Who’s Winning the Global Battle for AI Leadership?

Washington Executive Magazine just released their most recent list of Top 10 Execs to Watch, and I’m finding it illuminating to see what categories they’re choosing to highlight, such as security, cyber and, most recently Artificial Intelligence (AI), and data science. I was honored to learn that I was being included with such a distinguished group of executives who are using AI to drive change and transform the country.

It’s equally interesting that data science and AI are focal points of the latest Top 10 list because I believe we’re in the midst of a global race for supremacy right now, and it’s not an arms race or a space race, it’s a race for AI leadership.

And the U.S. needs to move fast, and decisively, if we’re going to come out on top.

What’s at stake?

  • PWC says AI could contribute $15.7 trillion to the global economy by 2030.
  • Accenture projects the AI healthcare market will hit $6.6 billion by 2021 (a compounded annual growth rate of 40%).
  • IDC predicts global spending on AI will reach $57.6 billion by 2021.
  • And there’s untapped potential: only 15% of enterprises are using AI today but 31% say it’s on their agenda, according to Adobe.

Data analytics, machine learning, cognitive computing, and artificial intelligence give businesses and government the opportunity to boost operational efficiency and launch new delivery models. Imagine more efficient methods of screening for fraud, accelerating response times for first responders to emergencies or natural disasters, or reallocating routine tasks to machines so public servants have the time to tackle more complex cases for citizens. Imagine a day when lining up to renew a driver’s license at the DMV or apply for government services is a thing of the past.

Making AI a National Priority

On July 31st, the government announced the 2020 Administration Research and Development Budget Priorities, which include:

  • Security of the American People—Calling on investment in emerging technologies including AI, cyber and autonomous systems.
  • American Leadership in AI, Quantum Information Systems and Strategic Computing—To deliver on the promise of scientific breakthroughs that can transform the American way of life.
  • American Connectivity and Autonomy including 5G networks.
  • Manufacturing—Next-generation manufacturing enabled by machine learning, AI and the Internet of Things.
  • Space Exploration—And the opportunity to use machine learning capabilities for applications in space and on earth.
  • Medical Innovation—Where AI and machine learning have the potential to positively impact treatment.
  • Agriculture—Where sensors, data analytics and machine learning can reduce inputs and improve crop yields.

Of the eight areas prioritized for investment, seven leverage AI to advance or accelerate breakthroughs. That’s how important AI is becoming to our country (and the world).

Accelerating AI Adoption

I talk a great deal about the need to accelerate AI adoption and with good reason: If we don’t get to the benefit of AI quickly, we may not get to the benefits at all.

Realizing the benefits of AI and data science is highly dependent on our ability to do some very difficult things very quickly. One of those difficult things is finding talented, skilled people who want to work in government. With millions of open jobs in data science in the U.S., finding those skilled people is a tall order and will impact our ability to deliver on the AI promise.

The next challenge revolves around the data itself. We have, and are generating, unprecedented amounts of data, but to make it meaningful, we must be able to label and classify it so that we can access and utilize the right data at the right time for the right reasons. Addressing this challenge overlaps with the skills shortage: We are going to need a workforce trained, available and willing to do the work of analytics. More data can’t equate to more work!

Building a Solid AI Foundation

As a data scientist, I know the potential (and pitfalls) of AI adoption. The right ecosystem of hardware, software, and accelerators from the cloud to the edge will facilitate innovation in AI deployments and get us to the benefits we are seeking faster.

In tandem, we must address a wide range of challenges from skills shortages and training to data management and the ethical questions that are inextricably linked to AI, so we can avoid some of the unintended consequences associated with outsourcing human cognition to intelligent systems.

We need to start working together as a cohesive, yet flexible group of academia, industry, and the government to put the pieces of this puzzle together so we can win with AI.