An interesting corporate observation has been floating around: a company buys thousands of AI seats, does a big internal launch, and then only a small fraction of people use it. While exaggerated, one has to admit some truth to it – and it shows the real gap between procurement and enterprise capability.
Buying AI licenses is a purchasing decision. Creating enterprise value from AI is a change in how work gets done, and that is evolving.
Seat Count as Hallucination
Seat count is easy to report. It looks decisive. It can even satisfy stakeholders in the short term. But it doesn’t answer the only question that matters: Did anything improve in the business through AI use?
When AI is rolled out as a general tool – “everyone should use it” – adoption becomes uneven. Some employees experiment, a few build it into daily workflows, and many don’t touch it again after the first week. Without clear use cases, guardrails, and accountability, it stays optional. And optional tools rarely become operational advantages.
Individual AI vs Enterprise AI
Individual AI use is real and valuable. It helps people draft, summarize, brainstorm, and move faster in daily tasks. Enterprise AI is higher level. Enterprise AI is when AI becomes part of a repeatable workflow with ownership, measurement, and continuous improvement. It’s not just people getting access. It’s using AI to get system-wide processing to run better. It’s measurable and goes beyond usage anecdotes.
Measuring AI Effectiveness
The cleanest way to evaluate whether an AI is working is to measure it in three layers –
Adoption → Proficiency → Impact
1) Adoption: are people using it consistently?
Adoption is not about how many seats were purchased. Adoption is whether the tool is being used repeatedly.
Good adoption indicators include how many licensed users are active in the last 7 or 30 days, how frequently they use it, and which teams and workflows it touches. If usage spikes during launch week and then fades, that’s not an AI problem—it’s a workflow problem.
2) Proficiency: are they using it well and safely?
Even when employees are using AI, the next question is whether it produces usable output with reasonable effort.
If results require heavy editing, constant verification, or repeated prompt trial-and-error, usage tends to collapse over time. Proficiency can be measured through task success rates, rework levels, error categories (factual errors, policy issues, tone issues), and time-to-acceptable output. This is also where governance matters: people need clear rules about what’s allowed, what data is sensitive, and when human review is required.
3) Impact: what is improving in the business?
Impact is where AI either proves itself or doesn’t – whether individual or enterprise-level. The best way to measure impact is to pick a few workflows and compare results to a baseline. That might be cycle time reduction, throughput gains, cost reduction, revenue lift, or risk reduction. It’s much more persuasive to say, “This workflow now closes tickets 18% faster,” than to “100 employees are now using AI”.
A practical way to frame it is: baseline → uplift → ROI. If there’s no baseline, there’s no uplift. And if there’s no uplift, there’s no business case.
Options to Buying Seats and Hoping
Enterprise progress usually starts smaller and more intentionally.
Choose three to five high-volume workflows where time, cost, or error rates are meaningful. Assign an owner for each workflow. Define a baseline and a target before rollout. Pilot with a small group, measure adoption and output quality, and then scale the workflows that show measurable gains.
That approach is slower on day one – but it is far faster over the course of a year because it creates repeatable wins instead of scattered experimentation.
The True Takeaway
Early on, many organizations are confusing tool rollout with organizational transformation. The good news is that the solution really isn’t complicated.
AI becomes an enterprise advantage when it is tied to specific workflows, measured against baselines, improved over time, and held to outcomes the business actually cares about.
