Businesses all over the world are moving the same way right now: handing
repetitive work to automation and agents, and building platform teams so
growth does not mean breakage. It is one of the largest shifts in how
companies operate in a decade, and unusually for a trend, quite a lot of
it has been measured in public.
Four of those results are below, each with a link to the source. Each one
also carries the part that did not go to plan, because a case study
without its correction is advertising. If you read one thing on this
page, read the caveats. None of these companies are clients of ours.
Automating busywork
Klarna's customer service assistant
Klarna put an AI assistant in front of its customer service queue. In
the first month it handled 2.3 million conversations,
roughly two thirds of all its customer service chats. Klarna reported
that people resolved their issue in under 2 minutes rather
than 11, that repeat enquiries fell by 25%,
and that satisfaction scores were about level with human agents.
The part most write-ups leave out: in 2025 Klarna walked some of this
back and brought human agents back for complicated cases. The widely
repeated "work of 700 agents" line was also a modelled equivalence,
not 700 people who left.
We think the correction is the more useful half. Automation absorbed
the volume and did not remove the need for people at the hard end.
That is exactly the shape we design for: the machine takes the
repetitive nine tenths, and a person is deliberately kept in place
where being wrong is expensive.
Source:
Klarna press release
Building what you grow on
Spotify's internal platform
Spotify grew to the point where its own engineers could not reliably
find their own systems. Rather than hiring around the problem, they
built one place to see everything, called Backstage. New engineers
reached their tenth piece of shipped work 55% faster.
Spotify open sourced it, and it is now used by
over 3,400 companies.
Be clear about scale: Spotify is enormous and you almost certainly
are not. Adopting their tool because they use it is how companies end
up maintaining a developer portal nobody asked for.
The transferable finding is not the software. It is that the hours
engineers lose to not knowing where things live is a real and
measurable cost, and that it can be roughly halved. On a team of ten
that is worth doing with a wiki page and some discipline, long before
it is worth doing with a platform.
Source:
Backstage by Spotify
Measured properly
The one that was a real trial
Most claims about AI at work are company blog posts. This one is a
controlled experiment. Researchers gave 95 professional
developers the same job, building an HTTP server, and gave
half of them an AI assistant. That half finished
55.8% faster. The result held up statistically,
with a p-value of 0.0017.
Read the confidence interval before you get excited: the true effect
sits somewhere between 21% and 89%. That is an
enormous range. It was also one self-contained task written from
scratch, which is the friendliest possible case, and nothing like
changing a large system you have to keep running.
We include it because it is the most honestly measured number on this
page, and because it still comes with a caveat that wide. Anyone
quoting a single confident percentage at you has not read their own
source.
Source:
Peng et al., arXiv 2302.06590
The whole field, not one company
Thousands of organisations, measured yearly
The four numbers on our home page come from the
DORA research programme,
which has surveyed engineering organisations every year for roughly a
decade. Its central finding is the one most people refuse to believe:
teams that ship more often also break things
less often and recover faster. Speed and stability
travel together.
It is survey data, so it is self-reported, and it shows correlation
rather than proof of cause. Being fast does not by itself make you
stable. The practices underneath, small changes and good tests, are
what produce both.
This is the evidence base we actually work from. One company's story
can be luck. A decade of the same pattern across thousands of them is
something you can plan around.
Source:
DORA research programme