Douglas Leal

Douglas Leal

Vice-President, Consulting - Data and analytics

The utility industry is facing a critical point where it must embrace the vast potential of artificial intelligence (AI) to transform energy production and distribution while addressing significant concerns related to regulations, data security, and ethical considerations. This juncture requires careful guidance from utility leaders to navigate the AI challenges and opportunities ahead.

How does one harness the transformative power of AI while safeguarding an ethical, responsible, and sustainable future?

Amidst this balancing act, utilities leaders grapple with legitimate AI implementation concerns. The unwavering commitment to data privacy remains at the forefront, requiring a pragmatic approach to safeguard sensitive customer information in alignment with stringent government regulations. Creating a robust defense against AI model bias is crucial to averting risks in fair pricing, resource allocation, and service delivery. Overlooking ethical considerations, on the other hand, can conceal pitfalls, erode trust, and intensify societal inequalities.

Enter the indispensable player in this narrative: AI governance. AI governance embodies principles that establish policies and ethical guidelines to regulate the development, deployment, and management of a utility’s AI solutions, ensuring responsible and accountable use. AI governance plays a crucial role in implementing responsible solutions. It's not just about mitigating risks; it's about shaping a future in which AI benefits organizations responsibly and ethically. Yet, beneath the surface lie challenges that, if unaddressed, could hinder AI’s effective rollout. Tackling these challenges head-on becomes essential to unleash the benefits of responsible AI use in the utility sector.

Strategic implementation of AI enables successful governance

The success of an AI governance strategy hinges on transparency in the end AI solution through seamless integration with the organization's operational framework. This framework offers a comprehensive perspective on AI-related business and technological activities.

Three key factors to consider when rolling out an AI data governance strategy include:

  1. Regulatory compliance for utility data governance

    The utility sector is complex and heavily regulated, and compliance poses a significant challenge to operations. Navigating the ever-shifting regulatory landscape around AI for utilities is a top concern for industry leaders who must ensure systems adhere to current data governance, cybersecurity, and fair business practices.

    The regulatory framework is continuously evolving. In the U.S., for example, a notable addition is the White House Blueprint for an AI Bill of Rights, encompassing five principles and their corresponding practices. This blueprint serves as a comprehensive guide for shaping the design, use, and implementation of automated systems, with the overarching goal of safeguarding the rights of the American public in the era of AI.

    Proactive engagement is equally crucial. Participating in discussions with regulators, collaborating with industry peers, and adopting best practices become indispensable strategies. These efforts ensure compliance with existing regulations and a readiness to adhere to new government guidelines as they emerge. Embracing these principles enables utility leaders to navigate the regulatory landscape while fostering a future in which AI is used responsibly and ethically in the service of the public.

  2. Data security and privacy

    At the heart of AI's engine lies data—the driving force behind the transformative potential for the utility sector. Companies amass vast amounts of customer data, including consumption patterns, billing history, and smart meter readings. Emerging AI solutions also expand the scope of data to include multiple media types (documents, text narrative, images, videos), which require data management to ensure ethical use and data protection. Yet, they operate within the confines of a strict regulatory landscape defined by data privacy regulations such as the EU’s General Data Protection Regulation (GDPR). Furthermore, the North American Electric Reliability Corporation Critical Infrastructure Protection (NERC CIP) provides a structured framework of compliance standards aimed at enhancing cybersecurity measures for utilities operating within the interconnected jurisdictions of the Bulk Electric System (BES) in North America.

    This juxtaposition creates a unique challenge: a delicate scenario where the power of AI must be harnessed while safeguarding the very data that makes it work. It's more than a checkbox on a completion list; it signifies a cultural shift demanding constant vigilance, ongoing monitoring, and proactive investment. It's about building in safeguards that behave as agile as AI solutions, constantly adapting to evolving threats and new regulations.

  3. Ethical considerations for utility AI

    Regarding AI, the potential is vast and diverse for utilities, from grid optimization to predictive maintenance, smart grids, and customer empowerment. Amid the excitement, organizations must remain vigilant and recognize the hidden risk: ethical assessment. Failing to address ethical considerations in AI implementations can amplify pre-existing biases, such as the potential for discriminatory pricing and inequitable resource allocation..

    Imagine the ramifications: an AI-driven solution determining rate adjustment pricing based on historical data with socioeconomic biases, triggering public backlash and regulatory scrutiny. Transparency is paramount, and AI systems must be built with full model explainability in mind, which is a concept where machine learning models and their output can be explained in a manner that is understandable to a human being at an acceptable level. All organizational stakeholders must understand algorithms' decision-making processes, actively identifying and mitigating potential biases before they become ingrained in the solution. This process requires regular audits, diverse testing datasets, and clear communication frameworks to explain AI decisions to consumers.

    However, transparency alone is not enough. Organizations must establish robust accountability mechanisms, assigning clear responsibility for developing, deploying, and monitoring trustworthy and reliable AI systems for utilities. Holding individuals and organizations accountable for biased outcomes is a powerful tool for encouraging responsible AI development. Furthermore, continuous monitoring and feedback loops are critical to detecting and addressing emerging biases throughout the AI life cycle. Striking this delicate balance between innovation and ethics is fundamental for AI's responsible and equitable integration in the utilities sector.

What lies ahead for AI in the utilities industry?

While the challenges are real, the rewards for navigating the AI governance and regulatory landscape are significant. AI-powered grids can optimize energy flow, predict equipment failures, and seamlessly integrate renewable resources. In parallel, AI-driven water management systems are pivotal in reducing leaks, minimizing waste, and ensuring equitable distribution. These opportunities are too significant to ignore. As utilities navigate this AI revolution, the journey shows the promise of innovation and a sustainable and equitable future for energy delivery.

Embrace the transition to AI with confidence

By incorporating the concept of "humans in the loop," utilities can define the principles, governance structures, and operational models for ensuring trusted outcomes, contributing positively to their internal operations and the broader societal landscape. Leveraging AI as a support for decision-makers is a key to ensuring greater efficiencies and a clear return on investment while ensuring safe solutions with human oversight as part of the process.

As utilities prepare and adapt to the growing tide of AI adoption and implementation, working with a trusted partner to help shape their AI journey, experiment with tangible use cases, establish future-ready and adaptive foundations, and scale up to accelerate value while operating responsibly is invaluable.

Utilities and their technology partners should collaborate to develop a comprehensive, ethical AI strategy and framework that navigates AI’s complexity and uncertainty. By harnessing AI’s potential responsibly, utility leaders can drive value from an approach that includes:

  • An adaptable AI strategy that aligns with business goals
  • Value-based strategic intent for the use AI, plus monitoring the AI benefits as part of operations to ensure the solution is meeting its intended purpose
  • Best practices for balancing ambition and practicality
  • Proven methods that integrate scientific rigor into AI solutions

At CGI, we are dedicated to advancing the responsible use of AI. Our approach extends beyond technology—we prioritize the human element, ensuring that AI is a force for positive change. By employing a proven risk framework and helping clients upskill their workforce, we contribute to the responsible application of AI for real-world benefits.

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About this author

Douglas Leal

Douglas Leal

Vice-President, Consulting - Data and analytics

With over two decades of expertise, Doug is a seasoned technology leader successfully executing critical data-centric solutions, big data initiatives, analytics and data engineering strategies.