The AI ‘Build vs. Buy’ Flip: The Hidden Costs Behind McKinsey’s 32% Stat
Have you noticed how quiet the B2B software sales landscape has gotten lately? There is a very specific reason for that. According to the highly anticipated ‘McKinsey State of AI 2026’ report published on August 25, 2026, a staggering 32% of organizations decided against buying one or more software products this year. Their alternative? They built the solutions internally using agentic coding tools.
This marks a historical shift in the traditional ‘build vs buy software’ debate. For decades, the golden rule of IT procurement was to buy off-the-shelf software unless the feature was a core competitive differentiator. Today, AI is flipping that script. But before you cancel all your SaaS subscriptions and hand the reins over to an AI agent, we need to look at what is actually happening beneath the surface.
Is it really cheaper to build internal tools with AI, or are we just trading subscription fees for a massive technical maintenance nightmare? Let us break down the data, the community reactions, and the hidden costs of this new era of software development.
The Enterprise Divide: Who is Actually Building?
It is tempting to look at that 32% statistic and assume every startup and local business is suddenly coding their own HR platforms. However, industry analysts emphasize that this figure is heavily driven by a selection effect among large enterprises with deep engineering benches.
When we look at ‘AI coding agents enterprise’ adoption, the numbers tell a story of scale. A massive 40% of organizations with over $1 billion in annual revenue are now scaling AI agents in at least one function, a significant jump from 27% the previous year. Meanwhile, adoption among small and mid-sized enterprises remains completely flat at 22%.
The sector breakdown is also telling. The shift away from software procurement is highest in the technology sector (41%), followed closely by healthcare payers and providers (39%), and professional services and energy (38%). Furthermore, among AI ‘high performers’—defined as organizations attributing at least 5% of their EBIT to AI—nearly half have skipped at least one software purchase in favor of building it themselves.
As Lieven Van der Veken, a Senior Partner at McKinsey, notes, leaders are fundamentally changing their tone. They are moving away from assuming AI is beyond their internal capabilities and are instead asking what infrastructure they need to build these tools themselves.
The SaaS Disruption AI: Death of the Thin-Wrapper App
If you are a B2B SaaS founder, this trend is likely setting off alarm bells. And it should. On developer forums and platforms like Reddit, B2B SaaS sales representatives are reporting that cold outreach pitching basic workflow automation is increasingly being ignored. Engineering leads simply feel confident they can spin up these internal tools in a matter of days.
There is a widespread consensus in developer communities, such as r/microsaas, that SaaS products lacking a proprietary data or integration network moat are the most vulnerable. If your product is essentially a simple CRUD (Create, Read, Update, Delete) app with a nice dashboard, it is highly susceptible to being replaced by internal teams wielding tools like Claude Code Cursor or other advanced coding agents. Some users are even speculating that existing S&P 500 software companies relying on traditional seat licenses will face massive market share losses as this ‘SaaS disruption AI’ wave continues.
The Illusion of ‘Free’: Token Costs and Shadow IT
While the initial speed of ‘internal tool development AI’ is mind-blowing—often taking just a week to build a working demo—the long-term financial reality is far more complex.
Tech analyst Artur Markus presents a sobering counterpoint: canceling a purchase order does not equate to saving money. He suggests that teams are merely shifting their spend from a highly trackable vendor line item to an untracked engineering capacity line item.
This shift is fueling a massive expansion of ‘shadow IT AI.’ Teams are building tiny internal tools without formal budget lines, IT oversight, or long-term ownership plans. Furthermore, the infrastructure to run these agents is not free. Approximately 20% of organizations reported in the McKinsey survey that AI-related operating costs, specifically token costs, have actively constrained their use of the technology. Building the app might be fast, but running continuous agentic loops to maintain it can quietly drain a budget.
Security Debt: The Maintenance Nightmare Nobody Wants to Talk About
Perhaps the most alarming aspect of this trend is what security experts are calling ‘Security Debt.’ It is one thing to have an AI write a script to sort your emails; it is another to have it build a piece of enterprise software that handles sensitive customer data.
A 2026 Veracode test across more than 150 Large Language Models (LLMs) revealed a troubling reality: only 55% of AI-generated code passed basic security tests. Even worse, over 15% of AI commits introduced at least one new security issue or vulnerability.
Community members are heavily debating these hidden costs. The true expense of agent-built software is not the initial week of development, but rather the years of maintaining it against upstream API changes, edge cases, and deeply embedded code smells that persist long-term in code repositories.
The Bottom Line
The ‘build vs. buy’ equation has fundamentally changed in 2026, but it has not become simpler. While AI coding agents are empowering large enterprises to replace traditional SaaS with rapid in-house builds, the real challenge has shifted from the initial cost of creation to the long-term burden of Total Cost of Ownership (TCO). Interestingly, despite this massive shift in how software is built, 37% of respondents reported AI contributed positively to EBIT, a figure that remains essentially unchanged from 2025.
If you are an engineering leader or IT procurement manager, the call to action is clear: Audit your current SaaS expenditures to identify viable internal build opportunities, but do not do so blindly. You must implement strict security, governance, and maintenance protocols for all agent-generated code to prevent your new internal tools from becoming tomorrow’s security breaches.
Frequently Asked Questions
How do the token costs of running coding agents over a 12-month period compare directly to the per-seat licensing costs of the SaaS tools they replace?
While building a tool internally eliminates the per-seat SaaS license, the continuous token costs required for the AI agents to maintain, debug, and update the code can accumulate rapidly. For compute-heavy applications, these token costs—combined with the untracked human engineering hours required for oversight—can sometimes exceed the original vendor subscription, which is why 20% of companies report token costs as a major constraint.
How are companies legally and technically addressing the security and compliance liabilities of internal tools built by non-engineers using AI agents?
Currently, this is a massive gray area driving the rise of ‘shadow IT.’ High-performing enterprises are beginning to enforce strict governance frameworks, requiring all agent-generated code to pass through automated security gates (like Veracode testing) and mandatory human-in-the-loop code reviews to mitigate the legal and technical liabilities of vulnerabilities.
