Artificial intelligence in government: How to be more efficient, effective and responsive

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As artificial intelligence (AI) increasingly comes into the mainstream, what better place to apply it than in the public sector, where vast troves of data can be leveraged to make government work smarter for its citizens. From infrastructure projects like repairing highways, to public services like reviewing patent applications, to policy decisions about staff allocation, AI can help streamline processes and improve decision-making.

Why AI?

You may have heard the terms analytics, advanced analytics, machine learning and AI. Let’s clarify:

  • Analytics is the ability to record and playback information. If you attach sensor to a road, you can record how often the road is used.
  • Analytics becomes advanced analytics when you write algorithms to search for hidden patterns. When does the road typically see spikes in use?
  • Machine learning is when the algorithm gets better with experience. The algorithm learns, from examples, to predict traffic on the road.
  • AI is when a machine performs a task that human beings find interesting, useful and difficult to do. Your system is artificially intelligent if, for example, machine-learning algorithms predict road wear and adjust maintenance in anticipation.

If you’re in the public sector, here’s how to make sense the terms analytics, advanced analytics, machine learning and AI. Click image to expand.

AI is often built from machine-learning algorithms, which owe their effectiveness to training data. The more high-quality data available for training, the smarter the machine will be. The amount of data available for training intelligent machines has exploded. According to an article on Forbes.com, by 2020 every human being on the planet will create about 1.7 megabytes of new information every second. According to IDC, information in enterprise data centers will grow 14-fold between 2012 and 2020.

And we are far from putting all this data to good use. Research by the McKinsey Global Institute suggests that, as of 2016, 90 percent of all European Union public sector administration data is born digital, but McKinsey reports we capture only 10 to 20 percent of that data’s value. Here’s what it looks like when you use AI to put public sector data to better use.

Here’s what it looks like when you apply industrialized AI in the public sector. Click image to expand.

We improve infrastructure

We need $57 trillion in infrastructure investment globally between now and 2030, according to a study by the McKinsey Global Institute. We could reduce that cost by increasing the productivity of our infrastructure. Scheduled maintenance wastes time fixing infrastructure that may not be broken. AI can learn to predict infrastructure failure. With predictive maintenance, we anticipate failure and spend time only on assets that need service. We spend less on maintenance and extend the life and productivity of our infrastructure.

We’re more efficient

The better we anticipate needs, the better we can serve citizens. Resource and service demand forecasts from AI make it easier to improve the use of both staff and infrastructure. With applied AI, the public sector would have the potential to redeploy staff to more productive positions and reduce operating expenses. We can increase the efficiency of public services by anticipating demand and optimizing our resources accordingly.

We’re more effective

Good policy and resource allocation decisions make for effective public services. AI can help spot policy problems by predicting citizen sentiment. AI can augment policy decisions by narrowing choices to only those options that will optimize the use of staff and resources. The McKinsey Global Institute found that this kind of applied AI has the potential for 0.5 percent annual growth in public sector productivity and 15 percent increase in citizen satisfaction. We see the big picture, make smarter choices and serve citizens better.

We’re more responsive

Red tape kills our ability to respond to citizen needs. From performing background investigations to approving routine requests, a big source of administrative delay is the time it takes to sift through piles of data.  AI can learn to spot anomalies in vast amounts of data. It can automate the process of reviewing documents (like applications for patents and licenses) and recommending administrative action. The McKinsey Global Institute found that applied AI has the potential to reduce some administrative error by 20 percent and decrease the cost of awarding contracts by 30 percent. We spend less time on routine tasks and more time responding to the needs of citizens.

Applied AI is a public service

If we see AI as technology, it makes sense to adopt it according to standard systems engineering practices: Build an enterprise data infrastructure; ingest, clean, and integrate all available data; implement basic analytics; build advanced analytics and AI solutions. This approach takes a while to truly benefit the public.

But AI can be a way to improve the lives of our citizens. When AI is seen as a differentiator, the attitude toward AI changes: Run if you can, walk if you must, crawl if you have to. Find an area of the government we can make as smart as possible as quickly as possible. Identify the data stories (like predicting fraud or predicting citizen sentiment) that you think might make a real difference. Test your ideas quickly using utilities and small experiments. Learn and adjust as you go.

It helps immensely to have a strong Analytics IQ — a sense for how to put smart machine technology to good public use. We’ve built a short assessment designed to show where you are and practical steps for improving. If you’re interested in applying AI in the public sector and are looking for a place to start, take the Analytics IQ assessment.

See more of Jerry Overton’s thoughts in Wired Magazine: Welcome to the Age of AI-Based Super Assistants.


Jerry Overton is a data scientist and senior principal in DXC Technology’s Analytics group. He leads the strategy and development for DXCs Advanced Analytics, Artificial Intelligence and Internet of Things offerings.

Jerry is the author of the O’Reilly Media ebook, Going Pro in Data Science: What It Takes to Succeed as a Professional Data Scientist. He teaches the Safari Live Online training course “Data Science at Enterprise Scale.” @JerryAOverton

RELATED LINKS

Managing the challenges of machine learning

Raising your Analytics IQ

Government executives: On being 21st century digital leaders

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