Forget about attracting talent, keeping up with rapid change, or complying with a myriad of regulations – what’s really keeping insurance executives up at night is scaling artificial intelligence (AI) for their business.
According to a recent report from Accenture, based on a global survey of 1,500 C-level executives across 16 industries (including 113 executives in Canada), 81% of insurance executives believe they risk going out of business in five years if they don’t scale artificial intelligence (AI), compared to 75% across all other industries globally.
Almost all (94% of) insurance executives acknowledge they know how to pilot AI, but they struggle to scale it across the business, found the report, AI: Built to Scale. This percentage is drastically different from other industries, where just over three-quarters (76%) say they know how to pilot AI but struggle with scale.
Released earlier this month, the report polled 1,500 C-suite executives from large companies in 12 countries across 16 industries to uncover the success factors for scaling AI.
The research found three distinct groups of companies with increasing levels of capability required to successfully scale AI:
Proof of concept factory: 80% to 85% of companies are in this category. Typically, they are:
IT-led, with a siloed operating model
Unable to extract value from data
Yielding low returns due to significant under investment
Burying analytics deep and AI is not a CEO-focus
Struggling to scale as unrealistic expectations on time is required
Strategically scaling: An estimated 15-20% of companies are at this stage. Capabilities include:
Intelligent automation and predictive reporting
Experimental mindset, achieving scale and returns
CEO-focus with advanced analytics and data team solving “big rock problems”
Ability to tune out data noise and focus on essentials
Industrialized for growth: Less than 5% of companies have evolved to this point. These companies offer:
Clear enterprise vision, accountability, metrics and governance to break down silos
A digital platform mindset; also, an enterprise culture of AI democratizing real-time insights to drive business decisions
Competitive differentiation that drives higher price-to-earnings multiples.
The companies in Accenture’s study collectively spent US$306 billion on AI applications in the past three years; the return on investment gap among them is major. How significant? There is a US$110-million difference between companies in the proof of concept stage and strategic scalers, the report said.
In fact, researchers found a positive correlation between successfully scaling AI and key measures of financial valuation. The average lift was 32% on enterprise value/revenue ratio, price/earnings ratio, and price/sales ratio.
So, how do you succeed at scaling at your brokerage or insurance company?
The research revealed the following critical success factors; these will separate strategic scalers from organizations in the proof-of-concept stage.
Drive “intentional” AI
Strategic scalers pilot and successfully scale more initiatives than their proof-of-concept counterparts – at a rate of nearly 2:1. Also, they set longer timelines. For example, they are 65% more likely to report a timeline of one to two years to move from pilot to scale.
To successfully scale, companies need structure and governance in place. The strategic scalers have both. Seventy-one percent say they have a clearly-defined strategy and operating model for scaling AI in place; only half the companies in the proof-of-concept stage report the same.
In addition, strategic scalers are far more likely to have defined processes and owners, with clear accountability and established leadership support with dedicated AI champions.
Tune out data noise
Strategic scalers recognize the importance of business-critical data, identifying financial, marketing, consumer and master data as priority domains. They are more adept at structuring and managing data. Sixty-seven percent integrate both internal and external data as a standard practice, compared to 56% of their proof-of-concept counterparts. Strategic scalers also use the right tools: for example, cloud-based data lakes, data engineering/data science workbenches, and data and analytics search.
Treat AI as a team sport
For strategic scalers, teams are most often headed by the chief AI, data or analytics officer. Teams are comprised of data scientists; data modellers; machine learning, data and AI engineers; visualization experts; data quality, training and communications, and other specialists. A full 92% of strategic scalers leverage multi-disciplinary teams.
“Scaling the exponential power of AI across the enterprise is a journey,” the report said. “Those that learn the lessons on each path will reach a place where the business is seamlessly fused with intelligence that boosts productivity and effectiveness. The result: industrialized growth through unassailable competitive strength in everything from organizational effectiveness to brand perception and trust.”