Using AI on your data can work, but only if the foundation is sound. Whether you get useful answers depends not on the model, but on whether your data models, definitions, and documentation are in order. We assess whether your data is ready for AI and are honest about what needs to happen first. That same foundation also makes your regular reporting more reliable, so the work is not wasted if your AI plans change.
Most organizations starting with AI analytics think they need the best model. But the model is rarely the bottleneck. What makes the difference is whether your data is sound: clear definitions, documented data models, and clear ownership.
The numbers make this very clear. Anthropic built an analytics agent internally and saw accuracy rise from 21 to above 95 percent, purely through better context and without changing the model. In a controlled experiment at a medical clinic, accuracy went from 0 to 92 percent, again without using a different model and solely by getting the context in order. In both cases, the model was not the bottleneck; the surrounding information was. The evidence and sources are available in our article.
This is not a new lesson. It is exactly what reliable dashboards have always required. An AI agent simply makes the problems painfully obvious: while a person may ask follow-up questions about a strange number, an agent will confidently provide an answer that looks right but is wrong. And that is also the good news. The work that makes your data ready for AI is the same work that makes your reporting reliable. So you are not investing in a gamble on AI; you are investing in a data foundation that has value regardless of whether you add an AI agent or simply rely on your dashboards.
Before using AI on your data, you need to know whether the data can support it. Our readiness assessment gives you that honest picture: what your data foundation is ready for and what needs to happen first. We do not assess the AI tool, but the layer beneath it, because that is what determines whether it succeeds or fails.
Are your models logically structured and documented so that an AI—or a new colleague—can understand how everything fits together? We assess the structure, field descriptions, and whether the documentation reflects reality.
What exactly do “active customer,” “revenue,” or “lead time” mean, and are those definitions documented unambiguously? This is precisely where things go wrong: an agent that does not know what a field means gives an answer that looks correct on screen but is wrong in practice. Clear definitions of your measures and dimensions are fundamental to a reliable answer.
Is there one agreed source for each data point, or do you have fifty tables that all contain something related to revenue? And does anyone know which one is correct? Without clear ownership, an AI does not know which source to trust—and neither do you.
Are there examples of good queries, and is the correct way to perform specific analyses documented? This knowledge often exists only in your analysts’ heads: which filters always apply, which exceptions exist, and why things work the way they do.
AI needs more than isolated definitions. It needs to know which sources it may consult, what pitfalls to watch for, and how to clarify a vague question. In Power BI, some of this is contained in the semantic model, and in Qlik, in curated datasets and central definitions. But the guidance layer above them determines whether an agent remains reliable. We assess whether that layer is in place or needs to be created.
An AI environment is never finished. Definitions change, tables are renamed, and without maintenance, reliability inevitably declines. That is why we also look at the people involved: is someone responsible for updating the documentation when something changes, and are there domain experts who can document their knowledge and assess whether an answer is actually correct? Without that, no AI solution will last.
After the assessment, you will know exactly where you stand and what, if anything, needs to happen first. Not generic advice, but a concrete picture of your data and your situation.
The assessment is the first step and often the most important one. Afterward, you will know whether your data is ready for AI and, if not, what needs to happen first. For some organizations, that is the complete answer: they address the issues we identified themselves or decide that it is still too early. That is also a valuable outcome, because it saves you from embarking on an AI initiative that would have stalled due to a shaky foundation.
If your foundation proves strong enough, or once you have put the basics in place, a proof of concept is the logical next step. Rather than immediately deploying an AI agent across your entire data landscape, you start with a clearly defined domain where you can see whether it works: one clearly defined part of your data, properly organized, with an AI layer on top. This lets you test the idea using your own data before committing to anything larger.
That is exactly what we are currently doing for a wholesaler. Within a clearly defined domain, we first get the data in order: we refine the definitions, document field descriptions and working methods, and make sure the models are correct. We then implement Qlik Answers, Qlik’s agentic AI assistant. First the foundation, then the AI. Not the other way around.
AI readiness is not a separate discipline; it means having a solid data foundation. And that is exactly what we have been doing for years. We build data models, establish definitions, and ensure that data is accurate and remains reliable. The questions that determine whether your data is ready for AI are the same ones we ask in every data project.
We also know how much domain knowledge this requires, because that is exactly our area of expertise. For years, we have been extracting knowledge from experts’ heads—what a field really means, which exceptions apply, and how a calculation should work—and translating it into sound technical solutions. That has always been at the heart of good BI work, and it is exactly what an AI agent needs. Because we work across a wide range of industries, from fresh supply chains and logistics to healthcare and public-sector organizations, we also know that terms such as “active customer” and “lead time” never mean exactly the same thing everywhere.
What sets us apart from firms now riding the AI wave is that we do not start with the AI, but with the underlying data. We have investigated what makes AI analytics work or fail in practice, we experiment with it internally, and we apply it in real-world settings. No promises about what might be possible someday, but an honest picture of where you stand now and what would make a next step realistic.
In a short call, we will determine whether an assessment makes sense and what you can expect to gain from it.
That depends on the size and complexity of your data landscape. It usually involves a few days to a little over a week of work: deliberately limited in scope, not a months-long engagement. In a brief conversation, we define the scope together so you know what to expect in advance.
You will only know once you understand where you stand, and that is exactly what the assessment tells you. You do not need to be “AI-ready” to get started. If your data is not ready yet, that is not a rejection but a starting point: you will know what needs to happen first, which is more valuable than discovering afterward that an AI initiative has stalled because it was built on a shaky foundation.
To a large extent, it is indeed about getting your data management in order. And that is exactly why it is worthwhile: the same work that prepares your data for AI also makes your regular reporting more reliable. So you are not investing in a bet on AI, but in a foundation that provides value regardless—whether or not you add an AI agent.
That is precisely when it makes sense. The assessment is not intended to determine whether you get a passing grade, but to clarify what is needed. The messier the situation is now, the more you will gain from an honest review: afterward, you will know exactly where the biggest problems are and where to start.
Yes. The foundation that makes AI possible is the same foundation that makes your dashboards reliable: clear definitions, documented models and clear ownership. The assessment examines exactly those areas. Whether you want to add an AI agent or simply be able to trust your figures, the foundation is the same.
This assessment is a specific form of strategic advice focused on whether your data is ready for AI. If the assessment shows that work on the foundation is needed, that becomes consulting: building and improving your data foundation. And as always, if you only need the advice and can take it from there yourself, that is fine too.
No, that depends on which solution is the right fit. Data security and compliance are among the considerations that determine the best direction: some AI solutions run in the cloud, while others keep your data closer to home. We include this in the assessment so you know not only whether your data is ready for AI, but also which route fits your organization’s security and governance requirements.
In two ways. First, we do not start with the AI, but with the underlying data, because that is where things go wrong in practice. This keeps the first step small and concrete: a clearly scoped assessment rather than a large engagement with an uncertain outcome. Second, the people doing the work are involved from the start. The consultant handling the engagement defines the scope together with you. This prevents knowledge from being lost in a handoff from a sales team to a delivery team that does not know your situation.
Whether you have concrete AI plans, are unsure whether it makes sense yet or simply want to know where you stand, we are happy to think it through with you. Email us, call us or schedule a meeting directly for a time that works for you.