Moving away from Agile: What’s Next – Martin Harrysson … — Transcript

Good morning, hello everyone, it's really great to be here.

So I'm Martin and I'm here with my colleague Natasha.

We're from a part of McKinsey, you may may not be as familiar with, we have a practice called Software X and we work with mostly enterprise clients on how to build better software products, which has meant mostly using AI in the in the past couple of years.

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.

If I take a quick step back in time and we just, you know, think through some of these the major technology breakthroughs that we've seen in the last few decades.

They tend to always come with a paradigm shift in also how we develop software, and so I still recall almost 20 years ago now, I started working as a software engineer, an entry-level developer in a tech company.

And the company I was working for was just switching to to Agile, we were using Kanban boards, we were doing stand-ups 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 if we think about some of the some of the things that are happening with AI in software development that we've seen at this 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, 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 about how do you scale that across a whole organization to really get get value.

If you sort of I'm talking to an audience here which is using AI 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 maybe even days and and and hours that are now taking only minutes, right?

So 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, they really shows that these tools work and they can be really impactful, and so yet despite seeing, you know, some of these improvements.

We've done some research to gauge, you know, where are our clients at the moment, we we recently surveyed about 300 companies, mostly enterprises, around what are they seeing in terms of productivity improvements.

So you have this and then they would say 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 these this big potential around AI as from the reality.

And so we we think that there is this gap because as we've started implementing AI, whether it's coding assistance or whether it's now using, you know, you just heard about.

You know, how AI is using agents and more complex workflows, what has started to emerge is a is a set of bottlenecks that that we're not necessarily there before.

Like for for example, as we now start moving much faster in certain in certain aspects of work, we haven't really changed how we collaborate among people and and team members, it'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, and then we also have this this theme which was recently highlighted in in even a research report from from Carnegie Mellon about how all the new code that is being generated is also amplifying 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 many companies from seeing the the real value that that they should be seeing.

Let me talk about maybe just a couple of examples to to make that 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 huge improvements and there are others where it it's not as effective, as you have that variability, you also have variability among people, some have have lots of experience now using these tools and and know how to pick that up and others 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 is is around how work is being reviewed, so agents are often giving given pretty fussy, you know, stories that are written in prose with pretty fussy acceptance criteria, 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 you've automated some things but we've generated more manual review, so these are some of the some of the examples of these bottleneck that we that we see coming up.

So we realized that rewiring the PDLC is not just a one-size-fits-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 code base, but also has clearly well-defined 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 greenfield 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 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 code dev.

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 the unit of work is moving from story-driven to spec-driven development, so that these PMs are iterating on these specs with agents rather than iterating on these 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, front-end and back-end engineers, there are more consolidated roles where product builders are managing and orchestrating agents with full stack fluency and also a better understanding of the full architecture of their code base, PMs are starting to create direct prototypes in code rather than iterating on these long PRDs.

And one example 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, but if you're a large enterprise predicated on the Agile model, what are some steps you can take, so in a recent client study with a leading international bank, we tested some team-level interventions to address the bottlenecks previously mentioned before, mainly around the sequencing of steps within the Agile ceremony and how to define the roles of agents and humans within the sprint cycle, so let's walk through some examples.

First, team leads would assign sprint stories using agents based on the data of the team velocity and delivery history, and then they would create co-create multiple prototypes and iterate with agents on the acceptance criteria around security and observability needs to have more consistent artifacts across teams.

This prevents downstream rework that was mentioned before, so that developers don't have to constantly be iterating with the agents during during the code process, the squads were also reorganized by workflow, so there would be one which would be focused on small bug fixes and another focused on greenfield development, in the background, agents would be used to look and impact look at the potential cross-repository impacts to prevent debugging time for developers.

And another example is that instead of for reducing the collaboration overhead and meetings that happen within the sprint cycle, instead of waiting for data scientists input, PMs would directly be observing the real-time customer feedback to reprioritize these features, and this would lead to an acceleration in the backlog within the same amount of time.

So we studied the impact of these interventions and found high promising results.

For example, not just the increase in agent consumption by over 60 times, but there was also an increase in the delivery speed that was tied directly to the business priorities for this bank, there was a 51% increase in code mergers, but also a decrease in an increase in efficiency.

The other aspect of this is is around the different roles and and the talent model, and so one of the biggest differentiators that we saw as mentioned was around whether you have actually changed the roles that that are involved in software development.

And so, you know, what what you all are seeing is that engineers are moving away from execution and and just simply writing code to being more of orchestrators and and thinking through more how to divide up work to agents, for example, and we also heard some examples of how the role of the product manager is changing, and so while this this may sound, you know, pretty straightforward to many of you here who are who are working with these tools like day-to-day that you have to change what you do.

The reality is that about 70% of the companies that we that we surveyed have have not changed their roles at all, and so you have this background expectation that people are going to do things differently, but the the role is still defined in the same way and there's the same understanding as it was, you know, a couple of years ago, what we are starting to see, you know, some companies changing this, so this is another example from a from another recent recent client.

