Editorial
While there's been growing consternation among the workforce about AI's effects, whether in terms of cheapening their work or automating it away completely, industry supporters still maintain AI will raise productivity and make things better for everyone.
I'll give you a minute to snort at such fanciful notions, but I suspect we can all agree that, just as the effects of AI will be unevenly spread across the workforce, so too will any productivity gains. A recent study from Stanford explored which jobs are more likely to benefit from AI.
AI's 'Beneficial' Impact
The researchers focused on the Chilean workforce and found that around half of all tasks performed across the 100 most common jobs in Chile could benefit from using GenAI. Most of these gains came in the form of speed, but the researchers found minimal impact on quality.
The findings are straight out of the augmentation narrative, whereby AI takes care of the routine tasks, freeing us up to perform more complex tasks. For instance, the researchers found that 80% of Chilean workers could use GenAI to speed completion of roughly a third of their current tasks.
All of this is, of course, rather theoretical. The reality is somewhat different. The newsletter Blood in the Machine chronicled the tales of dozens of tech employees, with most reporting that their bosses were going all-in on AI, not to make workers more efficient, but rather to infantilize them at best and automate them away at worst.
The Guardian reported similar tales from across the creative sector, with workers asked to babysit AI and make sure whatever it produced was roadworthy. It's a far cry from the image of AI freeing us all to be our best selves.
Penny Pinching
A study out of the School of Business, Central South University, Changsha, China looked at GenAI's impact on approximately 30,000 Chinese workplaces and found a clear distinction. The study found GenAI was able to significantly boost productivity when investments were made to augment human employees. When organizations used AI as part of cost-cutting regimes, no productivity gains materialized.
The finding is important, with the data showing that the significant upfront investments required in infrastructure, data and personnel make it much more likely to work when investments are made as part of innovation initiatives, not to save money.
This was shown in firms where AI investment was strongly linked with rises in patent output, with the researchers suggesting that the technology was boosting companies' ability to innovate effectively.
Competition also played a crucial role, with firms in competitive markets much more likely to achieve productivity gains than their peers in more benign markets. The lack of results achieved from deploying AI as a cost-cutting measure was also reinforced by the fact that capital constraints were strongly linked to poor productivity performance.
The message is clear: AI produces tangible results when firms in competitive markets use it to drive innovation, provided they have adequate resources for infrastructure, data and talent.
“Artificial intelligence is emerging not merely as a technological tool, but as a strategic lever for upgrading enterprise productivity,” the researchers explain. “Our analysis reveals that the productivity gains from AI are driven primarily by its innovation-enabling functions. However, these effects are context-dependent, requiring favorable market conditions and adequate financial resources to materialize. This nuanced understanding is essential for designing targeted strategies at both firm and policy levels.”
Getting AI Investments Right
The cautionary tale is echoed by research from the Kellogg School of Management. By analyzing nearly 300 studies, the researchers found AI's effects on the workplace plays out over years, not months. Crucially, the biggest risks of inequality come not from the AI itself, but from the choices in how it’s designed, developed and deployed.
From the training data used to build models, to the stakeholders included (or excluded) during implementation, to who gets the access and training on the tools, inequality emerges early, accumulates quietly and compounds over time. The researchers describe this as an “inequality cascade,” where small gaps in access or representation can snowball into structural divides.
As the Stanford research shows, even in cases where AI augments work, its value is contingent on the quality of its rollout. The tools don’t make people better by default. People make the tools work — if they’re given the resources to do so.
The cumulative message from these studies is clear: AI works best when it builds capacity, not when it strips it away. If firms treat it as a cost-cutting exercise, they may see some short-term gains, but they’ll also entrench inequality, stifle creativity and miss the bigger opportunity. Unfortunately, that seems to be exactly what many are doing right now.
Webinar
Dec
11
Fuel Your Front Line: Workforce Trends Shaping 2026
Get expert insight into the tools and tactics top frontline teams will need in 2026.
Register
Webinar
On demand
Fix the Disconnect Between Projects and Profit
Your margins are hiding in disconnected data. Let's find them.
Watch Now
Webinar
On demand
AI in Customer Service: Faster Resolutions, Happier Customers
Don’t let rising demand burn out your team. See how to build a smarter, more resilient support org.
Watch Now
Webinar
On demand
Accelerating Healthcare Ops with AI
Sign up to see how your peers are putting AI to work — and making it stick.
Watch Now
Webinar
On demand
From Nice-to-Have to Non-Negotiable: Prove the Value of Your EX Stack
Transform your tech stack: prove its value, secure your future.
Watch Now
Webinar
On demand
The Great Workforce Breakdown (And How to Stop It)
Disconnected employees. Failing communication. It’s time to turn things around.
Watch Now
Editor's Note: Read more about what's causing the growing chasm in GenAI results:
- 3 Years After ChatGPT's Launch, Has Work Improved? — Three years after ChatGPT launched, generative AI has made its way into every facet of our working lives, to mixed results.
- The Real Productivity Effects of Generative AI — AI won’t replace work so much as it will reshape who succeeds, how fast people learn and how organizations adapt. Leadership responses will define the outcome.
- AI Is Already Human-Centered. Maybe That's the Problem — The human effort behind AI's "thinking" gets lost in tech hype. Recent research argues we must revise our views of the relationship between humans and AI.
Learn how you can join our contributor community.
