AI governance is not a compliance checklist. When I say this, some executives visibly relax — ‘we already have too many rules, why add another layer?’ — while others shut down immediately. Both reactions miss the point. The real issue is this: by the end of 2025, a significant portion of Turkish enterprises had connected generative AI tools and early AI agents to live business processes. How many had a defined accountability chain in place? The pattern I observe on the ground is not encouraging. Efficiency gains are real — but most of those gains cannot be explained the moment a serious audit question or a customer dispute arrives. Efficiency achieved without AI governance resembles an uninsured vehicle: it moves fine on clear roads, and the bill only arrives after the crash.Consider a mid-sized insurance intermediary based in Ankara — 312 employees, four regional offices, and an active portfolio with high transaction volume. In early 2025 the company integrated a large language model-based agent into its customer service workflow. The agent classified incoming requests, answered standard policy questions, and routed cases to the appropriate unit. In the first three months the operations manager saw average resolution time drop from 61 hours to 19 hours. Solid result. Six months later, a corporate client filed a formal dispute over a payment discrepancy traced to an incorrect policy guidance. The question from management was: did the agent produce this guidance, on what data, and who approved it? The answer: nobody knew. A clean efficiency table had instantly become a missing accountability document. This is not a hypothetical scenario — I have encountered this pattern more than once over the past year.The core of AI governance is deceptively simple: for every AI-supported decision, someone must be named as accountable, the data source must be traceable, and the audit path must be defined before deployment — not after. Three conditions, simultaneously. Not just ‘which tool are we using.’ What I see across Turkish organisations right now is the tool decision made and the accountability chain left undrawn. Two concrete consequences follow. First, under the EU AI Act framework, Turkish companies serving the EU market now face conformity obligations for High Risk systems — insurance, credit scoring, and health applications sit squarely in that category. Second, the tension between KVKK data processing principles and generative AI pipelines remains unresolved, and treating it as a problem to handle later is no longer a defensible strategy in 2026.Institutions that accelerate AI without governance infrastructure typically fall into the same trap: agents produce efficiency metrics in the first few months, those metrics build trust at the management level, trust opens new use cases, and an untracked dependency complexity grows quietly beneath the surface. I call this ‘agent sprawl.’ At a mid-sized textile exporter in Gaziantep with roughly 280 employees, the sequence I observed last year was instructive: an agent was deployed for export documentation; a second agent was added for inventory planning; then a third integration layer was built so the two agents could exchange data. By the time three layers existed, no one in the organisation could trace which agent had produced which output. The company’s IT function was a single person; external support was mandatory, but the external provider had defined agent hierarchy documentation as a ‘client-side responsibility.’ The governance gap forms precisely at that handoff point.How do you actually build a governance framework? Four steps that can start on Monday morning. First, produce an inventory of all AI components currently in use — which tool, in which process, processing which data, and who is the named approver. A spreadsheet is fine to start; what matters is starting. Second, define a ‘human approval threshold’ for every AI component: which decisions can pass automatically, and which require human verification before execution? This line must be drawn before any agent goes live. Third, go back and retrospectively trace at least two critical AI-supported decisions: what data did they rely on, is that data still valid, was the outcome correct? This exercise surfaces broken data chains and calibrates institutional trust. Fourth, measure beyond throughput: error rate, human intervention frequency, and formal dispute count must be tracked alongside speed metrics. If the numbers deteriorate, the agent should be pulled back. That is not failure — that is governance working as intended.One qualification must be stated plainly: AI governance cannot be applied to every organisation at the same scope or the same moment. Expecting a five-person software team to produce a four-hundred-page governance document is absurd. At SME scale, governance must be transparent and executable — not a bureaucratic weight. But ‘lightweight governance’ is not the same as ‘no governance.’ The Ankara insurance firm and a five-person SaaS startup have different requirements; both still need an accountability chain defined. The scale of the framework differs; the obligation does not.Organisations that defer AI governance pay for efficiency today and settle the invoice later. The company in the insurance case eventually reached a resolution with the client — but the process consumed two months and a consulting fee that far exceeded the cost of building a governance baseline from the start. Artificial intelligence entering the operational layer of Turkish businesses is not imminent; it has already happened. The question now is practical and immediate: who monitors these systems, who is responsible, and how do you demonstrate that when something goes wrong? If those three questions have no clear answers, the efficiency figures on your dashboard are not yet reliable — they are provisional.
This article was originally published in Turkish by Gökhan MERCANOĞLU on July 29, 2026. The English edition has been reviewed and edited by the author.