Insight: Why Companies Are Using AI, but Still Not Getting Enough Value from It
- Ben Steenstra
- Jun 23
- 14 min read
Updated: Jul 3
Everyone is talking about AI, and more and more people are using it every day. Yet for many companies, the real added value still remains surprisingly limited. This is especially true for SMEs, where many entrepreneurs are struggling to keep up. But to be fair, medium-sized and large companies often do not perform much better. They may have more tools, pilots and presentations, but that does not mean AI is truly integrated into the way the company works, serves customers, makes decisions and creates value.
This raises an important question: where are the missed AI opportunities, and why do so many companies remain stuck in isolated experiments? Because while AI has now largely moved beyond its adolescence, many entrepreneurs still seem to be watching from the sidelines. Sometimes because of uncertainty, sometimes because of a lack of knowledge, and sometimes because the pressure of day-to-day business seems more urgent. Meanwhile, competitors that do use AI effectively are moving faster, smarter and more efficiently.
Key takeaways of this insight: why companies miss AI value in 4 patterns
Many companies do not miss AI value because AI is irrelevant to them. They miss it because the way they approach AI is too shallow, too fragmented or too unclear.
1. They use AI, but do not integrate it. AI often starts with individual use: writing emails, summarising documents or generating ideas. Useful, but limited. The real value appears when AI becomes part of workflows, systems, customer contact and decision-making.
2. They focus on tools before problems. Too many companies start with the question: “Which AI tool should we buy?” The better question is: “Where are we losing time, quality, attention or customer value?” AI only creates value when it is connected to a real business problem.
3. They underestimate data, trust and ownership. Poor data, unclear rules, privacy concerns and lack of ownership slow AI down. Without good information, clear responsibilities and basic governance, AI remains a risk instead of becoming a capability.
4. They wait too long to learn. Many companies are still watching, discussing or experimenting without momentum. But AI advantage is built through learning. The companies that start small, measure what works and improve fast will be harder to catch later.
The core message is simple: AI value does not come from using more tools. It comes from making sharper choices about where AI should improve the way the business actually works.
This insight shows how AI is currently being used within companies, which applications are popular, what obstacles businesses are facing and which benefits entrepreneurs are already seeing. It also shows which opportunities you may be missing, why you are not too late, but do need to take action soon, and what AI can concretely mean for the efficiency, customer contact, decision-making and growth of your company.
Who is ahead and who is falling behind?
AI has largely moved beyond the phase of being a curious playground. Anyone who has not been living under a rock in recent years has heard of it, experimented with it or already used it somewhere in their work. According to the Stanford AI Index, 78% of organisations worldwide used AI in 2024. So it is fair to say that AI has become mainstream.
But mainstream use is not the same as mature integration. In this insight, AI integration means that AI is not only used by individuals, but embedded in workflows, systems, customer contact, decision-making and measurable business processes.
That difference becomes clear when you look at Europe. According to Eurostat, in 2025 only 19.95% of European enterprises with 10 or more employees used at least one AI technology. That is a significant increase compared with previous years, but it also shows that AI is still far from structurally embedded in many companies.
The difference between large and small companies is particularly striking. Among large European enterprises, 55.03% used AI in 2025. Among small enterprises, this was only 17.00%. In other words: AI is everywhere in the news, but still far from everywhere in business operations.
This is an important signal, especially for SMEs. Not because smaller companies would benefit less from AI, but because they more often get stuck on knowledge, time, privacy questions, legal uncertainty and the simple question of where to begin. In larger companies, the challenge is often different. There are usually budgets, pilots and teams, but AI too often remains stuck in experiments that are not sufficiently connected to data, processes, customer value or measurable business results.
The advantage, therefore, does not simply belong to those who use AI. The real advantage belongs to companies that know how to translate AI into better work, faster processes, smarter customer contact and sharper decisions.
AI integration in companies: where it truly lands in the organisation
he problem is not only that AI adoption is still low, especially among SMEs.. The problem goes deeper. Even where AI is being used, it often remains superficial. An email is checked for content with ChatGPT, a newsletter is partly automated, or someone uses AI to create a text, idea or summary more quickly. That is useful, but it does not fundamentally change the company.
Broadly speaking, you can distinguish four levels.
