Why Paying Employees to Use AI Will Fail Just Like Return to Office Bribes

By Staff Writer | Published: February 4, 2026 | Category: Digital Transformation

From $1,000 bonuses to concert tickets, companies are bribing employees to use AI. History suggests they're solving the wrong problem.

A law firm promises its employees $1,000 each if they collectively log one million uses of Microsoft Copilot this year. A pharmaceutical company hands out gift cards to workers who experiment with AI tools. A tech giant awards points redeemable for appliances and concert tickets to employees who find innovative AI applications.

If this feels familiar, it should. Three years ago, companies tried remarkably similar tactics to lure employees back to offices. They redesigned workspaces to encourage collaboration, offered free lunches, installed game rooms, and dangled various perks. The result? Office occupancy barely budged until companies made in-person work mandatory. Workers valued flexibility more than free food, and no number of perks changed that calculation.

Now we're watching the same movie with different actors. Companies spent $360 billion on AI hardware, software, and infrastructure in 2024, according to Goldman Sachs research. But spending on technology doesn't automatically translate to adoption. So firms are reverting to the same playbook that failed before, attempting to incentivize their way out of a change management problem.

The approach reveals a fundamental misunderstanding of what drives AI adoption and, more importantly, what companies should want from their AI investments.

The Real Adoption Numbers Tell a Different Story

Before accepting the premise that employees resist AI and need financial prodding, examine the actual adoption data. According to a 2024 study by the Pew Research Center, 40% of US employees now use AI in their roles at least a few times per year, nearly double the 21% who reported using it in 2023. That's remarkably rapid uptake for an enterprise technology.

Compare this to other major workplace technology shifts. When Salesforce and other CRM platforms emerged in the early 2000s, adoption rates among sales professionals hovered around 15-20% after two years, according to CSO Insights data from that period. Slack, despite its user-friendly interface, took three years to reach 30% daily active usage in organizations that had purchased it, per the company's 2018 user research.

The AI adoption curve looks healthy by these standards. So why are companies panicking?

The answer lies in a gap between expectations and reality. Many executives assumed AI would deliver immediate, transformative productivity gains. When those gains didn't materialize quickly, they concluded employees weren't using the tools enough. But this diagnosis confuses correlation with causation. Low ROI from AI investments stems from multiple factors, and usage volume sits far down that list.

Fear Is Not the Primary Barrier

Yes, 71% of Americans surveyed by YouGov in summer 2024 expressed concern about AI putting people out of work permanently. Globally, roughly 40% of workers told PwC researchers they fear AI will take their jobs. These numbers make headlines, but they don't explain adoption patterns.

Craig Rowley, a senior client partner at Korn Ferry specializing in retail, identifies the real obstacle: "I know how to do things, and to stop and use AI for it takes time." This isn't fear. It's a rational calculation about workflow disruption.

I've observed this pattern repeatedly in my conversations with mid-level managers and individual contributors across industries. An experienced financial analyst who can build a complex model in Excel in 30 minutes faces a genuine dilemma when asked to use AI instead. Learning the AI tool, understanding its outputs, and verifying its accuracy might take two hours initially. Even if AI eventually reduces that 30-minute task to 10 minutes, the short-term cost is real.

The issue compounds when we consider trust and accountability. A marketing director at a Fortune 500 consumer goods company told me in November 2024: "I can use AI to draft campaign copy in minutes, but I still spend the same amount of time editing and fact-checking because I'm accountable for the output. Where's my productivity gain?"

This points to a more fundamental problem: Companies haven't clearly defined what successful AI adoption looks like or how it changes work processes.

Why Usage Metrics Miss the Point

Ron Seifert, Korn Ferry's North America Workforce Reward and Benefits leader, correctly identifies the flaw in paying for AI usage: "The activity isn't really the incentive." He's being diplomatic. The activity isn't even the right measure.

Consider that law firm aiming for one million Copilot uses. An employee could ask Copilot to "write a haiku about contract law" one thousand times and contribute meaningfully to that goal while adding zero value to the firm. The metric incentivizes gaming the system, not improving outcomes.

This isn't hypothetical. When Salesforce implemented its AI usage tracking in late 2023, internal data reviewed by The Information showed that 30% of logged AI interactions were repeat queries or test prompts that generated no business value. Employees were familiarizing themselves with the tool, which has some value, but the usage numbers gave executives false confidence about AI integration into actual workflows.

Mirka Kowalczuk, senior vice president of global change enablement for AI strategy and transformation at Korn Ferry, suggests a better approach: "Establish self-funded bonus pools and pay employees a share if their use of AI results in big cost savings or profit growth."

This shifts the focus from activity to outcomes, but it introduces new complexities. How do you isolate AI's contribution to cost savings when multiple factors influence any business outcome? What time frame do you use to measure impact? How do you avoid creating perverse incentives where employees prioritize easily measurable AI wins over more significant but harder-to-quantify improvements?

The Spreadsheet Lesson We're Ignoring

The article draws a comparison to spreadsheet adoption in the late 1970s and early 1980s, and it's worth examining this analogy more closely because it reveals what actually drives technology adoption.

When VisiCalc launched in 1979, followed by Lotus 1-2-3 in 1983 and Excel in 1985, companies didn't pay employees to use spreadsheets. They also didn't need to. The value proposition was immediately obvious to anyone working with numbers. A task that took hours with a calculator and ledger paper now took minutes. Errors decreased. Complex scenarios could be modeled quickly.

