Get More Out of Feedback Loops With AI

This article was originally posted on the Forbes Technology Council. Click here to read the original article.

On a crisp winter morning in Kitty Hawk, North Carolina, Wilbur and Orville Wright made history when they achieved the first powered flight. With Orville behind the stick, the plane traveled nearly 120 feet on its inaugural flight.

Those 12 seconds represent a watershed moment in aviation, but they were the culmination of nearly three years of trial and refinement. Between 1900 and 1902, the brothers tested hundreds of different wing shapes and airfoils. When early models failed to produce the expected amount of lift, they tried changing the wing size. When that failed to solve the problem, they built a wind tunnel to measure their own values for lift and drag.

Every failed launch and hard landing was part of a feedback loop that took performance data and folded it back into a constantly evolving design.

It’s an approach businesses still use today — only the soft, sandy beaches of Kitty Hawk have been replaced by a new kind of sandbox — computer modeling systems that enable rapid testing and iteration. These systems allow people to test the effectiveness of multiple designs before moving to production, lowering costs and providing a more comprehensive perspective.

What Are Feedback Loops?

As the name implies, a feedback loop is any process where the outputs of a system are plugged back in and used as iterative inputs.

Feedback loops exist just about everywhere. In nature, the evolutionary “arms race” between predators and prey is a classic example. In business, the practice of taking customer feedback (the output of a product or service) and using it to improve future processes is another commonly used feedback loop.

Today, rapid advances in artificial intelligence (AI) and machine learning are helping businesses do more with data. These systems — and their ability to analyze an inhuman amount of data — allow businesses to adjust algorithms, workflows and processes on the fly.

AI And The Future Of Feedback Loops

Just about every industry uses feedback loops to streamline their operations, but the recent shift toward digitization is helping businesses break down information silos and derive more value from these iterative cycles.

Previously, business leaders relied on slow, manually driven methods of capturing, uploading and analyzing data. Over time, the widespread adoption of mobile apps and an increasing number of sensor-equipped devices helped them capture more data than ever.

Now, these businesses are using AI to capture and analyze data, at a speed and scale that would otherwise be impossible.

Semantics aside, there’s no practical difference between AI-driven feedback loops and the Wright Brothers’ wind tunnel experiments more than a century ago. The only difference is who (or rather, what) does the observation and analysis:

• First, the system observes user actions and system events to capture data for analysis.
• Next, it analyzes observed data against historical trends and data from other sources.
• Then, it predicts outcomes based on observed data and related analysis.
• Finally, it recommends specific actions.

Ideally, these cycles are a loop without an end, as the system continues to refine its recommendations based on the latest outputs.

Disrupting The Break-Fix Model With AI-Driven Automation

When I talk to telecom leaders, they often stress the importance of asset uptime. In an industry where every minute of unplanned downtime is money out the door (not to mention a hit to their brand), keeping assets up and running is mission critical.

Unfortunately, the traditional method of applying standardized maintenance schedules based on manufacturer recommendations is unlikely to catch problems before they occur. That’s why some telecoms are exploring ways AI can help them deliver predictive maintenance.

AI and automation can be used to solve a number of business problems, from predictive maintenance to better forecasting and resource allocation.

Over time, an AI-driven system can automatically trigger an off-schedule maintenance request, based on historical trends and real-time data. It’s a bit like always having a technician on-site, and all it takes is a little user feedback at the beginning.

Implementing Feedback Loops In Your Organization

The long-term goal of your business might be to drive automation, but you can’t simply flip a switch and check back next quarter.

Earlier, I wrote about tomorrow’s workforce being a blend of humans, AI and automation — and AI-driven feedback loops are a perfect example. The most successful ones will include human input and intervention along the way.

Before you can successfully implement AI-driven feedback loops in your organization, you need to:

• Determine the key metric or outcome you would like to improve with automation.
• Figure out the critical variables and components that impact these outputs.
• Audit your current technology systems to see where your data currently resides and break down any information silos that might “muddy” results.
• Provide critical feedback on early responses, leading to smarter, more effective automation.

Once you have the right systems in place, AI can help you create feedback loops where your product or service continuously improves with use. But like the Wright Brothers more than a century ago, the most effective loops will still be spurred by human ingenuity. As you examine ways to drive efficiency and automate more processes, remember to include the unique problem-solving ability of people — especially at the beginning.

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