Solving the right problem, in the wrong way…….
Continuing my AI deployment in the workplace series. In my previous post I explored what happens when AI solves for problems no one really has. This week: what happens when AI solves the right problems, but in the wrong way?
Research on cognitive offloading shows that humans naturally distribute mental effort across their environment. We use tools, notes, and routines to manage cognitive load. The "easy" tasks in a workday aren't just low-value activities to be eliminated. They provide rhythm, recovery, and a sense of accomplishment between harder work.
When AI automates these tasks, employees are left with more cognitively demanding work. The breaks disappear. The quick wins vanish. What remains are the complex items and decisions. Organisations celebrate the efficiency gains. But is there a longer-term cost?
Microsoft's 2025 Future of Work Report suggests the productivity promise of AI isn't materialising as expected. While 96% of C-suite leaders expect AI to boost productivity, 77% of employees say AI tools have actually added to their workload. 71% report greater feelings of burnout. The report identifies "workslop": AI-generated content that appears useful but lacks substance, forcing recipients to interpret, correct, or redo the work. This may explain why individual productivity gains aren't being fully seen at the organisational level.
This connects to decades of research on job design, too. Hackman and Oldham's Job Characteristics Model identifies skill variety and task identity as core drivers of work meaningfulness. When AI removes variety from the workday, leaving only complex, demanding tasks, it may inadvertently undermine the engagement that makes workers effective.
This is the second type of Design-Reality Mismatch in my ADOPT framework. AI deployment programs often see the automation of low-value tasks as purely beneficial. The reality is those tasks can sometimes serve a hidden psychological function. The mismatch is therefore about misunderstanding how work actually feels.
What this means for organisations:
1.Audit the full workday, not just individual tasks. Which tasks provide recovery? Which offer quick wins?
2.Preserve or replace the rhythm. If AI removes tasks that provided breaks, design alternatives.
3.Monitor wellbeing, not just productivity. If efficiency rises but wellbeing falls, the design needs revisiting.
Next up in the series: Why do people resist AI even when it's clearly better? The answer lies in what we fear losing, not what we stand to gain.
