Generative AI has quickly found its way into both employees' everyday tools and the agenda of management meetings. Even so, the questions "What is generative AI, how does it differ from traditional AI, and where exactly is it useful in a company?" are often brushed aside without a clear answer.
In this article we explain the concept based on international definitions, describe in plain language how it works, look at current usage data from Türkiye, and cover common business use cases through example scenarios.
What is generative AI?
The US National Institute of Standards and Technology (NIST), in its Generative AI Profile (NIST AI 600-1) published in July 2024, takes the definition in US Executive Order 14110 as its basis and defines generative AI as the class of AI models that emulate the structure and characteristics of their input data in order to generate synthetic content derived from that data. According to the document, this content can include images, video, audio, text and other digital content.
In short, generative AI refers to systems that, instead of returning a classification or a score in response to a question, produce new text, images, audio or code.
The difference between generative AI and AI
"AI" is the broader umbrella concept. According to the definition the OECD updated in 2023, an AI system is a machine-based system that infers, from the input it receives, how to generate outputs such as predictions, content, recommendations or decisions. According to the explanation published on OECD.AI, the word "content" was added to the definition to make clear that it also covers generative systems that produce text, video or images.
Within this framework, the difference can be summarized as follows:
- AI (general): also includes systems whose output is mostly a prediction, score or decision, such as demand forecasting, fraud detection, image-based defect detection and recommendation systems.
- Generative AI: the subset of this family whose output is new content; it produces email drafts, report summaries, product descriptions, images or pieces of code.
In practice, the two approaches are often used together: while one model scores a risk, a generative model can turn the same result into a summary the employee will read.
How does generative AI work?
According to the NIST profile, generative models are designed to produce outputs that approximate the statistical distribution of the data they were trained on; large language models, for example, generate text by predicting the next word or word fragment in a sentence.
This way of working has an important consequence for companies. In the same document, NIST notes that this kind of statistical prediction can produce accurate and consistent outputs, but can also produce outputs that are factually incorrect or internally contradictory. The document refers to this phenomenon, which is popularly known as "hallucination", as "confabulation" and points out that incorrect content is presented with confidence. According to the document, this risk is more pronounced in long, open-ended responses and in topics that require domain expertise.
For this reason, generative AI output, especially in decisions that have consequences, should be treated as a draft and checked by a person.
How widely is it used in Türkiye?
The Artificial Intelligence Statistics, 2026 bulletin that TÜİK (the Turkish Statistical Institute) published on October 2, 2026 paints two different pictures:
- Individuals: the share of individuals who say they use generative AI rose from 19.2% in 2025 to 37.6% in 2026. Use increases with level of education; the rate is 62.7% among higher-education graduates and 45.4% among high-school or vocational high-school graduates.
- Enterprises: among enterprises with at least 10 employees, the rate of using any AI technology was 14.0% in 2026.
- Sectors: use is highest, at 63.7%, in telecommunications, programming and IT activities, followed by publishing, radio-TV and content activities at 57.1%.
The sector ranking stands out: work that is intensive in content and software production is among the groups that use AI the most. Because the two rates measure different units (individuals and enterprises), they cannot be compared directly; even so, the speed at which individual use is spreading is a reminder that employees may start using these tools before a corporate framework is in place. This topic is covered separately in the article on managing employees' use of AI.
What are generative AI tools?
Regardless of product names, the generative AI tools found in companies can be grouped by the content they produce:
- Text-generating tools: chat assistants; they produce draft correspondence, summaries, translations and first versions of reports.
- Image- and video-generating tools: they produce design drafts, product images and promotional content.
- Audio tools: they convert text to speech or extract a transcript and summary from a meeting recording.
- Code assistants: they offer software teams code suggestions and draft tests and documentation.
- Assistants embedded in existing software: generative features added inside office, email, ERP or CRM software. Taking an inventory of these features before buying a new tool is often a faster way to start, as described in the article on embedded AI in existing software.
Business use cases and examples
The examples below are example scenarios; they do not belong to any specific company and contain no figures.
Example scenario 1: Customer correspondence. A support team writes replies to similar questions from scratch every day. Generative AI prepares a draft reply based on an approved knowledge source; the employee checks the draft and sends it. The measures are response time and correction rate.
Example scenario 2: Marketing content. Product descriptions, campaign copy and versions in different languages are prepared by hand. A generative model produces the first draft, while brand and accuracy checks stay with the team. How to get measurable results in marketing and sales is covered in the article on AI in marketing and sales.
Example scenario 3: Document and meeting summaries. Long contracts, technical specifications and meeting records are read and summarized. A generative model produces a summary and an action list; whether the summary stays faithful to the source document is checked.
Example scenario 4: Internal knowledge access. In an organization with many procedures, documents are scattered across different folders and employees struggle to find the right one. An assistant restricted to internal documents answers a question by citing the relevant procedure.
The four scenarios have one thing in common: generative AI speeds up the draft, while the final check and responsibility remain with a person.
Three checks before you start
- Data: Put in writing what data may be entered into the tools. The personal-data dimension, which falls under KVKK (Türkiye's data-protection law), is covered in detail in the article on a KVKK-compliant internal generative AI policy.
- Accuracy: Define in which cases the output must go through human approval and how the source will be verified.
- Measurement: Before you start, set a success measure for each use case, such as time, error rate or the amount of rework.
Conclusion
Generative AI is the subset of AI that produces new content; both its power and its risk come from this way of producing. The TÜİK data shows that individual use is spreading quickly, while corporate use is further behind.
From Orient Global's perspective, the most productive way to start is to pick a few repetitive, draft-type workflows, write the data and control rules up front, and widen the scope by measuring the result.
Why logistics experience matters here. The distinction drawn above is one that logistics operations live with every day. A forecasting model that outputs a number is a familiar tool in warehousing and distribution, while the exception notes, handover summaries and carrier or customer messages built around that number are the kind of draft work generative AI can speed up, with a planner or supervisor keeping the final check. The three checks above (written data rules, defined approval points and a success measure fixed before the start) are just as familiar in KPI-driven operations. 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, building and leading teams of 100+ people and delivering 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. An industrial engineer, he now brings that supply-chain discipline to SMEs through AI and automation. Meet our consultants.
This article is for general information purposes only and is not a substitute for legal advice.
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