63% of Energy CEOs surveyed expect to realize value from generative AI and automation
At Distributech 2024, IBM unveiled additional findings from its global study findings that show that 74% of Energy & Utility companies surveyed have implemented or are exploring using AI in their operations.
Within the sector, among surveyed IT Professionals, 33% are focusing AI projects on HR/Talent Acquisition and 27% are focused on AI Monitoring & Governance. The findings are from IBM’s Global AI Adoption Index 2023 global study* administered by Morning Consult and commissioned by IBM, based on interviews with 2,342 IT Professionals at enterprises located across 20 countries.
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This new data echoes insights from the IBM Institute for Business Value’s 2023 study, ‘CEO decision-making in the age of AI,’ which also interviewed 420 Energy & Resources CEOs thinking around AI. The fresh analysis of the Industry C-suite data shows that:
- 63% Energy & Resources CEOs surveyed are more likely than their global peers to expect to realize value in the next three years from generative AI and automation
- 61% of CEOs surveyed express concerns about the sources of data used in generative AI
When considering the impact of transformational technologies, Energy Industry CEOs surveyed appear to place Generative AI first in terms of expected value. To help the industry chart a path forward to realize the value of generative AI, at Distributech 2024, the leading annual transmission and distribution event, IBM at booth#2743 today showcased watsonx – its enterprise ready AI and data platform.
Importantly, to help, Energy & Resources companies to navigate data-related challenges, including unclear data calculation and, critically, a lack of insights, IBM is highlighting its watsonx.governance toolkit for AI governance which allows a Utility company to direct, manage and monitor its AI . It employs software automation to strengthen a company’s ability to mitigate risks, manage regulatory requirements and address ethical concerns for both generative AI and machine learning models. While not all models are created equal, every model should have governance to drive responsible and ethical decision-making throughout the business.
Casey Werth, Global Energy Industry General Manager IBM said, “Energy & Utility CEOs have moved beyond experimentation with AI to focusing on where they can drive the most business value with AI. As they manage ongoing transformation efforts they can also capitalize on the great opportunities of generative AI and foundation models. In doing so they need to remember to focus on their own data, how it is gathered, accessed and used within their workflows along with the governance that should be baked into their tools and processes.”
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A good example of how Utility companies can tap large language models is to augment their internal compliance process. For a Utility company this can allow them to automate and align internal compliance processes to their specific business needs. For example, a utility can:
- Provide a single repository of o********* management to classify complex requirements.
- Enable their organization to govern their environmental, social and governance (ESG) programs and sustainability.
- Track provenance and document model performance against key performance indicators determined by the business.
- Provide visibility to key stakeholders through dynamic, user-based dashboards, charts and dimensional reporting.
At #DISTRIBUTECH24, IBM has several executive speaking sessions designed to help show how the industry can digitally transform at pace. Topics include; Quantum in the Energy Sector, Digitally Managing Emergency Response & Mobilization, Digital Twin for advanced and predictive Load Management, Preparing the Grid for tomorrow, Applying weather & climate risk analytics to create more sustainable and resilient grids. IBM’s Energy CTO Bryan Sacks, will also lead an industry roundtable focused on AI for Grid Management discussing how companies in the energy and utilities industry are thinking about AI, use cases, policy and governance.
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