Buying AI tools isn't enough: two reports show strategy, not spend, drives enterprise returns

Sixty-six percent of frontline white-collar employees who use AI regularly say they have received little or no guidance on how to use the time they save. That single figure, drawn from Boston Consulting Group's 'AI at Work' survey of nearly 12,000 workers, captures a gap that two new reports now put squarely in front of enterprise operations leaders: the distance between deploying AI and actually profiting from it.

Spend intensity predicts growth, but only up to a point

Ramp, a financial technology company, and Revelio Labs, a workforce intelligence platform, studied behavior across nearly 22,000 U.S. firms to understand what separates companies that realize gains from AI from those that don't, according to Business Insider's reporting by Lakshmi Varanasi. Their central finding: sustained, high-intensity AI investment correlates with measurable workforce growth, but the mechanism is not the spending itself.

The study defined high-intensity adopters as companies spending an average of roughly $34 per month on AI, against a comparison group spending under $3. Over the 24 months following adoption, the high-intensity group grew total headcount by more than 10%. Entry-level headcount rose 12%. Those companies also tended to be larger, more technically oriented, and already on a faster growth trajectory before they introduced AI.

The authors were careful to note that spending alone does not explain the gap. According to Business Insider, the report stated that realizing AI's benefits requires complementary investments, organizational change, and learning inside the firm. A set of enterprise chatbot subscriptions, in other words, is a starting point, not a strategy.

Strategy outperforms access in BCG's worker-level data

BCG's parallel findings reinforce that conclusion from the employee's vantage point. The firm's survey found that AI use has spread rapidly: 74% of frontline white-collar employees now use AI daily or several times a week, a 23-percentage-point increase from 2025, as reported by Business Insider. Penetration is no longer the primary challenge.

The more pressing problem is direction. BCG found that 58% of regular AI users are not reinvesting the time they free up into more strategic work. And when the firm compared two groups, those with strong strategic clarity but limited AI tool access and those with broad tool access but weak strategic direction, the clarity group won. Roughly 80% of the first group reported measurable impact, versus about 60% of the second, according to Business Insider.

That result has a direct implication for how enterprises frame AI programs. The variable with the highest return is not the number of licenses purchased or the sophistication of the models deployed. It is whether workers understand what to do with the capability once they have it.

Who owns the training gap?

A related tension runs through both reports: accountability for AI skill-building remains unresolved in most organizations. Business Insider noted that some CEOs expect employees to develop AI proficiency on their own time, while workers broadly believe that responsibility lies with the employer. BCG's data, showing two-thirds of regular AI users operating without meaningful guidance, suggests the two sides have not yet reached a working agreement.

For operations and IT leaders, this is not an abstract HR debate. A workforce that uses AI tools without strategic framing does not compound productivity gains; it disperses them. The Ramp and Revelio Labs data shows that the firms capturing workforce growth are not just buying more, they are building the organizational infrastructure to absorb what the tools produce.

What this means for your team

• Audit guidance, not just licenses: Before expanding AI seat counts, assess what percentage of current users have received structured direction on how to redirect saved time toward higher-value work.
• Tie AI spend to workflow redesign: The Ramp/Revelio Labs study found returns flow from organizational change alongside investment. Map which workflows change when a tool is introduced, and assign owners to those redesign decisions.
• Set a strategic clarity baseline: BCG's data shows that workers with clear direction outperform those with better tool access. Define what 'good AI use' looks like in each function before rolling out the next platform.
• Resolve the training accountability question: Whether upskilling sits in L&D, IT, or line management, establish it explicitly. Ambiguity is what produces the 66% guidance gap BCG documented.