Transforming the Friction of AI Into Flow

In this article:

• Turn the AI Tax Into Hidden Potential
• Upgrade Your Metrics to Match Your Tools
• Reinvest Savings to Fuel the Future
• How to Unlock Your Team's Genius
• The Work We’re Meant to Do

There is incredible energy in our workforce right now. If you look at the dashboards, the numbers are climbing. Adoption of new tools is skyrocketing, and people are moving faster than ever before.

But capability is not the same as impact.

For the last few years, we have been laser-focused on gross efficiency—how many tasks we check off, tickets we close, and code we ship. We have become excellent at volume. But in an AI-driven world, raw speed is just the baseline.

To unlock the untapped potential of our teams, we must pivot to a new metric. We need to stop asking how fast did we finish? and start asking how well did we solve it?

Turn the AI Tax Into Hidden Potential

Let’s look at the data with clear eyes. We know that AI is driving productivity—77% of employees report they are more productive today than they were a year ago. That’s a huge win.

But we’re also seeing a few growing pains. New research shows that for every 10 hours of productivity gained, we pay back about four hours in rework—correcting, clarifying, and refining AI output.

Some call this an "AI Tax." I prefer to call it hidden potential.

Think about that. Nearly 40% of possible gains are silently lost to rework. If we can reclaim that time by focusing on quality over quantity, we don't just get those hours back—we get the brainpower back.

We can turn that friction into flow. We just have to shift our focus from doing things fast to getting things right.

The Anatomy of the Friction

To reclaim this time, we first have to understand where it is going. The AI Tax is composed of specific types of operational drag that are slowing down our most enthusiastic teams.

• The Trust Gap: Employees are spending hours fact-checking AI output, double checking for hallucinations—plausible but fictitious data that AI creates. When trust is low, employees are forced to audit AI work with the same or more rigor than human work. This verification and correction loop turns high-paid talent into expensive fact-checkers, eroding the speed advantage AI was meant to provide.
• The Context Void: While AI is excellent at structure, it often fails at nuance. It produces hollow work, documents that might be grammatically perfect but lack the institutional context required for action. Employees are forced to heavily edit this generic output to make it relevant to the specific politics or history of the company, a process that can take just as long as drafting from scratch.
• The Prompt Iteration Cycle: Many people use AI frequently and with confidence but get stuck in a loop of tweaking prompts to get the right result. They might spend 30 minutes iterating on a prompt to automate a 10-minute task. While they feel productive, they are actually absorbing a hidden layer of work.

To eliminate this invisible drag, we must look past the illusion of speed and fundamentally change how we define success.

Upgrade Your Metrics to Match Your Tools

This is where net value comes in. It is the upgrade our metrics have been waiting for.

Gross efficiency counts the motion. Net value counts the progress. It asks: Did this work create a lasting outcome?

When we measure net value, we encourage our teams to pause, think, and verify. We move from speed (running fast) to progress (running fast in the right direction).

And the best part? We already know what success looks like.

We see a group of employees called Augmented Strategists emerging in the data. These are the people who have figured it out. They aren't just using AI to do more work; they’re using it to do better work. They are converting time savings into high-quality outcomes.

Here’s the most exciting part: 98% of these Augmented Strategists would recommend their organization as a great place to work.

They are happier. They are more effective. They are the proof that when we get the balance right, work becomes something we can truly enjoy.

Reinvest Savings to Fuel the Future

So, how do we turn everyone into an Augmented Strategist? How do we take the Low-Return Optimists—those enthusiastic employees who are trying hard but getting stuck in rework—and help them soar?

We have to confront executive disconnect.

While leaders believe they are preparing the workforce for an AI future, the investment isn't reaching the front lines.

When a team saves 10 hours using AI, the default organizational instinct is often to bank that efficiency as immediate cost savings—to cut headcount or simply double the volume of tasks. It is a short-term rush that looks good on a spreadsheet but often leads to burnout and zombie workflows, where data moves faster but provides less value.

To break this cycle, we must embrace a human-centered approach to reinvestment. This means taking the efficiency gains we are seeing and pouring them back into our people. Right now, the data shows we are missing the mark: on average, organizations are reinvesting more of their AI savings into technology (39%) than into their own workforce (30%).

And, the employees losing the most time to AI rework are receiving high levels of wellness investment (67%) but low levels of actual skills training (36%). We are trying to treat the symptoms of burnout with perks, when the cure is strategic reskilling.

Leading organizations are flipping this script. They aren't using AI simply to cut costs. They are explicitly authorizing their teams to use saved time for connection, strategy, and innovation. They understand that if you fill the saved time with more busy work, you kill the creativity AI was meant to unleash.

How to Unlock Your Team's Genius

To move from gross efficiency to net value, we need to change the infrastructure of our expectations. Here is how we operationalize the shift.

1. Rewrite the Role for Reality

Right now, 54% of employees are trying to force 2026 tools into 2015 job descriptions. We haven’t done the hard work of redefining roles for the AI era. We need to conduct role audits in high-friction departments like HR and Legal. Let's update our job descriptions to explicitly reward output verification and strategic thinking, so employees feel safe taking the time to audit AI work rather than just rushing to hit a volume target.

2. Train the "Who," Not Just the "What"

We need to pay people for who they are—their creativity, leadership, and adaptability—not just what they know. Currently, the training budget is getting lost; while 66% of leaders say skills training is a priority, only 37% of the employees struggling the most with rework are actually seeing it. We must pivot our training from simple how-to mechanics to judgment-based skills, teaching employees how to transition from making content to auditing it.

3. Upgrade Your Metrics: The Net Value Swap

You cannot measure a new way of working with old rulers. Speed without direction is just chaos. We must abandon generic volume metrics and adopt metrics that measure how fast we can make a correct, risk-aware decision.

Here is what that swap looks like in practice:

• In Customer Service: Stop obsessing over Average Handle Time (speed). Start measuring Customer Sentiment & Lifetime Value (impact). Fast service that alienates customers is a loss, not a win.
• In Talent Acquisition: Stop tracking Time to Fill (efficiency). Start measuring Quality of Hire (outcome). It doesn't matter how fast you fill the seat if the person isn't the right fit.
• In Operations: Stop counting Total Output (volume). Start measuring First-Pass Yield (quality). High volume is irrelevant if 40% of it requires rework.
• In Engineering: Stop measuring Lines of Code or Ticket Volume (activity). Start measuring Feature Adoption Rate and Decision Velocity (value). Are we shipping code that actually solves the problem?

The Work We’re Meant to Do

We are standing at the doorway of a new era.

During the Industrial Revolution, we optimized for physical repetition—building assembly lines to produce more goods, faster. In the early days of the AI era, we made the mistake of applying that same logic to our minds, obsessing over gross efficiency and task volume. But in a world where AI can generate infinite content instantly, average work is becoming a commodity.

True economic value no longer comes from processing tasks, but human judgment, connection, and strategy.

If 2025 was about waking up to the potential of AI, then 2026 is about realizing human ingenuity.

We have the tools to make work more meaningful, less repetitive, and more human than ever before. The friction we are feeling right now—that AI Tax of rework and confusion—is just the sign that we are leveling up.

By shifting our eyes from the speedometer to the compass—from gross efficiency to net value—we can stop spinning our wheels and start going places. We can turn that busy work into breakthroughs.

The potential is there. It is real. And it is waiting for us to unlock it.

AI is delivering speed, but many organizations struggle to turn that efficiency into net value. New global research reveals how rework, reinvestment choices, and work redesign shape whether AI delivers sustainable growth.