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AI is often evaluated on measured speed and productivity gains, but its impact on team durability can be harder to spot. As companies weave AI into everyday workflows, leaders must honestly evaluate whether the technology is strengthening employees’ skills, judgment and resilience or making them more dependent on automated support.
Knowing and spotting the signals that reveal how well people can still think, adapt and recover when systems fail or change has always been an essential leadership skill, but AI is making it more critical than ever. Below, members of Forbes Technology Council share indicators leaders can monitor to ensure AI adoption supports long-term adaptability and performance, not just short-term productivity.
Measure the value delivered to your customers and how long it took, not just the activity—lines of code written or PRs merged. Don’t measure value delivered. For a software company, delivering features that are used and valued by customers is what your team is there for. If you’re introducing AI and this is not changing—or worse, is degrading—then something is not right. - Mark Williamson, Undo
I think a pragmatic measurement is how much work was done pre-AI versus with AI. In software engineering, it’s simple: You can measure the number of pull requests and the code committed, as well as signals related to novel feature development. In some cases, it might not be straightforward to measure, but there can be ways around it. - Sumit Oberoi, Wispr Flow
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One metric leaders should be tracking is human-initiated challenge: how often your team questions or overrides AI outputs in meetings, docs and decisions. As AI accelerates execution, teams can default to acceptance over thinking. If pushback, critique and debate decline, adaptability erodes over time. Rising output with less questioning isn’t progress—it’s compliance. - Aditya Agrawal, Creed
The signal leaders overlook most is the skill atrophy rate. The clearest measurable signal is the rate at which team members can perform core tasks accurately without AI assistance—sometimes called the unassisted competency rate. We developed assessment software that allows us to know this metric by comparing performance with and without AI. - Oleg Fonarov, Program-Ace
Leaders should monitor how AI impacts application maintainability over time, including code quality, governance intervention and the team’s ability to sustain constant iteration. If systems become harder to understand or evolve, or if iterations generate more work, then AI-driven productivity gains are undermining long-term performance, ultimately eroding the productivity AI is meant to create. - Tiago Azevedo, OutSystems
We should be less concerned about team success and more concerned about the business outcomes generated by AI. Is it enhancing efficiencies and improving profitability? Is it creating growth opportunities? Of course, team success should be kept in mind, but it should not be the No. 1 priority. - Rao Tadepalli, Digitran Technologies Inc.
KPIs can be defined to assess rework percentage; 0% or less than 10% tells you that your team has outsourced 100% to AI. The approach I usually employ is asking team members a “curiosity” question: “When was the last time you caught an issue with an AI response, and how did you fix it?” If the team struggles or can’t remember when they last caught an issue, you know adaptability is in the red zone. - Muralidharan Nair, Infosys Limited
The most important signal is the expertise compounding rate. How quickly is your expertise (skills, reusable IP and delivery methods) improving independently of individuals? If AI is working correctly, expertise should be scaling across the organization, not trapped in prompts or individuals. This can be measured through the reuse of deliverables and assets across projects. - Sarah Edwards, Kantata
Track how often your team resolves new and ambiguous problems—the kind that fall outside established workflows—without resorting to AI-generated answers or escalating to senior leadership. If this rate is falling quarter by quarter, your team is getting faster at what they already know but has lowered performance around unknowns. AI is compressing execution time while silently stunting judgment. - Alessio Alionco, Pipefy
Adoption is one of the simplest forms of measurement. Adoption from both subjective and objective viewpoints should be gauged to see how teams are embracing AI for day-to-day use. When AI is adopted as an accelerator to the team’s capabilities, it’s no longer perceived as a replacement. Measuring deliverable quality alongside productivity helps ensure AI is improving team performance, not just increasing output. - Nicholas Nelson, Integris
Team adaptability should be measured by decision speed as well as cultural improvements like higher-quality meetings, shorter cycles and higher employee NPS. One way to measure this is by looking at the calendars of employees across the team. Has AI reduced the amount of time spent on internal meetings? How has AI changed how time is spent at work? - Edmund Zagorin, Arkestro
Monitor the growth of non-human identities relative to your workforce—sometimes this ratio can be as high as 50-to-1. When AI agents, bots and service accounts scale faster than governance, risk starts to spiral. If you can’t see, classify and control these machine identities in real time, you’re trading short-term productivity for long-term fragility and increased risk. - Praerit Garg, One Identity
One measurable signal is decision quality over time. Why? AI can boost speed, but if teams begin to rely on it blindly, their critical thinking and judgment degrade. How? The percent of AI-assisted outputs requiring rework or correction, escalation rates, and variance in outcomes. A great metric to see: speed increases without a rise in errors or rework. That means teams can challenge AI outputs and measure their quality, not just accept them. - Benu Aggarwal, Milestone, Inc.
Start with one question: “Should this be automated?” If the answer is “yes,” the next question isn’t “Can we use AI?” but “What’s the most responsible tool?” That could be a script, RPA, workflow or AI. Define the process, the measurable benefit and the experience gained. The goal is leverage, not novelty. Solving a $1 million problem with $3 million of AI is waste—AI should not equal 42. - Jeff Schmidt, ECI
Team cohesion is an important, measurable signal for gauging potential degradation in long-term departmental adaptability. AI empowers employees with new skill sets. With such utility, it can become easy for team members to start going down their own productivity rabbit holes, creating potential disconnects in cohesive workflows. - Daniel Keller, InFlux Technologies Limited (FLUX)
One signal to watch is how teams spend their meeting time, because AI adoption reduces administrative burden and frees time for creative problem-solving and collaboration. Leaders can monitor this by measuring time spent on critical thinking and teamwork, such as calendar tagging for working sessions that generate new ideas. - Diana Cano, Cambium Learning Group
A key signal is the quality of escalation. Strong AI should know when to hand over and do it well. When teams step in, are they picking up with full context or fixing confusion? If handoffs create friction or rework, performance will suffer. Done right, AI should enhance human capability, not quietly erode it over time. - Pete Hanlon, Moneypenny
The signal to watch is whether your team is getting better at handling complexity. If AI is taking routine work, people should be improving in areas that require judgment, adaptability and problem-solving. When that progression slows or plateaus, AI is redistributing work without increasing capability. - Craig Crisler, SupportNinja
Monitor the ratio of decisions your team can explain versus decisions they’ve outsourced to AI without understanding why. We call it “reasoning atrophy.” If your team can’t manually do what the AI does—even slowly—you’ve built a dependency, not a capability. The test: Turn off the AI for one day per quarter. - Nimit Mehra, Zeo Route Planner
Innovation in AI is moving so fast that today’s breakthrough technology is tomorrow’s outdated artifact. The real metric we are monitoring most closely as we implement new enterprise AI initiatives is not efficiency, it’s agility—how quickly and effectively people are able to make better, more informed decisions and strategic pivots based on available resources. - Vivek Jetley, EXL
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