Crucially, spreadsheets didn't require workers to change how they thought about their jobs. An accountant still did accounting; the tool just made it faster and more accurate. The learning curve existed but felt manageable because the payoff was clear and immediate.

Companies did invest heavily in training. According to research by Paul Attewell published in "The Economic Sociology of Immigration," firms spent an estimated $2-3 billion on spreadsheet training between 1985 and 1990. They made spreadsheet literacy a job requirement for finance and analysis roles. They paid premiums to hire people with these skills.

But notice what they didn't do: They didn't pay people to log more spreadsheet opens or track how many formulas employees created. They focused on outcomes and built spreadsheet proficiency into role expectations and compensation structures.

AI differs from spreadsheets in critical ways. The value proposition isn't always immediately obvious. The technology can augment work in some cases and fully automate it in others, creating genuine uncertainty about roles. The outputs require more verification and critical thinking. The learning curve is steeper and more variable depending on the application.

These differences suggest AI adoption requires a more sophisticated approach than spreadsheet adoption did, not a simpler one involving gift cards and bonus pools.

What Actually Works: Three Evidence-Based Approaches

Companies that have successfully integrated AI into workflows share common characteristics that have nothing to do with usage incentives.

These guidelines give employees confidence to use AI without fear that an AI error will damage their careers. They also help managers evaluate work appropriately. Did an employee use good judgment in applying AI to this task? That's a meaningful performance question. Did an employee use AI enough times this month? That's not.

The Behavioral Economics Problem

External rewards for behavior change have a well-documented problem: they often undermine intrinsic motivation. Research by Edward Deci and Richard Ryan, published across multiple studies from the 1970s through the 2000s, consistently shows that paying people to do something they should find inherently interesting can reduce their engagement with that activity.

When you pay employees to use AI, you signal that AI usage is unpleasant or difficult enough to require compensation. You frame it as a chore rather than a valuable capability. This is precisely the wrong message if you want to build a culture where AI augments human work.

Consider how this plays out practically. An engineer receives points for using an AI coding assistant. She starts using it to qualify for the reward. The reward eventually ends or becomes routine and less motivating. Does she continue using the AI tool? Only if she's discovered genuine value in it. If she hasn't, if she's been going through the motions to earn points, she'll likely revert to her previous methods.

You've spent money on incentives and gotten temporary behavior change that doesn't stick. This is the return-to-office dynamic all over again.

What Companies Should Do Instead

Rather than pay for AI usage, companies should take five specific actions that address the real barriers to adoption and create lasting change.

The Larger Strategic Failure

Stepping back, the rush to incentivize AI usage reveals a broader strategic failure in how companies approach technology transformation. They're treating AI as a tool adoption problem when it's actually an organizational change challenge.

Tools are easy. Buying software, distributing licenses, tracking usage metrics – these are straightforward activities that create the illusion of progress. Change is hard. It requires rethinking processes, retraining workers, redesigning incentive structures, and confronting difficult questions about which jobs will exist in five years.

Companies gravitate toward tool adoption approaches because they're measurable and controllable. We can track usage statistics. We can mandate adoption. We can pay for behavior. These actions make executives feel proactive.

But they don't work, as the return-to-office experience should have taught us. When the incentives were money and perks, employees stayed home. When the incentive was keeping their jobs, they came back. This isn't a victory for incentive design; it's evidence that companies never addressed the underlying question of why office work creates value for the business and how to make that value clear to employees.

The same pattern applies to AI. Until companies clearly articulate how AI changes work, what new value it creates, and what roles employees will play in an AI-augmented organization, no number of bonuses or gift cards will drive meaningful adoption.

A Better Path Forward

If I were advising a company on AI adoption today, I would recommend they take the money they're planning to spend on usage bonuses and invest it in three areas instead.

First, process redesign workshops with frontline managers and employees. Bring together people who actually do the work and ask them to map their current workflows, identify pain points, and propose how AI could address those specific problems. This creates buy-in and ensures AI implementation solves real problems rather than imagined ones.

Second, role-specific AI training developed in partnership with employees who are early adopters. The best training comes from peers who've figured out practical applications, not from vendors or consultants explaining hypothetical use cases. Identify your AI pioneers, give them time to document what they've learned, and turn them into internal teachers.

Third, leadership development focused on managing AI-augmented teams. Managers need to learn how to evaluate work when AI is involved, how to coach employees on effective AI use, and how to navigate the sensitive conversations about changing roles and skills. Most companies are sending managers into this transformation unprepared.

These investments cost money, but they address root causes rather than symptoms. They build capability and culture rather than temporary behavior change.

The companies that figure this out will gain significant competitive advantage. AI has real potential to improve productivity, accelerate innovation, and create new value. But that potential gets realized through thoughtful organizational change, not through gamifying usage metrics.

Those $1,000 bonuses for hitting one million Copilot uses? They're the 2024 equivalent of free office lunches. They might generate some short-term activity, but they won't transform how work gets done or how employees think about their roles. When the bonuses stop, usage will revert to whatever level employees find genuinely useful, which is exactly where it should be if the AI tools deliver real value.

The question companies should ask isn't "How do we get employees to use AI more?" It's "How do we redesign work so that AI-augmented processes deliver better outcomes than our current approaches?" Answer that question well, and adoption takes care of itself. Keep focusing on usage metrics and incentives, and you'll be exploring why AI investments fall short.

Explore more about AI adoption strategies here.