AI in energy starts and ends with data
AI starts and ends with data: Key steps to move your AI strategy forward
Market insights
January 29, 2026
7 min read
AI implementation is a journey — not a just switch, you turn on overnight. Preparing your organization for AI starts with understanding that journey and, not least, understanding the importance of your data in this regard.
With help from Senior Software Engineer, Kevin Krämer, this blog post explores some of the common misconceptions about AI, where to begin if AI is on your company agenda, and which considerations and approaches can help your AI strategy succeed.
The 3 C’s of AI success – Clean, consistent, complete (data)
AI is already shaping everyday operations across the energy industry. Service platforms use it to guide technicians by suggesting the most efficient steps when handling support cases, helping teams respond faster in the field. Asset management systems use AI to detect early warning signs in turbine performance, such as unusual vibrations or declining output, so operators can plan service before a shutdown. And HR teams experiment with AI to identify the most suitable candidates.
But have we come as far as we had hoped? Most likely not.
And the reason companies are hindered from pursuing their AI adventure is not a lack of capabilities. It is data. Not the lack of data, because companies tend to have inexhaustible quantities of data. No, it is about the quality of data and the fact that the quality of data determines the make-or-break of your AI strategy.
“The quality of your data defines your AI success. Poor, inconsistent, or biased data limits the reliability of AI outcomes. I have seen it many times; even organizations that see themselves as “data-driven' are surprised to uncover gaps such as duplicated records, siloed information, low-trust data sources, or incompatible formats between IT and OT environments,” says Kevin Krämer, Senior Software Engineer at Opoura.
When organizations embed AI into targeted areas, such as ticket intake, classification, knowledge retrieval, external data correlation, or predictive maintenance, they open the door to faster response times, higher data quality, and better decision-making across both technical and business domains. But many organizations move too quickly toward AI without understanding the groundwork that is required. As readiness frameworks highlight, AI is only as reliable as the data underpinning it. In practice, AI tends to amplify existing data inconsistencies rather than compensate for them. Minor variations, missing fields, or implicit assumptions that humans handle intuitively can lead to disproportionate effects when introduced into AI-driven processes.
And this is a wide-ranging challenge. Across industries, many companies have come to discover that the quality, accessibility, and governance of the data feeding their AI models are, in fact, the biggest barrier. Without clean, consistent, complete data, even the most advanced AI solutions produce inconsistent outputs.
When data is complete and organized, however, AI can uncover meaningful insights that support better planning, faster execution, and stronger collaboration. By investing early in data quality, companies position themselves to use AI responsibly and confidently as it becomes a core driver of future business performance. Read more about the importance of data in energy – download our Playbook.
“A strong foundation of structured data and standardized processes ensures long-term impact and avoids wasted investment,” says Kevin Krämer.
AI is many things – magic is not one of them
To set expectations correctly and thereby help ensure success, it may be a good idea to start by clarifying what AI can and cannot deliver, as there exist many unrealistic ideas about what AI can accomplish.
One of the most common misconceptions about AI is that it can fix operational issues, almost like magic. Of course, this is not the case. AI does not automatically clean your data, standardize your processes, or optimize your workflows. It can, but it needs to be fed clean data and the right information to do so,” says Kevin Krämer.
In other words, preparation is key. Otherwise, AI can become unreliable, a waste of time and money, and a burden to maintain.
This is why fostering a strong culture and addressing governance early are essential: by embedding a culture of accountability, ownership, and responsibility, and by establishing clear policies, roles, and processes from the start, organizations can keep AI efforts on solid ground. When governance and culture are neglected, data quality issues, unclear ownership, and privacy risks grow alongside the technology, creating costly rework and slowing the ability to scale. Strong governance doesn’t slow innovation but enables it by creating a trustworthy foundation that supports safe, sustainable growth in AI capabilities.
Key steps to move your AI strategy forward
To realize your AI aspirations, Kevin Krämer suggests six steps to consider, which can make a launch across your organization smoother.
1
Audit data readiness
Asses completeness, duplication, bias, and documentation of foundational criteria.
2
Define high-value use cases
Choose tasks where improved speed, quality, or visibility generates measurable benefits.
3
Strengthen governance
Build structures for ownership, privacy, risk, and responsible AI.
4
Prepare integration and infrastructure
Ensure that data flows reliably between systems, especially between IT and OT.
5
Invest in people and process
Ensure user trust, including adoption through training, clear communication,and change management.
6
Scale gradually
Start with narrow improvements, measure results, and expand as confidence grows.
In summary, if AI is a top priority, move forward with AI by focusing on readiness. Successful AI adoption depends less on advanced algorithms and more on strong data foundations, realistic expectations, and disciplined execution. Clean, consistent, and complete data is the cornerstone of reliable AI outcomes, while clear governance and a shared sense of ownership help keep initiatives on track. By starting with high-value use cases, investing in people and processes, and scaling step by step, organizations can turn AI into a practical tool that supports better decisions, stronger collaboration, and long-term business value.
