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Repair Windows errors before they cause bigger problemsFix Now →Scan for outdated or missing drivers - takes under a minuteDriver Scan →“Wild code” is a useful label for software, AI agents, and automations employees build or deploy outside the technology team’s view. The risk is not simply that code is unfamiliar: IT may not know an asset exists, what data it touches, or who is responsible for it. In a Tines-sponsored CIO BrandPost published September 30, 2026, Tines Field CTO Brad Rumph argues that repository-based controls can miss business-built workflows that never enter a source-code repository. IBM’s 2026 survey findings point to a related governance challenge—but do not establish how common this specific kind of unseen automation is at every organization.
What “wild code” means—and why it can escape IT
In the sponsored CIO article, “wild code” describes ungoverned, unapproved, or simply unseen code and automation spreading inside an organization. It can include AI-generated applications, agents, scripts, and workflows made by staff outside traditional software teams.
The visibility problem is that these tools do not all follow the same path. A developer’s application may be stored in a source-control repository and pass through established review and scanning processes. A finance, operations, or HR employee may instead create an automation in a workflow platform. If that automation never enters a repository, repository scanning alone will not reveal it.
Rumph describes the gap this way: “The automation nobody can see is built by people in finance, operations, and HR, who don’t think of themselves as developers. It never enters a repo, so code scanning will never find it.” That is a statement from Tines-sponsored content, not an independent assessment of how every organization’s controls work.
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IBM’s June 8, 2026 announcement of its 2026 Tech Leader Study reports that 70% of surveyed technology executives said business teams were deploying technology faster than IT could track, and 77% said AI adoption was outpacing their organizations’ current governance capabilities. IBM says the global study surveyed 2,000 C-level technology executives. These are survey responses, not measurements proving that every organization has the same problem.
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IBM CIO Matt Lyteson described the challenge as “scaling AI systems that operate continuously and autonomously, often within governance models and architectures designed for a far slower, more predictable environment.” As AI tools become easier to deploy, controls designed around scheduled reviews and familiar software-release paths may not keep pace with how work is actually being automated.
The sponsored article also recounts a Spring 2026 developer anecdote involving AI-assisted code. The account is not independently verified in the available sources, so it should not be treated as evidence of prevalence.
Why a periodic approval checklist may fall behind
Rumph cautions that “Governance built on predicting specific bad behaviours ages badly,” because “the behaviours change faster than the review cycle that produced the list.” The practical point is not that policies or reviews are useless; it is that a list of prohibited actions cannot tell IT about assets it cannot see, and a periodic check can become stale as tools and workflows change.
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A stronger approach begins with an inventory that can be updated as systems are created or changed. For each application, agent, or automation, IT needs enough information to answer three questions: what is it, what data can it access, and who is accountable for it? Rumph recommends “continuous visibility into what AI-generated code and automation exist, what data they touch, and who owns them.” This recommendation comes from the sponsored article.
What an organization needs to govern
Inventory is necessary, but it is not the whole governance job. IBM’s 2026 Tech Leader Study frames readiness around three complementary areas:
- Infrastructure adaptability: Can the technology foundation evolve as AI capabilities and deployment requirements change?
- Governance by design: Are appropriate controls built into systems and deployment processes, rather than relying mainly on periodic manual reviews?
- Portfolio discipline: Can leaders track AI investments, adjust initiatives that are not working, and retire them when needed?
The study page also reports that 11% of respondents said they were fully prepared for the scale of AI-agent deployment they expected over the next year. That is a survey finding, not a prediction about any individual company’s readiness.
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How to assess visibility and governance
When reviewing an existing program or comparing approaches, ask practical questions across the whole lifecycle:
- Inventory coverage: Does the inventory include repositories as well as business workflow tools where applications, agents, and automations may be built?
- Ownership and data: Can each asset be tied to a named accountable owner and a clear view of the data it uses or can reach?
- Controls in the workflow: Are governance controls part of creation and deployment, or does the organization depend mostly on reviews after the fact?
- Adaptability: Can the underlying infrastructure and controls change as AI deployment needs evolve?
- Portfolio oversight: Can leaders see which initiatives are active and decide whether to continue, change, or retire them?
These criteria help expose gaps without assuming that one governance product is best. The sources cited here provide no independent comparative test of governance platforms and do not show that buying any one product will solve an organization’s visibility problem.
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What Tines says its 3B environment does
The Tines-sponsored CIO article describes Tines 3B as a single environment where workflows, agents, and automations are visible from creation. That is the vendor’s description in sponsored content, not an independently tested product finding. Organizations evaluating it—or any alternative—should verify whether it covers the tools they use, links assets to owners and data access, and fits their governance and infrastructure requirements.
There is no verified statistic here for the prevalence of “wild code” as the sponsored article defines it. IBM’s survey data establishes that many surveyed technology leaders report a gap between technology deployment, AI adoption, and governance capacity; it does not prove that every business-built automation is unsafe or that a single tool closes the gap.
Sources
- CIO: Tines-sponsored BrandPost, September 30, 2026
- IBM Newsroom: 2026 Tech Leader Study announcement, June 8, 2026
- IBM Institute for Business Value: 2026 Tech Leader Study
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