Coding Agents Are Changing Enterprise Software's Build-Versus-Buy Equation

For years, the build vs buy software decision has started from a fairly simple constraint. An organisation might be capable of building something itself, but that doesn't mean doing so makes sense. Engineers are expensive, development takes time and every internal project competes with something else the team could be doing. 

That has helped make buying software the easier answer to a lot of enterprise problems. Even when an existing product wasn't perfect, paying someone else to build, maintain and improve it could be more economical than tying up scarce internal capacity. AI coding agents are starting to change that calculation. 

McKinsey's August 2026 State of AI survey found that 32 per cent of respondents said their organisations had already decided against purchasing at least one software product or feature because agentic coding tools made it possible to build the functionality internally. That takes the conversation beyond whether AI can help developers write code faster. 

If greater development capacity is already changing purchasing decisions, the more useful question is what happens to enterprise software procurement when building stops being as expensive as it used to be.

Coding Agents Are Moving The Build-Versus-Buy Break-Even Point

Building software has never been expensive for only one reason. But engineering capacity has traditionally put a fairly hard limit on what organisations can sensibly create themselves. There are only so many developers, only so many hours and usually far more requests for software than teams can realistically deliver. 

Coding agents loosen that constraint. They can take on more of the work involved in producing, testing and refining code, which means the same engineering organisation may be able to deliver more without growing at the same rate. Software development costs don't disappear, but one of their biggest components starts to move. 

The effect is already stronger among organisations getting more measurable value from AI. McKinsey found that its AI high performers were twice as likely as other respondents to be scaling software coding agents. Nearly half had also decided against buying one or more software products or features because they could build them internally, compared with 31 per cent of other respondents. 

So how are coding agents changing build-versus-buy decisions? They are making some internal projects economically possible that may previously have lost to an off-tlf product before the organisation had even seriously considered building them. That doesn't make the traditional calculation obsolete. It changes where the break-even point sits. 

And once that point starts moving, decisions made under the old economics are worth looking at again.

Cheaper To Build Doesn't Mean Cheaper To Own

This is also where the excitement around agentic development can become misleading. Producing the first working version of an application is only one part of owning software. Coding agents can reduce that cost to create without necessarily reducing everything the organisation becomes responsible for afterwards. 

Those responsibilities start almost immediately. Software has to work with existing systems, stay secure, survive changing requirements and keep working after the people who originally created it move on. It needs testing, support, documentation and updates. Depending on what it does, it may also need to meet regulatory or compliance requirements. 

Current developer research shows why faster creation doesn't automatically translate into lower effort everywhere else. Sonar's 2026 State of Code Developer Survey found that developers estimated 42 per cent of committed code was already AI-generated or assisted. 

Yet 96 per cent didn't fully trust AI-generated code to be functionally correct, and only 48 per cent said they always verified AI-assisted code before committing it. Some of the work is simply moving. Sonar found that 38 per cent of developers said reviewing AI-generated code required more effort than reviewing code written by colleagues. 

GitLab's June 2026 AI Accountability Report found a similar pattern, with 85 per cent of respondents agreeing that AI had shifted the bottleneck from writing code towards reviewing and validating it. Then there is the cost to own. 

GitLab found that 73 per cent of respondents were concerned about maintaining AI-generated code, while 82 per cent believed it could create technical debt their organisations weren't prepared to manage. The code may arrive faster, but someone still inherits it. AI itself also adds an operating cost. 

McKinsey found that one in five organisations had already constrained some AI use because of costs including tokens, while roughly one in 10 reported cost constraints specifically around software coding agents. The comparison therefore needs to change without becoming artificially simple. 

Buying carries licences or subscriptions, implementation, integration and dependency on the vendor. Building carries creation, AI consumption, verification, integration, security, maintenance and organisational ownership. Total cost of ownership still decides whether the cheaper option at the beginning remains cheaper over time. 

Coding agents can move one side of that calculation quite dramatically while leaving much of the rest where it was.

The Unit Of The Build-Versus-Buy Decision Is Getting Smaller

There is another detail in McKinsey's finding that's easy to overlook. Respondents weren't only avoiding whole software products. They were avoiding products or features because they could build the required functionality themselves. 

That creates a much more immediate change for enterprise software procurement than the idea that businesses will suddenly start rebuilding their major SaaS platforms from scratch. Most organisations don't need their own version of every core system. But they regularly encounter smaller gaps that existing systems don't quite solve. 

A missing workflow might once have justified another specialist tool. An awkward integration could have pushed a team towards a product with better connectors. A particular internal process may have required enough custom development that buying a broader platform still made sense, even if most of its functionality wasn't needed. 

As agentic coding reduces the effort involved in creating narrower applications and capabilities, some of those decisions become less obvious. The question moves from "Should we replace this platform?" towards "Do we really need to buy another product to solve this particular problem?" 

That could make the early effect on SaaS far more incremental than some of the bigger predictions suggest. Organisations don't have to replace an entire application for coding agents to change software spending. They only need to stop adding another one when a smaller internal build can close the gap. 

Over time, enough of those decisions can change an application portfolio without a dramatic migration programme ever taking place. Software consolidation may happen one avoided purchase at a time.

