The percentage of enterprises employing artificial intelligence (AI) grew 270 percent over the past four years, signaling a slow but sure halt to hesitation surrounding the early value of AI. In the 2019 Gartner CIO Survey, a quarter of respondents believe that AI and machine learning are game-changing technologies that are expected to be met with increased funding and continuing investment.
By 2022, Gartner predicts 25 percent of utilities globally will use artificial intelligence (AI)-augmented digital customer service agents to interact with customers’ virtual personal assistants (VPA) and home Internet of Things (IoT). This is just a first step in an AI-driven utility transformation wherein personal agents and multi-agent systems representing organizational, human and artificial actors will be applied to personal productivity and enterprise augmented intelligence capability.
"Democratizing AI means taking steps to ensure that everyone in the utility can access, embrace and derive the most value from it"
As AI continues to drive transformation in the utility industry in unprecedented ways, how can utility CIOs adequately prepare? The answer is to connect AI with business value at scale across the full range of business activities. Areas such as data processing, decision support, process automation, and IoT perception in addition to VPA conversational interaction are business activities of high potential value.
In Gartner’s work with CIOs who are leading utility digital transformation, we identify the three key success factors.
Focus on Specific High-Value AI Use Cases
Strong use cases help the utility accelerate toward operational AI. This allows CIOs to capitalize on the momentum garnered from early experimentation, quickly move toward delivering real AI business value, and develop further capability in existing business and technology areas. Further, starting with use cases puts a focus on human-machine interactions, the area where AI delivers the highest value.
Developing viable AI use cases gives utility CIOs a clear understanding of how early AI scale can be obtained by adding AI-powered features to existing products and services. This in turn sets the stage for AI to be systematically applied across the other business departments and functions in the enterprise to optimize redesigned processes, introduce new innovative business practices, products or services, and build the exponent of future technology capabilities.
Balance AI’s Technical Capabilities with Business Opportunities and Risks
Utility CIOs are being asked to not only assess technical suitability for AI solutions, but also to evaluate AI “fitness” with business context. To do so, they must think about strategy, business and technology capabilities, and business outcome expectations all at once.
Use the below “consider-embrace-experiment-avoid” model to organize and prioritize AI use cases and to contextualize these within the utilities’ overarching digital business plan.
Consider-Embrace-Experiment-Avoid Model for AI Use Case Prioritization
Set a strong foundation for democratizing AI
Democratizing AI means taking steps to ensure that everyone in the utility can access, embrace and derive the most value from it. Utility CIOs must set a strong foundation for democratizing AI by fostering AI development and scale by integrating AI use cases into the larger digital business platform. Further, utility CIOs reinforce the foundation for democratizing AI by using good governance principles, clear policy, strong stewardship, and robust empowerment inside IT and across the business. This robust empowerment includes developing enterprise:
• Capabilities to deploy and manage AI application models
• Talent and skills needed to support and exploit continuously evolving AI tools and techniques
• Structures and processes, such as center of excellence, to curate new AI products and steward AI services provided by internal and external partners
Remember, AI’s promise and potential is realized first in creative use and second in democratizing it —scaling around purpose and ubiquity.
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