After the productivity gain, what do companies actually get?

Since I started building AI products myself, I’ve become a bit more sensitive than before to the question of “how much value AI actually creates.”

On one side are more and more productivity numbers: writing code faster, customer service handling more issues, knowledge workers saving time.

On the other, once you actually build an AI feature, you find that behind it there are model calls, search, data, evaluation, human review, monitoring, failure handling, and a lot of costs that don’t easily show up on an API bill.

So in this round of research, what I really wanted to figure out wasn’t:

is doing AI worth it for companies?

That question is too big.

What I wanted to know more was:

once AI enters real companies, what exactly do we get, and what do we pay in full?

And, of these inputs and outputs, which already have relatively reliable evidence, and which are still speculation.

The evidence for this round runs up to September 6, 2026.

I mainly looked at a dozen or so core sources, including randomized experiments or quasi-experiments in customer service, software development, knowledge work and retail, companies’ actual operations and financial disclosures, enterprise surveys like McKinsey’s, and the cost and governance frameworks of FinOps and NIST.

Different evidence answers different questions.

• Randomized experiments are better at telling us whether a given task actually improved.
• Company disclosures are closer to real operations, but a company saying “AI saved us this much money” doesn’t mean the causal link has been independently verified.
• Surveys can tell us what companies as a whole are going through, but a set of survey numbers alone can hardly prove how much money a particular AI project made.

In the end I split this round of research into three posts:

Part one: after the productivity gain, what do companies actually get?

First follow productivity onward, to see how improvements at the individual or task level become value the company actually gets.

Part two: where should AI’s costs be counted?

Move outward from the most visible API / token fees, putting data, integration, evaluation, people, operations and failure handling back into the full workflow.

Part three: once benefits and costs are put together, how should AI projects be accounted for?

Finally, discuss what fairly reliable methods we currently have for judging, for a specific AI use case, whether it’s worth continuing to scale.

This post does the first set of accounts:

what do companies actually get?

Some AI productivity gains are no longer just “it feels faster”

First, a fact I think I have to admit after the research:

AI’s productivity gains on some specific tasks already have quite good real-world evidence.

A study later published in the Quarterly Journal of Economics followed 5,172 customer service agents.

After the company gradually gave agents a generative AI assistant, the study estimated the number of issues resolved per hour rose by about 15%.

This wasn’t AI independently replacing agents, but AI assisting real employees handling real customer issues.

Software development has similar evidence too.

A randomized experiment published in Management Science in 2026 covered three companies and 4,867 developers. The pooled results showed developers using an AI coding assistant completed about 26% more tasks, with especially clear improvements for junior developers.

So saying broadly today:

“AI’s so-called efficiency gains are all just marketing.”

doesn’t quite fit the existing evidence either.

But as the research went on, I found another question more worth looking at.

Once task efficiency improves, what happens next?

A 2026 McKinsey enterprise survey happened to give a very interesting background signal.

Of 1,719 respondents from 97 countries:

80% reported AI had improved individual productivity;

but only 37% reported AI had already contributed positively to company EBIT.

These two numbers can’t simply be subtracted.

Both are self-reported, and “37%” certainly doesn’t mean the other 63% of companies are losing money on AI.

But put together, they do raise a question worth chasing further:

between “one person working faster” and “the company actually getting economic value,” what exactly happens?

I’d now roughly keep looking along four layers.

Task improvement → workflow realization → capacity use → value capture

Layer one: did the task itself really improve?

This looks the simplest, but it’s already easy to get wrong.

AI generating a report in 30 seconds doesn’t mean the work went from two hours to 30 seconds.

If afterwards you still need an hour to check facts, rewrite key parts and handle failed results, what should really be compared is:

once verification, revision and exception handling are included, is the full task faster, better, or cheaper?

And “using AI means higher productivity” can’t be assumed in itself either.

In 2025, METR ran a very interesting randomized experiment.

Sixteen senior developers familiar with mature open-source codebases completed 246 real tasks. The result was the opposite: in that specific setting, being allowed to use the AI tools of the time increased the time taken to complete tasks by about 19%.

A 2026 follow-up experiment has started showing results in the speedup direction, but the confidence interval crosses zero, and there are clear participant and task selection biases, so the research team itself didn’t interpret it as a new general productivity figure.

Taken together, these coding studies give me a very simple reminder above all:

measure the full task first; don’t assume up front that AI will raise efficiency.

With different tasks, people, codebases, tools and acceptance criteria, the results could be completely different in the first place.

So what layer one really needs to confirm is:

in my own real business, did this task really get better?

Layer two: one task getting faster doesn’t mean the whole workflow gets faster

Suppose layer one holds.

Writing a piece of code used to take a day; now it takes half.

Does the company gain half a day?

Still not necessarily.

If what really limits delivery speed is requirement decisions, code review, testing or approvals, producing code faster may just make a longer queue at the next step.

AI made one local step faster, but the throughput of the whole workflow didn’t change by the same amount.

A randomized experiment covering 66 companies and 7,137 employees over six months shows this difference well.

Employees who actually used generative AI tools cut the time spent on email by about two hours a week in the second half of the experiment, and their after-hours work time fell too.

These are all real changes.

But the study didn’t detect any clear change in the amount of other tasks or in how work was structured.

In other words:

time really was saved.

But the study didn’t go on to show:

that this time automatically became more output for the company.

I think this distinction is very important.

Because when we see “each employee saves X hours a week,” it’s easy to take the next step of multiplying the hours by wages and writing it down as “value.”

Reality isn’t that direct.

Employees working less overtime can of course be a kind of value in itself.