The first level is individual use. Employees use AI themselves for writing, analysis, ideas, summaries or preparation. This is often where most companies start, sometimes even without management knowing exactly how often it happens.
The second level is functional support. AI is then used within specific departments, for example for marketing content, sales emails, customer service responses, reports or support with coding. Output increases, but the underlying process often remains largely the same.
The third level is process integration. This is where AI becomes part of workflows, systems and decision-making. Think of automatic ticket routing, customer questions that are immediately summarised, documents that are processed, sales opportunities that are prioritised or internal knowledge that becomes available at exactly the right moment.
The fourth level is organisational transformation. At this stage, the company is not merely applying a tool, but changing the way the business operates. Processes, roles, data, customer contact, decision-making and governance are redesigned with AI as a structural layer in the organisation’s operations.
Research by Gartner shows that AI maturity makes a major difference. In highly mature organisations, business units trust AI solutions much more often and are much more willing to use them than in low-maturity organisations. Highly mature organisations are also more likely to have dedicated AI leaders, clear metrics and AI projects that remain in production for longer. This shows that real AI integration does not happen by itself. It requires direction, ownership, trust and measurability.
Deloitte sees the same pattern. Globally, 37% of organisations still use AI mainly superficially, with little or no change to existing processes. 30% are redesigning important processes around AI, and 34% use AI to transform more deeply, for example by developing new products, services, processes or business models.
This is especially painful for SME companies. They often have the most to gain, precisely because they need to do more with fewer people. Yet AI often remains limited to isolated applications. As a result, they are still using only a fraction of what AI can truly mean for efficiency, customer contact, knowledge, speed and decision-making.
Most Popular AI Applications Within Companies
The most popular applications of AI are much closer to everyday work than they would have to be, or perhaps should be. They are found in text. In knowledge work. In customer contact. In marketing, sales, service, administration, analysis and software development. Exactly in the places where people lose time every day searching, writing, summarising, checking, following up and repeating.
According to Eurostat, the most commonly used AI technologies within European enterprises in 2025 are mainly focused on language, content and data. Think of text analysis, generating images, video or audio, generating text, speech or code, speech recognition, machine learning, process automation, image recognition and autonomous machines. What stands out is that physical autonomous machines are used the least. So the robot is not the main character. The real work happens behind the screen.
Gallup shows the same pattern at employee level. AI is mainly used to bring information together, generate ideas, learn new things and automate basic tasks. Only later do applications such as customer interaction, making predictions or controlling complex equipment appear. That also says a lot. At this moment, AI is mainly being used as a thinking, writing, searching and organising assistant.
For entrepreneurs who do invest in AI, this matters. Because it means you do not have to wait for a major technical AI project to create value. The first gains are often found in the boring, everyday things that take a lot of time and are not very distinctive. Summarising customer questions. Preparing sales emails. Making internal knowledge searchable. Answering frequently asked questions. Sharpening proposals. Speeding up reports. Structuring marketing content. Checking code. Summarising meeting notes. Processing documents. A concrete case of that kind of practical gain is 4 hours of time saved per employee per day.
That is where the first layer of AI value lies.
Not because it is spectacular, but because it directly affects time, attention, speed and quality.
Companies that use AI intelligently therefore do not start with the question: “Which tool should we buy?” They start with a better question: “Where do we lose time every week on work that could be done smarter, faster or more consistently?”
AI benefits for companies and entrepreneurs
The benefits of AI are often either greatly exaggerated or greatly underestimated. On the one hand, you hear stories as if AI will take over entire companies tomorrow. On the other hand, it is still dismissed as a handy text generator for people who do not feel like writing an email themselves.
The truth lies somewhere else.
The first real benefits of AI are mainly found in efficiency, better decision-making, cost reduction, customer relationships and innovation. Not because AI suddenly solves everything, but because many companies are still full of work that is slow, repetitive, fragmented or unnecessarily manual.
That is where the gain lies.
Deloitte reports that organisations mainly see benefits in productivity and efficiency. 66% of organisations mention this as a realised AI benefit. This is followed by better insights and decision-making, cost reduction, better customer relationships and innovation. That is telling. AI usually does not start with a completely new business model, but with working faster, gaining better overview and losing less time on work that can be done more intelligently.
Microsoft shows that employees themselves also feel these benefits. 90% of AI users say that AI saves time. 85% say that AI helps them focus on more important work. 84% feel more creative and 83% experience more enjoyment in their work.
