Employee Training Crucial for AI Investment Return

Key Quotes

"I literally can't tell you the number of times that I've talked to business leaders who have spent their companies anyways are spending usually millions of dollars on ai yet they haven't formally trained their people and it's almost baffling to me right because here we are with this generative ai technology powered by large language models arguably some of the the biggest technological shifts ever and it changes almost daily yet why aren't companies investing in their people to make sure that they understand what the technology does understand what it can and can't do and the cultural and process changes needed to actually get a return on ai so that's what we're going to be talking about today going over the ai training crisis and why companies are spending so much money on ai but not spending the time and the resources to educate their people"

Jordan Wilson highlights the paradox of companies investing heavily in AI technology while neglecting employee training. He expresses bewilderment that businesses are quick to adopt AI tools but fail to equip their workforce with the necessary understanding of these rapidly evolving technologies, their capabilities, limitations, and the required cultural shifts for successful implementation and return on investment.

"it's it's actually much more of a of a cultural shift that it is just a retooling of the team and of course it's important to provide tools and provide space and time but but it actually has to be treated like a cultural shift and an evolution of the culture of the company to be a company that embraces ai knows how to use it has expectations and -- and even has like i think of them as cultural moments where ai comes to the forefront and it highlights it for people and it highlights it for people and gives them permission and expectation and things like that so the cultural exchange has to be very well managed and then like the security of applications the data ability the tooling the the training that people talk about i think that's the biggest surprise is how much of the culture impacted it actually is"

Dan Lawyer emphasizes that adopting AI is primarily a cultural transformation rather than just a technical update. He explains that companies must foster an environment where AI is embraced, understood, and integrated into the company's evolution, supported by deliberate "cultural moments" that normalize its use and set clear expectations for employees. Lawyer points out that the impact of culture on AI adoption is often underestimated compared to the focus on tools and training.

"and and i actually think one of the biggest gaps and how people think about you know getting value from ai it's the combination of training but also the expectations they're like in order to get a good out from from ai you know generative ai is non deterministic businesses don't survive that very well they need to you know predictable outcomes and so you have to teach people that to get good outcomes from ai they actually have to ground the ai in what a good job looks like you have to ground the ai in you know the reality of like this is how our work gets done at our company if you want to automate that work and and so you need to like actually back people up and teach them okay you have to actually have a a fair amount of documentation that you can provide to the ai about how your company works and about what a good job looks like before you can then get the highest value from ai and so it takes some preparation and some forethought and some domain specific knowledge to be able to do it well"

Dan Lawyer identifies a critical gap in achieving AI value, stemming from the combination of insufficient training and misaligned expectations. He argues that because generative AI is non-deterministic, businesses need predictable outcomes, which requires training employees to "ground" the AI in what constitutes a good job and the company's specific workflows. Lawyer stresses that this grounding process, involving documentation of company operations and desired outcomes, is essential for maximizing AI's value and requires preparation and domain expertise.

"and we think about this at lucid a lot we talk about it as the last mile problem right which is there's you know if you think of logistics right you build a bunch of distribution centers -- that doesn't matter actually unless you can get it from the distribution center to people's homes and and similar in ai it's like if you can have ai a licensed tool -- but if you if you can't actually you know pass to the ai information about how your company works which requires you to actually go through and document your processes and document things and get the knowledge that's scattered that's a lot of people's heads and get it all together people can see it and then to make it worse like if you pass the ai a bad process you'll still get a bad outcome and so you actually have to you have to document how your company works and then you have to refine that and that's partly essential training is like you have to teach people that that part of what they need to do to get the most value from ai is to document how they work so that they can share that with ai to have a good examples of good outcomes so that they can share that with the ai and so there's a fair amount of like teaching and expectation setting i think that has to happen there"

Dan Lawyer likens the challenge of AI adoption to the "last mile problem" in logistics, where the final delivery to the end-user is crucial. He explains that simply having AI tools is insufficient; companies must document their internal processes and knowledge to effectively "pass to the AI information about how your company works." Lawyer emphasizes that this documentation and refinement of processes are essential training components, as feeding the AI incorrect or poorly defined workflows will lead to suboptimal outcomes.

"and and i've even like like i wouldn't go all the way there and maybe mentally sometimes i like i tend to personify my ai assistants quite a bit i tend to talk to them as if they're real people -- i but then i have to back them off because then they tend to talk to me like like i worry that you know my ai assistants try to flatter me and i have to tell them i'm i'm like look i don't want i don't want you to flatter me i want critical thinking like i don't i don't need you to tell me this is good or like i need honest assessment i actually have to have to say things to ai to get it to give me more critical feedback otherwise it's just telling me everything i do is great which is not true yeah so like there's like just like a whole work paradigm that we have to think through"

Jordan Wilson shares a personal anecdote about how he consciously adjusts his interaction with AI assistants to avoid them becoming overly flattering. He explains that while he sometimes personifies AI, he actively steers the interaction towards seeking critical feedback rather than simple affirmation. Wilson suggests that this deliberate approach is necessary to obtain honest assessments and that a fundamental shift in how we work with AI is required to move beyond mere validation.

"and so like like that is a critical thing and and pretty much any part of any company can figure out like where are their cultural moments where we give the airtime to ai to to like start working on the behavioral change and help people realize it's safe to play now you also have to create the space right like expecting people