Setting off into the AI abyss takes a strong stomach, especially when companies are under pressure and learning at hyperspeed. Building the track while the train is running is hard enough already without a destination that keeps changing. An August report from MIT, which found that 95% of companies are seeing no return on their AI investments, hasn’t helped ease the journey either.
It begs the question: How should a company implement AI? And can you give yourself a grade along the way? We spoke with three companies—Asymbl, Shopify, and Hapax—embarking, and trying to keep themselves in check, on the AI journey.
Aim small, miss small
“You’ve got to think about the outcome,” said Asymbl CEO and founder Brandon Metcalf. The recruitment and staffing solutions company wants to approach AI with a smaller scope to achieve large results.
“The way we look at AI and digital labor is: What’s the impact that it’s going to have on the business? And what’s the impact that it’s going to have on our employees?” Metcalf said.
Within his sales organization, Metcalf created an AI SDR named Theodore that handles most of the tasks that a human SDR can for his team.
“We saw a need in the business for someone to interact with all these leads that we had coming in in a meaningful way,” Metcalf said. “We decided to give Theodore a try as our first agent…then we saw him starting to perform well in the test case of leads that he had.”
Metcalf said that each AI investment is made with a specific outcome in mind. “What’s your game plan?” he said. “Make sure it’s grounded in realistic results that you can measure, not just what’s the activity the AI is doing.”
Asymbl then created its own benchmarks for tracking success and hired a chief digital labor officer, who oversees the AI deployment across every department.
“The game plan is by 2026, 40% of our labor will be digital,” Metcalf told Revenue Brew. “ Our game plan for this year is 20%. We’re already ahead of the 20% from the success that we’ve seen, and we’re getting better at it.”
Metcalf says the technology is a moving target, requires focus, and is time-consuming. “Keeping our internal team up to speed is our biggest challenge,” he said.
Creating an AI culture
Shopify has not been shy about communicating its mandatory AI policy. While CRO Bobby Morrison says most companies have taken the stance that AI is solely the purview of engineers, data scientists, and the like, Shopify has “taken a different approach, where we’ve democratized AI and made it available for everyone across the company.”
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Morrison created a grading spectrum that measures where the company, and its employees, is in its AI commitment, which spans AI naive, AI curious, and AI native. Once each employee gained access to AI tools and the spectrum was created, Morrison said the next step was to create a culture where employees feel emboldened to create their own AI solutions, what Shopify calls tinkering.“There are a set of behaviors that you can see in your business that really help you identify where you are along that curve,” he said.
When asked where he feels Shopify is on that scale, he thinks it is beyond AI curious and on its way to being native, but he added that because AI is changing so fast, being native is only partly attainable.
“It may be one of those unachievable goals because once you think you’re AI native, the next version comes out, and you realize now you’re all the way back down to curious,” he said.
Better safe than sorry
“This is a journey, not a sprint,” said Kevin Green, co-founder and CMO of AI solutions company Hapax, referring to the sea change businesses are experiencing as the AI revolution unfolds. Hapax is encouraging clients, primarily banks and other financial services providers, to slow down and build a safe and scalable foundation.
The big question for Hapax’s CRO Aaron Kwan: “I always go back to ‘why’…Why are you trying to deploy AI? What are you trying to solve? Let’s step back,” said Kwan.
The company prides itself on being transparent and deliberate in implementing protections it says will pay dividends in the future. For Hapax, building a strong AI use case is paramount but just as important is developing a systematic process for ensuring deployment is responsible.
“We’ve got to sit back and plan and take the safest approach that also solves the most problems or creates the most opportunity for us,” said Kwan.
Meanwhile, Green highlights other major pitfalls, such as the potential for compromised data. “I don’t think people understand the security vulnerabilities in the Big Tech solutions,” he said.
Kwan echoed the sentiment. “You have to have guardrails in place to protect your customer data and your company data. If you don’t have that in place, it’s not worth pursuing AI at all, because you’re putting everything at risk if you don’t.”