Unlocking data strategy success: Practical AI, robust governance and agile management

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In the latest episode of our Energy Transition Talks series, CGI Vice-President, Consulting – Data and Analytics Doug Leal discusses with Peter Warren the evolving landscape of data use in the energy and utilities sector, particularly in light of new AI applications. In the first instalment of this two-part conversation, they explore the challenges of scaling AI models, the move away from experimentation toward practical solutions and two key approaches to data management: the Data Lakehouse and the Data Mesh—both of which are shaping the future of data strategies’ success.

Utility organizations are facing increasing pressure to leverage data effectively for decision-making. This involves the integration of various data sources, such as Advanced Metering Infrastructure (AMI) and outage management systems, to enhance operational insights. While some organizations are already progressing in this area, Doug says, many are still in the early stages of their data journey.

Doug and Peter discuss two distinct approaches to AI: one that treats it as a novel tool to explore, and another that focuses on practical problem-solving. The latter, Doug says, is essential for developing a strategic approach to AI implementation, ensuring that solutions are not only effective for immediate challenges but also adaptable for future developments

“We need to be able to build a model or any type of AI solution in a way that will enable the organization to scale—not only scale that model for production, but also for everything that comes after that model, the innovation that comes after that model.”

The challenge of transitioning from proof of concept (POC) to production

Typically, a business unit recognizes the potential of a technology or model and decides to invest further. However, without a well-defined operational process to transition from proof of concept (POC) or proof of value to full production, this can create significant challenges and bottlenecks.

As Doug shares, only 53% of models successfully progress from POC to production, making it an expensive endeavor when roughly half fail to deliver results.

Shifting focus to Minimal Viable Products (MVPs) and practicality

Peter agrees, citing a current client’s approach that skips the POC entirely, jumping ahead to develop minimal viable products (MVPs) right away. He explains their strategy involves creating solutions that are aligned with their organizational goals and can be effectively scaled. This ensures that the IT team can support the growth of these products and that the business can derive tangible value from them.

Doug has also noticed a shift in mindset among clients. As he sees it, there’s a growing emphasis on how to effectively transition ideas into production rather than just experimenting, reflecting an increased understanding of the importance of assessing the real value and return on investment for these initiatives. Given the substantial costs associated with infrastructure, data scientists and machine learning engineers required for model development, organizations are increasingly cautious about treating these efforts as mere experiments.

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