There is a pattern visible in Turkish mid-market companies right now: AI budgets get approved, tools get purchased, and twelve months later nobody can name a single measurable business outcome. A food processing plant in Konya with 312 employees approved three separate SaaS AI solutions in late 2024 — a demand forecasting module, a vision-based quality control system, and a supplier risk scoring tool. Fourteen months later, only one of the three is genuinely embedded in the production workflow. The other two remain in a permanent ‘evaluation phase.’ The problem has nothing to do with product quality. It has everything to do with the fact that nobody ever answered three questions for each tool: who owns this decision, who owns this process step, and what business metric tells us whether this is working. Buying AI is not building an AI Operating Model — and confusing the two is the only reason the pilot graveyard keeps filling up.An AI Operating Model is not a technology architecture document. It is a management framework that answers three questions for every AI application in the company: who has decision authority here, who owns the affected process, and which output metric is being tracked? The decision rights question is particularly sharp in Turkey’s SME context, where owner-approval remains the dominant governance mechanism. When an AI agent automatically issues a purchase order to a supplier, who made that decision? The agent? The IT consultant who configured it? The procurement manager? When this question has no written answer, the first error triggers a full blame cascade — and the system either gets switched off or continues running while everyone quietly distrusts its outputs. Neither outcome justifies the investment.Process integration is more layered than most companies expect. Back to the Konya plant: the demand forecasting module is technically functioning. Its predictions for raw material quantities are directionally correct. But the procurement team does not trust the outputs because they cannot interrogate the reasoning. A RAG-based explainability layer — one that presents each forecast alongside the historical order data and stock movement patterns that produced it — addresses the ‘why this number?’ question directly. However, deploying that technical fix is not enough. The procurement workflow itself needs to be redefined in writing: at which step does the model’s output become an input, at which step is human judgment mandatory, and at which step does a flagged anomaly trigger escalation? Companies that complete the technical integration but leave the process map unchanged consistently find themselves in operational confusion within six months.With the EU AI Act in force since 2025, these questions have moved from best practice to compliance obligation for Turkish companies with AB market exposure. The Act’s high-risk AI classification covers quality control in food production, employment decisions, credit scoring, and medical diagnostics — areas directly relevant to a wide range of Turkish exporters. A Turkish food manufacturer signing a contract with an EU buyer now routinely receives a supplier questionnaire asking whether human oversight mechanisms exist for AI-assisted decisions, whether outputs are logged, and whether the system can be overridden or stopped. The answer to that questionnaire is not a technical datasheet — it is the AI Operating Model itself. The Konya plant’s vision-based quality control system remains in evaluation precisely because the EU buyer’s documentation requirements have not been met. The model works. The governance record does not exist yet.Performance management is the most neglected layer of the AI Operating Model. When companies struggle to define success metrics for AI tools, two failure patterns appear reliably. The first is measuring tools by technology metrics: model accuracy, API latency, uptime. These are IT operations metrics, not business value metrics. The second is deriving expectations from budget rather than from process: ‘We spent this amount, we should save this amount.’ This reflects a fundamental misunderstanding of how AI generates value. The correct metric design works from the process outward: which specific process step does this AI application affect — what is the current cycle time and error rate at that step — and after deployment, are those two indicators measurably different? For the demand forecasting module that actually is working in Konya, the business case was ultimately expressed this way: weekly hours spent on manual demand planning dropped by 48 percent, and emergency supplier orders fell from 23 to 9 annually. These are business metrics. They tell the factory manager and the owner whether the tool earns its keep — in language that does not require a data science background to evaluate.One important qualification: an AI Operating Model should not be uniformly complex across all company sizes and risk levels. For a mid-scale food plant with five AI applications, a sufficient framework is a decision rights matrix that fits on a single page, two or three business metrics per application, and a monthly review cycle that takes less than an hour. The oversight mechanism required for an SLM-based internal document assistant is categorically different from what a high-risk quality control system requires — treating them identically creates unnecessary bureaucratic friction and, paradoxically, obscures real risks by burying them in compliance theatre. Be skeptical of ‘one-size-fits-all’ AI governance packages sold as complete solutions; a framework without calibrated scope is governance performance, not governance substance.The factory manager in Konya now knows, for the one tool that is running, who approved it, what it is being measured against, and under what conditions it gets stopped. The other two tools are still waiting — but the reason is now specific: one lacks defined decision rights, the other lacks a business metric. That specificity is progress. In a period when ‘transformation’ is the word most attached to AI conversations, a factory manager who knows which tool is working and why it is working stands on considerably more solid ground than a competitor who has launched fifteen pilots and measured none of them. The AI Operating Model will not accelerate your results. But without it, every result you report will be contested — including the ones that are actually true.
This article was originally published in Turkish by Gökhan MERCANOĞLU on January 26, 2026. The English edition has been reviewed and edited by the author.