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Is Your Data Ready for AI?
- Adsotech
- 5 hours ago
- 5 min read
Why “AI-Ready Data” Matters More Than Ever
Espoo, Finland - 18.11.2025 Is your data ready for AI? Many organizations today are moving beyond testing and are now using artificial intelligence in their everyday operations. From process automation to analytics, AI is changing how businesses work and compete. But according to Gartner, only 4% of organizations say their data is truly ready for AI.
As Precisely’s Trusted AI 101 and Cutting Through the Chaos: The Case for Comprehensive AI Governance e-books explain, AI success does not start with algorithms — it starts with data.
In our work with Nordic enterprises, we see this every day. Many companies want to modernize their SAP systems, automate workflows, and explore AI, but their data isn’t ready. Inconsistent, incomplete, or siloed SAP data can delay projects, cause automation errors, and make AI results unreliable. To build trustworthy AI, the foundation must first be trusted data.
To be ready for AI, organizations must start with trusted data. Reliable, consistent, and well-governed SAP data ensures that AI delivers accurate, compliant, and valuable results. Without data integrity and governance, even advanced AI tools can produce unreliable or biased outcomes.
Why Data Integrity Still Comes First
AI can only be as accurate as the data it learns from. The Trusted AI 101 e-book highlights a simple truth: garbage in, garbage out. When data is incomplete or incorrect, even the best AI systems produce flawed or biased results.
Data integrity means that your data is:
Accurate – It correctly reflects what happens in the real world.
Consistent – It is harmonized across all systems and departments.
Contextual – It contains the background information needed to make sense of it.
For companies using SAP, keeping these qualities in check can be difficult. Over time, material masters, vendor records, and hierarchies often become duplicated, fragmented, or outdated. These issues can disrupt reporting, planning, and automation.
Improving data integrity helps organizations avoid costly errors, speed up S/4HANA migrations, and create a reliable foundation for AI and analytics.
AI readiness begins with data readiness. Before automation or AI can work effectively, the data that feeds them must be accurate, consistent, and governed properly.
Beyond Clean Data: The Governance Imperative
The Cutting Through the Chaos e-book introduces another key requirement — governance. As AI becomes part of everyday business, companies must manage not only data quality but also transparency, accountability, and compliance.
Without a central governance framework, both data and AI models can become scattered across teams and systems. This “sprawl” leads to confusion, duplication, and risk. No one knows exactly which data or model version is being used, who owns it, or how it is being applied.
Good governance solves this by creating visibility and control. In SAP environments, this means having clear rules for data ownership, validation, and change control. When governance is part of daily work, errors are prevented before they spread — and organizations have a full audit trail to meet compliance requirements.
This is especially important now that the EU AI Act is introducing new standards for transparency, accountability, and human oversight across AI systems in Europe.
Data governance is the first layer of AI governance. Trusted AI cannot exist without governed, traceable, and well-understood data.
The Nordic Experience: Common Challenges
Across the Nordics, many enterprises face similar data challenges as they prepare for digital transformation.
Manufacturing companies often deal with inconsistent material master data and complex hierarchies that disrupt automated workflows.
Retail and consumer goods organizations manage fragmented product and customer data across different systems, leading to unreliable reports and missed opportunities.
Energy and utilities providers struggle with siloed maintenance data that reduces the accuracy of predictive models.
When SAP master data lacks integrity, the results are predictable: failed S/4HANA migrations, automation breakdowns, and stalled AI projects. These challenges are not caused by technology but by poor data management — and solving them requires a new way of working and sharing responsibility.
Empowering Business Users to Take Ownership
The fastest way to improve data is to give control to the people who know it best — the business users. This idea, known as citizen development, allows teams to safely update, validate, and improve data without always depending on IT.
When business users can manage their data quality directly, they become active owners of data integrity. This speeds up work and ensures that data is always relevant and accurate.
Tools like Precisely Automate make this possible. They allow users to perform mass updates, create workflows, and validate information directly in SAP — with built-in controls and full auditability. The result is faster, cleaner, and more reliable data that supports every transformation goal.
Empowered business users create better data — and better data powers better AI.
From Data Integrity to Trusted AI: A Practical Path Forward
Becoming AI-ready is not one single project — it’s a continuous process that starts with solid data and grows through strong governance. Based on the ideas from Precisely’s Trusted AI 101 and Cutting Through the Chaos e-books, here is what this looks like in practice for SAP-driven organizations:
1. Integrate your data sources
Many companies still operate with disconnected systems — ERP on-premise, analytics in the cloud, and spreadsheets everywhere in between. Connecting SAP and non-SAP data into one trusted environment gives a complete view of business operations.
2. Clean and standardize information
Duplicates, outdated records, and inconsistent names are common issues. Automating these corrections directly in SAP saves time and prevents data errors from spreading across the business.
3. Govern data where it lives
Effective governance isn’t about new policies — it’s about making governance part of daily work. When users can make approved changes with audit trails automatically recorded, compliance becomes effortless.
4. Add context and keep improving
Finally, enrich your SAP data with business, geographic, or sustainability insights. Context turns raw data into meaningful intelligence, and continuous monitoring ensures it remains reliable over time.
These steps represent data integrity in action — practical ways companies across the Nordics are already succeeding with Adsotech’s expertise and solutions powered by Precisely.
Conclusion: Trusted AI Starts with Trusted Data
AI is no longer experimental — it’s a core part of how modern businesses compete. But no matter how powerful AI becomes, it will only work if the data behind it is clean, consistent, and well-managed.
By improving data quality, empowering business users, and embedding governance in daily operations, organizations can confidently move forward with S/4HANA migrations, automation, and AI initiatives.
Because in the end, trusted AI starts with trusted data — and trusted data starts with the people who manage it every day.
Is your data ready for AI? Let’s find out together.
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