AI Alone Won’t Guarantee Success. Your Strategy Will.

Remember just two years ago, when simply using AI was considered the new, competitive move? Back then, companies that experimented early were hailed as visionaries. But the tech has moved fast—and it’s not slowing down. Today, nearly every organization has access to the same models, platforms, and tools. The real advantage is no longer who has AI, but who’s ready for it.

And by “ready,” we don’t mean the most enthusiastic or eager to experiment. Any company can invest in AI, but the payoff often stops short of the bottom line. For most, the barriers are structural—unclear ownership, missing skills, employee pushback, and outdated infrastructure that make integration difficult. Without addressing these issues, AI won’t deliver real impact. In fact, a recent study found that over 97% of companies still struggle to prove the business value of their generative AI initiatives.

That’s why we created the AI Impact Score—a clear, data-driven tool and framework to turn AI potential into performance. Built on Korn Ferry’s decades of job architecture research and proven success profiles, it analyzes job descriptions and responsibilities to reveal how AI affects each role and provide a roadmap for where to invest for the biggest payoff.

AI’s true promise has never been about replacing people with machines. It’s about knowing where each adds the most value. Success comes when leaders understand how AI transforms roles and amplifies human strengths, turning technology into a catalyst for what people do best.

Which Roles Are Most Exposed—and Most Resilient—to AI?

We know from our analysis that AI doesn’t impact every role in the same way. Jobs built around structured, repeatable, and data-intensive tasks—like customer service reps and purchasing clerks—are the most exposed to automation. Meanwhile, roles grounded in human judgment, emotional intelligence, or specialized expertise—think nurses, teachers, or employee relations advisors—are proving far more resilient.

The greatest pressure falls on jobs centered on knowledge work, coordination, and management—what we call “squeezed middle” roles. These jobs manage tasks that AI handles efficiently, such as analyzing data, drafting reports, and keeping projects on track. Some roles have started to feel this shift, particularly in positions like billing clerks, data entry operators, and service representatives.

Jobs that rely on hands-on skills, human care, or complex decision-making are showing more resistance to AI’s impact. For example, home health aides and customer relationship managers depend on empathy, adaptability, and trust—qualities that AI still struggles to replicate. And we know from our research that even in roles where AI could technically take over—like sales, coaching, and leadership—people often prefer the human touch.

AI’s impact isn’t uniform across roles and functions, so leaders will need a clear view to make informed decisions. That’s where our AI Impact Score comes in.

4 Strategies to Turn AI Potential into Business Value

Productivity alone doesn’t guarantee ROI—the focus should be on bottom-line results. To maximize value, leaders need to redesign roles evolving under AI’s influence while strengthening the human capabilities that drive performance. And it all starts with accurately assessing roles and processes.

But understanding where AI creates the biggest shifts is just the start. Leaders also need a clear path to turn those insights into action. Korn Ferry’s Quantity × Quality Upside framework shows organizations how to convert AI potential into measurable business outcomes.

It highlights two upside options:

• Quantity Upside: increasing output only where extra volume delivers measurable ROI.
• Quality Upside: improving accuracy or personalization only if it translates into financial gains.

Based on the type of impact AI can have on a role or process, we see four ways to capture that value:

1. Cost Takeout & Redeployment

High Impact × Low Quantity × Low Quality → Cost Takeout & Redeployment

In roles with limited demand to produce more or improve quality, the focus shifts to completing the same work more efficiently. This is where automation delivers cost savings and frees up time, streamlining work, redeploying people to higher-value tasks, and building foundational AI skills for what’s next.

2. Quality Reinvestment

High Impact × Low Quantity × High Quality → Quality Reinvestment

When AI won’t increase output but can improve quality significantly, the smart move is to reinvest saved time into doing better work. Use AI to sharpen precision, reduce risk, personalize experiences, and elevate the customer journey. The payoff will come from excellence and trust, not just efficiency.

3. Throughput Play

High Impact × High Quantity × Low Quality → Throughput Play

When demand is strong, and AI can increase output without compromising quality, it’s time for a throughput play. Here, AI accelerated production to scale volume, shorten cycle times, and ease pressure on capacity. The focus is on meeting market demand and fueling growth through smarterspeed.

4. Redesign Zone

High Impact × High Quantity × High Quality → Redesign Zone

AI is more than a productivity tool—it also has the power to amplify both scale and quality. This is where organizations can innovate faster, personalize deeper, and create entirely new sources of customer value. The goal is redefining what’s possible.

Turning AI Ambition Into Scalable Outcomes 

Challenges with AI adoption are popping up left and right. But the biggest issue isn’t the tech—it’s the transformation. Leaders who tackle organizational and people shifts now will pull far ahead of those stuck in endless tool testing. This is the moment for companies to redefine how their people work, compete, and lead in an AI-driven world.

At Korn Ferry, we understand both the promise and the limits of AI—making us the right partner for transformation. Our data-backed insights reveal where AI can create the greatest impact, helping companies prioritize actions, launch credible pilots, and scale what works. We don’t make assumptions about AI’s potential; we know it can reshape the way work gets done. But AI can’t do it alone. People are the power source—and no one understands people, their drivers, and their potential like we do.