Implementing AI Starts with Data – What Every Company Needs to Understand

I've spent the past two decades working with data. I vividly remember the times when we talked about “advanced analytics” – long before AI became part of our everyday business language. Today, artificial intelligence (AI) is at the core of business strategy, but its true potential only becomes real under one condition: the data must be managed well enough.

In this article, I’ll explain why AI doesn’t work without well-managed data – and how companies can take their first steps toward impactful AI adoption.

AI doesn’t function on its own. It always relies on the data that’s available for it. If the data is flawed, biased, or poorly structured, the outcomes AI produces will reflect those issues.

I often hear statements like, “We’re not ready for AI because our data isn’t perfect.” The good news is – perfect data doesn’t exist.

The real question is: where is the data good enough to start, and where can it deliver the most business value?

One of the most important lessons I’ve learned over the years is this: smart data management is not about a single tool or technical solution. I frequently encounter the assumption that choosing the "right" tool will fix data management. Unfortunately, it’s not that simple.

Smart data management only works when technology, people, and processes are aligned. Tools do matter, but without clear roles, ownership, and shared practices, they often remain underutilized – or even unused.

Each organization is different. Architecture, business needs, and data maturity vary. That’s why there is no one-size-fits-all solution. You need to find the combination of technology, processes, and operating models that creates value in your business context.

Success also requires business engagement. Data management is not just responsibility of IT – it demands collaboration, shared ownership, and above all, a culture that treats data as a strategic asset, not just a technical resource.

I’ve seen firsthand how organizations with solid data management move to AI adoption with much more speed and confidence.

Yes – and it’s one of the most tangible, high-impact uses of AI today. Data management has traditionally been a manual effort: building data pipelines, completing metadata, spotting errors, cleaning duplicates, documenting field definitions. Now, AI can automate much of this work.

For example:

Generating metadata automatically:

AI can recognize the meaning of data fields and suggest descriptive definitions or value ranges – even without human input.

Assessing and correcting data quality:

AI can identify anomalies, missing values, or inconsistent formats. These can be automatically corrected or flagged for review.

Supporting data engineers:

Repetitive tasks like data transformation, basic integrations, or cleanup can be shifted to AI-powered tools – freeing up experts for more valuable work.

But it’s important to understand this: AI does not replace data management – it requires it.

AI can assist, accelerate, and improve quality – but only if built on a well-organized, responsible, and goal-driven data foundation. Without that, AI is working in the dark – or worse, reinforcing existing errors and biases.

I’ve been involved in multiple projects where AI helped detect and fix data quality issues. In one Finnish organization, we analyzed the completeness and consistency of customer data. AI helped us quickly identify fields missing critical values like customer IDs or contact information, and detect patterns that deviated from expected formats.

We used a machine learning model to learn what "normal" looks like and suggest corrections for outliers. In addition, metadata was enriched automatically: fields that had never been documented were now contextually described based on AI-driven insights.

As a result, the organization was able to:

• Improve customer segmentation
• Sharpen campaign targeting
• Reduce manual data cleanup significantly

In this case, AI didn’t just use the data – it made the data better and more valuable for the business.

AI makes bad decisions. A classic example is an AI-powered recruitment tool that starts favoring only certain candidate profiles – simply because the training data, i.e. past hiring history was biased. If the data includes mostly CVs from one gender, AI will “learn” to prioritize those, because it models the past.

Bias isn’t the AI’s fault – it’s a reflection of the data. If the input is not diverse, the model narrows, and AI begins to repeat human errors – but now at larger scale and with automation.

Organizations that take data management seriously are truly one step ahead. When your data is well-managed and reasonably high-quality, adopting AI becomes faster, safer, and more impactful.

Instead of spending time fixing data or questioning model reliability, you can focus on realizing business value.

This doesn’t mean everything must be perfect. But the more you have clear roles, structured processes, sound architecture, and quality assurance in place, the more quickly and reliably AI can generate results. Good data management isn’t just support for AI – it’s a strategic edge.

A common question is: How should we start with AI and data management? The answer isn’t always simple – and that’s okay. Every organization starts from a different place. Some begin from scratch, others are improving what they already have.

At adesso, we help you right from the start to assess where to begin.

Together, we’ll go through:

• What does your current data landscape look like?
• Where is the data already good enough?
• Where can AI bring the first measurable business value?

From there, we can identify a suitable use case, plan the roadmap, and build the right foundations: processes, roles, and technologies.

Finnish companies are now at a pivotal moment. AI is no longer the future – it’s already here. But to truly deliver value, it needs the right foundation: well-managed, high-quality data.

If this resonates and you’re wondering how to get started, we at adesso are here to help you right where you are – no out-of-the-box answers, just a practical path built together toward meaningful, impactful AI.