Late last year I spoke with the operations director of a mid-sized food manufacturer in Gaziantep — 295 employees, supplying both domestic retailers and export channels. After ChatGPT’s December launch, the company had acquired six AI tools: a demand forecasting platform, a customer e-mail classification service, a supplier quote summarizer. The result? ‘Making decisions got harder,’ he told me. ‘Every tool says something different.’ This paradox sits at the heart of what most companies get wrong about being AI-First.Being AI-First is not a technology decision. It is an organizational design decision. Most companies ask: ‘Which AI tool should we buy to see results?’ The right question is: ‘Which of our recurring decisions are structurally suited to receive AI-generated input, and which are not?’ The distance between those two questions is the single clearest predictor of whether an AI initiative will generate real value or simply accumulate subscriptions. Buying a tool is not an AI strategy. Redesigning decision flows, assigning data ownership, and rebuilding skill sets — that is the strategy. The tool is a consequence, not a cause.The concept gained urgency in Turkey following ChatGPT’s release. Within weeks, executives across every sector were announcing AI adoption plans. But that enthusiasm obscures a structural question the hype cycle rarely surfaces: how many of the company’s decision points are actually designed to be fed by data? Back to the Gaziantep manufacturer. The demand forecasting tool runs. But the weekly production planning meeting still operates on the intuitive assessment of three people around a table every Tuesday morning. The tool sits outside the process. It generates no value because it was never wired into the decision flow. This is the pattern that repeats: companies deploy AI at the boundary of a process rather than inside it, then wonder why adoption stalls.So what does being AI-First actually mean in operational terms? The shortest accurate definition is this: an operating model in which every significant decision is preceded by a structured AI-generated assessment, and in which that assessment is recorded and traceable. That definition has three components, each requiring a different kind of maturity. First, process design: the company must have mapped where decisions are made and which data feeds them. Second, model governance: a rule set that defines when AI output goes directly into action and when it is subject to human review. Third, capability planning: the people in the organization must be able to operate this model, not merely observe it. Build only one of these three and you recreate the Gaziantep paradox. The tool arrives. The process has not matured. The tool goes unused.In Turkey’s context, there is a fourth layer that most Western AI-First frameworks ignore entirely: KVKK compliance. Turkey’s personal data protection law, in force since April 2018, governs how personal data is processed, where it is stored, and for what purposes it may be used. Following guidance issued by the Personal Data Protection Authority in early 2023, it became clear that sending customer data to ChatGPT and similar generative AI APIs carries significant legal ambiguity, since processing occurs on infrastructure outside Turkey. A mid-sized insurance intermediary in Izmir with 378 employees discovered this the difficult way: a ChatGPT-based e-mail drafting tool used by the sales team was passing policyholder names and premium figures to the model API without the data ever being processed on Turkish servers. The legal team shut the tool down after three months of use. The question of which data had been transmitted, and to where, could not be fully reconstructed. This KVKK-generative AI blind spot is a genuinely Turkish problem. The global AI narrative rarely accounts for it. Any AI-First strategy designed for a Turkish company must resolve the compliance architecture before the first model is deployed, not after.Here is the counter-argument, and it deserves serious weight. Is becoming AI-First necessary for every company? Is it appropriate at every scale? No. For a 295-employee food manufacturer, the instruction to ‘redesign your entire decision architecture’ almost certainly exceeds the available management bandwidth and sets the wrong priority. What that company should actually do is identify two or three high-frequency decisions that are already fed by data, build a structural AI layer only at those points, and leave everything else untouched. AI-First is not a mandate to transform all processes simultaneously. Selective depth generates more value than shallow breadth. Even at enterprise scale, Gartner data published in late 2022 showed that more than nine in ten organizations fail to move AI pilots into production. That figure does not reflect only technology readiness — it reflects process design debt and skill gaps. Knowing when not to pursue AI-First is itself a form of strategic maturity.Where does a company start on a Monday morning? Three steps, and sequence matters. The first is mapping: list the decisions your company makes on a weekly or daily basis that repeat reliably — purchasing volume, pricing timing, customer prioritization, staffing adjustments. That list reveals where AI will generate the highest return. The second step is a data quality audit at each of those decision points: is the data that would inform this decision currently clean, complete, and accessible in near real time? If the answer is no, fix the data first. Buying a model before the data is ordered is the single most common and expensive mistake in AI adoption. The third step is KVKK resolution: document which data categories cannot leave Turkish infrastructure, which processing operations require explicit consent under your aydınlatma obligations, and which tools require contractual data processing agreements. These three steps, completed in sequence, create the minimum viable foundation for a genuine AI-First operating model. The label without the foundation is a slide deck, not a strategy.I returned to the Gaziantep operations director with a concrete proposal. Retire five of the six tools. Pick the demand forecasting tool, choose one decision — weekly production volume — and build a closed feedback loop. The tool’s output becomes a documented input to the Tuesday meeting. When the team overrides the forecast, they record why. After three months, examine the pattern of overrides and use it to refine the data feeding the model. That cycle — one decision, clean data, recorded feedback — is the actual anatomy of being AI-First. The other five tools did not make the company AI-First. That loop does. The question worth asking before the next tool purchase is not ‘what can this AI do?’ but ‘which specific decision will this change, and how will we know?’
This article was originally published in Turkish by Gökhan MERCANOĞLU on April 3, 2023. The English edition has been reviewed and edited by the author.