KPMG has rolled out an internal AI usage dashboard across its US advisory division, designed to move beyond simply encouraging AI adoption and start measuring whether that usage is actually driving productivity gains.
The Details
The tracking system gives leadership visibility into how employees are engaging with AI tools on a day-to-day basis, shifting the conversation from "are people using AI?" to "is AI use producing results?"
Key points from the rollout:
- The dashboard was deployed across KPMG's US advisory division in late 2024
- The tool tracks individual and team-level AI usage patterns, not just aggregate adoption rates
- The goal is to connect AI activity data to measurable productivity outcomes, not just license utilization
- KPMG has been one of the more aggressive Big Four firms in pushing AI integration into professional services workflows
The move reflects a broader maturation in how large enterprises think about AI investments. Early-stage AI programs focused on getting employees to try the tools. The next phase is accountability: proving the tools deliver value at scale.
Why This Matters for Service Providers
For MSPs and telecom resellers advising clients on AI deployments, KPMG's approach signals where enterprise expectations are heading. Clients are going to start asking harder questions about ROI, and "we deployed the AI" will no longer be a sufficient answer.
This creates both a challenge and an opportunity. Service providers who can offer clients visibility into how AI tools are being used, and tie that usage to business outcomes, will have a stronger retention and upsell story. Those who just provision tools without supporting adoption analytics risk being commoditized.
The actionable takeaway: If you're helping clients deploy AI voice agents or other AI-driven communication tools, build outcome tracking into your service from day one. Usage dashboards and call analytics are not just nice-to-have features; they are increasingly what separates a sticky managed service from a one-time sale. The conversation around using call analytics to predict client churn is directly relevant here: the data you surface for clients builds trust and justifies ongoing fees.
If you are still working out how to frame the value of AI services to clients, how to pitch AI voice agents to your MSP clients covers the objection handling and ROI framing you will need as these conversations get more sophisticated.
Looking Ahead
Watch for more enterprises following KPMG's lead by tying AI tool access to demonstrated usage benchmarks, which will push vendors and service providers to deliver better reporting and accountability layers. If you are not already thinking about how to measure and communicate AI ROI for your clients, now is the time to build that capability.
For the full story, read the original article on UC Today.
