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What's your AI success story? What made you realize you had to change? And what does a hopeful future with AI look like to you? AI Hope with Geoff Livingston asks three simple questions of people doing the hard work of adapting and thriving in the AI era.
Efficacy versus Efficiency
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[00:00:00] Hey everybody, my name is Jeff Livingston, and this is AI Hope. My book, Now Is Gone, is about never-ending change. This podcast is about what comes after the stories, the people, and the reason for hope.
Hey, everybody. Welcome to the latest edition of AI Hope. It is October 18th, and I am recording it day of release. So, interesting week, but before we get there, I do wanna say you're gonna be seeing more of these types of podcasts from me. I do have a couple of guests coming up, but I do wanna make them special, right?
~Like, ~I want these things to be valuable and not just the usual, ~uh,~ "Hey, I got a guest," and voila. ~You know, ~they-- I want somebody that's got some insights for you, and again, we're just ~kind of ~trying to figure out this AI thing as it's happening. With that in mind, I did speak at the GAIN Conference on Thursday, and this [00:01:00] is business to government marketers, so a lot of the government contractors, professional services, marketing firms that are all serving, ~uh,~ federal, state, and local government.
I thought it was really interesting to get a vibe for what's going on given the election and the budget year coming up. And it's interesting because it just goes to show you again how, ~uh,~ the federal market's just a little bit different. People are s- just starting to recover from the whole DOGE thing, which was, ~uh,~ for this market, particularly Washington, D.C.,
extraordinarily rough. But from an AI standpoint, they're also expected to do really well And that's in part because of the federal government's desire to use AI, but also because the government contractors tend to mirror their, ~uh,~ customers, and they too have a lot of downward pressure on AI. So I spoke to them on this topic, and one of [00:02:00] the things that we really started to discuss was efficacy versus efficiency.
And if you listened to last week's podcast, I gave about 45 seconds, a minute on that. ~Uh, ~it really resonated with them. It's starting to resonate with nonprofits I'm talking to. It's resonating with professional services I'm talking to. So I thought we would have this episode and really dive a little bit deeper into it.
Now, obviously, efficacy and efficiency have the same Latin root, ~uh,~ and thus the similarity between the two words, but they mean very different things in the cur- common vernacular. And I think as we discussed last week, efficiency really is kinda code in AI for cut costs, cut people, cut, cut, cut, cut. ~Um, ~and while there is some conversation about productivity around it, generally speaking, I think people are very sensitive to the conversation of them becoming more efficient using [00:03:00] AI, and with good reason, right?
~I mean, ~most of these employers that have deployed these solutions to date haven't really promised to take their employees with them to the Holy Land if AI is successful. Only thing that they're promising is that you can do more work. Isn't that exciting? And in some cases, maybe that is a big win, ~you know, ~the productivity thing.
A lot of statistics that have been released, particularly as it relates to labor this year, are showing that, ~uh,~ eh, not so much, right? The productivity increases that are promised by AI haven't really happened. So maybe we're seeing efficiency, but at what cost? I think what is most disturbing to me about that whole logic is how does that help you attain your business goals outside of, "Hey, we're doing things cheaper"?
That's a big question, and I don't think a lot of executives [00:04:00] have the answer. When I look at the general fall down that's occurred with AI and corporate adoption, almost always it comes back to the ELT, the executive leadership team, and how they've failed to really understand this technology and have simply used it as kind of the new shiny object that's gonna make everybody better and their, their jobs easier.
Plus, their boards are sitting on them, so they're just ~kind of ~like knee-jerk reacting as opposed to really coming to understand what the technology is and how it can help them And if you think about the way a lot of executives have approached various business roles, it's really painful. ~Um, ~one of the examples that came up at that GAIN conference that, ~uh,~ I spoke about on stage was if anybody remembers the old advertising, ~um,~ analogy where the executive would show up like, "Look, I wrote your new a- [00:05:00] advertisement for you because you don't know what you're doing.
This is so easy to do." And the same thing happened with social media. "You should have a Facebook page. Where's your Facebook page?" Right? And, ~uh,~ on, and on, and on. ~Um, ~it just shows shoddy leadership. Good leadership, and this is true of individuals with your own careers, my own career, with the teams that we're working with, and of course, our larger organizations look at efficacy.
And efficacy is where the hope is with AI, in my opinion, because that's the mission. Okay? That's, that's the North Star. That's where we wanna go. And if we have efficacy in mind, basically becoming more effective in achieving our goals, okay, so for example, if I put in 40 hours and I help 35 people in a week with AI, maybe with AI [00:06:00] and tuned towards my mission and making me more effective, I either reach more people or the quality of my aid, my instruction, or my training, whatever you might wanna call it, increases, right?
So instead of, hey, Jeff helps about 85% of the 35 people he touches every week, he's now reaching 92% of the people. Now, efficacy as a concept is pretty well understood in the nonprofit world. However, it is something that is becoming more and more relevant for businesses, and I think that smart team leaders are gonna start talking about efficacy.
Everybody in that GAIN conference understood what was being talked about when we started discussing the effectiveness of our ability to achieve mission. Efficacy is obvious. Efficacy is the goal. It's the [00:07:00] brand promise. It is everything that a company is trying to achieve when it serves its customers, and I am using words intentionally there.
Serve is an important word. I unfortunately think too many of companies today think serve means make investors happy. Unfortunately, without customers, you have no business, and we're seeing that also come to play more and more frequently So efficacy, what is your North Star and how does AI help you achieve it?
I think these are really good questions. Once we understand that, we can start looking at use cases. Is this a good idea or is this a bad idea? And a good idea is going to be on point, on mission, and is gonna make you more effective. A less useful idea is just, hey, this is new and it l- looks really cool.
We should be doing it. Hey, let's go into Joe's office. I'll bet this new meta AI that's gonna compete with [00:08:00] ChatGPT is the answer that we've been looking for with his complaints about how LLMs work. This new one for Mistral is really it. ~You know, ~by the way, I did see the Meta announcement with Mistral.
Another one on the corner. It's ~kind of ~like Saab or I don't know, some secondary car brand. Cool. It's cool that it's there. It's not cool that it's Meta, but it's cool that it's there. But I don't know if it's gonna make any impact. ~I mean, ~at this point, deploying new LLMs, unless you have something that's lights out incredible, why bother?
~I mean, ~the market's pretty much established at this point. So with that in mind, I think I'll, I'll leave it at that. ~You know, ~what is your North Star? How can you choose AI use cases that are gonna increase your efficacy as opposed to increase efficiencies to make executives that don't understand AI happy?
Efficacy, efficacy, efficacy. Have a great week. We'll be back next week. Cheers.