Enterprise governance must evolve as AI makes custom software accessible to all.Presented by Retool
The logic used to be: buying software is cheaper, faster, and safer for most use cases. Building was reserved for companies with large engineering teams, deep pockets, and problems so specific that no vendor could address them. But now, the cost to code a piece of software has dropped to zero.
Anyone can build their own software now, but enterprise and governance models have yet to catch up. Retool’s 2026 Build vs. Buy Shift Report, based on a survey of 817 builders, traces exactly how this shift is playing out.
The cost curve changed; SaaS pricing didn’t
Two years ago, a custom internal tool might have taken an engineering team weeks or months and cost six figures. Today, an operations lead with the right platform can have a working prototype in a day or two. This structural shift is driven by AI-assisted development and the maturation of enterprise app-building platforms.
Meanwhile, SaaS pricing hasn’t adjusted, still charging per-seat for generic software that requires customization and integration costs on top. When the cost of building drops by an order of magnitude but the cost of buying stays flat, the math changes for every company, not just the ones with large engineering teams.
The data reflects this. Retool’s report found that 35% of teams have already replaced at least one SaaS tool with a custom build, and 78% plan to build more custom tooling in 2026.
Workflow automations and admin tools are among SaaS tools at risk
The shift isn’t happening uniformly. The top SaaS tools respondents have replaced or considered replacing include workflow automations (35%) and internal admin tools (33%), followed by BI tools (29%) and CRMs (25%).
A purchased workflow automation tool has to serve thousands of customers, so it optimizes for the average case — and the average case is nobody’s actual case. Every company’s internal workflows are different. They reflect org structure, compliance requirements, data systems, and business logic unique to that organization.
Internal admin tools carry the same problem: they’re inherently company-specific. These categories were always the most awkward fit for off-the-shelf software, and there’s now an affordable, accessible alternative (MIT’s State of AI in Business reported $2-10 million in savings annually for customer service and document processing tasks).
The replacement pattern tends to be additive rather than wholesale (nobody is just ripping out Salesforce). They’re replacing the specific pieces that never quite fit: an approval flow that required three workarounds, the dashboard that couldn’t connect to their actual data … but those narrow replacements add up. Once a team builds one tool that works better than what they bought, the default question shifts from “What should we buy?” to “Can we build this?”
Builders go around IT, signaling broader procurement challenges
The clearest evidence that procurement processes haven’t kept up with building capability is the scale of shadow IT now occurring inside enterprises. Retool’s report found that 60% of builders have created tools, workflows, or automations outside of IT oversight in the past year — and 25% report doing so frequently.
Even experienced, high-judgment people choose speed over process. Two-thirds of total survey respondents (64%) are senior managers and above. Existing procurement cycles weren’t designed for a world where building software takes days rather than months. When people love to quote the 95% generative AI pilot failure rate they’re not accounting for the robust grassroots adoption happening under executives’ noses.
Shadow IT at this scale is a demand signal. The people closest to the problems are telling organizations that the existing process can’t can't keep up — 31% of those going around IT do so simply because they can build faster than IT can provision tools. So, suppression isn’t a productive response. The challenge is that the tools being built in the shadows are also the ones most likely to stall before they become useful.
A vibe-coded prototype running on sample data is impressive. A production tool connected to your actual Salesforce instance, with role-based access and a security review, is useful. The report found that 51% of builders have shipped production software currently in use by their teams, and among those, about half report saving six or more hours per week.
When building happens in an ungoverned environment, organizations get neither outcome reliably. Someone connects an AI-powered tool to production data with no audit trail, no access controls, and no owner. Multiply that by dozens of builders across an organization, and you have an expanding security surface that IT doesn’t even know exists.[1]
The teams whose homebuilt solutions reach production tend to have three things the others don’t: connectivity to real data sources, a security and permissions model they trust, and a review process for what gets deployed. Channeling builder energy into governed environments, where speed and security aren’t in conflict, is how organizations avoid shadow IT becoming a liability.
Governance will define the next era of SaaS
The build vs. buy shift is already underway. The more important question now is who controls the environment where that building happens.
Ungoverned building invites security risks and makes the ROI case difficult to close. You can’t measure time saved by tools IT doesn’t know exist, or are only run in one individual’s workflow. You can’t enforce access controls on a prototype that someone connected to production data last Tuesday. And those aren’t hypothetical risks: in Deloitte’s 2026 State of AI in the Enterprise survey of 3,200+ leaders, data privacy and security ranked as the top AI concern at 73%, with governance capabilities close behind at 46%. The 35% of organizations with no AI productivity metrics are missing more than just a dashboard. They’re missing the accountability infrastructure that justifies building over buying in the first place.
The organizations that treat governed environments as a prerequisite for building at scale will be the ones that can actually prove it’s working. The ones that don’t will find out when something breaks.
For a closer look at the data, including how enterprises are approaching AI-assisted building, read the full 2026 Build vs. Buy Shift Report.
[1] The cost of which can be steep: IBM’s 2025 Cost of Data Breach Report found that AI-associated cases cost organizations more than $650,000 per breach.
David Hsu is CEO at Retool.
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