Better work experience and less exhaustion can’t be counted as zero just because they don’t show up immediately on the income statement.

But if we want to claim:

“AI saved the company this much money.”

the next step of evidence is still missing.

Layer three: where did the saved time end up?

This may be the layer in the whole chain that’s easiest to skip.

Suppose a 100-person team each reliably saves two hours a week thanks to AI.

In theory, 200 hours of new capacity appear each week.

Then what?

It might become:

• the same people handling more customers;
• backlogged work starting to get done;
• faster product iteration;
• not having to keep hiring as originally planned once the business grows;
• employees working less overtime;
• or just everyone having some scattered bits of time in different places that are hard to reorganize.

The value of these outcomes isn’t the same.

One thing that struck me in the research was:

AI can free up capacity, but the organization still has to decide how to use that capacity.

Technology can’t do this step for the company.

• If demand is limited to begin with, producing 30% more content isn’t necessarily valuable.
• If downstream teams don’t have more capacity to handle it, faster output upstream may just pile up.
• If a company originally planned to hire 20 more people once the business grew, and now the same team can support the growth, AI’s value may show up mainly as avoided future cost, rather than laying off 20 people today.

So “how much time was saved” and “how much economic value the company got” are best recorded separately.

The last layer: the value companies really get isn’t only saved labor

This is also where, after the research, I felt my original thinking had been a bit narrow.

When discussing AI ROI, we easily only ask:

how many fewer people did we use?

But in the existing research, some of the clearest value doesn’t come from layoffs.

An experimental study on a large cross-border retail platform compared seven kinds of generative AI workflows.

Among them, offering consumers a pre-sales chatbot raised sales by about 16.3% compared with a baseline group with no real-time service.

Here AI’s path to value is very different.

It didn’t simply replace an existing employee.

Originally, a large number of consumers got no instant one-to-one pre-sales service at all. AI let a service that might not have made sense cost-wise reach a much larger scale for the first time.

So the value came from:

more service coverage → fewer information barriers in the buying process → more transactions.

In the same study, AI search optimization brought a sales lift of about 2.9%, while AI marketing pushes showed no statistically significant sales improvement.

That reminds me too:

“using generative AI” isn’t itself a source of value.

What problem the specific workflow actually solves is.

Pulling together the cases I saw in this round, I’d now roughly sort the value companies might get into several kinds:

Actual cash savings

Really reducing outsourcing, overtime, software or other cash spending.

New capacity / avoided future costs

The same people can support more business, or growth doesn’t require adding investment as originally planned.

New revenue or profit

For example more conversions, transactions or retention, or offering services that couldn’t be covered economically before.

Improvements in quality, timeliness and risk

Less rework, faster handling, lower losses from errors, more stable service.

New capabilities

Things that wouldn’t have been done at all because of labor, cost or expertise barriers can now be done for the first time.

All of these can be real value.

But they’re best not mixed together.

Above all, don’t write “employee time saved in theory” directly as “cash costs already reduced.”

So next time I see “AI raises productivity xx%,” I’ll keep asking four steps

What this post’s research left me with in the end isn’t the line “AI productivity isn’t ROI.”

I think what’s more useful is this chain:

Task improvement → workflow realization → capacity use → value capture

After seeing an AI productivity number, you can keep asking:

1. Task

Once verification, revision and failure handling are included, is the full task really faster, better or cheaper?

2. Workflow

Did this improvement carry through downstream?

Or did it just move the bottleneck from one place to another?

3. Capacity

How was the freed-up time and capability used in the end?

More business, less backlog, avoided hiring, less overtime, or not reorganized at all?

4. Value

What actually remains in the end?

Cash savings, capacity, revenue, quality, risk improvement, or a new capability that couldn’t be offered before?

I think “productivity gain” itself is a real and important result.

More and more research supports it now too.

But if we want to go on to discuss enterprise value, we need to keep following this chain onward.

You can’t jump from “one task got 26% faster” straight to:

“the company therefore got a 26% ROI.”

At this point, only one side of the enterprise AI economics has actually been accounted for:

what we might get.

The other side hasn’t been laid out yet.

To get these results reliably, what do companies actually pay?

Models and APIs are the most visible bill.

But how should data preparation, system integration, evals, human review, monitoring, exception handling, governance, and what’s left behind after a project fails be counted?

That’s the question the next post wants to look at:

“The real economics of enterprise AI,” research 2/3: where should AI’s costs be counted?

About this round of research

This is the first post in the research series “The real economics of enterprise AI.”

The whole round isn’t trying to answer “does AI have value,” nor to find a so-called “average enterprise AI ROI.”

I want to break down the two sides of the real-world accounts as clearly as possible:

what companies actually get, and what they pay in full to get these results.

This post mainly uses three kinds of evidence.

The first is randomized experiments and quasi-experiments in real work settings, including customer service, software development and knowledge work. They’re better at answering whether a given task actually improved, but usually don’t fully measure project costs and company profit.

The second is experiments directly tied to business metrics, like sales changes in retail. They’re closer to enterprise value, but the results can still only explain the specific workflow involved.

The third is enterprise surveys like McKinsey’s, used to observe the broad background between individual productivity and company-level economic outcomes. Surveys can find trends, but they’re self-reported, correlational evidence, so I won’t use them to calculate a single “AI success rate.”

One very important principle for this round of research:

only say as much as the research measured.

Task productivity gains aren’t automatically called ROI; savings self-reported by companies don’t automatically become independent causal evidence; and where full costs weren’t measured, improvements in benefits won’t be written up as net returns.