That last point is often underestimated.
AI is not only about saving costs. It is also about winning back attention. Less time spent searching, repeating, rewriting and checking means more room for customers, choices, creativity and quality. This is especially important for entrepreneurs, because they are often the ones drowning in work that seems important, but adds very little value.
So the question is not only: how many people can AI replace?
That is often the wrong question.
The better question is: how much time, energy and attention can people regain when AI supports the work that currently takes up too much unnecessary space?
For entrepreneurs, that may be the biggest opportunity of all. AI does not automatically make a company better. But it can reveal where time is leaking away, where customers are waiting too long, where employees get stuck and where decisions can be sharper.
Anyone who sees AI only as a way to cut costs is missing part of the story.
Anyone who sees AI as a lever for time, attention, speed and better choices comes much closer to its real value.
Disadvantages, Risks and Why Many Companies Have Not Started Yet
The main reason why companies have not yet started working seriously with AI is usually not that they consider AI useless. That is too easy. The real barriers lie deeper. In a lack of knowledge. In poor data. In privacy concerns. In legal uncertainty. In systems that do not connect properly. In costs that are not clear upfront. And above all, in the absence of a clear business case.
Many entrepreneurs do feel, somewhere, that AI is important. They see the examples, hear the stories and notice that competitors are experimenting with it. But there is a large gap between “interesting” and “properly implemented”. And that is exactly where many companies get stuck.
Eurostat shows this clearly. Of the European companies that have considered AI but ultimately do not use it, 70.89% cite a lack of expertise as the reason. This is followed by unclear legal consequences and concerns about data protection and privacy. Only 20.68% say that AI is not useful for the company.
That is an important signal.
So companies are not saying en masse: “AI has no value.” They are mainly saying: “We do not really know how to approach this safely, sensibly and practically.”
The same pattern is visible in the Netherlands. According to Statistics Netherlands, 74.6% of companies that considered AI but do not use it cite a lack of experience as the main reason. This particularly affects SMEs. Not because smaller companies have fewer opportunities, but because they often have less time, less specialist knowledge and less room to experiment calmly.
In larger companies, the problem is often different. There is usually budget, but AI projects too often remain stuck in pilots, proof-of-concepts and isolated initiatives. Gartner points out that GenAI projects often fail because of poor use case selection, insufficient business value, poor data quality, weak risk management and rising costs.
In other words: small companies often remain stuck before the start. Large companies are more likely to get off the starting line, but then run into complexity.
There is also an additional risk: shadow AI. If companies do not provide clear direction themselves, employees will simply start using AI on their own. They use their own AI tools, often without policy, without proper agreements and sometimes with sensitive information. That is understandable, but also risky. Not because employees have bad intentions, but because the organisation is too slow to provide clarity.
The lesson is simple.
AI does not only require curiosity. It requires leadership, choices, rules, data quality and an honest business case. Skip that, and you do not get an AI strategy. You get isolated experiments, risks and confusion.
And if you wait too long, you run another risk.
Not that AI will suddenly take over everything.
But that competitors learn earlier, improve faster and build an advantage that will later be much harder to catch up with.
Customer Contact, Customer Service and Call Centres
Customer contact may be one of the most concrete areas where AI quickly becomes visible. Not because it is so futuristic, but precisely because it is so practical. Every company with customers recognises the same problems: many recurring questions, waiting times, handovers, frustration, searching for information, call notes, employees under pressure and customers who simply want to be helped quickly.
AI fits that almost uncomfortably well.
Customer contact contains a lot of text, a lot of speech, a lot of repetition and many measurable costs. That makes it a logical domain for AI. A chatbot that answers frequently asked questions is only the beginning. The real shift lies in AI agents that independently handle customer questions, support employees live, summarise conversations, route tickets, retrieve customer history and prepare follow-up actions.
Gartner predicts that by 2029, agentic AI will autonomously resolve 80% of common customer service issues without human intervention. According to Gartner, this could lead to a 30% reduction in operational costs. Gartner also expects that by 2028, at least 70% of customers will start their customer service journey via conversational AI.
Salesforce sees the same movement. According to Salesforce, service professionals expect AI to resolve 50% of service cases by 2027, compared with 30% in 2025. In addition, 89% of service professionals say that conversational AI increases self-service resolution, 88% say it accelerates resolution times and 87% say AI frees employees to focus on more complex customer questions.
