A new study – IBM’s Global AI Adoption Index 2023 – reveals that 74% of global Energy & Utility companies have implemented or are exploring using AI in their operations – with 33% focusing AI projects on HR/Talent Acquisition and 27% focused on AI Monitoring & Governance.
The new research is based on interviews with 2 342 IT Professionals at enterprises located across 20 countries and 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 realise 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 realise the value of generative AI, IBM showcased watsonx – its enterprise ready AI and data platform – at this week’s Distributech 2024.
Importantly, to help Energy & Resources companies to navigate data-related challenges including unclear data calculation and, critically, a lack of insights IBM is also 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.
“Energy & Utility CEOs have moved beyond experimentation with AI to focusing on where they can drive the most business value with AI,” says Casey Werth, Global Energy Industry GM at IBM. “As they manage ongoing transformation efforts they can also capitalise 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.”
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 obligation management to classify complex requirements.
* Enable their organisation 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.