# The AI Productivity Paradox: Why 87% of Workers Use AI, But Only 13% of Companies See Real Gains

## The uncomfortable number

Here is the statistic that should stop every CIO, CHRO and CEO cold this quarter:

> **87%** of digital workers now use AI at work. **75%** say it makes them personally more productive, saving them roughly **11 hours a week**. Yet only **13%** say their organisation is performing significantly better because of it.

That is not a rounding error. It is a 62-point gap between what individuals feel and what companies can measure. And it lines up almost perfectly with the U.S. Bureau of Labor Statistics data that Tom Davenport pointed to last week: aggregate non-farm productivity growth over the last seven years — half of which is the genAI era — has been **2.1%**, exactly the long-run average since 1947. Q1 2026 clocked in at **0.3%**.

Two of the most-read pieces of the past week try to explain this gap from different angles:

*   The [Glean Work AI Institute's *Work AI Index*](https://www.glean.com/work-ai-institute/reports/work-ai-index), based on a survey of 6,000 full-time digital workers in the US, UK and Australia (Dec 2025 – Jan 2026).
    
*   Tom Davenport's Substack post, [*"Ten Reasons Why We Won't See Productivity Improvements from GenAI"*](https://tdavenport.substack.com/p/ten-reasons-why-we-wont-see-productivity).
    

Read together, they paint the same picture from two directions: individually, AI is doing a lot; institutionally, almost nothing is showing up. Below is what they found, why it happens, and — most importantly — what leaders can actually do about it.

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## Where the 11 hours are going: botsitting and botshitting

Glean's most useful contribution is a pair of words for what we have all been doing without naming it.

*   **Botsitting** *(n.)* — the largely unrecognised, unbudgeted labour of making AI usable: feeding it context, checking its outputs, debugging its mistakes, re-prompting, and cleaning up after it.
    
*   **Botshitting** *(n.)* — shipping AI-generated work that the worker has not verified, doesn't fully understand, or couldn't defend if asked.
    

The numbers are not subtle:

| Metric | Value |
| --- | --- |
| Time workers spend botsitting each week | **6.4 hours** (most of a workday) |
| Share of AI-related time that goes to botsitting | **37%** |
| Share that goes to actually using AI to produce work | **36%** |
| Share that goes to learning tools and building agents | **27%** |
| AI sessions that "fail" outright and require a full restart | **36%** |
| Workers who admit to botshitting | **69%** |
| Workers who ship AI output they cannot explain | **41%** |
| Workers who have blamed AI for their own mistakes | **28%** |
| Frequent botsitters who are actively job-hunting | **73% more likely** |

Where does all that hidden labour come from? Glean's breakdown:

![](https://cdn.hashnode.com/uploads/covers/651bff05e4455a8ac9ec7688/6e8ebe3e-d1d6-4905-8653-af3d286e8c9f.png align="center")

Feeding AI context alone eats **2.3 hours a week**. Supervising outputs takes **2.2 hours**. Debugging burns **1.7 hours** — and it carries a **1.4× exhaustion multiplier**, meaning it wears people out faster than any other AI-related activity.

The really damaging finding: for every 10% more time workers spend feeding AI context, they are **25% more likely to report feeling worn out**. Glean calls it the *context tax*. Davenport, from a different angle, calls it reason #3 on his list: *"If you use genAI the 'right way,' you don't save a lot of time and effort."*

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## The cycle that keeps grinding forward

Both sources describe the same self-reinforcing loop. Here it is as a diagram:

![](https://cdn.hashnode.com/uploads/covers/651bff05e4455a8ac9ec7688/db944552-33f0-4637-aa3b-48cfa3d6a3cf.png align="center")

Stanford's Bob Sutton has a name for the "response" step: **addition sickness** — the reflex to solve every problem by piling more on top. Its GenAI variant is **tokenmaxxing**: rewarding people (or letting people reward themselves) for burning more tokens, regardless of whether the tokens produced anything useful. Meta ran an internal leaderboard until early 2026 that ranked engineers by token consumption; the "winner" averaged **281 billion tokens/month** at a compute cost of hundreds of thousands of dollars. Whether any of that was useful was, as Glean drily notes, "beside the point."

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## Tool sprawl and the AI toggle tax

Part of the reason botsitting eats so much time is that nobody uses just one AI tool. Glean's data:

*   **77%** of AI users bounce between multiple tools every week; **33%** juggle four or more.
    
*   Only **0.5%** of Claude users use Claude alone; the average Claude user runs four other AI tools alongside it.
    
*   **60%** of workers rerun the same prompt across multiple tools because the first output wasn't good enough.
    
*   Workers who juggle multiple tools are **35% more likely** to be frequent botsitters.
    

Each switch costs context, focus and time. Glean calls it the **AI toggle tax**. MCP and APIs help with plumbing but do not solve the deeper problem: **context**. Knowing which file is authoritative, which "Q3" you mean, or which unwritten rule keeps the workflow moving — those live in people, not in your data warehouse.

So the worker becomes the integration layer. They paste context into one tool, re-paste it into another, then referee disputes between two confident answers, neither of which is fully right.

