Can Artificial Intelligence Replace Executive Decision Making?

For the time being, countless decisions still require human engagement.

Awash in data, executives dream of a time when the Jetson utopia finally manifests — and they find themselves sipping coffee and cashing checks while machines slave away for them, uncovering unexpected business insights and learning optimal ways to manage organizations.

Despite improvements in cognitive technologies, that dream managerial scenario is still far from reality. Decisions that executives face don’t necessarily fit into defined problems well suited for automation. At least for the time being, countless decisions still require human engagement.

Consider machine learning. To oversimplify, machine learning emphasizes algorithms that use numerous examples as inputs. In an ideal world, machine learning would reveal connections between observations and outcomes with minimal human guidance. In other words, machines would excel at finding patterns and making data-based predictions.

Recent advances in machine learning and cognitive technologies have been remarkable. We’ve seen impressive inroads in areas such as radiology, and accounting. Nevertheless, executives resist using these approaches for decision making for many reasons, including …

  • Algorithmic approaches typically require numerous examples. Organizations rarely have the number of examples needed to understand relationships between everything in the world that can affect an organization. This is the managerial version of the “curse of dimensionality.” The ratio of “examples of past similar decisions” to “stuff that might be important for those decisions” can be abysmally low.
  • Even with ever-increasing data collection, many known explanatory variables are still difficult to capture. Algorithmic performance is always better when more information is known, structured, and available. In particular, it is difficult to incorporate data about events that didn’t happen but could have, or that did happen but had no data collected about them.
  • Beyond that, executives can have a broad view of new information that didn’t exist before, but could make a difference in the future — such as coming legislative, regulatory, or technology changes. It is harder to make out-of-sample predictions than in-sample, particularly when extrapolating and boldly going where no data has gone before.
  • Executives don’t have multiple organizations that would enable them to make randomized A/B tests. Ideally, learning from past decisions could occur by observing similar scenarios with alternative decisions. Instead, executives must estimate counterfactuals based on limited information.

As a result, current cognitive technologies focus on the easiest problems. And while this makes sense, the questions these approaches can answer may not be the foremost question in an executive’s mind. IBM emphasizes the progress in solving difficult problems — such as helping teachers personalize curriculum — while also noting current limitations, by pointing out that managing children is a much more difficult unstructured problem than current technology can solve.

I don’t want to trivialize the current state of cognitive technologies. Progress has been so amazing and fast that we have quickly become inured to amazing progress. A definition of artificial intelligence, for example, is “systems able to perform tasks that normally require human intelligence.” But the definition of “normal” is changing. Machines that autocorrect spelling and check grammar in real time as you write would have been fantastical not too long ago; now I’m annoyed when my machine guesses my mistyping incorrectly. Uncanny predictive ability would once have led to a trial in Salem; now it leads to a corner office on Wall Street.

Cognitive technologies will increasingly absorb the easiest aspects of executive jobs; that seems inevitable. The question, then, is what changes. Like other technology changes, there will be a mix of good, bad, and ugly. Executives may be liberated from the mundane and able to use time more creatively and productively. They may face increasing competitive threats from automated virtual workers who do the easiest tasks, leaving the rest of us with the most difficult jobs. Or, worst case, executive may lose the ability to contribute at all. It’s likely to be a mix of all of these. However, it is unlikely that the dream scenario of getting paid to sip coffee while machines work is a realistic option. We’ll need to add value irrespective of how “normal” changes.

3 Comments On: Can Artificial Intelligence Replace Executive Decision Making?

  • Praveen Kambhampati | June 28, 2016

    Great insight. The JD of an Executive management role in the realm of AI seems gaining more visibility except the stakes and remuneration perspective. That would be taken care of too, by the AI enabled Finance function.

    Recently i had to answer the question “who would you prefer between a senior citizen and a young beginner, to engage on an assignment? ” My answer was “it it depends !!. For an airplane pilot ii would be the youngster and for Trainer or Teacher, all youngsters would be screened out, unfit”!!.
    For challenging AI in the context of this article, i get this question of ” what if the candidate is a middle experienced professional ? As it stands today, An executive decision maker would depend more on the human wisdom than machine data.
    But lets face it, if a robot can take and smartly reject complex questions from human audience, the executive decisions would be taken care of for sure very soon.

    The role of Executive Management would be redefined, in the AI enabled corporate eco system. Just like the ERP and dash boards have brought more visibility, a higher level of data based facts, interpretations and wisdom would be enabled to chose. The Leader possibly, has to select a higher level of wisdom enabled Risk ownership on the strategy. A sip of the coffees can boost the adrenalin kick a little higher but surely an AI savvy and not a trainee CXO , would help both the organisation and the apex level positions in the run for their money.

    We still have a lot of policy dependent intelligence which looks beyond data and machine learning. Even with data and records, should an organisation take a specific path is largely dependent on the executive call of the CXO. Conflicting situations which show “AI is right but a human override is preferred for the current situation”, could largely be the solution. Do we envisage a totally AI enabled corporate world ? well, a very superior extrapolation of AI deployment and dependence, and no easy answer for ignoring sophistication. The journey is to conquer more planets and create more employment, for now, for an answer.

    With all the transformations happening around AI, we are still at the door step of this fantastic higher intelligence sophistication of data and decision support. Much is in the reckoning.

  • Chandra Pandey | June 29, 2016

    The biggest multiplier effect of AI evolution is in time boxed use cases where understanding & addressing the data that represents different dimensions are brought to the decision making table. With explosion of data the emerging challenge is to separate the signal from the noise. Low cost productivity & accuracy augmentation in providing curated view is the real benefit.

    AI is better suited where there is lower risk to financial or reputation loss as algorithm at best are probabilistic in correctness in current stage of evolution. How these refined data points can be linked into actionable with shortest path to result is context sensitive and requires human due diligence in roll out to address the timing aspects & operating constraints. From GRC point of view any org would always have clear accountability defined till the last mile connectivity to the end user. As AI evolves to provide more open API’s the use cases of integration will open up which is critical for transitioning from silos to integrated world.

  • Jose Miguel Rodriguez | July 20, 2016

    Hi Sam, Praveen and Chandra

    I´ve been working AI applied to managerial decisions for a few decades.
    Of course never this approaches will substitute the managers in the decision making process.
    My best experience that I published in Mexico in my book “How to make your business an intelligent one” not translated to english, was the logical approach using the association rules, a completely heuristic approach.
    As all of you have stated the most important thing in an organization is to implement the knowledge management process in order to be an intelligent organization. If you don´t achieve this, it´s very difficult to use the Business Analytic algortiths for supporting the decision making.

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