Using AI to Enhance Business Operations

How organizations can improve processes and capture value through enterprise cognitive computing.

Reading Time: 21 min 


Permissions and PDF

Image courtesy of John Hersey/

Artificial intelligence invariably conjures up visions of self-driving vehicles, obliging personal assistants, and intelligent robots. But AI’s effect on how companies operate is no less transformational than its impact on such products.

Enterprise cognitive computing — the use of AI to enhance business operations — involves embedding algorithms into applications that support organizational processes.1 ECC applications can automate repetitive, formulaic tasks and, in doing so, deliver orders-of-magnitude improvements in the speed of information analysis and in the reliability and accuracy of outputs. For example, ECC call center applications can answer customer calls within 5 seconds on a 24-7-365 basis, accurately address their issues on the first call 90% of the time, and transfer complex issues to employees, with less than half of the customers knowing that they are interacting with a machine.2 The power of ECC applications stems from their ability to reduce search time and process more data to inform decisions. That’s how they enhance productivity and free employees to perform higher-level work — specifically, work that requires human adaptability and creativity. Ultimately, ECC applications can enhance operational excellence, customer satisfaction, and employee experience.3

ECC applications come in many flavors. For instance, in addition to call center applications, they include banking applications for processing loan requests and identifying potential fraud, legal applications for identifying relevant case precedents, investment applications for developing buy/sell predictions and recommendations, manufacturing applications for scheduling equipment maintenance, and pharmaceutical R&D applications for predicting the success of drugs under development.

Not surprisingly, most business and technology leaders are optimistic about ECC’s value-creating potential. In a 2017 survey of 3,000 senior executives across industries, company sizes, and countries, 63% said that ECC applications would have a large effect on their organization’s offerings within five years.4 However, the actual rate of adoption is low, and benefits have proved elusive for most organizations. In 2017, when we conducted our own survey of senior executives at 106 companies, half of the respondents reported that their company had no ECC applications in place. Moreover, only half of the respondents whose companies had applications believed they had produced measurable business outcomes. Other studies report similar results.



1. ECC applications are distinct from other kinds of enterprise software in that AI tools, rather than human deduction, are used to figure out what logic will optimize business outcomes. AI software tools apply computational and analytical techniques, such as neural network analysis, machine learning, and Bayesian statistics, to large sets of structured and unstructured data to create AI algorithms that will classify, cluster, predict, and match patterns. These algorithms become part of the logic of the ECC application.

2. J. Bughin and E. Hazan, “Five Management Strategies for Getting the Most From AI,” MIT Sloan Management Review, Sept. 19, 2017,

3. M. Tarafdar, C.M. Beath, and J.W. Ross, “Enterprise Cognitive Computing Applications: Opportunities and Challenges,” IEEE IT Professional 19, no. 4 (August 2017): 21-27.

4. S. Ransbotham, D. Kiron, P. Gerbert, et al., “Reshaping Business With Artificial Intelligence,” MIT Sloan Management Review research report, Sept. 6, 2017.

5. S. Norton, “Machine Learning at Scale Remains Elusive for Many Firms,” The Wall Street Journal, April 27, 2018; J. Bughin and E. Hazan, “Five Management Strategies”; and Ransbotham, et al., “Reshaping Business.”

i. C.M. Beath, M. Tarafdar, and J.W. Ross, “OneBankAssure: Customer Intimacy Through Machine Learning,” working paper, MIT Center for Information Systems Research, Cambridge, MA, March 12, 2018; M. Tarafdar and C.M. Beath, “Wipro Limited: Developing a Cognitive DNA,” working paper, MIT Center for Information Systems Research, Cambridge, MA, April 27, 2018; and J.W. Ross, K. Moloney, and C.M. Beath, “Pharmco: Becoming a Data-Science Driven Company,” working paper, MIT Center for Information Systems Research, Cambridge, MA, Feb. 21, 2019.

Reprint #:


More Like This

Add a comment

You must to post a comment.

First time here? Sign up for a free account: Comment on articles and get access to many more articles.

Comment (1)
Dennis Rick
I agree with this aritcle. The ECC need not to be higher end like driver less cars as you said. For example, I run a SEO company where my people need to sit and submit directories and weblinks on many directories and newsletters. There are many works to do as such and they do not get time to explore the rest of the tasks.
I have spoken to my developers to sort this out but practically it is not possible to do so. The challenges are due to the non-consistent outside links. They don't submit always in the same set of pages. Because we have to do more search on other urls which are designed with different variable names.
I suggest this AI should be more or less improved and should be known to everyone in their jobs.

As you explained, the manufacturing companies will have the alarms for maintenance time. Yes we do have adopted this technology to remind the users when the visitor count is diminished on one url. So, we can raise a flag and work toward it to see and research the reasons for diminished results. For more experience on microsoftliveassist,com is a tool developed to track the case history and followup the pending case information.