The AI Maturity Framework
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Source: Irving Wladawsky-Berger, CogWorld Think Tank member
I recently attended a seminar, The Art of AI Maturity, by Accenture executives Philippe Roussiere and Praveen Tanguturi as part of MIT’s Initiative on the Digital Economy (IDE) lunch seminar series. The seminar was based on their recently published article The Art of AI Maturity: Advancing from Practice to Performance.
“Today, so much of what we take for granted in our daily lives stems from machine learning,” wrote the authors in the article’s executive summary. “Every time you use a wayfinding app to get from point A to point B, use dictation to convert speech to text, or unlock your phone using face ID ... you're relying on AI. And companies across industries are also relying on - and investing in - AI to drive logistics, improve customer service, increase efficiency, empower employees and so much more.”
To determine the true state of AI maturity in the marketplace, Accenture conducted a survey in August and September of 2021 of over 1,600 C-suite executives at nearly 2,000 of the world’s largest companies across 16 industries with headquarters in 15 countries. In addition, they interviewed 25 CEOs, Chief Data Officers and Chief Analytics Officers, as well as a number of Accenture AI experts, and developed over 40 case studies on AI transformation.
Overall, the survey found that AI maturity is increasingly important to business success. Firms that discussed AI on their earnings calls last year were 40% more likely to see their share price increase - up from 23% in 2018.
“AI maturity comes down to mastering a set of key capabilities in the right combinations - not only in data and AI, but also in organizational strategy, talent and culture.” These include foundational AI capabilities - like cloud platforms and tools, data platforms, architecture and governance - that are required to keep pace with competitors; and differentiation AI capabilities, including a well-written AI strategy, solid C-suite sponsorship, and a culture of innovation.
Accentures’s survey identified four different levels of AI maturity based on a firm’s foundational and differentiated capabilities:
AI Achievers (12% of firms), have advanced both their strategic and operational AI maturity to achieve superior growth and business transformation;
AI Innovators (13%), have strong strategic capabilities but struggle to operationalize them because of their average foundational capabilities;
AI Builders (12%), have strong foundational capabilities but average strategic capabilities; and;
AI Experimenters (63%), have only average AI capabilities and make up the majority of firms.
Despite the increasing presence of AI in our lives, when it comes to making the most of AI’s potential, most organizations still have relatively low levels of AI maturity.
AI Achievers are thriving. There is increasing evidence that scaling AI beyond experimentation and proofs of concept has a significant impact on financial metrics, customer experience and enterprise-wide transformation. Achievers are 25% more likely than Experimenters to have scaled AI pilots across the enterprise and scored 8% higher in customer experience. In addition, Achiever are 3.5 times more likely than Experimenters to see their AI-influenced revenue surpass 30% of their total revenues.
What accounts for the superior performance of AI Achievers? By itself, AI isn’t the secret to their superior performance; it’s their approach to AI that makes them different. AI maturity is as much about people than about technology, and as much about strategy than about execution. “Achievers are not defined by the sophistication of any one capability, but by their ability to combine strengths across strategy, processes and people,” said the report, citing five key factors for the success of Achievers. Let me briefly discuss each of these factors.
Champion AI as a strategic priority for the entire organization, with full sponsorship from leadership. “Companies can create strong AI strategies, but unless those strategies receive enthusiastic support from the CEO and the rest of the C-suite, they’re likely to flounder, competing with other initiatives for attention and resources.”
The survey found that 83% of Achievers have formal senior sponsorship for their AI strategies compared with 56% of Experimenters, and 48% of Achievers have a culture of innovation compared to 33% of Experimenters. Their AI strategies tend to be bold, which in turn leads to increased innovation. And, to further encourage AI innovation, 16% of Achievers share ideas and collaborate with colleagues across the company, compared to only 4% of Experimenters.
Invest heavily in talent to get more from AI investments. “With a clear AI strategy and strong CEO sponsorship, organizations are more likely to invest heavily in creating data and AI fluency across their workforces.”
Achievers prioritize efforts to build AI literacy and AI-related skills across their workforce. 78% of Achievers, compared to 51% of Experimenters, have mandatory AI training for different groups of employees, from product development engineers to C-suite executives. 44% of Achievers have employees in their firms with consistently high AI skills, compared with 30% of Experimenters, who on average have significantly fewer such employees.
In addition of investing in their in-house talent, Achievers generally develop other proactive talent strategies to keep up with leading edge industry trends, including partnering with services and consulting companies, collaborating with universities and research institutions, and so on.
Industrialize AI tools and teams to create an AI core. “Another priority for Achievers involves building an AI core: an operational data and AI platform that taps into companies’ talent, technology and data ecosystems, allowing firms to balance experimentation and execution.”
Building their AI applications on an end-to-end core platform helps organizations seamlessly integrate AI into their existing applications, which helps productize and bring to market their new AI capabilities. It also provides end-to-end data capabilities that follow the appropriate usage, monitoring, and security policies for both internal and external data; and facilitates the management of the various stages of the machine learning lifecycle, including workflow, model training, and model deployment.
In addition, Achievers are 32% more like than Experimenters to develop custom-built machine learning applications or work with a partner that offers solutions-as-a-service.
Design AI responsibly, from the start. “As companies deploy AI for a growing range of tasks, adhering to laws, regulations and ethical norms is critical to building a sound data and AI foundation.”
Achievers are 53% more likely than Builders and Innovators to design and deploy responsible AI applications that engender trust and confidence by being fair to customers, employees, business partners and society in general. Being responsible by design positions Achievers to better meet future requirements and create sustainable value for themselves and their stakeholders.
This is particularly important given the potential for regulatory changes. A separate Accenture survey of 850 C-suite executives found that “Nearly all (97%) respondents believed that regulation will impact them to some extent, and 77% indicated that compliance is a company-wide priority.”
Prioritize long- and short-term AI investments. “To avoid being left behind, most companies need to aggressively increase their spending on data and AI.”
Achievers get more out of AI because of their higher investments. In 2018, Achievers devoted 14% of their total technology budgets to AI; in 2021 they devoted 28%; and in 2024 they plan to devote 34%. They realize that they have only “scratched the surface of their AI transformations and that the quality of their investments matters just as much as the quantity.” These continued investments will enable them to expand the scope of AI across the business.
“As AI technologies become more prevalent, the future of all businesses is going to look very different - some will lead the change, and some will be subjected to it,” said the Accenture report in conclusion. “Those who transform will be the ones whose teams master the art of AI maturity, using cloud as the enabler, data as the driver and AI as the differentiator.”
Irving Wladawsky-Berger is a Research Affiliate at MIT's Sloan School of Management and at Cybersecurity at MIT Sloan (CAMS) and Fellow of the Initiative on the Digital Economy, of MIT Connection Science, and of the Stanford Digital Economy Lab.