A logistics company’s operations director ran a small machine learning experiment last year focused on demand forecasting. It worked — warehouse utilization improved, excess inventory costs dropped. Now the real question is how to scale that result across the rest of the organization. Which data to collect, which capabilities to build in-house, which processes to prioritize first. Many companies in Turkey are standing at exactly this crossroads. Artificial intelligence has moved off the ‘future consideration’ list and onto the boardroom agenda; but turning a pilot into a corporate strategy requires an entirely different set of capabilities.
Machine learning is a class of algorithms that enables a system to identify patterns in data and make predictions or classifications without being explicitly programmed with rules. The distinction from conventional software is fundamental: instead of writing the logic yourself, the system learns it from data. Customer churn prediction, product recommendation engines, visual quality control in manufacturing, natural language processing for customer service automation — all of these are machine learning applied to business processes. In Turkey, early institutional examples are appearing in finance, retail, and manufacturing. But in most companies, these efforts remain isolated projects driven by IT departments or individual data analysts, disconnected from broader business priorities.
Elevating machine learning to a corporate-level strategy rests on three pillars: data infrastructure, human capability, and use-case prioritization. On the data side, most companies have accumulated substantial structured data in their ERP systems, e-Invoice records, and CRM tools. The problem is that this data is often fragmented, incomplete, or inconsistent across systems. Machine learning models require clean data at sufficient volume; investment in data quality therefore has to precede any AI initiative. This is not an IT problem — it is an operational discipline problem, and it demands ownership from senior management.
The talent picture is more challenging still. The pool of data scientists in Turkey remains limited, and finding a competent machine learning engineer carries significant time and cost implications. Most companies are choosing to buy this capability externally rather than build it internally. That approach has short-term logic, but it creates long-term dependency. A sustainable AI strategy requires at least a small internal analytical capability — one or two people who can direct external vendors, evaluate model outputs critically, and translate business problems into technical briefs. Some companies are addressing this through university partnerships or structured internship programs, which is a pragmatic starting point.
Use-case prioritization is the least discussed but most decisive element of the strategy. Not every process is suited to machine learning, and not every suitable process generates the same business value. Projects launched without a proper ROI framework tend to lose organizational support even when they produce technically sound results, because impact cannot be measured. The right sequence is: define the business problem first, then ask whether the data to solve it actually exists, then evaluate technical feasibility. Reversing this order — asking ‘we have this algorithm, where can we apply it?’ — is one of the leading causes of failed corporate AI initiatives.
Another practical risk is underestimating total cost of ownership. The cost of a machine learning project does not end at model development. Deploying the model to a production environment, monitoring its performance over time, retraining it as data patterns shift, and keeping it integrated with live business processes all require ongoing resources. Cloud infrastructure has made this more manageable — significant computing capacity is now accessible without large capital expenditure. But the operating cost of a cloud-hosted model needs to be in the budget before the project starts. A common mistake among executives is approving the development budget while overlooking the running cost entirely.
For executives who want to make AI a genuine component of corporate strategy rather than a recurring pilot cycle, a practical sequencing looks like this: start with an honest assessment of current data infrastructure and document the gaps. Then identify two or three high-impact business problems and define them with measurable success criteria. Next, establish at least a minimal internal analytical capability and position that person to manage external providers rather than simply receive their output. Finally, secure ownership from the relevant business units before integrating any pilot into operational processes. AI projects run exclusively by the technology department rarely translate into operational change when business units are not actively involved. Strategy comes before technology selection — every time.
This article was originally written in Turkish by Gökhan MERCANOĞLU on March 13, 2017 and has been automatically translated into English and other languages using machine translation.