AI promises a lot, but only if your data is in order

The truth is simple: AI works as well as the data it’s built on. If reporting is slow, fragmented, or unreliable, AI won’t fix it. It will only amplify the problem. That’s why efficient data and reporting are the essential foundation for any AI-ready organisation.

The hype vs. reality of AI in business

The hype around AI creates urgency. Leaders see competitors experimenting with predictive models or generative tools and fear being left behind.

But the reality inside many organisations looks different: data scattered across systems, financial reports built manually in Excel, and KPIs that differ depending on who you ask.

When AI is added on top of this chaos, the results are disappointing. A CFO might launch an AI forecasting project, only to discover that inconsistent data makes the model unreliable. An HR leader might try to apply machine learning to workforce analytics, but poor-quality reporting turns any possible insight into guesswork.

Read more: Use HR Data to Make Better Decisions for Your Workforce

AI doesn’t solve messy reporting. It magnifies it.

Trash in, trash out – this is why inefficient data blocks AI adoption

Before AI can deliver value, the basics need to work. When your data is messy and your reporting is in shambles, it creates invisible barriers that no algorithm can overcome:

• Fragmentation: Data lives in multiple systems, from ERP and CRM to payroll and spreadsheets. Data consolidation is manual and error-prone.
• Manual reporting: Teams spend more time cleaning and combining data than analysing it.
• Lack of trust: Leadership doubts dashboards because KPI definitions vary or reports arrive late and incomplete.

These challenges make proper AI adoption almost impossible. An AI model trained on inconsistent or incomplete data produces unreliable results. The cost of bad decisions grows faster than the promised efficiencies.

Efficient reporting, on the other hand, makes AI’s hype concrete. With clean, trusted data flowing seamlessly across systems, AI will elevate itself from being just a fun gadget to a real business value creating powerhouse.

What does this mean in practice?

Efficient data and reporting aren’t just abstract ideals from the most beautiful daydreams. When the foundations are solid, concrete business outcomes are easier to reach. A few examples of this:

• Process mining reveals hidden inefficiencies
  When A. Ahlström applied process mining to their finance operations, they discovered bottlenecks and unnecessary manual steps that had been invisible in traditional reporting. By streamlining these processes, the company saved time and freed up resources, making their reporting not only faster, but also ready for automation and AI-driven insights.
• Automated consolidation enables real-time forecasting
  CapMan, a leading private equity company, struggled with time-consuming manual reporting across funds. By automating consolidation and reporting, they cut hundreds of hours of manual work. The improved data flows also paved the way for predictive analytics, where AI can support fund-level forecasting with confidence.
• Interim experts build readiness where it’s missing
  Not every organisation has in-house capabilities to clean data, build BI pipelines, or define KPIs consistently. That’s where interim services come in: an interim CIO or data analyst can quickly build a reporting framework, introduce tools like Power BI or BI Book, and ensure the organisation has a scalable foundation for AI.

Why and how to build the foundation for AI?

For CEOs, CFOs, and COOs, the question isn’t whether AI will change business - it already has. The real question is whether your organisation is prepared to use it effectively.

Without a solid data foundation, AI will waste resources instead of saving them, deliver inconsistent results that undermine trust and delay, rather than accelerate, decision-making.

Read more: Practical Use Cases for Process Mining

AI can become a multiplier of value, able to forecast scenarios, optimise resources, and even suggest process improvements.

This doesn’t happen by accident, though. Every AI journey starts with the same three steps:

1. Audit your data reality
   Where does your data live? How much time does reporting currently take? Where are the bottlenecks? Process mining is one of the fastest ways to get this picture.
2. Establish consistency and trust
   Define KPIs, automate consolidation, and ensure leaders can access real-time dashboards. Tools like BI Book make this step practical and accessible.
3. Scale with the right expertise
   If your organisation lacks the capabilities to set up reporting properly, consider interim data or analytics roles. It’s faster and safer than learning through costly mistakes.

AI isn’t magic. It won’t clean your data, fix broken processes, or build trust in reporting. But if those foundations are in place, AI can be transformative. Efficient data and reporting turn AI from a gamble into a growth engine.