A recent opinion piece by Jason Widjaja states, “To paraphrase Eric Beinhocker from the Institute for New Economic Thinking, there are physical technologies that evolve at the pace of science, and social technologies that evolve at the pace at which humans can change—much slower.”  He goes on to suggest that physical technologies enabled by data science and artificial intelligence (AI) will ultimately be hindered if social considerations like technological mistrust cannot be overcome.

Heading into 2020, Widjaja believes this dynamic will shape how we view and implement AI in the new decade. The following are among his 2020 predictions:

  • Data science and AI roles will continue on the trend towards specialization. He writes, “For all the value of the multi-talented performer, they are not a comparative advantage when it comes to building and scaling large data science teams.” In the years ahead, there will be a greater need for data scientists to focus on either engineering-heavy work (i.e., data/ML/AI engineer roles) or science-heavy functions (i.e., data scientists, business analytics professionals or analytics consultants).
  • Executives understanding of data science and AI will become more critical. Business leaders are growing increasingly aware that the technological savviness—or lack thereof—of their organization can be a bottleneck to more widespread AI adoption. As such, Widjaja believes more companies will institute training programs and invest in other avenues to accelerate their AI maturity.
  • Ethics will emerge as its own discipline. Incidents like the Cambridge Analytica scandal and Amazon’s biased AI recruiting tool have brought the conversation of AI ethics into the public consciousness. Widjaja believes we will see data science and AI ethics become a distinct discipline, noting, “Technology usually outpaces regulatory paradigms by a few years, but regulation is catching up. This will cause short-term pain as data science and AI teams learn to work within new constraints, but will eventually lead to long term gains as credible players are separated from bad actors.”
  • Obtaining actionable insights from AI and data science will still take work. Widjaja writes, “Creating value from data science and AI is not only hard but requires discussion and consensus beyond the data scientist and machine learning engineer alike.” While the new decade will bring new tools and (hopefully) greater trust in AI technologies, a significant amount of work, collaboration, and consensus will still be necessary in order for organizations to derive value from these solutions—and the data that feeds them.

For more on what we can expect from artificial intelligence in the coming years, check out this recent APEX of Innovation post.


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