5 Ways Businesses Can Ramp Up AI
It’s still tough for many organizations to get beyond pilots to create scalable, revenue-generating AI capabilities
By Paula Klein
As more organizations race to embrace AI and make serious financial and resource investments, why are some seeing more value than others?
That’s one of the overarching questions Accenture’s research group set out to answer in a large-scale study conducted over the last year. Seeking to determine what they call “AI maturity,” the consulting firm rolled out a multi-faceted research project that included a survey of 1,615 global executives at 1,176 companies, interviews with 25 C-level executives, and 40 case studies. In addition, the researchers created machine learning models to identify key AI capabilities, and used a natural language processing tool to measure the speed of AI transformation compared with the speed of digital transformation.
The results? In a report, “The Art of AI Maturity: Advancing from Practice to Performance, published in June, Accenture found that while many more companies are moving beyond the pilot project phase, only 12% can be categorized as “AI Achievers.” According to Praveen Tanguturi, Principal Director and Global Research Lead of Applied Intelligence at Accenture, those with average capabilities are AI Experimenters (63%). Pre-pandemic (in 2019), Achievers already enjoyed 1.5 times greater revenue growth, on average, versus their peers, he said.
It’s common for AI projects to be stuck in the pilot stages – where the perfection of a product or tool delays scaling of the technology, Accenture said. Many industries with legacy technologies fall into this category and struggle to transform their digital core and turn pilots into production. But things are beginning to change.
Bigger Budgets, Bigger Expectations
Accenture sees tremendous opportunity to advance AI maturity across industry sectors like, automotive, aerospace and defense, life sciences, banking, and healthcare — not just in high tech.
The research shows that nearly half (49%) of all of the surveyed companies expect to devote more than 30% of their technology budgets to AI over the next two years — a positive sign of what’s to come, Accenture said.
“When AI is implemented at scale it results in better decision making, automation, product and service innovation, and revenue growth,” said Philippe Roussiere, who leads Innovation and AI at Accenture Research. All of those are huge incentives to ramp up efforts, he said. Tanguturi and Roussiere presented their findings at a recent MIT IDE seminar. Accenture is a partner and founding member of the IDE.
Accenture sees AI as far more than a tactical play for businesses today compared to three years ago: it’s now a strategic, revenue-generation tool. Firms want to boost customer experience and new product development via AI processes and technologies, too. With investments come expectations:
Business leaders told Accenture they expect the share of corporate revenue “influenced by AI developments” to triple by 2024 — another positive indicator that AI is top of mind.
A Multi-tiered Approach
To reach the next level and measure success, Tanguturi prescribes a five-pronged strategy that includes: AI strategy and sponsorship from the top of the organization; development of AI talent and corporate culture that sparks and maintains interest; widespread implementation of enterprise-wide data and core AI management practices; a keen awareness of responsible AI, and prioritizing both short and long-term investments.
Taken together, these strategies will create true competitive advantage for the organization and will accelerate AI and the resulting benefits. Accenture offers an assessment tool and questions for CEOs and the C-Suite to help them nudge the process along at their organizations.
Tools alone won’t yield results, however. All of the strategies need to be used in concert, Tanguturi said.
“You can’t be a fast-follower.” Innovators need to focus on practical use cases as well as small experiments. And AI builders can’t ignore strategic goals and be out of sync with others.
There’s also growing awareness about designing responsible AI, but it is still an emerging discipline, Tanguturi said. Only a small fraction of global companies surveyed (6%) had implemented responsible AI practices — which include diversity, accuracy, and recognition of privacy concerns. Accenture is working with MIT Sloan professor Renee Gosline to help leaders understand the importance of models with these goals baked in from the start. Responsibility goes far beyond compliance, he said, and “there is much more to be done. We need to do better.