As the rapid uptake of AI in the enterprise continues across the board, a new report by Deloitte Consulting has found that there are big differences in the results and workflows in the AI practices being explored by businesses. In its latest “State of AI in the Enterprise” report, Deloitte looked for the commonalities that mark successful AI practices, as well as the practices associated with lower achievement.
The fourth annual AI report, “Becoming an AI-fueled Organization,” collected survey input from 2,875 IT and business executives from 11 countries in the Americas, EMEA and APAC to determine how they are using AI, what kinds of results they are getting, and their underlying practices. The report, which was produced by the Deloitte AI Institute and the Deloitte Center for Integrated Research, was released on Oct. 28 (Thursday).
The 28-page report grouped the participating companies into four main groups, based on the volume of AI projects undertaken by the respondents and their success rates. “Transformers,” which accounted for 28 percent of survey respondents, were characterized by high outcomes and a high number of AI deployments, while “Pathseekers,” which accounted for 26 percent of respondents, reported high outcomes, but a low number of deployments. “Underachievers,” which represented 17 percent of respondents, reported low outcomes and a high number of deployments, while “Starters” – the biggest group, accounting for 29 percent of survey respondents – reported low outcomes and a low number of deployments.
Several characteristics stood out among the Transformers and the Pathseekers, according to Deloitte’s survey.
For starters, representatives from these two groups are more likely to say that AI differentiates their organization from competitors. They are also more likely to have an enterprise-wide AI strategy, as well as senior leadership who communicate a transformative AI vision. Lastly, Transformers and Pathseekers are more likely to lean on AI initiatives to remain competitive in five years, although the difference on that question was negligible.
While these high-achieving companies are focused on using AI for a competitive advantage, there is a counter-intuitive principle at work among the most successful enterprises: True differentiation comes from business outcomes, not AI tech, the respondents reported.
“The strongest AI strategies start without ever mentioning AI,” Deloitte says in its report. “Instead, they should begin with the organization’s north star: the core business strategy… Ultimately, AI strategy should function as the fuel to the business strategy, aligning to the same KPIs that have been crafted to incentivize and grow competitive advantage.”
Lower achievers tend to ask data scientists or the IT department to drive their AI strategy, which is not ideal, the report continues. Rather, the AI strategy should flow from the business, and it should encompass the entire enterprise, if possible. Deloitte cited the example of Jeff Bezos, who in 2010 mandated that every leader at Amazon.com come up with a strategy for how they are going to use AI and machine learning. This drove unparalleled growth, Deloitte says, and was a catalyst for the company’s amazing growth.
There is another interesting commonality among Transformers and Pathseekers: they tend to enact more growth-oriented AI projects, while the Underachievers and Starters tend to focus more on cost-cutting AI projects. In fact, both types of AI projects are typically needed, according to Deloitte, which also questioned the efficacy of embarking upon just a handful of AI projects. In Deloitte’s view, the more AI projects, the merrier.
Succeeding at AI is not a one-off project, but rather a continuous process says Nitin Mittal, Deloitte AI co-leader, and principal at Deloitte Consulting. “Becoming an AI-fueled organization is to understand that the transformation process is never complete, but rather a journey of continuous learning and improvement,” he says.
Deloitte also investigated the workflow habits of high-achieving AI groups and found that a dedication to MLOps processes correlate to success.
“Organizations that document and enforce MLOps processes are twice as likely to achieve their goals to a high degree,” the company stated in its report. “They are also four times more likely to report being extremely prepared for risks associated with AI, and three times more confident that they can deploy AI initiatives in a trustworthy way.”
Deloitte identified trust, data fluency and agility as additional characteristic that differentiate between high- and low-achieving AI groups. Organizations that invest in change management to manage their AI implementations are 60 percent more likely to report that AI initiatives exceed expectations and 40 percent more likely to achieve outcomes than those that do not, Deloitte says.
Interestingly, fear is another factor that shows up in high-achieving AI groups, but it is not fear of AI replacing jobs. Instead, “fear may be a positive indicator that an organization’s AI vision is bold,” the report says.
Another characteristic of high-achieving AI groups is a reliance on a broad ecosystem of partners and tool providers. Deloitte’s survey found that 83 percent of the Transformers and Pathfinders “create a diverse ecosystem of partnerships to execute their AI strategy.” An overreliance on a smaller number of partners and tool vendors also puts a company at risk of vendor lock-in, Deloitte says.
The risks associated with AI remain top of mind for executives, says Beena Ammanath, executive director of the Deloitte AI Institute. But that should not keep them from moving forward, she added. “We found that high-achieving organizations report being more prepared to manage risks associated with AI and confident that they can deploy AI initiatives in a trustworthy way,” said Ammanath.
This article first-appeared on sister website Datanami.