The AI, machine learning, and data science conundrum: Who will manage the algorithms?

Artificial intelligence and machine learning are being adopted at a rapid clip, and the management headaches are just about to begin.
Written by Larry Dignan, Contributor

Artificial intelligence and machine learning are being adopted into the enterprise at a rapid clip and adoption is likely to surge in 2019. What comes next is the real business challenge: How will we manage technology that we likely don't understand?

The issue is likely to bubble up in the year ahead. For now, most of us are lulled into thinking more algorithms are better and even assuming we can outsource critical thought to models. Why hurt our brains when we can trust Einstein, Watson, Alexa, Google Assistant, and other software tools to think for us?

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According to IDC, global spending on AI and cognitive technologies will reach $19.1 billion in 2018, up 54.2 percent compared to a year ago. By 2021, AI and cognitive spending will hit $52.2 billion. If you're not spending on AI, you may need some computer-assisted help to set your budget.

Here's a look at the top use cases from IDC.


And here's a reality check: Not all of these AI implementations are going to go well. Why? Managing AI may be one of the biggest business challenges in the year ahead. AI will be like most enterprise technologies -- ERP, CRM, HCM, blah, blah, blah -- and fail to be a magic bullet.

Consider the following moving parts:

  1. Every business technology leader needs an artificial intelligence and machine learning strategy. Why? The board is asking.
  2. In response to this demand, cloud providers such as AWS, Microsoft, and Google are starting to package AI services to address horizontal functions such as recommendations, contact center and HR recruiting. Those moves will make AI easier to consume.
  3. There is a shortage of data scientists available to truly kick the tires on models and algorithms. A few companies I've talked to have a data science team to put AI tools through their paces, but most don't.
  4. There is little transparency in the models being sold, inherent bias, or fine print. IBM Research recently proposed an effort to add the equivalent of a UL rating to AI services. IBM Research is on target.
  5. Universities are adding data science programs to business schools, but these students won't become C-level executives for a while.
  6. As technology buyers we are trained to buy black boxes that will fix our problems and offer us "solutions."

That list is a lot to digest. Rest assured we're going to get a collective case of AI implementation indigestion due to algorithm sprawl, project management issues, and vendor hype. We're also likely to hit Gartner's trough of disillusionment soon. Maybe even tomorrow.

(Image: Gartner)

Gartner's Emerging Technology Hype Cycle has AI all over it. Gartner is betting that AI technologies such as AI platform as a service (PaaS), Artificial General Intelligence, autonomous driving, conversational AI platform, deep neural nets, and virtual assistants will be mainstream in the next two to five years. According to Gartner, AI technology will be "virtually everywhere over the next 10 years."

As far as marketing goes, AI is already everywhere.

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The trick will be figuring out how everyone from mid-level managers to C-level execs are going to use this AI and manage what they aren't likely to fully understand. The real fun is just starting.

More AI:

How ubiquitous AI will permeate everything we do without our knowledge.

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