That is exactly the promise for entrepreneurs. Not less customer contact, but better customer contact. Less time spent on standard questions, and more room for the customer moments where human judgement, empathy and creativity are truly needed.
Deloitte’s contact centre research shows that AI-centric contact centres perform better than organisations that are less mature in AI. They are more often profitable, more often rate the customer experience positively, also rate the employee experience more positively and are better able to deliver personalised experiences. In other words: AI does not only help the customer faster. It can also relieve employees.
But customer contact is also exactly where the downside appears.
Customer contact is sensitive. People are quite willing to accept that AI helps, but they do not want to feel fobbed off by a stupid bot, get stuck in an endless chat or see their data disappear into a system that no one understands anymore. Salesforce reports that only 42% of customers trust companies to use AI ethically. That is a warning.
Companies that use AI well in customer contact therefore do not blindly automate everything. They ensure transparency, good data, clear escalation to humans and AI that genuinely helps the customer move forward. Not AI as a wall between the customer and the company, but AI as an accelerator of attention, clarity and resolution.
That is where the difference lies.
Bad AI makes customer contact cheaper, but colder.
Good AI makes customer contact faster, smarter and more human in the moments where that truly matters.
From Insight to Action: Where Do You Start as an Entrepreneur?
When you look at all the numbers, a clear picture emerges. AI is no longer a hype, but most companies are still nowhere near getting the full value out of it. There is a lot of testing, a lot of discussion and increasingly more use, but real integration is lagging behind. And that is exactly where the opportunity lies.
Because AI is not just about technology. It is about how a company works. How quickly customers are helped. How easily knowledge can be found. How much time employees lose to repetition. How decisions are made. How well data is organised. And how much room remains for the work where people truly make the difference.
That is why the most important question is not: which AI tool should we buy?
The better question is: where are we currently losing the most time, attention, quality or customer value?
That is where it starts.
For one company, the first gain lies in customer contact. Less waiting time, better answers, smarter ticket routing and more room for complex conversations. For another company, the gain lies in marketing, sales, administration, reporting, knowledge management or software development. The right starting point is not where AI sounds most impressive, but where the problem is most clearly felt.
At the same time, AI requires more than curiosity. Anyone who wants to use AI seriously must also look honestly at data, privacy, ownership, risks, training and measurable value. Without direction, AI becomes a collection of isolated experiments. Without rules, shadow AI emerges. Without good data, answers become unreliable. And without a clear business case, AI remains stuck in impressive demonstrations that change very little.
The companies that will benefit from AI in the coming years are therefore not necessarily the companies with the most tools, the biggest budgets or the most beautiful pilots.
They are the companies that make sharper choices. That know where AI needs to add value. That start small enough to learn, but seriously enough to follow through. That do not use AI as a toy, but as a lever for better work, faster customer contact, smarter processes and better decisions.
For entrepreneurs, that may be the most important lesson.
You do not have to do everything at once. You do not have to reinvent your entire company tomorrow. But continuing to wait is no longer a strategy either. Every month that AI remains only a topic of conversation and does not become part of business operations, the distance grows between you and the companies that are learning, testing, improving and integrating.
AI will not simply replace the entrepreneur.
But entrepreneurs who understand AI will overtake entrepreneurs who keep waiting.
So the real choice is not whether AI will become important. We are past that stage.
The choice is where you begin.
Sources used in this insight
Stanford AI Index: Used for global AI adoption and the broader development of AI in organisations.
Eurostat: Used for AI adoption in European enterprises, company size differences and barriers to AI use.
CBS: Used for AI adoption among Dutch companies and the reasons Dutch companies have not started using AI.
Used for GenAI project failure, AI maturity, data quality, agentic AI and customer service predictions.
Used for AI maturity, process transformation and realised business benefits.
Used for the performance difference between AI-centric contact centers and less mature service organisations.
Used for how employees use AI at work and which AI tasks are most common.
Used for employee experience, time savings, productivity, shadow AI and organisational AI adoption.
Used for AI in customer service, service case resolution, conversational AI and contact center trends.
Used for customer trust, ethical AI concerns and the human side of AI in service.
For more examples, strategy and case studies, explore the AI in Business articles.
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