. buy, and the question nobody asks first
25:09 The rise of the business engineer
25:50 The digital twin that knows your stack
28:06 Data infrastructure: the real enterprise blocker
30:05 Closing the loop: telemetry, usage, decisions
34:12 Psychological safety and toxic token usage
39:32 Security guardrails: MCP safety, DLP, sandboxes
42:54 Who builds beyond product and engineering?
46:54 Teaching product skills instead of staffing PMs

Notable Quotes

"AI puts the credit card at the hand of the employees. But without the oversight and training, costs can spiral. But high usage doesn't really mean a bad thing. It's not good or bad. The question is what's the impact that you get out of that." — Tomer Elias

"Once you implement AI in your organization, it surfaces your DNA and the organizational culture that didn't change for a while and now needs to change if you really want to push impact with AI." — Tomer Elias

"Any improvement away from the bottleneck or from the constraint is meaningless, and any improvement directly at the constraint is a real multiplier." — Yuval Yeret

"If it's a healthy DNA, people would feel safe to experiment. And if it's a toxic culture, you would get toxic token usage and activity theater." — Yuval Yeret

Links and Resources
Eliyahu Goldratt, Theory of Constraints and The Goal
Real options and innovation accounting for staged funding of experiments
Tools referenced in the conversation: Glean, Zapier, Salesforce, Gong, Datadog, Claude, ChatGPT
Tomer Elias on LinkedIn: https://www.linkedin.com/in/tomer-elias1

If you're working on turning AI activity into business impact, I write about this every week in the Scaling w/ Agility newsletter.

Creators and Guests
Yuval Yeret — Host, AI transformation advisor
Tomer Elias — Product executive, AI and data

Scaling AI: From Activity to Impact — yuvalyeret.com
The Scaling w/ Agility Newsletter – yuvalyeret.com/insights
Yuval's Linkedin – https://www.linkedin.com/in/yuvalyeret/