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Yes—but the evidence points to a changing build-versus-buy mix, not companies abandoning software vendors. In McKinsey’s 2026 global survey, 32% of respondents said their organization had decided against buying one or more software products or features because it could build them internally with agentic coding tools. That is a reported decision, not a measure of how much software has been displaced or how many companies have permanently replaced SaaS.
What the latest survey says—and what it does not
McKinsey’s 2026 global survey offers the most direct evidence for the shift: 32% of respondents said their organizations decided against buying at least one software product or feature because they could build it internally with agentic coding tools. The result signals that AI-assisted development is affecting some purchasing decisions. It does not mean that 32% of companies replaced their software stack, or that 32% of software products have been displaced.
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The survey also found that about 20% of respondents said AI-related operating costs constrained their organization’s AI use. And 37% said AI had contributed at least some EBIT impact to their organization, a share McKinsey reported as essentially unchanged from the prior year. Those figures underline why a new ability to build is not automatically a cheaper or more valuable alternative.
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There is no directly comparable survey measurement establishing what share of all companies permanently replaced purchased software with internally built AI software. The available figures cover different populations, periods and questions, so they should not be combined into a single market-wide replacement rate.
Companies are building targeted tools, while still buying plenty
The internal-build use cases reported so far are often specific to a team or workflow rather than a wholesale substitute for a major enterprise platform. In EY’s AI Pulse Survey Wave 5, among senior leaders whose organizations were investing in AI and had fully deployed or were piloting AI development for internal use, 60% cited team-specific workflow and productivity tools. Other reported categories included experimental tools and AI enhancements to existing enterprise software (39% each), replacements for existing enterprise software and tools once considered too resource-intensive (33% each), tools once considered too time-intensive (31%), and niche internal tools previously not economically viable (29%). These are reports from that defined group of leaders, not verified deployment rates across all businesses.
Meanwhile, external products remain a common route to adopting AI. In the UK Department for Science, Innovation and Technology’s 2025 AI Adoption Research, 3,500 business interviews were completed between 12 February and 2 May 2025; 16% of UK businesses said they were currently using at least one AI technology. Among businesses using each technology, 71% reported buying external software or ready-to-use systems for natural language processing or text generation, compared with 14% developing those systems in-house. For machine learning, 55% bought external solutions and 24% developed in-house. The survey examined AI adoption, not later decisions to forgo general-purpose software purchases because of coding agents.
Older evidence points in the same broad direction. An OECD, BCG and INSEAD 2022–23 survey of AI-adopting enterprises in G7 countries found that more than 70% of enterprises in both ICT and manufacturing reported carrying out AI R&D for their own use. At the same time, 53–64% relied on customized third-party systems or purchased off-the-shelf software or hardware. This is useful sourcing context, not a current estimate of agentic coding’s effect on software buying.
What build-versus-buy looks like in practice
Building internally can make sense when a team needs a workflow that existing products fit poorly, wants to connect internal systems or data in a particular way, or wants to test an idea that previously seemed too costly or time-consuming to develop. A focused internal tool may complement an existing platform rather than replace it.
Retool’s 2026 Build vs. Buy Report illustrates both the interest and the limits of the evidence. In a late-2025 survey of 817 Retool customers and builders, 35% of respondents said they had replaced at least one SaaS tool with a custom build, while 78% expected to build more custom internal tools in 2026. The first figure is a vendor-survey report, not a representative estimate for all enterprises; the second is an expectation, not a completed outcome. Retool identifies workflow automation and internal administration as categories facing replacement pressure, alongside CRM, BI, project management and customer support. Those findings reflect its survey and commercial perspective, not a neutral census of the software market.
The same survey found that 60% of respondents had built software outside IT oversight in the prior year, and 25% said they did so frequently. That can make experimentation easier, but it also raises questions about ownership, security and support. Retool’s Harmonic example—33 internal apps connected to Salesforce, Gong, Slack and internal APIs—is a reported implementation at one company, not a typical result or proof of general cost savings.
Why companies still buy software
A ready-to-use product can be faster to adopt and easier to stop using than a custom application is to build and own. In UK government interviews, businesses described limited technical expertise, uncertainty about what they wanted to build and the significant cost of software development as barriers. One small-business interviewee in construction, currently using AI, told UK DSIT, IFF Research and Technopolis Group: “With any software development there will be fairly significant cost, whereas if you buy something off the shelf, you can pick it up and drop it.”
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Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →Outbyte PC Repair FREERepair Windows errors before they cause bigger problemsFix Now →Buying also shifts some development and product-maintenance work to a vendor. An internal tool, by contrast, needs someone to review its code, manage access, respond to failures, document changes and maintain integrations. The more teams depend on it, the more important those responsibilities become. EY highlights who will maintain, govern and secure internally built tools as an open operational question.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to decide whether to build or buy
AI can lower the effort required to produce a prototype; it does not remove the cost of operating dependable software. Compare the full lifecycle and the consequences of choosing wrong, not just the time to create a first version against a subscription fee.
- Fit: Is the workflow distinctive or poorly served by available products, or is it a standard capability that a vendor already handles well?
- Total lifecycle cost: Include AI usage, engineering review, integrations, security, maintenance, upgrades and the cost of keeping a knowledgeable owner available.
- Time to value: Would an off-the-shelf product meet the need sooner, or would adapting one create more delay than building a focused tool?
- Data and integration: Identify which internal systems the tool must connect to and how it will handle sensitive data and access controls.
- Skills and ownership: Assign responsibility for review, support, documentation and maintenance after the original builder moves on.
- Risk and governance: Set requirements for security, privacy, compliance, auditability, reliability and change control before expanding beyond an experiment.
These factors do not yield one universal winner. As EY’s Dan Diasio put it, “’AI saves time’ is no longer a sufficient business case when the costs are mounting. Priorities must be focused on doing different things, not the same things differently.”
The likely shift is selective, not a SaaS wipeout
Agentic coding tools are making it feasible for some organizations to say no to a software purchase and build a tailored alternative. The strongest current figure is McKinsey’s 32% of survey respondents reporting at least one such decision. But continued reliance on ready-to-use products, the costs of AI and the work of governing internal applications all argue against reading that result as proof that software vendors are becoming obsolete. The more grounded conclusion is that companies are reassessing individual workflows and making build-or-buy choices case by case.
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