If you ask managers today where they want to use AI in the company, you often get similar answers:
"We want to automate orders."
"We want to automatically process requests."
"We want the system to do less manual work."
However, most of these tasks are not about artificial intelligence at all, but about properly set digitalization.
In the previous article, we showed that:
- digitalization is not an IT project, but a growth strategy,
- AI cannot replace dysfunctional processes,
- successful digitalization begins with understanding the business model,
- only on a quality digital foundation does artificial intelligence make real sense.
If you haven't read the first article yet, I recommend starting with it. Strategic Digitalization of a B2B Company 1/3: Why AI is Not the First Step.
Let's imagine a completely ordinary situation. A customer sends an order. The system checks stock availability, reserves the goods, creates documents in the ERP, sends confirmation to the customer, prepares materials for the warehouse, continuously informs about the order status, updates the real stock status after dispatch, reports sales to statistics.
That is not AI. That is just a well-designed digital process.
Similarly, automatic order approval according to set rules, individual pricing conditions, repeat orders, notifications of unavailable goods, or automatic informing of the salesperson about changes in the order status belong here.
That is not AI either. It is digitalization.
And this is where one of the biggest misunderstandings arises. Many companies today label everything that the system does automatically as AI. In reality, it is standard process automation that enterprise systems have been handling for years.
If you are not even at this step yet, it is good news for you. Because this first phase of digitalization is also the one that brings the fastest return on investment.
By eliminating routine, repetitive tasks such as retyping orders, manual approvals, checking stock levels, or sending the same information to customers, companies can often reduce operating costs by single to double-digit percentages. This is not about saving at the expense of quality. On the contrary, processes are faster, more consistent, and less prone to errors.
AI Needs Quality Data
When talking about artificial intelligence today, most of the discussion focuses on the tools themselves. Much less is said about what these tools are based on.
The best product recommendation does not arise because you use the latest AI model. It arises because you have quality product information, order history, correctly categorized products, a connected ERP system, and data you can trust. Simply put, a quality dataset.
A common misconception is the idea that AI only works when all company data is stored in one place. Modern solutions, such as FLUIDUM, can safely work with data across ERP, CMS, CRM, or other systems through the MCP connector without the need for centralization.
However, this does not change the essence. AI needs data to exist somewhere and be reliable.
If you do not log customer behavior, do not keep order history, business rules remain only in the heads of experienced colleagues, or product information is not complete, AI will work with an incomplete picture of your company. And incomplete inputs lead to incomplete results.
Therefore, companies should not only ask where their data is stored, but especially whether they systematically collect, store, and can rely on it. Because the quality of data is the foundation of every successful AI initiative.
AI Will Not Bring a Revolution in One Weekend. It Comes Gradually.
Perhaps this is why there are conflicting opinions about AI. Companies expect immediate transformation from it, expect that after implementation they will have no worries with it and it will function autonomously. In practice, however, an evolutionary approach works much better.
Start where the risk is low and the benefit immediate. For example, with automatic translations of product information. Generating product descriptions. Assisting in product categorization or supplementing technical parameters.
Later, AI can help salespeople prepare offers, recommend related products, or alert them to customers whose activity is starting to decline.
Only in the next phase do more complex scenarios come, such as demand predictions, intelligent assistants, or AI agents capable of handling part of customer communication.
This approach has one great advantage. The company learns to work with AI gradually, without large investments and without great risks. But mainly on a quality foundation.
The Second Digitalization is Not About Technology. It's About Corporate Know-How.
There is another area that appears much less in discussions about digitalization. Know-how.
In most large B2B companies, there are people who "know everything". They know the specific conditions of individual customers. They know why a particular product is handled differently. They remember the history of business cases, know the exceptions. Simply put, they are irreplaceable.
And that is precisely the problem. If most of the know-how remains only in the heads of individuals, the company becomes dependent on specific people. Digitalization is therefore not only about speeding up processes, but also about preserving corporate memory.
Modern B2B systems today can store business knowledge, technical documentation, internal procedures, and communication history so that individual experience becomes a shared corporate asset and so that their experiences remain in the company even ten years from now.
What Would I Address Today as a CEO?
If I were managing a manufacturing or distribution company today, I would not start with the question of what AI tool we need. I would start with much simpler questions.
- Can we handle double the number of orders without doubling the number of administrative staff?
- Does the customer have access to all the information they repeatedly request from us?
- How much time do our salespeople spend on actual sales and how much on administration?
- Do we make decisions based on data or intuition?
- And do we have a digital foundation created so that AI can truly bring us a competitive advantage in two or three years?
If the answer to most of these questions is negative, the problem probably does not lie in the fact that the company lacks AI.
It lacks the foundation on which AI can successfully function one day.
Digitalization is Not an IT Project. It is a Growth Strategy.
In the coming months, the B2B market will not be divided into companies that use AI and those that do not. It will be divided into companies that can grow without having to proportionally increase the number of administrative staff with each new order, and those that remain dependent on an ever-increasing amount of manual work.
That is, in my opinion, the true essence of digitalization. How to create a company that can grow faster than its costs, use the knowledge of its people more efficiently, and provide customers with a level of service that they will consider standard in five years? And that is why digitalization is no longer a topic for the IT department.
It is one of the most important strategic decisions a CEO can make today.
Artificial intelligence is one of the strongest technological topics today, but it is not a solution in itself. It is a tool that can exponentially increase the value of well-set processes, quality data, and thoughtful digitalization. However, if a company does not have these foundations, AI will not create them for it.
And this is precisely the topic we will address in the final part of this series – what a practical roadmap for digitalizing a B2B company looks like and where it makes sense to start so that the individual steps build on each other and bring real business value.
