> Organizations are using agentic coding tools to build software in-house in lieu of purchasing it. Nearly a third of respondents (32 percent) report that their organizations have decided against buying one or more software products or features because they could be built internally with agentic coding tools.
People think they can do it themself... kind of like how everyone build their own database using Microsoft Access in the 90's. When all software developers needed to repair all the issues...
I am at a point in my career where I'm figuring out where I want to spend the last 15 years of it. I don't want to work in corporate for a long stretch of it. It seems to me, being a consultant to fix all those messes could be a good seam of code face to get stuck into...
No, it's been my experience asking LLMs to explain complicated code to me. They can do a pretty good job of it. You can also ask them to write a bunch of tests and then rip out the old implementation and fix it with a new one. LLMs aren't perfect but they are really good at understanding and writing code.
It depends on what one's idea of an implementation is and what one's expectations are.
If an implementation is "a black box that works," absolutely, LLMs are fine. They can create complex programs in full.
But if one expects a tight implementation... it's a horror. LLMs vastly overengineer code (I believe they're intentionally designed to do so). I really struggle to make LLMs generate lean code.
In the best case, they generate bloated designs (in terms of complexity, not necessarily performance), but in the worst case, they generate gaps in the specification. And even in the first case - cognitive load is a problem also for LLMs, not just for humans (although obviously at a larger scale).
I think LLMs are essentially modern compilers, with similar problems, but designed more like "deoptimizing compilers" than optimizing ones. :)
These days ppl often (usually?) simply ask an LLM to replce something rather than debugging it (or if they ask the LLM to debug they get a different snarl of code).
So rather than debug a steaming pile of code you might be be able to treat it as a greenfield opportunity.
Yeah, unreasonable deadlines (these are the norm) spent trying to untangle a mass of overly verbose spaghetti code, and chances are there is no budget for you to do it properly, they contracted you only for a ‘bugfix’. It’s just more technical debt on top of technical debt.
No, thank you. I’ll leave the underpaid disinfestation job to someone else.
The best fun is when you’ll be called in not to untangle the LLM mess, but to build new features on top of its nonsense architecture and API design, but expected to do it by hand because they have lost confidence in AI.
It will be messes of python scripts running on some ancient version of python that you have to switch between scripts. All in a folder on some laptop with an outdated OS that you cannot update or everything will break. Business logic obviously not documented anywhere, just mountains and mountains of slop readmes that might be accurate, but also might not be.
On the other hand, if you let in-house devs own your enterprise software, in many places it's going to be months of meetings to get the smallest change implemented.
Plenty of companies run most of their internal operations on Excel and Access for exactly this reason. And most developers barely know anything about databases anyway.
Except that isn't how it played out. Most of the Access DBs, spreadsheets, and even SharePoint, Notes, and early Salesforce apps worked just fine without any help from devs. Yes, the devs got called in when those solutions needed to scale beyond a single department, but most of them never did.
It is far more plausible that we'll see that pattern again - small, department-level vibe coded apps will be around. And things that need to scale bigger will need professionals.
We can do it ourselves. Simple custom tools are so much more effective than a conglomeration of general purpose ones. I've been ripping out SaaS offerings left and right, it's great.
I dunno is creating chaos that requires many expensive rewrites damaging to the industry?
I kid of course - their recommendations on management of technical teams are fly-in, fly-out out of context garbage in my experience. Usually results in poor but justified decisions from senior leaders that happen in cycles of 3-5 years.
Microsoft comes to mind, with their anti-open-source era, their bullying of security researchers, and several other infractions. Plus I suspect they're gearing up for some shenanigans re: their auth portal.
> Please hire more McKinsey consultants who say they’re AI experts
You have to recognize that a vast majority of enterprises fall in the category of "Dumb IT". To them, IT is a cost-center and overhead as their main line of business is something else. And, they look up to the likes of McKinsey for wisdom.
Yeah, you’re right about that. They view IT as a “cost center” to be cut to the bone (despite being essential to their product and strategy in most cases), while they have no problem with paying consultants astronomical hourly rates for nonsense like this.
I get that these companies (airlines, widgets, whatever) don’t see IT as a thing that generates revenue, but it’s like having electricity or running water. You don’t think about it much, but if you don’t have it, you’re fucked.
Correct, which is why we went through the initial dotcom bubble, the initial enthusiasm didn’t match the ROI and resulted in a massive resource misallocation. A tiny bit like the AI rollout?
> About four in ten respondents (37 percent) report that AI has contributed positively to their organizations’ EBIT, essentially unchanged from 2025—despite growth in the share of organizations scaling AI technologies. But respondents do cite other organization-wide benefits: most notably, improvements in innovation, competitive differentiation, customer satisfaction, and employee satisfaction.
I view this mostly as a case of uneven diffusion. Consider your priors on the tech savviness of the bottom 50% of enterprises, let alone the bottom 25%. Many companies won’t adopt AI for 5 years. Now model how a 5% difference in growth plays out; not enough to sink stragglers immediately.
The other interesting bit is a 5% increase in EBIT could be a smaller productivity increase in the margin, eg you lowered prices by 2% and some fraction of your competitors’ customers noticed.
Basically, nothing revolutionary but I think it’s an error to read this as “we don’t see ROI yet”.
The other interesting point is one of legibility; the difference between “employees using AI” and “business function has rolled out AI”.
> Among AI tools, chatbots are the most widely scaled, with 47 percent of respondents saying their organizations are scaling them across the enterprise
We should look into the details, but I read this as: ~everyone is using AI ad-hoc, the 47% is the consultant-y “leadership has stood up a measurable initiative to scale AI…”. By construction, Gartner cannot measure the on-the-ground adoption.
As expected the thread is filled with smug comments on how it is definitely not ROI positive.
The thing I think about is AI is already at that spot where you don’t need a survey or a Randomised Controlled Trial to prove it makes one productive. Sometimes things are that obvious. But these kind of tests and surveys appeal to a certain kind of person who considers IMO the wrong type of things as rigorous.
It makes organizations more productive at producing code and doing other tasks, but translating that into something that affects PnL is different. Where I work it's sped up individual tasks I've worked on but I don't think it's sped up delivery timelines of any of the major projects I'm involved in. We just added additional verification work with the extra cycles the engineers have now. It's not like that work is useless. It will probably mean I have less debugging to do in the future, but when you look at how the business makes money I don't see it making a big impact.
I think it is easy for companies to waste time and money on lots of things when it is hard to measure the impact. For example, I think most people would agree that the big tech companies are bloated and have too many employees for the amount of work they have. There's not really an incentive to fix problems, and in some cases (like with headcount) there are counter-incentives (managers want to have more reports).
I think companies will get better at measuring the impact of AI and attributing it to increasing profit or decreasing costs, and that the companies that are better at this will have an advantage over those that are worse, so eventually overall efficiency will improve.
People think they can do it themself... kind of like how everyone build their own database using Microsoft Access in the 90's. When all software developers needed to repair all the issues...