If you’re an avid reader of Forbes, TechCrunch and other tech publications, you’re probably tired of hearing about how AI and machine learning technologies are changing business.
At this point, these inescapable terms have been reduced to buzzwords and marketing jargon in the minds of many. But assuming the innovations are irrelevant is a mistake.
The hype is justified.
I know, because I run a professional services company that’s currently developing AI-powered products, including commercial real estate software and corporate performance management software. And I also lead strategy for a company that uses AI to run a recruiting job candidate-matching software.
So, I’d like to offer a glimpse into a few specific, practical ways AI technology is changing everyday business.
Using AI, recruiting is overcoming bias and accounting for every good candidate.
A key component of recruiting software is the ability to find the best candidates for the job. To achieve this, the majority of technology today relies on keyword search and requires the use of boolean—a system of logical thought.
But this technology fails to account for two major realities about recruiting. First, it can skip over great candidates who don’t include the right keywords in their resumes. Second, some people (who may not be great at their jobs) know how the system works and hack it by dumping keywords throughout their resumes.
Fortunately, AI technology is poised to solve the problems of keyword search through the use of ontology. Tom Gruber, an AI specialist at Stanford, defines ontology as “the specification of conceptualizations, used to help programs and humans share knowledge.” An ontological approach can match the right candidate with a role in a way keywords just can’t.
Here’s a real example that my team at ThisWay Global encountered: A pharmaceutical manufacturing plant had a job opening for a technician/pipe fitter. They needed someone who not only had the technical ability but who also could be trusted with a high clearance level and was willing to work closely with a potentially hazardous chemical.
Ontological technology was incredibly useful in this job search because it accounted for all factors of the role, even those that would be near impossible for a human recruiter to piece together. With the help of the technology, the pharmaceutical technician role was filled by a nuclear engineer who was working on a nearby military submarine base.
Who would have recognized this person was right for the job? Not the average recruiter, that’s for sure. But AI-backed software took a holistic and comprehensive look by going beyond keywords to understand how the data is connected—without introducing bias.
Machine learning technology is making life a lot easier for commercial real estate brokers and agents.
Until recent years, the real estate industry has never truly embraced tech—mainly because it has never had to. And it’s even worse in commercial real estate (CRE), where, unlike residential real estate, there’s no universal listing software. CRE brokers and agents spend a lot of time finding properties for clients on their own, the old-fashioned way.
But new technology can gather data from many sources to consolidate all the information a broker needs in one place. Applying machine learning to that data can create powerful features. For example, it can take a tenant’s specific needs and provide a list of available properties that meet those needs, without the broker having to call half a dozen other brokers to get the details on the properties.
Of course, humans can do all of this. But the software does it a lot faster—and more accurately.
AI is changing how to forecast outcomes for virtually any business.
At my last company, I spent nearly ten years creating a massive series of Excel workbooks I called “The Beast.”
For a non-tech tool, it was impressively accurate. It allowed me to predict almost everything about my business. It eventually became so expansive that it gave my team the ability to model out scenarios ranging from product pricing and sales quota, all the way to what markets to enter and what to hire for.
But every old-school approach can be improved by new innovations.
When I was in the process of setting up “The Beast” for an advisory client, they suggested it would be an amazing software—and I had a light-bulb moment. I realized that if I combined “The Beast” with machine learning, the technology would be able to map out vast amounts of business data. It could equip companies to use pricing as a strategy rather than just a percentage above cost, hire based off true needs rather than perceived needs, and more.
Now, the resulting technology proves most useful for finding insightful patterns too complex for a human to track. It doesn’t replace people, because you still need human leadership and problem-solving in any process. But it gives leaders a new set of intelligence in a much shorter time period, without extensive errors.
Even as an AI-powered technology developer, I often find myself relying on my gut instinct when making business choices. But it doesn’t have to be one or the other. Both instincts and technology are incredibly valuable for decision-making.
Although, if you don’t begin using AI-powered technology to optimize your processes and help make business decisions soon, you’ll be swinging in the dark compared to your AI-equipped competition.