Leveraging AI to Transform the Utility Mindset

The need for an overhaul

Increasing free power generated from renewables. Falling energy storage prices. More involved and collaborative regulatory approval constructs. Among others, these trends theoretically serve to decrease prices for and protect ratepayers, yet utilities suggest that this resulting headroom facilitates price increases. The Energy Information Administration predicts 2.4 to 3.6 percent CAGR in nominal dollars for electric prices from 2017 to 2050, representing a doubling of today’s electricity prices.¹

What’s driving this? Billions of dollars of capital investments geared towards modernization and security.² Considering utilities’ track records of average operations & maintenance (O&M) increases of about one percent for every dollar invested³, customers are going to continue seeing increases in energy prices. If we consider Hawaiian Electric’s situation as a unique case study into a future with high levels of renewables and energy storage adoption, we see declining electric revenue, decreasing net income and decreasing ROE at the utility. While decoupling is a convenient reason to maintain relevancy, utilities need to drastically overhaul their mindset and operating model for agility when transforming the core and pivoting to the new. By doing this, utilities can create a step change in efficiency to not only drive value to the ratepayer while increasing their relevance in the future.

Using AI for value and growth

Accenture research indicates companies on average in the United States can use AI to do the same with 35 percent less resources.⁴ Further, utilities can drive 2x growth with AI over 10 years. Embracing key value levers enabled by AI, such as intelligent automation, intelligent products and enhanced trust offers an opportunity to drive an average 30-50 percent increase in enterprise profitability over the same period⁵—translating into real customer value. Accenture forecasts $6.9B to $17.3B in value from transforming core utilities businesses to growing and scaling the new (Figure 1), and the use of AI in these initiatives can drive five-year transformation net profitability increases of $800M to $2.5B⁶. Utilities should focus on leveraging AI to increase the accuracy of work, reduce the labor intensity of work and perform new analyses enabled by the ability to process and connect complex datasets. Finally, automation is key to drive agility and value. Through these opportunities, utilities can enable meaningful change in not only cost competitiveness but more importantly operational prowess, which can enhance the company’s competitive position in the industry.

Key opportunities for utilities – Valued at $6.98 - $17.38

Figure 1.

VIEW ENLARGED CHART

Utilities need to drastically overhaul their mindset and operating model for agility when transforming the core and pivoting to the new.

How can companies leverage AI to pivot to new ways of doing business? There are four key steps:

Set the AI strategy, leadership and governance: An AI roadmap is essential. The roadmap should be a plan to grow the business, incorporating AI as a critical enabler. As such, it is incumbent on leaders and strategic planners from across the business to have a good grasp of AI to transform existing business plans, define key decision points and guide appropriate investment decisions. AI teams should be integrated into strategic planning, program design and process excellence groups for iterative success.
Create an open AI culture: Corporate culture must adapt to the presence of its new AI employees. Humans and machines will be collaborating, teaching and learning from one another. This demands trust, openness and transparency, just as any co-working relationship. Utilities leaders have a responsibility to explain the risks and opportunities that a hybrid workforce brings. But they can also shape the culture and guidelines that minimize those risks and maximize the opportunities.
Prove simple, pilot big: Reduce risk by quickly validating and yielding identified business value on simple projects; also see what the dynamics of the team / organization is like after successfully implementing AI. Based on those lessons learned, optimize the more complex, larger-scale programs that have a greater return to increase the probability of success. Key lessons should drive understanding in areas including technology infrastructure needs, implementation costs and effort, organizational design, and talent implications.
Iterate, solidify and scale: Continue applying lessons learned towards advancing strategy and culture, and achieving goals. Consider how these capabilities can efficiently and sustainably be established as a standard. Further, apply concepts externally and evaluate partnerships between providers and customers to examine how to extract value out of ecosystems.