Four Things You Need to Know About AI in the Era of Automation

Intelligent automation: it’s one of the defining topics of our time.

Our Chief Technology Officer, Greg Goff, recently had the chance to sit down on Bloomberg Live with some fellow leaders in tech and discuss some big ideas around AI, automation, the cloud and what it really means to get insights from data.

Here are a few key takeaways that have been resonating with our customers:

1. The role of AI in automation is different from what you think.

Businesses, lawmakers and consumers around the globe know that AI will play a big role in building the next era of intelligent automation — the question is how? Cut through all the hype and buzz and you’ll see that some of the biggest questions about AI and automation are still being answered. Questions like: What is the role of AI in automation? When it comes to data and AI, what does having a competitive advantage look like? How will automation affect the labor market?

We won’t know the full scope of some of these questions for quite some time but we are beginning to understand how AI can help automate repetitive, complex tasks involving large volumes of data.

Today, when businesses talk about “AI,” most often they’re talking about machine learning, a subset of AI. Machine learning is the use of statistical computing to understand tendencies, patterns, characteristics, attributes and structure in data, to inform decisions with new observations.

That sounds way less interesting than robots being trained to mimic human decision-making and creativity or self-driving cars. Though it’s not the stuff futuristic dreams are made of, the advanced statistical modeling that drives machine learning today has a lot to offer modern businesses.

2. Finding patterns in extremely large data sets is highly valuable.

Advanced pattern recognition among extremely large data sets is an incredibly useful tool that solves a large number of business challenges. Machine learning correlates large data sets and finds patterns faster than humans can. Many companies across all industries are now using machine learning to analyze specific data sets to solve real-world business problems.

In industrial businesses, machine learning is used to better understand the health of machines and equipment, increase productivity and reduce unplanned downtime. For example, pattern recognition in wind turbines could be used to reduce downtime by just one percent across the existing U.S. wind fleet, resulting in enough additional energy to power one million homes.

3. Success depends on deep industry expertise.

At the end of the day, math is math. As more and more businesses begin to act on the value that machine learning and automation can deliver, they have access to the same math as you. What sets you apart is your unique expertise.

Machine learning doesn’t make machines more like people or replace people. It can ingest data and spit back insights but it still depends on people to interpret those learnings and make them actionable. It’s necessary to have subject matter experts that have a deep understanding of your business and your industry.

The winners in the automation race won’t be the best at math, they’ll be the best at using machine learning plus human insights to solve unique business challenges.

The impact of innovation is always overestimated in the short term and underestimated in the long term.

Roy Amara

4. The adoption of new technology is always slower than technology development itself.

Even though people generally adopt new technology slowly — they will adopt it eventually. In the next fifteen years, many professions will undergo significant transformations. This is a scary thought for most. A technician who has been working on a machine for forty years is naturally going to be intimidated by an algorithm saying “you’ve actually been doing it wrong all this time.”

So the question becomes: how do we get people to open up to the idea of new technology?

As a society, we’ll need to work together to adopt new technology and retrain the workforce. We’ll need to help people understand that in augmented intelligence, machines won’t replace people. They’ll just make them better and faster at their jobs, enabling them to focus on higher-level, more creative tasks.

At the end of the day, we’re not living in a world that’s run by robots, but automation and machine learning are here to stay.

They will certainly change the shape of the world as we know it — and we need to be ready. Our own Greg Goff summed our situation up nicely by quoting Amara’s law: The impact of innovation is always overestimated in the short term and underestimated in the long term.

If you want to learn more about how machine learning will impact the future of business, check out the full panel discussion below.