AI in Context

Artificial intelligence is everywhere in our daily lives, from Alexa to messenger apps automating communications to electronic toothbrushes. AI has so many different applications that it likely means something very different from one person to the next depending upon their experiences and perspective. In fact, it means so many different things to so many different people that its positive business impacts are being compromised. This is a real issue for those businesses where AI is core to their offering.

In my role at Fetcher, a recruiting platform leveraging AI to fill open positions, I see the technology’s potential in industries like HR being highly touted. It can bring you closer to prospective customers and/or candidates, drive conversions, free up resources and so much more. But ask yourself: How is it doing so far within your own business environment? Are you getting the most out of it or really just automating a few routine tasks with incremental benefits at best?

Ai in Context #2AI is packaged into “solutions” and put to work in cookie cutter ways to sell B2B and B2C products. These products and platforms that claim to be driven by AI may or may not get the job done. It’s important to understand not only the potential of AI but its limitations, as well — particularly within a given industry. If processes are not optimized, AI will only further expose what’s already not working for you. And, if you choose the wrong technology for your business challenges, you could end up working for the tech instead of the tech working for you.

It’s vital to have a firm grasp on the bigger business problems you’re trying to solve when you start thinking about AI. If you are already using AI, are you seeing the results you expected based upon what was promised? Is it solving your biggest business problems? Understanding the broader business context and understanding your current gaps before applying AI is critical. While the technology can learn anything, those using it must still provide the feedback that helps the technology align to the bigger business context.

HR Tech centered around talent acquisition has mostly focused on one key question: How do you find someone? While the applications of AI for recruiting are still in their early stages, some results have been imperfect or even disappointing based upon limited application. When applications have failed it’s usually because there’s a disconnect between how the technology works and how the discipline works. With AI and HR Tech, understanding the fundamental nature of how recruiting works, how to align a search (self-directed vs automated) and understanding exactly what AI is not is key.

At Fetcher, we deploy AI in a way that works around the clock to support your bigger business context. It centers around the job description and discovers people you may never have even considered for a role. We also understand the limits of the technology. That’s why we combine human expertise with AI and apply that to help the technology learn and ensure you get the best results possible.

MLJ AI in ContextOne of our earliest customers was an entrepreneur named Mary Lou Jepsen. Her startup built opto-electronic and holographic systems to look inside the human body. Mary came to us desperate to find a “Senior Hologram Architect”, which, like the business itself, was an extremely sophisticated and complicated role to fill. Yet, because of our approach and technology we were still able to unearth five candidates within the highly specialized criteria she was looking for. Ultimately, she hired one of our candidates and has since gone on to hire a COO and a Senior Ultrasound Imaging Physicist using our platform. Finding that needle-in-the-haystack candidate at scale is the perfect demonstration of Fetcher’s unique application of AI in talent acquisition.

The future of AI and its ability to help solve some of the biggest challenges in recruiting is exciting. There are a lot of smart people in the world now looking at artificial intelligence and how to apply it in this space. I’m just happy to be a part of that process, shaping the application of AI for our particular industry while continuing to try and understand (and explain) AI in its broader context and particular applications.

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