In this week’s Data Diaries, we kick off a seven-part series on enterprise AI with the question every CFO eventually asks: how do you actually know what this stuff is worth?
Start with TRIPS — Trust Insights’ five-factor screen for whether a task even belongs near an AI system. Take any task on your desk and score it for Time, Repetitiveness, Importance, Pain, and Sufficient data. High scores across all five mark a candidate task for AI; low scores mark a task you keep with a human.
The screen replaces the vendor pitch with a workbench test you can run before you spend a dollar. Now lock the financial vocabulary. ROI sits as a very strict financial measure: earned minus spent, divided by spent. Most AI savings claims do not clear that bar — they show up as time returned to people, not dollars on the income statement.
Adoption runs near-universal across the Fortune 1000 today; outcomes do not. The discipline gap, not the spend gap, separates the leaders from everyone else.
So what does that gap actually look like in practice? If your team measures the hours a given task consumes and you know what you pay that person, you can infer a savings figure. That is an inference, not a calculation. Suppose Chris spends one hour on a task that used to take twenty — the math says you saved nineteen hours of payroll.
If Chris then spends the other nineteen hours playing video games at work, you really haven’t saved any money. The savings exist only when you redeploy the hours into work that moves a number leadership already tracks. All of this requires good existing measurement. If you do not have good existing measurement, you cannot infer what AI is worth.
That measurement gap lands as the hardest part for most enterprises, and it sets up everything else in this series. Departments with line of sight to sales objectives prove value easily. HR, Legal, and Finance prove value only when they connect the freed hours to something the board already counts.
What do you do with that discipline? Map every initiative to one of three horizons, and locate each horizon on the TRIPS curve.
Horizon 1, Defensive Efficiency, automates the highly templated work that scores hot on every TRIPS dimension and returns hours inside existing workflows.
Horizon 2, Core Augmentation, drives margin gains inside your core value chain by pairing humans with AI on tasks that score high on Pain and Importance but lower on Repetitiveness.
Horizon 3, Net-New Disruption, builds entirely new products around problems your TRIPS screen flagged as too important to leave templated. A company living only in Horizon 1 watches a Horizon 3 competitor eat its lunch by next fiscal year.
Run that map through the 5P Framework by Trust Insights™ — Purpose, People, Process, Platform, Performance — before you scale anything. At the enterprise level, leaders express corporate strategy through AI, stand up governance ahead of scale, and pick a hub-and-spoke operating model with central oversight.
Mid-market companies sit outside the EU AI Act’s primary crosshairs, but enterprise customers push the requirements down through procurement RFPs, so the playbook still applies.
At the SMB end, you compete by staying fast, focused, and credible. Agencies productize what enterprises now need — audit trails, evaluation rubrics, ISO/IEC 42001-aligned documentation — and carry a service line competitors lack.
The leaders do not pull away because they stockpiled more AI. They pull away because they apply more discipline around AI.
Next week we walk the governance layer — the AI Council, the EU AI Act enforcement clock, the U.S. state laws, the Bartz precedent, and what every company needs in writing before the next regulator letter lands. Stay tuned!
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