The integration of AI into software development represents a significant paradigm shift, moving beyond individual productivity gains to necessitate fundamental changes in operating models, team structures, and organizational processes to unlock true value and achieve substantial improvements in delivery speed and quality.
Mind Map
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คลิกเพื่อสำรวจ Mind Map แบบอินเตอร์แอคทีฟฉบับเต็ม
Good morning. Hello everyone. It's
really great to be here. Uh so I'm
Martin and I'm here with my colleague
Natasha. Uh we're from a part of
Mckenzie. you may may not be as familiar
with. We have a practice called software
X and we work with uh mostly enterprise
clients on how to build better software
products which has messed mostly using
AI uh in the in the past couple of years.
years.
Uh and so what our talk is about today
is really more focused on the people and
the operating model aspects of
leveraging AI for software development
and and that we believe that that has to
change quite significantly and and
that's what we're excited to talk to you about.
about.
If I take a quick step back uh in in
time and we just uh you know think
through some of these the major
technology breakthroughs that we've seen
in the last few decades uh they tend to
always come with a paradigm shift in
also how we develop software and so I
still recall uh almost 20 years ago now
I started working as a software engineer
an entry- level developer um in a tech
company and the company I was working
for was just switching to to agile we
were using camb boards we were
uh standups and and other ceremonies.
This was a big change. It was a massive
change for the for the company. And now
with everything that is happening
happening in AI, we're at the precipice
of another such paradigm shift. And
And um
um
if we think about some of the um some of
the things that are happening um with AI
and software development that we've seen
at this um at this conference, there's
no doubt that this is a new paradigm
that is about us. And so we'll talk
about two things. uh we'll first touch a
little bit about how do you go from
these things that we're seeing at
individual productivity to scaling that
to the whole team and what that what
type of changes we think that implies
and then we'll talk a little bit uh
about how do you scale that across uh a
whole organization and to really get get value
um
if if you sort of I'm talking to an
audience here which is using a agents
all the time and I thought if I If I
asked you about some examples, I'm sure
you could rattle off, you know, 10
different ones where you would say,
"Look, there was this thing that I used
to do. It it used to take uh maybe even
days and and and hours that are now
taking only minutes, right? There's no
shortage of those those stories and you
can go over to the expo and and talk to
any of the companies there about all
these all these great use cases. It
really shows that these tools work and
they can be really impactful." And so
yet despite seeing you know some of
these uh improvement uh improvements
uh we've done some research to gauge you
know where are our clients at the
moment. We we recently surveyed about
300 uh companies uh mostly enterprises
around what are they seeing in terms of
productivity improvements. So you have
this and then they would say uh on
average we're often seeing only 5 10 15%
improvements overall as as a company. So
we're in a place where there's a bit of
a disconnect between this this big
potential uh around AI as uh from the reality
reality
and so we we think that um there is this
gap because as we've started
implementing AI whether it's um you know
coding assistance or whether it's now
using you know you just heard about uh
you know how open AI is using agents and
more complex uh workflows what has
started to emerge is a is a set of bottlenecks
bottlenecks
uh that that were not necessarily there
before. Like for for example, as we now
start moving much faster in certain in
certain aspects of the work, uh we
haven't really changed how we
collaborate among people and and team
members. That's not quite keeping up.
We started generating way more more
code, but we're it's still being
reviewed in a in a pretty manual way in
in many companies, right? And then we
also have this this theme which was
recently highlighted in in even a
research report from from Carnegie Melon
uh about how all the new code that is
being generated is also amplifying uh
the generation of tech debt in some in
some cases and actually generating
complexity. And so there are these
bottlenecks. They're not impossible to
overcome but this is what we believe is
limiting uh many companies from seeing
the the the real value that that they
should be seeing.
Let me talk about maybe just a couple of
examples to to make that uh come to life
a little bit more. One of the things
that we see as a big rate limiter at the
moment is around how work is allocated.
And so what what we've learned over the
last couple of years is that the impact
from AI and agents is highly uneven.
There are some tasks which where it
works amazingly well today and you see u
huge improvements and there are others
where it it's not as effective and so
you have that variability. You also have
variability among people. Some have have
uh lots of experience now using these
tools and and know how to pick that up
and others uh are less experienced.
Right? And so what that means for for
team leaders, for engineering managers
and so on is it's very highly
non-trivial to know how to allocate work
and resources in in a good way. And this
is creating a lot of inefficiencies.
Another example uh is is around how work
is being reviewed. So agents are often
giving given pretty uh fuzzy uh you know
stories that are written in pros with
pretty fuzzy acceptance criteria uh
which which means that the code that
comes back is not always what it was
intended to be and and for many
companies the only mechanism to control
that is is often manual review. So
you've automated some things but we've
generated more manual review. So these
are some of the some of the examples of
uh this bottleneck that we that we see
coming up
and as mentioned what what has that has
resulted in so far is that most most
large companies today uh are are stuck a
little bit in in a world of relatively
marginal gains. Uh they're working in
ways that was developed with constraints
that we had in the past paradigm of
human development. So you have you you
know if you go out to most companies you
see 8 to 10 person teams you see working
in two week sprints you have all these
these elements that were largely parts
of like an of an agile operating uh
model and that is and that is uh putting
in some some limits to what they can
see. Over the past year, we've been
working with lots of clients to to sort
of break that model a bit uh and develop
new ways of of working in smaller teams,
in new roles, uh in with shorter cycles.
And when you do that, we see really
great performance improvements. And
that's what gives you gives us this uh
path to where we see things are going to improve.
improve.
So we realized that rewiring the PDLC is
not just a one-sizefits-all solution.
For example, different types of
engineering functions across enterprise
along the product life cycle may require
different operating models based on how
humans and agents best collaborate. So
if we take the example of modernizing
legacy code bases, this task requires a
high context of potentially the entire
codebase but also has clearly well-
definfined outputs. So an example
operating model could look like a
factory of agents where humans provide
an initial spec and final review with
minimal intervention.
For new features for green field and
brownfield projects, the operating model
may look like an iterative loop because
they may benefit from the
non-deterministic outputs and increased
variation where agents act as
co-creators um providing more options to
facilitate faster feedback loops.
So, as we mentioned, we did a survey
among 300 enterprises globally to
understand what sets these top
performers apart. We found that they are
seven times more likely to have AI
native workflows which meant scaling
over four use cases across the software
development life cycle rather than just
having point solutions for just code
review or for just codev. They were also
six times more likely to have AI native
roles which meant having smaller pods
with different skill sets and new roles.
To enable these shifts, these
organizations were investing in
continuous and hands-on upskilling,
impact measurement, and also incentive
structures to incentivize developers and
PMs to adopt AI.
This led to five to six times increase
in time to market and delivery speed as
well as higher quality and more
consistent artifacts.
So when we talk about AI native
workflows, we mean that these
enterprises are moving away from
quarterly planning to continuous
planning and also um the unit of work is
moving from storydriven to spec driven
development. So that these PMs are
iterating on these specs with agents
rather than iterating on long PRDS.
On the talent side, AI native roles
essentially means that we're moving away
from the two pizza structure to one
pizza pods of three to five individuals.
Instead of having separate QA frontend
and backend engineers, there are more
consolidated roles where product
builders are managing and orchestrating
agents with full stack fluency and also
better understanding of the full
architecture of their codebase. PMS are
starting to create direct um prototypes
in code rather than iterating on these
long PRDs.
And one example um that we've described
in our article, we've studied some AI
native startups and realized that
they've actually implemented all of
these shifts to accelerate their
outcomes. And in our article, we've
described how cursor actually operates internally.
