A few months ago, Babson College professor Tom Davenport gave a talk on the state of AI in the enterprise at the annual conference of MIT’s Initiative on the Digital Economy. His talk was based on two recent US surveys conducted by Deloitte, the first one in 2017 followed by a second in 2018. Davenport was a co-author of both reports.
The 2017 survey was focused on the responses of 250 US executives who were leading the applications of AI in their companies. The larger 2018 survey reached out to 1,100 IT (46%) and line-of-business (54%) executives from US-based companies (64% at the C-level) and 10 different industries. All of these respondents were early AI adopters compared with their counterparts in an average company, – 90% were directly involved in their company’s AI projects, and 75% said that they had an excellent understanding of AI.
Davenport started his talk by summarizing the key findings in the Deloitte surveys:
- 20-30% of enterprises are early adopters, having implemented at least one AI prototype or production application;
- Many projects are in pilots but some are already in production;
- Relatively simple low hanging fruit projects prevail over more ambitious and complex moon shots;
- Only 24% cited “reducing headcount through automation” as one of their top AI priorities;
- The great majority of respondents believe that AI leads to moderate or substantial changes in job roles and skills;
- Implementation, integration, data issues and talent top the list of challenges faced by early adopters;
- Further AI growth is inevitable.
Overall, the 2018 survey found that “Early adopters are ramping up their AI investments, launching more initiatives, and getting positive returns.” Compared to executives in average companies, early adopters have been implementing key AI technologies at a growing rate, including machine and deep learning, natural language processing and computer vision. 63% of respondents had adopted machine learning, an increase of 5% over the 2017 survey and 50% were using deep learning. 62% had adopted natural language processing, compared to 53% in 2017, while 57% were using computer vision.
Early adopters have been increasing their investments in sophisticated AI technologies, with 37% of respondents saying that their companies had invested $5 million or more in AI technologies while 82% said that their AI investments are paying off. Across all industries, the median return on these investments is estimated at 17%.
Not surprisingly, technology companies lead both in AI investments and returns, followed by media and entertainment and telecommunications. Health care and life sciences are also investing in AI, but, so far, they have less to show for it. Public sector and education are at the other end of the spectrum, with both low AI investments and low returns. Surprisingly, the Deloitte survey found that financial services industries are on the lower side of the investments/returns spectrum.
Another major reason for the growing use of AI is that it’s becoming easier for companies to acquire AI capabilities. Enterprise software vendors are increasingly integrating AI technologies into their ERP, CRM and similar applications, such as Salesforce Einstein, and SAP Leonardo. 59% of respondents said that their companies use these methods, – the most popular and easiest path to AI.
Another popular way of acquiring AI capabilities is through cloud-based services, used by 49% of survey respondents. A number of cloud providers offer a variety of AI-as-a-service offerings, including IBM’s Watson and AWS AI. Cloud-based deep-learning services can give companies access to advance AI algorithms, pre-trained models, data management tools, and the huge computing power necessary to extract insights from very large data sets.
44% of respondents cited “enhanced current products” as the top AI benefit to their companies, followed closely by “optimizing internal operations” at 42%, and “make better decisions” at 35%. More respondents viewed AI as enhancing innovation-related activities that reducing headcount. “Free workers to be more creative” was cited by 31% of respondents, “create new products” by 27% and “pursue new markets” by 24%, while only 24% cited “reduce headcount through automation” as a key benefit.
In his MIT talk, Davenport discussed and contrasted two very different approaches to AI projects. Moon shoots tend to be highly ambitious, CEO-driven, large-budget projects with very high media visibility. In contrast, low hanging fruit projects are modest, invisible, and generally CIO-driven, designed to improve operations, customer satisfaction and financial returns. In health care, for example, the use of AI to transform the treatment of cancer patients is an example of a moon shot project that’s turned to be much more difficult than originally anticipated. But, modest health care AI projects have been much more successful, including patient- and family-centered care, assisting patients with health care bills, and helping staff with IT problems.
In a 2018 HBR article, Davenport offered advice to companies on how to begin building their AI capabilities. They should look at AI through the lens of business opportunities, rather than technologies. As has been the case with the great majority of new technologies, highly ambitious, multi-year moon shot projects are less likely to be successful than low-hanging fruit projects that enhance business processes. It’s the best way to get on the AI learning curve, and the least expensive and easiest capability to implement, since companies have long been engaged with the automation and enhancement of business processes.
Early AI adopters struggle with an array of basic challenges. Around 40% of respondents cited implementation difficulties, integrating AI into the company’s roles and functions, and data privacy and related data issues as their top challenges, not surprisingly for a complex technology in its early stages. Lack of skills was cited by 31%, while 21% said that they need business leaders to help select the best use cases and interpret AI results.
Most current AI projects are focused on augmenting the capabilities of the workforce. Automation to cut jobs received the lowest rankings among the list of top AI benefits, the choice of only 24% of respondents. However, the vast majority of respondents agree that AI is transforming many jobs, leading to moderate or substantial changes in roles and skills, both now (72%) and three years from now (82%). 78% believe that AI empowers employees to make better decisions, and 72% said that it will increase job satisfaction. “Perhaps the biggest advantage could be new ways of working that blend the best of what machines do with human experience, judgment, and empathy; 78 percent of executives believe that AI-based augmentation of workers will fuel new ways of working.”
Davenport concluded his presentation with a set of recommendations to help leverage AI for competitive advantage:
- Think big – figure out how AI can transform your business processes, business models and overall strategy;
- Start small – start with pilot projects and less ambitious goals;
- Scale up – develop a pipeline toward production, and move along it;
- Skill out – offer AI training to augment the skills of the workforce and help them transition to new jobs;
- Put an ethical framework in place; and
- Appoint a company-wide AI leader.