This article is a collective view from our team. No long explanations about why AI matters, just real cases from real businesses showing what AI readiness looks like today. We also share our overall view of the market. You may recognize your own situation here, but it’s better to prepare now than spend a lot of resources later and still get no real results. Let’s go.
AI has become so common in our daily work that many companies now feel constant pressure to add it, otherwise they risk losing their place on the market. And sadly, that part is true. But before bringing AI into your internal processes, there are three very important things you need to understand:
And this third point is exactly where I want to focus. Over the past year, we received many requests that sounded like “we need AI.” After talking to many companies and visiting events across the EU and the US, we created our own AI readiness index based on what we see happening on the market.
Below you’ll find how we calculated this score, but here’s the short spoiler: most companies are not ready for AI. If they add AI into their processes tomorrow, it might not work as initially planned. And what’s even more important - many companies don’t realize how critical AI readiness is or what risks they face without it.
To calculate the score, we ran an internal survey with our colleagues who work with projects and partners every day. What’s interesting is that the answers were very different - there was no single trend. But we want to highlight the most common problem areas to help your business avoid issues and extra costs in the future.
It’s important to say that this article reflects the experience of our whole Moravio team across projects of different sizes and complexity. This is our view at the end of the year, moving into 2026, based on what we see when companies talk about AI integration today.
Pay attention to these points - they show different pain areas companies face. We share them so you can spot them early in your own business. If you ignore them, you risk losing time and money. Some points may not apply to small businesses, but we included them because they are critical for larger companies and corporations.
We also rated each point from 1 to 10, where 10 means fully ready and 1 means not ready at all. But from project to project and client to client, the results were very different. So we took the median values.
Most companies already store data in some structured form, but the way it’s organized often leads to duplication and limited accessibility. Data is scattered across tools, and many firms still run on legacy or even paper-based systems, so a big preparation step is needed before using AI. Sensitive data is usually well protected, but datasets often require cleanup. People also expect more from AI than their current data can realistically support.
Most companies use tools that support integrations and have basic APIs, so connecting systems is usually possible. Smaller companies tend to be flexible, while mid-sized and larger ones are often slowed down by legacy systems and strict security rules. Moving to the cloud helps, but it’s not enough on its own, and many infrastructures still can’t scale well for AI. Sometimes outdated internal systems without APIs require custom workarounds before AI can be added.
Many businesses lack clear workflows. Also readiness varies by industry: some areas have predictable steps, while others are complex with many exceptions. In most cases, clients need workflow revision before AI. Working through the process with them usually helps clarify what can be standardized. Fully well-defined processes are rare, but repeating tasks are generally suitable for automation.
When it comes to data security, most companies pay attention to it, and the bigger the company, the stricter the policies usually are. However, many people still use tools like ChatGPT without understanding data protection risks.
Some businesses rely mainly on the built-in security of the products they use, and legal or compliance topics are often not discussed at all.
Security is sometimes used as a reason to delay AI adoption, even when the actual risks are manageable. Newer systems often include better auditing, but overall AI-related governance is still not well established.
Interest in AI pilots is high, but maturity is mixed. Some companies have clear ideas, others only know that they want AI and need our help to find realistic use cases.
There are almost always quick win pilots we can launch after a short feasibility check, even if timelines depend on their internal processes and people, while for our own operations we can test and ship small AI improvements very fast.
AI leadership is still concentrated in one role, mostly the CEO, and proper ownership is missing. Some organizations rely fully on external experts, while others only start thinking about ownership after seeing a small working AI feature.
Larger companies are beginning to form dedicated AI roles or teams, and interest is growing fast as management pushes for AI adoption. In some cases, AI use is distributed across the whole company, with each person responsible for using AI in their work, but this model is still not common.
AI literacy varies a lot. Younger or digitally oriented teams use AI tools daily and understand their limits, while others have only basic experience or no interest at all. Most companies don’t offer structured AI training, so knowledge grows mainly through personal use of tools like ChatGPT.
In some teams AI is already part of everyday work, and not using it means falling behind. In others, employees still wait to see “what AI can bring” and need guidance to understand how it can improve their workflow.
Most companies don’t have a dedicated AI budget yet. Interest in AI is growing, but investment decisions depend heavily on clear ROI. In many cases, discussions sound like “this could help us, let’s see what it would cost.” Dedicated AI funding exists only in rare cases, and many companies are still cautious or unsure where to invest.
As our COO Barbora said:
"We should all calm down and not jump on the bandwagon. AI is a marvel, it speeds up everything, tech, healthcare, law, business in general. But as of now, we should use it as a tool, not as a universal answer to everything. Like any other tool, its effectiveness depends on the one who’s using it. It’s a great enhancement, but a bad master. It’s artificial intelligence, not outsourced intelligence."
Each company is unique, and AI integration looks very different for small businesses and large ones. For smaller companies, even adding AI to basic daily tasks like handling customer inquiries can show results within a week. For corporations, it’s a complex process that needs time, money, employee training and more tools for proper integration.
But if you prepare for AI integration the right way, you’ll see real digital transformation and a clear boost in how your business works.
So the final answer is this: we see that most companies still have major gaps to fix before adding AI into their processes. But there is good news - we also see companies that already have automation in place, and even a small AI integration can help them speed up. Maybe that’s you?
And one more thing. We’ve already guided many different businesses through this journey – small and big, prepared and unprepared, with clean data or none at all. Our team knows how to understand your operations, highlight the right places to start, and shape AI solutions that actually fit your business. If you want to explore where AI can bring real value for you, we’re here to help.
If this article was interesting for you and you want more research like this, follow us on social media (LinkedIn, Facebook, Twitter, Instagram) and send us a direct message - we will see it. And if you have specific topics you want us to cover, write to us as well and we’ll prepare material with answers to your questions.
Where did we get our inspiration? From the collective experience of our team and a detailed industry report by Cisco.
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Jakub Bílý
Head of Business Development
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