"How do we use AI in sales and marketing, and how do we measure the result?" For most companies in Türkiye that use AI, this question is no longer theoretical. According to the bulletin that TÜİK (the Turkish Statistical Institute) published on October 2, 2026, the share of enterprises using AI in 2026 is 14.0%. The purpose these enterprises cite most often is marketing or sales.
Widespread use does not mean that results are visible as well. In this article, we read the data for Türkiye and Europe together, look at recent research findings on marketing executives' AI investment, and propose a practical framework for tying a use case to a measurable result.
The picture in Türkiye: marketing and sales is the most common purpose
According to TÜİK data, 51.0% of the enterprises using AI in 2026 used this software and these systems for marketing or sales. This was followed by R&D or innovation activities at 46.3% and production or service processes at 43.4%. Because enterprises could select more than one purpose, the percentages add up to more than 100.
Another notable finding in the bulletin, from a marketing standpoint, is the personal-data dimension. Of the enterprises using AI, 16.6% declared that they use these technologies to process personal data such as gender, age, address, purchase records or facial images. The bulletin does not break this share down by purpose of use; even so, the data types listed are of the kind commonly used in marketing applications such as customer segmentation and personalization. For this reason, an AI project in marketing also puts data-protection questions on the agenda from the outset. We addressed this topic separately in our in-house policy article.
First place in Europe too
Eurostat data paint a similar picture. In 2025, 34.70% of the EU enterprises using AI used these systems for marketing or sales; the organization of business management and administrative processes was in second place at 31.05%.
The sector breakdown shows that marketing use is not specific to retail alone. In manufacturing, the most common purpose of AI is marketing and sales, at 30.41%. Use for marketing and sales is at 58.82% in the accommodation sector and 48.18% in retail trade.
Because TÜİK (2026) and Eurostat (2025) cover different years and different samples, comparing the percentages one to one would not be right. The common message is this: in both datasets, marketing and sales is the business area where AI is used most often.
Investment is rising while maturity lags behind
Growing use does not make it easier to earn a return on investment. According to a survey Gartner conducted in January–March 2026 with 401 marketing executives in North America, the United Kingdom and Europe, an average of 15.3% of marketing budgets is allocated to AI initiatives. While 70% of participants regard leading in AI as a critical goal for 2026, only 30% say they have mature or fully developed AI readiness. Likewise, 70% acknowledge that their internal marketing processes are not yet ready to apply and scale AI effectively.
Another survey by the same organization, of 402 marketing executives, shows how high expectations are. The executives expect the share of marketing work automated by AI to go from 16% in 2026 to 36% in 2028. Gartner also points to the risk of a "competency trap," in which, after a certain point, early successes fail to deliver the expected return despite additional investment. In the same survey, 80% of the executives see employees' anxiety as a barrier to experimentation.
These two surveys mostly cover large-scale companies outside Türkiye; they do not directly reflect the picture in Türkiye. Even so, the lesson they convey is a general one: as the number of tools grows, if process, data and measurement infrastructure do not develop at the same pace, results remain invisible. We addressed the gap between productivity and financial results earlier; marketing is one of the areas where this gap opens most easily.
A practical path to measurable results
The steps below contain no source figures; they are a general framework proposed for putting the findings above into practice.
- Define the use case in terms of a business outcome. "AI in content production" is a tool, not a goal. The goal should be tied to a single business indicator, such as quote preparation time, the conversion rate from lead to sale, or production time per campaign.
- Measure the current situation before you start. Without a point of comparison, an improvement seen afterward cannot be proven. Collect at least a few weeks of baseline data.
- Set up three-layer measurement. An activity indicator (content produced, requests answered), an efficiency indicator (time spent per task) and a business-outcome indicator (conversion, sales, customer churn) should be tracked separately. Reporting only the first layer carries the risk of mistaking activity for results.
- Use a control group. Where possible, run the AI-assisted process in one customer segment or region and keep the old process going in a comparable group. Only this way can seasonality and campaign effects be separated out.
- Answer the data and legal questions up front. Which customer data will be used for what purpose, where it will be processed and how long it will be retained must be put in writing before the pilot. The sourcing route affects these answers; we compared the options of ready-made tools, external providers and in-house teams in a separate article.
- Define the human checkpoint. An approval step should be defined for texts that go out to customers and for recommendations that affect quote terms or the contract. When the team knows which tasks it hands to AI and which decisions it makes itself, that also reduces anxiety.
- Set a stopping rule before you scale. At the start of the pilot, write down the criterion: "at what result do we expand, and at what result do we stop?" When returns begin to diminish, it may be necessary to redesign the process rather than add more tools.
- Translate the result into management language. Link the measurement to a monthly management report so that marketing, sales and finance look at the same indicator.
Conclusion
TÜİK data show that companies in Türkiye that use AI turn to it most often in marketing and sales; the situation is similar in Europe. International research, however, reveals that investment is growing rapidly while organizational readiness lags behind. What makes the difference is less the tool itself than tying the use case to a business indicator and measuring the result comparatively.
From Orient Global's perspective, when starting with AI in marketing, a single use case, a single main indicator and a measurement plan written in advance make a healthier starting point than putting several tools into operation at once.
What logistics teaches about measuring results. Logistics runs on the same habits this article recommends: a measured baseline, a few indicators tracked on a fixed cadence, and a clear rule for when to expand and when to stop. One of Orient Global's consultants brings more than 30 years in operations, logistics and supply chain: at a technology distribution company of roughly one billion dollars in scale, he ran logistics, warehousing, technical service and administrative operations end to end, built and led teams of 100+ people and delivered lasting gains in operational performance through KPI-driven programs. He has also managed nationwide warehouse and transport operations, large-scale distribution networks, and network and capacity planning at leading retail, logistics and distribution organizations. In businesses that move goods, it also pays to read a sales or marketing indicator next to an operational one, such as order lead time or on-time delivery, because a campaign's gain can be lost between the order and the delivery. An industrial engineer, he now brings that supply-chain discipline to SMEs through AI and automation. Meet our consultants.
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