The End of Productivity Theatre: Why We Need to Rethink What Makes a “Good Worker” in the AI Era

For years, organizations have linked productivity with visible effort: worked hours, packed calendars, rapid email responses and long task lists. Data shows employees are working longer than ever, with nearly one-third of workers back in their inboxes by 10 p.m. Yet more activity doesn’t necessarily translate into greater impact.

Artificial intelligence (AI) is exposing that we’ve become remarkably good at measuring busyness while often overlooking what moves the business forward. When employees can automate hours of repetitive work and redirect that time toward solving higher-value problems, we should re-think productivity.

That shift is redefining what it means to be a “good worker.” More than ever, leaders need to ask who is creating the greatest value for the business rather than who is doing the most work.

Productivity theatre is becoming impossible to hide

Every organization has tasks that keep people busy without creating much value.

Scheduling meetings across multiple calendars. Chasing approvals. Copying information between systems.  

With AI that is changing quickly. Routine work can be completed in seconds. As a result, measuring employees by the volume of those tasks tells us little about their contribution. With productivity theatre getting exposed, it’s important to understand how employees use the time they’ve gained from AI efficiencies to uplevel their work and contributions. 

What makes a “good worker” in the AI era

The real opportunity with AI lies in differentiating between work that no longer or still requires human effort. 

Take recruiting. Coordinating interviews across multiple hiring managers, candidates and time zones can consume hours every week. It’s necessary but doesn’t require strategic thinking. When talent coordinators recognize this and automate scheduling, they gain time to build relationships with candidates, advise hiring managers and improve hiring decisions. 

Research supports this shift. PwC’s 2025 AI Jobs Barometer found that industries most exposed to AI have experienced nearly four times higher productivity growth since generative AI became mainstream, while revenue per employee has grown three times faster than in less AI-exposed industries.

Employees who thrive in the AI era will certainly become faster. More importantly though, they’ll develop a stronger instinct for where human expertise creates the greatest value and where technology should handle their routine work. 

They’ll also be curious enough to experiment with new ways of working with AI, continually build their AI fluency and openly share what works and what doesn’t with their teams to help everyone move along quickly. 

Helping more employees develop that mindset is where organizations have a great opportunity.

Equipping employees with the right AI strategies for better productivity

Simply giving employees access to AI tools isn’t enough to deliver meaningful productivity gains.

Many organizations have spent the past two years introducing AI assistants, chatbots and copilots. While these tools can save time, adding isolated AI solutions onto existing ways of working rarely changes outcomes on its own.

The greater opportunity comes from redesigning how work happens.

Boston Consulting Group found that 80% of employees working within a clear AI strategy reported measurable business impact, compared with 60% of employees who had access to AI tools without a clear strategy. Gartner has similarly warned that many organizations struggle to realize meaningful productivity gains because they haven’t fundamentally redesigned work around AI.

The biggest gains derive from designing repeatable workflows that allow AI to handle routine tasks across an entire process while employees remain responsible for oversight, decision-making and exceptions. Agentic workflows make this possible by coordinating multiple actions into a single connected process instead of treating AI as another standalone tool.

Consider customer service. If a customer needs to change an order, traditionally, a service representative might spend valuable time confirming account details, checking inventory, updating an order management system, processing payment changes and arranging shipping. Agentic AI can orchestrate those routine steps across multiple systems within a single workflow.

Having that repeatable workflow changes the employee’s role. Time can now be invested in resolving complex issues and making sure each interaction ends with the best possible outcome. Those are the moments customers remember and that influence loyalty, retention and business performance.

Human judgment and expertise become more important

As organizations build more hybrid workforces—where human employees collaborate alongside AI—people’s competitive advantage increasingly shifts toward uniquely human capabilities. 

An AI system may recommend the fastest solution but people understand whether it’s the right one. People can weigh competing priorities, recognize organizational and cultural context, build trust with customers and colleagues and know when a situation calls for flexibility instead of following the standard process.

AI literacy is quickly becoming a table stakes workplace skill. What separates high-performing employees is their ability to recognize where technology removes friction and where their own expertise has the greatest influence on customers, colleagues and organizational outcomes.

If this is what high performance and a “good worker” looks like, organizations need to rethink how they recognize and reward this work.

Leadership must redefine success

As AI expands the ways employees can create value, HR leaders should reassess whether their current performance frameworks fully capture those contributions. Activity-based measures such as hours worked, tickets closed and responsiveness have long been useful indicators, but they increasingly tell only part of the story in AI-powered organizations.

AI isn’t just helping employees complete the same work more efficiently. As routine tasks become automated, employees have greater capacity to step into more strategic work: solving complex challenges, reducing business risks, and uncovering entirely new growth opportunities. Those contributions often have a broader and longer-lasting impact on the business than the individual tasks AI has taken off their plate.

Performance conversations should evolve to recognize the additional ways that employees add value. Alongside established business outcomes metrics and traditional measures, leaders should recognize employees who use AI to create value that helps the entire organization adapt more quickly.

Workforce planning also needs to evolve for the hybrid workforce. Decisions about hiring, job design and career development can no longer happen independently of technology planning. Before organizations decide which roles they need tomorrow, they first need to understand which workflows AI can automate and where human expertise creates the greatest value.

Demand for AI fluency—the ability to use and manage AI tools—has grown sevenfold in two years, faster than for any other skill in US job postings. But creating an AI-fluent workforce requires more than organizations providing access to the newest tools. People are naturally cautious about change, particularly when it affects how their work is measured. Leaders have a responsibility to provide practical guidance and training, give employees opportunities to experiment with AI and learn from one another, and set clear expectations about how success measures are evolving. 

One example of practical guidance is through a hub-and-spoke model. A central steering committee can establish responsible AI practices and strategic priorities, while AI champions across different business functions can identify use cases to experiment applying AI to. This allows businesses to scale AI adoption consistently while allowing for flexibility to solve the unique challenges each business function faces.

Ultimately, the definition of a “good worker” is evolving in this new environment. The employees who stand out will continue to build their AI fluency, understand where technology creates efficiency and human expertise makes the greatest difference, and share their learnings and best ways of working with others.

Organizations have a critical role to play in helping employees achieve this new success. By redesigning workflows, modernizing performance measures and fostering a culture where learning and experimentation with AI are encouraged, they can create workforces that are more productive, greater business value contributors and better prepared for the future.