Buying AI Could Mean Buying Less Software

There is a slightly strange dynamic emerging here. Enterprises are paying for coding tools that expand what their own technology teams can build. If those tools work well, one software purchase may ultimately reduce the need for several others. That doesn't mean software vendors are disappearing. 

Gartner estimates that up to $234 billion of enterprise application spending could be exposed to what it calls "agentic arbitrage" by 2030, equivalent to roughly 20 per cent of enterprise application SaaS spending. 

Its research describes agents completing work across systems and reducing reliance on traditional user-facing software rather than coding agents specifically replacing purchased applications. The mechanisms are different, but they point in the same economic direction. AI is changing what businesses believe they need to pay software vendors for. 

Gartner itself describes the likely outcome as a transformation of SaaS rather than its destruction, with buyers putting more weight on outcomes and less on adding tools, interfaces and features. At the same time, the buy side isn't standing still. 

Zylo's 2026 SaaS Management Index reported average annual SaaS spending of $55.7 million across the organisations in its dataset, with an average portfolio of 305 applications. Among surveyed IT leaders, 78 per cent had encountered unexpected charges linked to AI features or consumption-based pricing. That creates pressure from both directions. 

Internal development is becoming more accessible while parts of purchased software are becoming harder to predict financially. Neither trend automatically makes building the better option, but together they give technology leaders a good reason to reopen assumptions that previously felt settled.

What Becomes Worth Building When Development Gets Cheaper?

The useful decision isn't whether businesses should suddenly build more software. It's identifying which capabilities have crossed the economic boundary because agentic coding changed a constraint that helped determine the old answer. That requires looking back at why the organisation bought something in the first place. 

A decision driven mainly by limited engineering capacity deserves a different review from one driven by complex regulation, specialist expertise or a maintenance burden the business never wanted to own.

Has development cost been the main reason we buy this?

Some software earns its place because recreating it would be difficult, expensive and distracting. Other products win because building even relatively straightforward functionality hasn't been worth assigning scarce engineers to it. Coding agents affect the second group much more directly. 

If development time was the main barrier, greater internal capacity can genuinely change the economics. If the vendor's real value lies elsewhere, cheaper code generation may barely change the answer.

Does owning the capability create strategic value?

The same shift can make custom software development more attractive where the capability reflects how the organisation actually competes. Proprietary workflows, internal processes or specialist knowledge can be difficult to squeeze into standard products without changing the way people work around the software. 

Again, this isn't a new rule that differentiated capabilities should always be built. The question is whether coding agents have reduced the development burden enough to change a previous decision. Owning something only creates value if the organisation also wants the responsibility that comes with it.

Which costs haven't moved?

This is where the calculation needs to stay grounded. Maintenance, integration, security, compliance, support and technical debt don't become irrelevant because the first version arrives faster. Some may even become more demanding as organisations produce more internal software than their existing processes were designed to manage. 

The greater the long-term operational burden, the less the initial development saving tells you. A cheaper build can still become an expensive asset if the organisation hasn't included its full lifecycle in the calculation.

What are we actually replacing?

Not every avoided software purchase carries the same consequence. Building one missing feature is very different from replacing a point solution, and both are different again from taking ownership of a major system of record that supports critical business operations. This is why software consolidation may be a more useful lens than wholesale SaaS replacement. 

Coding agents expand the range of gaps enterprises can potentially close themselves. They don't make every layer of the application portfolio equally sensible to own. The practical opportunity is therefore to revisit the margins of the portfolio first. 

Those are the places where development cost may have been doing most of the work in the original buying decision.

The Best Build-Versus-Buy Decision May Be Different Next Year

There is one final complication. The numbers behind this calculation aren't fixed. Coding agents are still improving. Development practices around them are changing, verification tools are getting better and organisations are learning what agentic software development actually costs once it moves beyond isolated use. 

At the same time, AI consumption costs and the resources needed to manage growing code volumes are becoming clearer. Software vendors are changing too. Pricing models, embedded AI capabilities and agentic products can alter the value of buying just as quickly as internal development becomes more capable. 

A strong software procurement strategy therefore can't assume that a build-versus-buy decision remains correct simply because it was correct when the contract was signed. Something that remains more economical to buy today may move closer to building as internal capacity improves. The reverse can happen too. 

An application that looked remarkably cheap to create may become far less attractive once several years of maintenance, integration changes and operational responsibility are visible. Build versus buy is starting to look less like a permanent classification and more like a calculation worth revisiting when the economics underneath it change.

Final Thoughts: Cheaper Software Still Has A Cost

There was a good reason enterprise software teams became used to buying capabilities they could theoretically build. Internal development wasn't free, engineering capacity wasn't unlimited and software vendors could spread the cost of maintaining complex products across many customers. 

Coding agents don't erase any of that. What they do is change one of the assumptions strongly enough that some old answers no longer hold. The organisations that respond well won't necessarily become organisations that build everything. 

They'll become better at recognising where lower development effort genuinely changes the economic case, and where paying a vendor to absorb complexity, maintenance and risk still represents better value. AI can make software cheaper to create. It doesn't make software free to own. 

As those economics keep moving, the boundary between internally built capability and purchased enterprise software is likely to move with them. EM360Tech will continue following how that shift changes technology strategy, procurement decisions and what organisations ultimately decide is worth owning.