KPMG is accelerating its push to get employees using AI with the rollout of a new internal dashboard that tracks its use in day-to-day work. Rolled out across its US advisory division late last year, the dashboard tracks not only how often employees use AI tools but also the productivity gains from that usage.
The move signals a shift away from simply encouraging adoption toward understanding how effectively these tools are embedded in workflows. Rather than treating AI as a broad productivity enhancer, KPMG is attempting to quantify how it is used and whether it delivers tangible value.
This reflects a broader evolution in enterprise AI strategy. Many organizations have already cleared the first hurdle of getting employees to try AI tools. The next challenge is ensuring those tools are used in ways that meaningfully improve output, decision-making, and efficiency.
Inside KPMG’s AI Usage Dashboard
At its core, the dashboard tracks how frequently employees engage with AI tools during the workday and benchmarks that activity against internal targets. It also enables employees to compare their usage with peers, introducing a degree of transparency and potential competition in how AI is adopted across teams.
The rollout covers roughly 10,000 employees within KPMG’s US advisory arm, making it a significant test bed for understanding enterprise AI behavior at scale. The firm reports that a large majority of its US workforce already uses AI tools at least weekly, suggesting that baseline adoption is no longer the primary concern.
Instead, KPMG is focusing on depth of usage. The company argues that employees who engage more consistently with AI tend to produce higher-quality work, experience reduced stress levels, and free up time for more strategic tasks.
To support this shift, KPMG has integrated a mix of internal AI tools alongside platforms such as Microsoft 365 Copilot. The dashboard is complemented by training programs and incentives designed to encourage employees to move beyond basic interactions, such as simple prompts, toward more sophisticated workflow-level integration.
From Experimentation to Measurable Impact
KPMG’s initiative sits within a wider trend of large enterprises attempting to better understand how AI influences productivity and work patterns. However, this added benchmarking of productivity shows the next stage of AI adoption, moving from simply using tools to generating meaningful impact.
Organizations including JPMorgan Chase, Amazon, and The Walt Disney Company are also experimenting with systems that track AI usage, signaling a broader industry push toward accountability. The Walt Disney Company monitors metrics such as how often employees use AI and the volume of tokens generated, while Amazon tracks how deeply AI tools are integrated into daily workflows and whether they deliver meaningful outcomes.
However, KPMG’s approach has not been without criticism. Some employees have raised concerns about how accurately the dashboard captures real-world usage. For example, basic actions such as submitting a single prompt may be counted as engagement, potentially inflating usage metrics without reflecting genuine productivity gains.
There are also questions around visibility gaps. Certain developer tools and workflows may not be fully tracked, leading to incomplete or skewed data. These limitations highlight a broader challenge for enterprises: measuring AI usage in a way that reflects true impact rather than surface-level activity.
Ultimately, the shift underway is significant. AI adoption is no longer about whether employees use these tools, but whether they meaningfully integrate them into how work gets done. KPMG’s focus on tracking both frequency and quality of usage underscores this transition from experimentation to operational maturity.
What This Means for Enterprise AI Strategy
KPMG’s dashboard initiative points to the next phase of enterprise AI: governance, measurement, and optimization. As organizations move beyond pilot programs, there is increasing pressure to demonstrate return on investment and ensure that AI is used effectively across the workforce.
The emphasis on visibility and benchmarking suggests that AI usage could soon become a managed performance metric, much like other productivity indicators. This raises important questions about how organizations balance accountability with flexibility, particularly in knowledge-driven roles where outputs are not always easily quantifiable.
At the same time, employee concerns highlight the risks of overreliance on simplistic metrics. If usage data fails to capture the nuance of how AI contributes to complex workflows, organizations may struggle to draw meaningful conclusions or risk incentivizing the wrong behaviors.
Despite this, KPMG’s approach is likely an early indicator of where the market is heading. As AI becomes more deeply embedded in enterprise environments, the focus will increasingly shift from access to outcomes.