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## Why the individual gains never roll up: Davenport's ten reasons

Where Glean documents the *behaviour*, Davenport explains the *economics*. His ten reasons for why individual productivity claims don't show up in company or macro numbers:

1.  **You need to redesign end-to-end processes around AI capabilities.** That takes years, and most companies won't do it.
    
2.  **Training is generic.** People get a webinar on summarising emails, not on their actual Tuesday-morning workflow.
    
3.  **Doing it right doesn't save much time.** Multiple prompts, hallucination checks, editing out clichés, adding your own voice — often no faster than writing it yourself.
    
4.  **Measuring aggregate individual gains is genuinely hard.** Few companies do proper before/after task timing across enough jobs.
    
5.  **We don't know what people do with the saved hour.** More work? More streaming? Layoffs almost never follow the "we could reduce headcount by 1/8" logic.
    
6.  **Token costs are rising.** Any real productivity gain now has to be netted against a real, growing cost line.
    
7.  **Personal infrastructure investment is rare.** Yes, some people build agents that automate half their job. Almost no one you actually know does this.
    
8.  **Organisations don't run controlled experiments.** A/B tests on AI usage are academic novelties, not corporate practice.
    
9.  **Workslop and process slop.** Bad AI output that lowers *other people's* productivity — and worse, degrades trust in whole cross-org processes.
    
10.  **Agents help but don't solve it.** Somebody still has to supervise the agents, and that supervision is itself botsitting at scale.
     

Davenport's punchline: *"AI providers are over-valued by the market, GDP growth largely driven by data-centre construction is unhealthy, and generative AI is not enough to power the economy on its own."*

Uncomfortable, but hard to argue with when Q1 2026 productivity growth is 0.3%.

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## The three paradoxes that keep the gap open

Glean crystallises the whole thing into three paradoxes worth memorising:

![](https://cdn.hashnode.com/uploads/covers/651bff05e4455a8ac9ec7688/ee42e921-bb28-47e4-9b10-a2af7ebf859b.png align="center")

Two more findings that deserve their own callout, because they cut against intuition:

*   **"Smarter" tools produce more botshit, not less.** ChatGPT and Claude users report the biggest productivity gains — *and* the highest rates of botshitting (71% and 92% at least monthly). Better output makes people stop watching. Aviation psychologists have called this *automation complacency* since the 1970s.
    
*   **Fear correlates with more AI use, not less.** Workers most afraid AI will eliminate their role are the ones using it most, automating the most of their own work, and wanting to automate even more. Visible AI usage has become a form of career insurance.
    

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## What can actually be done: the human infrastructure of AI

Here is where both sources converge. Glean's finding is that the 13% of organisations that *do* see performance gains are not spending more time inside AI tools. They spend **less** — 27% of their AI-related time inside the tools, versus 49% at low-impact organisations. They spend the rest on **the work around the tool**: setting context, defining what "good" looks like, catching errors, and deciding when *not* to use AI at all.

That "human infrastructure" has to be built at three levels. Think of it as a temple: a pediment declaring the goal, three load-bearing pillars, and a foundation of actual business impact. Knock out any one pillar and the whole thing comes down.

![](https://cdn.hashnode.com/uploads/covers/651bff05e4455a8ac9ec7688/1393fb87-beb6-4f0b-9527-7b6c02decb3f.png align="center")

### At the individual level

1.  **Draw a Centaur line.** Wharton's Ethan Mollick uses the term for workers who explicitly split tasks between what they will do themselves and what they will hand to the model. High AI achievers spend **38%** of their AI time on core tasks; low achievers spend **48%**. Keep the judgement work; hand off the mechanical work.
    
2.  **Botsit on purpose.** High achievers spend *more* time supervising, not less — and they treat every bad output as free training data about what the model can and cannot be trusted with. They are **2.4× more likely** to rate AI itself as a valuable teacher.
    
3.  **Guard the dividend.** When AI gives you an hour back, do not spend it doing 20% more of the same task. Spend it building the skill you did not have — running agents, writing better prompts, learning where AI is *wrong*.
    
4.  **Practise restraint.** Only **33%** of workers are extremely confident they know when *not* to use AI. This is the hardest skill and the highest-value one. If prompting and verifying would take twelve minutes and you can write the function in eight, write it.
    

### At the team level

1.  **Frame AI as a teammate, not an employee.** BCG's 2026 randomised study found that when AI was framed as an *employee* rather than a *tool*, workers felt less accountable for its output and reviewed it less carefully. Teammate is the sweet spot: you argue with a teammate, you accept a tool's output.
    
2.  **Invest in cross-functional AI builders.** Employees are **5.6× more likely** to adopt AI when a cross-functional teammate uses it, versus 2.4× for a leader. Cross-functional builders design for the messy version of work, not the tidy fantasy version.
    
3.  **Managers: reclaim your job.** High AI-achieving managers delegate **32% more** of their coordination work to AI. They don't compete with AI on status updates. They use the reclaimed time for the coaching and mentoring they were supposed to be doing all along. **44%** of workers already say AI is fairer than their manager; the number climbs with span of control.
    

