Over the course of the past several years, artificial intelligence (AI) has seen widespread implementation in industries across the globe. As its popularity has expanded, many business leaders have raced to embrace it and the potential cost-cutting, time-saving measures it has the potential to bring. However, in this mad dash to incorporate new technology, proper implementation and long-term considerations have often fallen to the wayside.
Many executives can say how many AI licenses their company bought. Far fewer can say what those licenses changed. Usage dashboards report logins and prompts, but productivity shows up somewhere else: in cycle times, error rates, resolved tickets, and the hours people get back for more complicated projects. Closing the gap between those two sets of numbers has become a pressing management problem, and the tools that address it look less like AI products than like measurement infrastructure.
The first requirement for more successful utilization of AI tools is allowing for visibility into how work actually happens. Companies building toward an AI enterprise often find that adoption is lopsided: a few teams build durable habits while others open a new tool once and drift back to old routines. Digital adoption platforms, including WalkMe, layer over existing software and can record where employees click, stall, or abandon a task.
That may help leaders tell whether an AI feature is part of a real workflow or has only been tried, bridging the gap between license counts and outcomes. Without this insight, a company may celebrate rising prompt volume even as the workflows it hoped to speed up remain untouched.
For as crucial a first step as behavioral data is, it is only the beginning. This information shows whether people use a tool, but it doesn’t show whether the work improved. For that, leaders are realizing that it may be best to lean on systems they already own, such as ticketing platforms, which can record handle times and reopen rates.
The discipline of comparison is what produces credible numbers. A study of more than 5,000 customer support agents found generative AI raised productivity by 15%, with the biggest gains going to less experienced staff. Federal Reserve research cited in the same piece estimates that workers using AI save about 5.4% of their hours, a little over two per week. Those figures exist because someone measured defined tasks against a baseline, not because someone counted prompts.
Credit: UnsplashWhen it comes to a tool going company-wide in its usage, it’s often best to set baselines and expectations well before that rollout actually occurs. Teams that run small, bounded pilots can capture a few weeks of pre-AI performance, introduce the tool to one group, and compare results against a group that has not yet received it.
Sandbox environments, where employees can experiment without touching production data, can make those pilots safer to run and easier to interpret. Some experts argue that success should be defined by the quality of work people do, not by efficiency alone, a useful check against optimizing for speed at the expense of judgment.
Measurement also depends on people knowing how to use what they have been given. A workforce that understands what a tool can and cannot do has often proven capable of producing cleaner data, because people often use it for the tasks it suits. Policymakers have noticed.
As reported, a new plan is asking federal agencies to track AI adoption through maturity assessments and proposes tax-free reimbursement for private-sector AI training. Whatever one thinks of the politics, the underlying logic is one business leaders can borrow: training and tracking work best together.
No platform settles the question alone. Dashboards can show that a team saved two hours a week, but they can rarely say whether those hours went to better work or to more meetings. That judgment still belongs to managers, which is why the survey’s point that leaders cannot delegate understanding holds up.
Among the most credible approaches is one that can combine behavioral data, outcome metrics, controlled comparisons, and honest employee feedback, reviewed on a regular schedule and adjusted as tools and roles change. To this end, leaders who build that habit can explain not just that their company uses AI, but what it gets in return.
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