State of AI in the Enterprise

Useful view from recent surveys Deloitte in 2017 and 2018 by Deloitte.

Irving Wladawsky-Berger reports on The State of AI in the Enterprise

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.  .... "