They were set up in a way that is, you know, pretty common for for many companies in a kind of typical two pizza team model with with the types of roles that you'd be familiar with, we ran a bunch of experiments on frontrunners and and tested new models that were had much smaller pods that had new roles which consolidated some of the tasks that were previously done by different roles.

And so so by doing that, we could we could create basically more pods or more teams with with the same number of people, but retaining the expectation that each pod is is uh is uh performing at about the same level as as it were before, and so so we also see really really positive results from that with with maintaining and even improving in some case the quality of the code that was generated.

In particular, there was a there was a high speed up in in terms of the output from from the different teams and you can see some of the metrics here.

Let's shift gears a little bit and and and go from talking about just the team levels, so how does this now scale across a big organization, the reality is that many many companies don't just have like one or two of these these teams, but often hundreds of teams even and thousands or even tens of thousands of people who are working in in this way.

And this is where one of the biggest differences that we that we saw between those that are stuck a bit in the in in getting only 10% or so change improvements from those who are seeing outsized improvements is around how you manage that how you manage that change, and change management I guess is like one of these a little bit of an often catch or elusive term for for a lot of different things.

But but I think in some ways it's not a bad way to think about it, I I usually say that the change management is about getting a lot of like small things right, and so the crux to like actually scaling this is often about getting 20, 30 or even more things right at the same time that involve the way you communicate what this means, the way you incentivize people, the way you upskill them and it all has to come together.

And when it's not, we we we see what happens, and so this is an example from a from another tech company that we worked with, where initially we're rolling out new AI tools for them that that hit different parts of the product development life cycle, we we rolled we rolled out the tools, there was some usage, but often it dropped off, it was either not used or it was it was sort of used in very sub-optimal ways.

So that's the sort of jagged part that you're seeing on the on the left-hand side here, despite kind of adding more users, the overall impact did not change at all, so we had to do a quite a reset and and start over effectively, reset the expectations, what should what what does this mean if you're a developer day-to-day, what does it mean for a PM, we had much more hands-on upskilling, there was could bring your own code, there were, you know, coaches available, especially those first like few sprints before you get make this a habit and work it into the way that you develop software day-to-day, it's a very critical time and that's when when this matters a lot, and having a bit of a measurement system as well, so you know what's changing and and you're able to to see what's what's what's improving.

Another example just to to this alive a little bit, as mentioned, like this is about getting a lot of things right, and it's each one of these individually may not seem as it's the biggest deal, but put together, they really make a make a huge difference, like this is for this is some of the top interventions that another client had to go through.

For them, it really helped having, you know, setting out code labs, for example, really, you know, instituting a new set of certifications that help motivate and drive people to to change what they do day-to-day, and these these things really added up to the change they needed.

But building a robust measurement system that prioritizes outcomes and not just adoption is important not just to monitor progress, but also pinpoint issues and course correct quickly, so one surprising result from the survey was that these enterprises that were bottom performers were not even measuring speed and only 10% were measuring productivity.

Our goal is to make our clients top performing organizations, so we've worked with them to create a holistic measurement system that captures impact all the way down to inputs, so for inputs, this would include the investment into coding tools and other AI tools, but also the time and resources in upskilling and change management, these inputs would lead to direct outputs, but a lot of organizations are just focusing on how the increased breadth and depth of adoption with of AI tools is leading to increased velocity and capacity increase, however, it's also important to understand how developers have a different NPS scores and if they're enjoying their craft more rather than feeling more frustrated, and it's also important to understand whether the code is becoming more secure and have has better quality, but also more resilient, and one proxy for resiliency that we used for our client was the meantime to resolve priority bugs.

Now if we look at economic outcomes, which is priority for the C-suite executives, they look into what is the time to revenue target, what is the increased preferential for higher quality features or expanding the number of customers to meet the feature demand, and also what is the cost reduction per pod for reduced human labor, in aggregate, having these larger economic outcomes can also lead to for organizations to understand how there is an increased reinvestment in greenfield and brownfield development, but as these tools evolve, the proxies for these metrics will also evolve, but hopefully this provides a framework as an initial starting point.

So what's next, the future of course is difficult to predict, let alone in the next five years, what we hope that with our vision of a new software development model, even as agents increase in their intelligence and humans become more fluent in AI, that this model still stands, so hopefully this model that includes shorter sprints, smaller teams, but large smaller but larger number of teams will set enterprises up for success in the long term.

So just leave you with some some key takeaways, start now, I would say to to our clients, this is a human change and it takes some times and it's a big change and and it's going to be a journey.

And so I think, um, this is something that everyone needs to go on, I think it's also important to figure out which model works for you and set a really bold ambition.

And with that, say thank you so much for listening to us and and uh we have an article here if you're more interested in in the research that we've conducted, thank you so much for having us.