Blog article
See all stories »

Is Automation Always Better?

“Science, my lad, is made up of mistakes, but they are mistakes which it is useful to make, because they lead little by little to the truth,” wrote Jules Verne.

This is a reprint from my recent article for the Forbes Technology Council

 

I’ve been in the field of automation long enough to see that many of the most impressive advancements are still quite new. Take driverless cars, for instance. A decade ago, they were little more than a fantasy, but in February 2019, Elon Musk predicted that we’d have the technology for a fully self-driving car by the end of this year.

Driverless cars promise to transform transportation, but something like machine learning promises to transform everything. Smart systems that can learn from experience have applications in just about every aspect of life. In healthcare, researchers found that machine learning systems can correctly classify echocardiograms up to 92% of the time, whereas human doctors can only do so 79% of the time.

 

As automation technology has grown, so has its accessibility. Advances in natural language processing promise to liberate AI from purely technical settings and integrate it with more aspects of daily life. Amazon has sold 100 million Alexa-enabled devices, and almost every household technology staple is now available in a “smart” model.

I’m optimistic about this new era of automation, but I’m also cautious. Forward thinkers have always understood that technologies have the potential to create challenges as well as solve them. If we’re going to realize the highest potential of AI and automation, we need to not only acknowledge the worst aspects, but also do our best to avoid them.

When Automation Isn’t An Asset

We don’t need to imagine a futuristic scenario to see what bad automation looks like. Most of us are already inundated with robocalls -- something that combines the annoyance of telemarketing with the persistence of automation. Robocalls may cut labor costs, but they alienate customers in the process. Unfortunately, this is just one of many examples of automation delivering shortsighted solutions.

Bad automation has made the news in recruiting, too. It’s a common misconception that AI is free of human bias. However, tools designed to automate candidate selection have been shown to exclude women and others because the algorithms they rely on are human creations full of subtle prejudices. In that way, AI can amplify the faults of humans rather than eliminate them.

Implementing The Better Automation of Tomorrow

My goal isn’t to discourage anyone from embracing automation. Rather, I want to underline the fact that it’s not an automatic improvement.

Companies that want to leverage it effectively need to look for the right applications, ones that deliver value for both the business and the end users. If you’re looking to implement AI and automation, I recommend the following best practices to ensure that the technology is an asset, not a burden:

1. Embody The End User

If the technology is customer-facing, you must first make sure it’s actually addressing a customer pain point. Otherwise, it’s not likely to be much more than an annoyance.

Focus on precision, not just volume. As an example, the database being used to feed chatbots, robocallers or auto-mailers information about customers must include their history and all touch points and interactions with the business to avoid repetitive communication. In the case of financial institutions, for instance, if a person has already been denied a line of credit, they shouldn’t receive mailers that continue to make that same offer.

Ensure your marketing department’s CRM software and customer database are linked and regularly cross-checked with those in related departments. To avoid the above case, the underwriting and marketing departments should be sharing data to avoid annoying potential customers.

2. Avoid Out-Of-The-Box Solutions

Even if you entrust a third-party vendor to implement automation for you, someone on your staff should have a solid understanding of the algorithms, training sets and metrics used to fuel the technology.

This is because no automated system is a purely out-of-the-box solution. Every business has different inputs, processes and other variables, and each tool should be fine-tuned accordingly. An employee who understands both the unique challenges of your company and the technology itself will be able to liaise between you and your automation partner.

Also, to identify where automation will really boost efficiency, management or technology-positioned employees should be able to clearly define the process that’s set to be automated. If they can’t outline the inputs, the processes and the expected output, the process is not yet ready to be fully automated. These tools need to be built with certain standards in mind and fine-tuned to meet specific needs.

3. Prove The Business Case

Establishing the business case can be difficult with AI and automation because a decrease in one set of costs may be offset by an increase in another.

It’s easy to imagine a business process that uses a certain amount of human labor and takes a certain amount of time. Sometimes, automated systems can lower both of those things -- but not always. The automated system may speed up the process, but the human labor required to monitor and assist it may increase, along with material input, utility and technology costs.

The point is that automating just because you have the resources to do so is not a productive strategy. To know whether your business should automate, crunch the numbers to estimate long-term value. Consider the full costs of employees who might be replaced -- including insurance, pensions, office space, etc. -- as well as the full expected savings of the new technology, including increases in the quality of your outputs.

Our automated future seems certain, but that doesn’t mean the evolution will be easy. Some companies will thrive because of automation, and others will stumble for the same reason. The difference often depends on which players are being realistic. Instead of treating automation like a panacea, companies should see it for what it is: a tool with tremendous potential, as long as it’s used in the right ways.

 

3221

Comments: (0)

Ron Cogburn

Ron Cogburn

CEO

Exela Technologies

Member since

12 Apr 2016

Location

Irving

Blog posts

10

Comments

0

This post is from a series of posts in the group:

Artificial Intelligence and Financial Services

Artificial Intelligence and Financial Services


See all