- Despite the proliferation of AI, many businesses are failing to generate returns on their investment.
- Training executives and others in AI consumption is as important as investing in AI production.
- Businesses need to be patient, as the value derived from AI does not come automatically.
By Jessica Sier
In 2018, US-based insurer Aetna used artificial intelligence (AI) to forge a powerful new corporate strategy following its US$70 billion merger with healthcare giant CVS Corp.
AI, the company discovered, could provide much more than traditional cost-saving applications such as preventing fraud and rectifying overpayments. Applied strategically, AI could entirely redesign the health insurer’s customer benefit schemes. That redesign led to a 180% jump in new member sign-ups.
“Aetna’s experience with AI – such as integrating AI capability-building with corporate strategy, using AI to reduce costs and grow revenue, and improving organisational alignment – is typical of leading AI practitioners in various industry sectors,” say the authors of Winning With AI, the 2019 MIT Sloan Management Review-Boston Consulting Group Artificial Intelligence Global Executive Study and Research Report.
While organisations the world over pour investment into AI, the researchers say the companies that actually capture value from their AI activities generally exhibit a distinct set of organisational behaviours.
Reward comes to those organisations that work hard to integrate AI strategies with an overall business strategy; that prioritise revenue growth over cost savings; and that are prepared to take on large, riskier AI efforts.
“With more executives perceiving both opportunity and risk from AI, the development of AI capabilities now has a sense of urgency,” says report co-author Professor Sam Ransbotham.
“But efforts to rebrand existing processes with a spiffy new label of ‘now with more AI!’ aren’t enough.”
Aligning AI and strategy requires businesses “to look backward from strategy, not forward from AI”, according to the report’s authors. It’s not a case of observing what AI can do and then seeing where it can support an organisation’s strategy; the better approach is to identify areas in the strategy that need support and then look for the best way to provide it.
“When organisations keep the focus on strategy, executives maybe in a better position to appreciate ways that AI can influence entire business models,” the report states.
‘Riskier’ AI investments bring bigger benefits
As AI proliferates, executives report a distinct fear they will fail to capitalise on the technology’s far-reaching power.
Forty-five per cent of survey respondents in 2019 perceived some risk to their business from AI, up from 37% when the same question was asked in 2017. Existing competitors could use AI to work smarter and faster, or non-traditional competitors might use AI to disrupt adjacent industries and create new threats.
The survey also reveals that management teams see AI as a foundation for future business models, and that they are prepared to wait for their ‘riskier’ AI investments to yield returns.
Indeed, of those that have invested in high-risk projects, 50% have already seen value to date, whereas low-risk projects are serving up only 23% of gains.
Apple and Amazon’s forays into payments and banking, with Apple Pay and Amazon Cash respectively, show just how quickly AI can shake things up and create competition for traditional companies.
“With massive amounts of data, the ability to apply AI and other technologies to capitalise on that data, and their loyal customer bases, the tech giants’ respective moves into financial services pose formidable threats to traditional banking and financial services companies,” notes the report.
AI producers must collaborate with AI consumers
Generally, when thinking of AI, most business leaders imagine a scenario in which experts direct powerful algorithms to crunch vast troves of data.
While the ‘production’ of these AI algorithms often grabs headlines, the researchers argue that without willing and capable ‘consumers’ of AI, businesses will fail to fully exploit the power of the technology.
The businesses that align the production of AI with its consumption, and invest in AI talent alongside a broader digital transformation, buck the trend of seven out of 10 companies achieving little return from their AI investment.
“This consumption side of AI is often underappreciated, if not wholly overlooked,” say the report’s authors. “Ensuring that investments in producing AI align with investments in consuming AI is critical.”
Such ‘investments’ can include putting executives through boot camps to teach them how to think about problems differently in an AI-powered world. Raising an organisation’s overall AI knowledge, through internal conferences that highlight AI projects, or staff training, is also useful.
To gain the best outcomes, AI producers must collaborate with AI consumers. People whose work is affected must not only like the idea, but be willing to provide feedback. At Siemens AG, for example, the AI lab takes people out of their daily jobs for a week to work with data scientists to come up with an early prototype of a desired solution.
“Growing consumption-focused talent also means investing in people who are geared towards buying what’s available on the market, rather than developing applications in-house,” the report advises.
“Consumption is especially vital to manage because many AI solutions simply don’t work right out of the box; organisations must calibrate or co-develop them for the business.”
Playing the long game
Despite their enthusiasm for AI’s potential, survey respondents insist that value from AI does not come automatically or quickly. Across the survey population, 65% are not yet seeing value from the AI investments they have made in the past few years.
Even among ‘pioneers’ – what the report’s authors call those who show the highest maturity on understanding and use of AI – 30% have yet to see business value materialise.
However, the authors argue that one thing is certain: “If AI initiatives are not core to a company’s business strategy, they are unlikely to create meaningful value and scale. Furthermore, if a company’s current business strategy ignores AI as a risk or as an opportunity, it probably needs revisiting.”
“If a company’s current business strategy ignores AI as a risk or as an opportunity, it probably needs revisiting.”