AI solutions for business do not mean the same thing to every company. In a firm of a few dozen people, setting up a ready product correctly gives the highest return; in an organisation with thousands of employees, development tailored to its own processes is needed. On this page Intelligence AI, an Istanbul-based enterprise AI company, explains which approach makes sense for your company size and process structure, where to start, and what is unnecessary.
You find out where AI will create value not by looking at your industry but at the nature of your processes. A textile manufacturer and a logistics company may have nothing in common, yet in both, any process with one of the following four characteristics is a candidate for AI.
If the same kind of decision has to be made dozens or hundreds of times a day, such as what price to set, which product this request refers to, whether this transaction is suspicious, AI pays off directly. It is designed for recurring operational decisions, not for one-off strategic ones.
One person can review a few hundred records a day. Tracking the prices of thousands of products, tens of thousands of transactions or a constant stream of social media mentions by hand is impossible. Once the volume exceeds human capacity, AI is no longer a choice but the only way.
A wrongly matched quote line, an overlooked inconsistency or a mispriced product is a direct loss of money. When the cost of error is high, a system that works consistently is more reliable than a person: it does not tire and its attention does not wander.
The value of some decisions decays fast. Noticing a competitor’s price two days later is useless; seeing a crisis signal the next morning is too late. When the speed of the decision determines the outcome, automation is required.
If none of these four exists in your processes, an AI investment may not be your priority. We are not afraid to say so.
At this scale the highest return comes from setting up a ready product correctly. Developing a model from scratch rarely makes sense in terms of either budget or data. Instead, a ready solution that does one specific job very well should be configured to work with the business’s own data.
For a marketplace seller, for example, price management is a ready product; setup takes a day and it starts working the next. Developing a pricing model from scratch for the same business would take months and would not produce a better result.
Here a hybrid approach usually works: ready products for standard needs, custom development for processes specific to the company. Document processing, for instance, is a standard need and can be met with a ready solution; but how those documents connect to the company’s own approval flow requires custom development.
What matters at this scale is deciding correctly which part to buy and which to build. Building everything means unnecessary cost; buying everything means bending your processes to fit the software.
In environments where processes are company-specific, data volume is high and the existing system investment is large, custom development comes to the fore. Even here, though, we recommend piloting in a single process and then scaling out, rather than building a large system from the start. The most common reason enterprise projects fail is that the scope is set too wide at the beginning.
Suspicious transaction and anomaly detection, cash flow forecasting, catching budget deviations as they occur, automatic classification of the incoming invoice flow. Because finance is the department with the most structured data, it is usually where results come fastest. Explore our Financial Intelligence solution.
Automatic matching of incoming quote requests to the catalogue, prioritising the opportunities most likely to close, early detection of churn risk. Time won on repetitive work such as quote preparation turns directly into sales capacity. Explore the Proposal Robot.
Demand forecasting, inventory optimisation, supplier performance tracking, location analysis for a new store or warehouse. The value of AI here comes from its ability to weigh a large number of seemingly independent variables together. Explore our Location Intelligence solution.
Real-time tracking of brand mentions, tone measurement, early warning on unusual activity, monitoring competitor communication. In a reputation crisis, hours matter; keeping this watch going continuously by hand is impossible. Explore our Social Reputation Management solution.
Bringing data from different systems together in one place, automating KPI calculation, taking management dashboards out of manual preparation. In most companies reports arrive a week late, and that delay directly lowers decision quality. Explore our Business Intelligence solution.
Reading, classifying and extracting data from invoices, contracts, delivery notes and application forms. Regardless of department, almost every organisation carries a document-processing burden. Explore our Document Intelligence solution.
The answer depends less on company size than on how standard the process is.
A ready product makes sense when:
Custom development is needed when:
If a ready product covers your need, we will say so; we will not try to talk you into custom development. Even though we have three products of our own, if the right product for you is someone else’s, we will tell you that too.
1. A clear problem. Projects that begin with “let’s do something with AI” exhaust themselves as the scope keeps shifting. A concrete sentence such as “preparing quotes takes us days” or “we notice competitor price changes too late” is enough. If you are not sure what your problem is, we can clarify it together in a discovery call.
2. Accessible data. The data does not have to be perfect; it can be scattered, incomplete or spread across different systems, and those are solvable. But if there is no data at all, you need to start accumulating it first. In that case we recommend a data collection infrastructure rather than AI.
3. Someone who knows the process. The resource we need most in a project is a few hours from the person who knows how the work actually runs on the ground. Only that person can explain what the data fields mean and why the exceptions exist. You do not need a technical team; you need a process owner.
Business profile: Sellers with hundreds of products on Trendyol, working with small teams.
Problem: Being unable to track competitor price changes. Cutting the price to win the buy box erodes profit; not cutting it loses position.
Approach: Ready product, SellBox. Setup in a day, no custom development. For every product, commission, shipping and cost are factored in to produce a price decision that never drops below the profit threshold. Explore SellBox.
Business profile: Anadolu Agency, Türkiye’s official news agency.
Problem: Spotting a developing event before rival agencies. Continuously scanning a multilingual, multi-source, very high-volume stream by hand is impossible.
Approach: Custom development, NewsTracker. Because the process was specific to the organisation and a direct part of its competitive advantage, a ready product was not suitable. The system can catch breaking news hours before it appears in the major agencies’ bulletins. Explore NewsTracker.
Business profile: Sales organisations with a wide product catalogue that receive quote requests as Excel, PDF or handwriting.
Problem: Matching quote requests with hundreds of lines to the catalogue by hand takes days, and human error is inevitable.
Approach: Hybrid. The document reading and matching engine is standard; adaptation to the company’s own catalogue structure and quote format is custom. 95% accuracy was achieved in catalogue matching. Explore the Proposal Robot.
Yes, if your processes involve repeated decisions or a data volume beyond human capacity. For small businesses, though, the right answer is usually to set up a ready product correctly, not to develop from scratch. Setup is completed within days and the investment stays limited. Cases where development from scratch makes sense are rare at this scale, and we will tell you so plainly.
It depends on the problem. For work like document reading you need almost none of your own historical data; the system runs on ready capabilities. For demand forecasting or churn prediction, on the other hand, at least a year of history is usually required. In the discovery phase we measure whether your data is sufficient and tell you from the start if it is not.
The systems we build sit inside your existing workflow; you do not have to learn a new screen that needs separate expertise. The price suggestion, for example, appears in the panel you already use, and the suspicious invoice arrives flagged in the existing list. At handover we train your team, and that training targets day-to-day use of the system, not AI expertise.
Enterprise AI adoption in Türkiye is growing fast, but most companies are still stuck at the pilot stage. Adoption is further along in finance, e-commerce and technology; in manufacturing, services and SMEs it is not yet widespread. That means you are not late, but waiting has a cost too, especially in areas where competitors gain an edge in pricing or operational speed.
In scenarios using a ready product, the first results appear within days. In custom development, a working prototype takes 2-4 weeks and full production setup 2-3 months in total. The purpose of the prototype stage is to let you see whether the approach works before any large investment; if the result does not meet expectations, you can stop there.
On critical decisions the system does not decide alone; it produces a recommendation and the final approval stays with a person. A suspicious transaction, for example, is flagged but not blocked; a price suggestion is made but cannot go outside the limit values. Which decisions are made automatically and which go to approval is agreed together at the start of the project, and the system is built so that it cannot step outside those limits.
If you are not sure which of your processes is suited to AI, this call is exactly for that. Based on your company size and priorities, we will work out together whether a ready product or custom development makes sense, and which process to start with.
Book a free assessment → For the detailed process, see our enterprise AI consulting page.
