Many executives are enthusiastic about the business potential of machine learning applications. But business leaders often overlook a key issue: To fully unlock the benefits of artificial intelligence, you’ll need to upgrade your people’s skills — and build an empowered, AI-savvy workforce.

Jeanne Ross is principal research scientist for MIT’s Center for Information Systems Research.

There is no question that artificial intelligence (AI) is presenting huge opportunities for companies to automate business processes. However, as you prepare to insert machine learning applications into your business processes, I recommend that you not fantasize about how a computer that can win at Go or poker can surely help you win in the marketplace. A better reference point will be your experience implementing your enterprise resource planning (ERP) system or another enterprise system. Yes, effective ERP implementations enhanced the competitiveness of many companies, but many other companies found the experience more of a nightmare. The promised opportunity never came to fruition.

Why am I raining on the AI parade? Because, as with enterprise systems, AI inserted into businesses drives value by improving processes through automation. But eventually, the outputs of most automated processes require people to do something. As most managers have learned the hard way, computers can process data just fine, but that processing isn’t worth much if people are feeding them bad data in the first place or don’t know what to do with information or analysis once it’s provided.

With my fellow researchers, Cynthia Beath, Monideepa Tarafdar, and Kate Moloney, I’ve been studying how companies insert value-adding AI algorithms into their processes. As other researchers and managers have also observed, we are finding that most machine learning applications augment, rather than replace, human efforts. In doing so, they demand changes in what people are doing. And in the case of AI — even more than was true with ERP systems — those changes eliminate many nonspecialized tasks and create skilled tasks that require good judgment and domain expertise.

For example, fraud detection applications may reduce the time that people spend looking for anomalies, but increase requirements for deciding what to do about those anomalies. An AI application might allow financial analysts to spend less time extracting data on financial performance, but it adds value only if someone spends more time considering the implications of that performance.

6 Comments On: The Fundamental Flaw in AI Implementation

  • Michael Zammuto | July 18, 2017

    Well thought out and a very interesting topic. I think the way we are accelerating machine learning shows we can extend the boundaries of it. As for general AI, I dont feel like we know the limits. From gaming to art to predicting cancer we continue to surprise ourselves and trace a new lune in the sand. We tell ourselves that there is something different between the way our brains work and AI. But that misses the point. The Turing test is irrelevant and algorithms dont need to function like brains to replace human judgement and creativity. Thank you for bringing up one of the critical issues of our time. How do we relate to our own inventions

  • David Johnston | October 12, 2017

    A very timely and relevant article that should be required reading for any C-Suite executive.

    In my experience, the article calls out and highlights some of the most important best practices such as (paraphrased) “there is little to no value in (further) automating existing processes” Instead one wants to envision / engineer new processes that will deliver significant change driving bottom line benefits”!

    Also, the article goes on to reinforce the need for “new” business / management processes that focus on translating the “results into “now what do we do” … requiring new management and control processes but particularly performance management processes and planning/change management processes — all together forming new views of an evolving “Enterprise Architecture”

  • Vicente vicente.miranda@br-asgroup.com | October 13, 2017

    I always comment about Bad data at the begin ! If you don’t close this door, you are delivering bad information as fast as before, and don’t present information is better than present bad information.

    Recently, I have made an interview to HBR Brazil and showed my vision about “impact of TD/IIot” and my observation is that Brazilian workforce will have troubles because people are nor prepared to change (Intelectual capital is not rich – people’s skills) !

  • José Antonio FLORES RODRIGUEZ | October 16, 2017

    I think that this topic has to be on the table again and again as is the case of this paper. It has so many aspects that we need to keep in mind where our organizations must choose to be able to flow in the maelstrom of changes that we are living and not only to resist. In particular resisting an AI initiative.
    Instead of looking for a solution that evokes Einstein, Leonardo, I propose a solution of AI that will help us to reflect as Socrates would. It is always good to start with a good question.

  • Raj Ramesh | December 28, 2017

    Right on spot! I used to be a hard-core technologist (specifically AI), and I was gung-ho about it. Then I moved over to the business. That’s when I realized that business problems are more nuanced.

    Technology has not been able to address ‘soft’ problems associated with the human side – like culture, intuition, creativity. Perhaps that will happen in 60 years after we have hit the singularity, but until then, we need human-machine collaboration to get useful things done.

    Many leaders sadly believe that simply bringing in artificial intelligence into the organization is enough. As you point out through your examples and our learnings form ERP implementations, that is not true.

    Hope leaders understand that earlier than later before they let the AI hype consume them and set unrealistic expectations on what AI by itself can deliver.

  • Kurt Hahlbeck | January 18, 2018

    My own anecdotal experiences in over 30 years of software development and deployment are completely consistent with your research perspectives. I look forward to future insights based on the progress of your research.

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