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Most Agentforce rollouts get measured the way a software license gets measured: seats provisioned, logins recorded, a project marked "complete." Then six months pass, someone in finance asks what the platform actually returned, and the team scrambles to backfill a business case that should have existed before launch.
MIT Sloan's review of 300 public deployments found that 95% of generative AI pilots produced no measurable profit-and-loss impact, and the pattern holds for agentic rollouts specifically. The problem is rarely the agent's capability. It's that nobody defined the Agentforce KPIs that would prove value, so nobody can point to the number when it's time to renew budget or expand scope.
The eight metrics below come from what we track for clients after implementation, cross-checked against Salesforce's own published research and third-party analyst data. Each one answers a different question a stakeholder will eventually ask.
Why Agentforce KPIs matter more after go-live than before it
Go-live is the easy part. A working agent, a passing UAT cycle, a demo that impresses the steering committee. None of that tells you whether the agent holds up against real customer conversations, real edge cases, and real volume.
McKinsey's research on AI high performers shows that companies attributing 5% or more of EBIT impact to AI consistently measure across four dimensions at once: business outcomes, model performance, operational efficiency, and risk. Rollouts that skip straight to "is it live?" without building that measurement layer tend to plateau at pilot scale, unable to prove the case for expansion.
That's the gap a solid Agentforce KPI framework closes. It turns "the agent is deployed" into "the agent resolved this many cases, at this cost, with this customer satisfaction score, and here's the trend line."
The 8 Agentforce KPIs worth tracking
1. Actioned Work Units (AWU)
Salesforce's own metric for Agentforce activity isn't logins or seats. It's the Actioned Work Unit: a discrete task the agent completed on its own, like resolving a case, updating a record, or triggering a workflow. By the end of fiscal year 2026, Salesforce had delivered 2.4 billion AWUs platform-wide, growing 57% in a single quarter. Track your own AWU count from week one. It's the closest thing to a native productivity meter Agentforce gives you, and it reframes the budget conversation from "how many licenses are active" to "how much work got done."
2. Containment rate
The percentage of conversations or cases the agent resolves without escalating to a human. This is the single clearest signal of whether the agent is trusted with real work or just fielding the easy questions. Track it by use case, not as one blended number. A service agent that contains 80% of password resets but 10% of billing disputes needs a different rollout plan for each.
3. Time-to-resolution
How long a case takes from open to close, agent-handled versus human-handled. Salesforce's own sales data shows sellers expect agents to cut prospect research time by roughly a third and email drafting time by more than a third once fully implemented. If your agent isn't moving this number in the first 60 to 90 days, the workflow design needs a second look before the vendor gets blamed.
4. Human override and escalation rate
How often a human steps in to correct, redirect, or reverse what the agent did. This is the closest proxy you have to an accuracy metric without running a full audit. A rising override rate over time is an early warning that training data has drifted or that the agent has been handed use cases outside its design scope.
5. Customer satisfaction (CSAT)
Salesforce surveyed over 3,000 customer service professionals and found the number one improved KPI after AI agent deployment was customer satisfaction, ahead of rep productivity, handle time, retention, and first-response time. That ordering matters. Teams that optimize purely for cost or speed often miss that the experience quality is what moves fastest, and what leadership actually wants to see in the business case.
6. Adoption rate among internal users
For Agentforce deployments that assist employees rather than customers directly, track how many eligible users are actually invoking the agent versus working around it. McKinsey's research also flags that power users complete tasks up to 77% faster than average, and a blended adoption number tends to mask that signal entirely. Segment adoption by role and tenure, not just by a single company-wide percentage.
7. Cost per resolved interaction
What it costs to close a case or complete a task through the agent, measured against the pre-Agentforce baseline for the same workflow. This is the number finance will ask for directly, so build the baseline before go-live, not after. Without a pre-rollout number, there's no "before" to compare against, and the ROI conversation stalls on methodology instead of results.
8. Time to measurable value
Salesforce found that 70% of customer service organizations that adopt AI agents see measurable value within 60 days of deployment. Track your own timeline against that benchmark. If 90 days pass without a defensible result on any of the seven metrics above, that's the signal to revisit scope, training data, or the use cases the agent was assigned, before the initiative loses executive sponsorship.
Turning Agentforce KPIs into a rollout scorecard
Individually, these eight Agentforce KPIs tell you how one workflow is performing. Together, they tell you whether the platform is ready to scale beyond its first use case, which is where most of the real value sits.
Gartner forecasts that 40% of enterprise applications will embed task-specific AI agents by the end of 2026, up from under 5% the year before, and the enterprises pulling ahead aren't the ones that deployed first. They're the ones that built a measurement discipline early enough to prove value, secure a second budget cycle, and expand past the pilot.
If your team is past go-live and still reporting on license counts, that's a gap worth closing before the next budget review, not after. TELUS Digital's managed services team builds these scorecards into the rollout plan from day one, not as an afterthought once someone in finance starts asking questions.
Our Agentforce implementations pair the platform's native AWU data with the broader KPI set above, tailored to whether the deployment is customer-facing, employee-facing, or both. For teams that already have Agentforce live and need the measurement layer retrofitted, our AI and data practice can build the reporting structure around your existing configuration without a re-implementation.
Curious how a specific use case would perform against these benchmarks? Our consultation service starts with exactly that kind of KPI baseline, before any build work begins. You can also see how these metrics played out for other organizations in our client stories, or talk to our team about building a scorecard for your own rollout.
