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Artificial intelligence (AI) is seen by many as the best path to competitive advantage and efficiency. C-level executives are taking this message to heart – three-quarters believe if they don’t move beyond experimentation to aggressively deploy AI, they risk going out of business over the next five years.
That increasing anxiety emerged in a recent study from Accenture, based on a global survey of 1,500 C-level executives. Still, organizations are scrambling to establish an AI foothold. Eighty-four percent say AI is now vital to their business strategies, but only 16% have moved out of the experimental stage. Seventy-six percent are struggling with figuring out how to scale AI across their businesses.
The team of Accenture authors, led by Ketan Awalegaonkar, took a deep look at these AI leaders, finding these top performers are achieving nearly three times the return from AI investments as their lower-performing counterparts.
What makes these organizations different? They “have a clear AI strategy and operating model linked to the company’s business objectives, supported by a larger, multi-dimensional team championed by the chief AI, data or analytics officer. The scaled AI is generally across point solutions, such as personalization.”
Awalegaonkar and his co-authors provide the following advice to bring AI out of the labs and into the mainstream of organizations:
Drive “intentional” AI: Nearly three-quarters of AI leaders (71%) say they have a “clearly-defined strategy and operating model for scaling AI in place,” while only half of the lagging companies. AI leaders “are also far more likely to have defined processes and owners with clear accountability and established leadership support with dedicated AI champions,” the survey shows.
Tune out data noise: “After years of collecting, storing, analyzing, and reconfiguring troves of information, most organizations struggle with the sheer volume of data and how to cleanse, manage, maintain, and consume it,” the Accenture authors state. These leaders can tune out “the noise” surrounding data. “They recognize the importance of business-critical data— identifying financial, marketing, consumer, and master data as priority domains.” They also use the right AI tools, the report continues, “things like cloud-based data lakes, data engineering/data science workbenches with model management and governance, data and analytics marketplaces and search—to manage the data for their applications.”
Treat AI as a team sport: Take AI out of the IT labs and put teams from across the organization in charge of it. For AI leaders, “these teams are most often headed by the chief AI, data or analytics officer. They’re comprised of data scientists; data modelers; machine learning, data and AI engineers; visualization experts; data quality, training and communications specialists.”
Ultimately, Awalegaonkar says the state organizations need to reach is “industrialized AI,” characterized by a “digital platform mindset and create a culture of AI with data and analytics democratized across the organization. They have scaled thousands of models with a responsible AI framework. They promote product and service innovation and realize benefits from increased visibility into customer and employee expectations.”
