Editorial
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The Future of Leadership Development: AI-Driven Manager Enablement
Did you notice anything different after the 2026 midyear review cycle? Our managers at Laserfiche reported that teams shattered previous execution-based metrics.
On paper, this looks like a historic triumph in productivity:
- IT teams are deploying new solutions faster than ever before.
- Marketing/communications teams are producing larger volumes of content and campaign assets.
- Customer service teams are closing out tickets in record time.
But looking at these numbers, we’ve realized that these KPIs — which seemed impossible just a few years ago — need to be reimagined for the AI-powered workplace.
If an employee can hit 100% of an output target simply by feeding an AI tool a prompt, then that KPI is no longer measuring human capability. It can be easy, in this environment, to fall into an AI efficiency trap, measuring how fast the machine can churn out work rather than how effectively our teams are solving real business problems.
Enterprise leaders must fundamentally redefine what value looks like for today's reality, and in some cases, completely overhaul how we measure success.
Speed Isn't the Win You Think It Is
AI is an unparalleled production factory. It can generate passable code, standard marketing copy or template-driven HR onboarding schedules in seconds. It gives us a higher baseline than we’ve ever had before. But “passable” work does not move a business forward.
The speed at which a task is executed won't differentiate leading organizations from the rest. The human intervention that converts that baseline into excellence will.
When a computer handles the mundane, repetitive elements of a job, the value of the employee changes. They are no longer an executor chained to a desk; they are a critical thinker, a cross-departmental collaborator and a high-touch consultant.
If our KPIs continue to reward only volume and velocity, we discentivize our people from stepping away from their screens to do the deep, high-value human work that AI cannot touch.
How to Shift Metrics from Output to Outcomes
To escape the efficiency trap, we have to look beyond traditional goals — like shipping a product — and start measuring its ultimate impact. Across any business area, whether technical or non-technical, traditional metrics must evolve from tracking what was produced to what was solved.
Here is how we must redefine success across four core corporate functions:
| Department | Old KPIs | New KPIs | The shift |
|---|---|---|---|
| IT | Projects completed | User adoption | AI can assist with code or help close tickets, but it doesn’t understand user frustrations. Success should be measured by how well technology actually works for the intended user. |
| Human Resources | Onboarding checklist completion rate | New hire retention and integration | AI can automate administrative logistics and prepare for a new hire’s first day. HR’s value, however, should be measured by how well they facilitate the new hire’s belonging and satisfaction in their role. |
| Customer service | Ticket resolution time | Client retention and proactive advisory | If AI chatbots can answer repetitive, low-lift questions, human customer service agents should be measured on their ability to act as high-touch advisors who help deepen client trust. |
| Marketing/communications | Content output volume | Audience engagement | AI can take some of the heavy lifting out of content development, but human marketers are the experts in determining audience alignment, injecting original research, authentic voice and emotional nuance that cuts through the noise. |
3 Steps to Fix Your KPIs Now
As we look toward the second half of the year, we can't let the green dashboards fool us into believing our organizations are running at peak strategic capacity. These three immediate actions will align AI productivity metrics with real human value:
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1. Find Your Ghost Metrics
Sit down with team leaders and look at your current goals. Identify any metric that AI can achieve without a human applying critical thought. If a metric requires zero human oversight, it can be deprecated or re-weighted, so that it only measures the strategic outcome of that output.
2. Reassign the Time AI Frees Up
When AI saves an employee five hours of mundane tasks, a reflex can be to assign them five more hours of mundane tasks. Break this cycle. Rewrite job descriptions to account for this found time. Require employees to spend those reclaimed hours on cross-departmental alignment, user interviews or direct client consulting.
3. Reward Critique, Not Just Completion
Shift team discussions to focus on evaluation of projects rather than simply celebrating that something was completed. Whether it is a software deployment or an HR initiative, managers should lead teams on critiquing, refining and elevating processes and projects. Collaboratively think about what AI misses, where its shortcomings are and how to take something from an AI-created “first draft” to an initiative that uniquely fits the organization and its customers’ needs.
The true promise of AI isn’t that it lets us work faster; it’s that it frees us to think better. Our job as leaders is to ensure our scorecards reflect that higher standard. Let the machines handle the baseline, and let your humans build the excellence.
Editor's Note
How else do we have to rethink performance when AI is in the mix?
- How Managers Weigh AI Use in Employee Performance Reviews — AI is reshaping performance reviews — but measuring usage alone isn't enough. Experts say the real measure is judgment, not frequency.
- Why Nobody Agrees on AI Productivity Metrics — As AI reshapes work, companies want new productivity metrics. The problem? Nobody can clearly define or fairly measure the alternatives.
- Performance Metrics Are Obsolete. AI Is About to Prove It — Why outdated performance metrics and legacy work systems are now organizational risks when AI is a digital collaborator.
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