Long-lived traditional models, protected by tight regulations and anchored in predictability are becoming outdated. New, more complex models are emerging, driven by real-time prices, smart technologies, decentralization and digitization.
Yet, the adoption rate of AI in the utilities sector remains low, largely because of the challenges that come with such digital transformations.
Few are those who understand the nuances of bringing AI technologies to the industry, and 40% of the current workforce will retire in 5-10 years. Replacing these individuals will be challenging. Automated support from ML solutions will reduce reliance on experience to make decisions and facilitate knowledge transfer.
Many approaches are based on old technologies, unsuitable for new analytics techniques. To adopt new technologies, companies need clear business values and propositions backing each initiative. Risks will have to be identified and mitigated.
Most utility players are locally or regionally focused and do not have the scale and scope to generate the necessary data, for example, about transformers. Open data could be incredibly important in accelerating AI. Furthermore, sharing and leveraging resources with external collaborations could help speed things up.
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AI might be new and partially unexplored, but it is also practical, realistic, and ready to implement. If you wait for your competitors to adopt, you will be stuck playing catch-up forever.
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Problem: Extensive paper archive of contracts to be sorted and reviewed. Manual analysis or basic automation tools were not a viable option given the vast amounts of documents and the thousands of different writing styles.
Solution: A tool that understood and sorted data in two stages - information extraction (dates, signatures) followed by classification (contract type, current legal validity).
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