Morning meetings sometimes challenge your thinking in ways you didn’t expect. Today, someone floored me with a simple question: Would you pay extra for higher intelligence—in people, or in digital agents?
On paper, it sounds like a no-brainer. Why wouldn't you want the brightest mind or sharpest AI for every job? But as the discussion unfolded, I found myself revisiting old assumptions, comparing how we hire humans to how we now design digital teams. The result? We don’t need “the best,” we need the right fit—for every role, every time.
Let’s face it, high intelligence doesn’t guarantee high performance—if the job doesn’t need it. In a traditional HR world, you wouldn’t ask a quantum physicist to manage a help desk, any more than you’d ask a world-class negotiator to stuff envelopes. Overqualification creates boredom and churn, not excellence.
With agentic AI, we face the same dilemma but with higher stakes. Boards are now considering what kind of intelligence to deploy, at what cost, in every digital role:
"Boards and C-suite leaders are already wrestling with these realities: Capability vs. Motivation: Imagine an agent (or person) capable of far more than the job requires. Will their performance plateau? Could their 'overqualification' cause operational or cultural friction? Return on Intelligence: What’s the incremental value of adding higher intelligence to each role, process, or transaction? Are you paying a premium for capabilities that go unused?"
Business reality isn’t about finding the most impressive agent—it’s about tuning every “worker” (human or digital) to the role that delivers maximum value for money, alignment, and engagement. This shift echoes through contemporary boardrooms:
• Agentic “IQ” as a Variable, Not a Goal: Enterprises must now design roles with an eye to how much intelligence is “enough”. Systems that are too smart may plateau, create friction, or simply cost more than their added value.
• Benchmarking Is Board Business: What separates leaders from laggards is the discipline to objectively benchmark AI agents—for efficiency, domain fit, compliance, and adaptability. Recent frameworks stress this is not just about technical performance metrics, but holistic business fit (Emergence AI, 2025).
• Use Cases Show the Power of Matchmaking: In recruitment, agentic AIs designed for best-fit roles have slashed costs and improved quality by making unbiased, context-aware matches—spotting “gems in the rough” and cutting screening costs by up to 75% (Hyreo, 2025).
"A jet can move faster than a car, but it’s the wrong choice for a trip to the grocery store. ... Humans and machines inherently have different strengths and weaknesses. Organizations that collaboratively reinvent work ... will outplay those who merely focus on ... endless automation without increasing total value output."
— VentureBeat, From AI agent hype to practicality: Why enterprises must consider fit over flash (2025)
Use Real Benchmarks: The world’s top AI teams (IBM, Sierra, VentureBeat contributors) increasingly test not just for “can this agent pass an exam?” but “does this agent deliver at business speed, cost, and scale?” IBM Research Blog, 2025
"Benchmarks should measure cost-efficiency. ... API costs, token usage, inference speeds, and overall resource consumption should be measured and reported to level the playing field. ... Well-designed benchmarks do more than just rank systems; they spotlight gaps, motivate new research, and sometimes surface surprising failure modes or unintended behaviors."
— IBM Research Blog, The future of AI agent evaluation (2025)
Rethink Collaboration, Not Just Automation: The top agentic frameworks now test how well agents work with us, not just for us. It’s about shared control, adaptability, and true partnership.
"𝜏²-bench challenges AI agents not just to reason and act, but to coordinate, guide, and assist a user in achieving a shared objective. ... It’s not enough to act autonomously. The next generation of AI must learn to act with us."
— Sierra, 𝜏²-bench: benchmarking agents in collaborative real-world scenarios (2025)
Continuously Redesign Roles (and Workflows): Dynamic, multidisciplinary teams now flex both their human and digital “muscle” to match strengths to tasks, whether that’s creative strategy, frontline support, or repetitive documentation.
• INSIST on benchmarking—aggressively—before any at-scale investment in new agentic AI systems.
• Challenge team leads to make the business case for “just enough intelligence” rather than “the most intelligence money can buy.”
• Ensure HR and IT collaborate to define digital roles—and the intelligence levels needed—for both humans and agents.
• Monitor for emerging risks, including agent overqualification, operational boredom, or new vectors of regulatory exposure.
• Keep the conversation rolling. As agents get smarter, the “best-fit” frontier will keep moving.
Boardroom Takeaway: Effective resource orchestration now means matching intelligence—whether human or digital—to the problem, not just the headline metric. Highest-performing teams don’t pay for superstars everywhere; they build the right fit for each critical task.