### At the organisational level

1.  **Kill the vanity metrics.** Tokens, logins, "lines of AI-generated code" — Goodhart's Law will eat you alive. Workers in organisations that measure only productivity botshit at **74%**; where quality is also measured, it drops to 64%. Track a basket of at least five dimensions: efficiency, quality, employee experience, adoption breadth ("intent diversity" — how many distinct use cases per employee), and revenue/cost impact.
    
2.  **Turn the AI policy into governance.** **40%** of workers have not read their AI policy. Review it quarterly, explain the *why*, enforce it visibly, and define clearly who can build and deploy agents. Otherwise, you get *agent sprawl*: three teams building three bots to do the same thing, two of them running on unsanctioned data.
    
3.  **Start with the work, not the vendor contract.** Employees at high-impact organisations are 33% less likely to say vendor lock-in constrains their AI strategy. If your "AI strategy" is a roll-up of what your Microsoft, Salesforce and Google licences already include, you have a procurement plan, not a strategy.
    
4.  **Fund the context layer.** Context-poor AI (workers say critical info is not accessible via their AI tools) correlates with dramatically more fatigue, cleanup, shadow usage and botshitting. **53%** of workers say the info they need isn't accessible through their AI systems. Fixing that — through retrieval, MCP servers, forward-deployed engineers, and yes, connectors — pays back faster than another tool licence.
    
5.  **Redesign work; don't just squeeze people.** **90%** of workers at transformative organisations say their employer treats AI as a chance to redesign the work, versus **54%** everywhere else. When AI is named as the reason for layoffs, **62%** of the survivors start job-hunting. That is the most expensive cost line in your P&L.
    
6.  **Have the CEO actually use it, visibly.** Employees who have seen their CEO personally use AI use it **67% more** than those who haven't. This is not theatre; this is the cheapest, highest-leverage adoption intervention available.
    

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## A concrete 90-day starting point

If you lead an enterprise function and want to move on this before the next board meeting, here is a compressed sequence that has worked with clients:

![](https://cdn.hashnode.com/uploads/covers/651bff05e4455a8ac9ec7688/607c76f4-bf6c-4424-84d8-c6ee76f67331.png align="center")

You don't need everything. You need **something you can measure**, in the messy conditions of your real work, that lets you defend the answer to the only question that matters at the next board meeting:

> *"Are we in the 13% or the 87%?"*

If you can't answer that, you're the 87%.

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## The bottom line

Two independent lenses — Glean's survey of 6,000 workers and Davenport's economics-of-work analysis — are telling the same story:

*   **AI is real for individuals.** The 11 hours and the 75% productivity self-report are not fabricated.
    
*   **The gains disappear on the way to the P&L.** Coordination neglect, botsitting, botshitting, workslop, process slop, token costs, missing measurement, and above all the failure to redesign end-to-end work.
    
*   **The winners are not the biggest spenders.** They are the ones who built the human infrastructure — measurement, governance, context, and management discipline — that makes the tool worth using.
    

You cannot buy your way out of this with another licence. You have to build it. And you have to start from the work, not from the tech stack.

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### Further reading

*   Glean Work AI Institute — [*Work AI Index: Botsitting, Botshitting and the Hidden Human Labor of AI at Work*](https://www.glean.com/work-ai-institute/reports/work-ai-index)
    
*   Tom Davenport — [*Ten Reasons Why We Won't See Productivity Improvements from GenAI*](https://tdavenport.substack.com/p/ten-reasons-why-we-wont-see-productivity)
    
*   HBR — [*AI-Generated "Workslop" Is Destroying Productivity*](https://hbr.org/2025/09/ai-generated-workslop-is-destroying-productivity)
    
*   Ethan Mollick — [*Centaurs and Cyborgs on the Jagged Frontier*](https://www.oneusefulthing.org/p/centaurs-and-cyborgs-on-the-jagged)
    
*   U.S. BLS — [Productivity data](https://www.bls.gov/productivity/)
    

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*If you're building or fixing GenAI enablement inside a large enterprise and want to compare notes on what's actually working, I'd love to hear from you.*

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## About the Author

**Siddhesh Prabhugaonkar** is a **Generative AI & Agentic AI Enablement and Adoption Specialist** with two decades as an Architect, Consultant, and Trainer across IT, Cloud, and Generative AI. He is a **Microsoft Certified Trainer**, a **Pluralsight Instructor**, and helps enterprises move from GenAI curiosity to production adoption at scale.

His consulting and training practice spans **GenAI, Azure, Microsoft Foundry, Anthropic Claude, GitHub Copilot, Amazon Q, Kiro, Google Gemini, OpenAI Codex, Cursor, Windsurf**, and modern full‑stack engineering (.NET, MEAN, MERN). Notable engagements include GenAI enablement for **ADP**, IoT platform consulting for **IIT Bombay's E‑Yantra** program, and early work on Microsoft's Repository platform (which later became **Entity Framework**).

> *Empowering organizations and individuals to adopt, build, and scale with Generative AI, Cloud, and Modern Software Engineering.*

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