B (3:40)

That's awesome. And that's kind of what we need right now. Things are moving faster and faster. You know me as a technologist and you of course knowing over the years technology has moved quickly, but now it's hard to keep up. And I like how you highlighted your two day program is different than your typical old school, the multi week cohort, because you know how to elicit the right information out of the technology, the AI, the LLMs today to be able to produce something that is going to get people to launch into something right away. So I really appreciate that. Chris, let's talk about your ignition process. What makes it unique and how does it help companies move from pilot AI ideas to full scale development across departments?

A (4:19)

We see a lot of companies and there is intermittent and sporadic introduction of AI and in some cases the leadership isn't even aware that their team is using it. The Industry term for that would be shadow usage. And what we find is that if they're like, no, we've told our employees they can't use it yet until we identify governance framework and training and things like that. And then we find out that people are already using it. And I think it's going to be hard to get your employees and your teams to withhold from using this tool, especially if they're already using it in their personal life and they're getting big wins. They're going to be tempted to introduce this into their day to day and just maybe not tell you now if you're getting the productivity gains, you're like, well maybe that's okay. It's not. There's a lot of risk if there is ungoverned and untrained usage of these models in a business environment, you might be violating an NDA, perhaps with a client. When somebody introduces that client's data to work with it in the models, they might be unknowingly introducing information that they shouldn't be. And if their settings aren't correct in their chat GPT accounts, that information is being used to train the model. And time and time again we've been able to show demonstrations of how one of our clients competitors has done that or somebody in their company has done that. And we're able to get some insights into what their competitors are doing and how they're using not only AI but also other business strategies because someone on their team was ungoverned and using this to what seems like a good idea. Let me get better strategy, let me get better insight into how we can support our customers. But if it's done the wrong way, obviously it's exposing that company to potential risk. So our framework starts with first we upskill the executive so that everybody on the executive team has the same baseline understanding and level of knowledge so that they're able to have a communication collectively and knowing that everybody is on the same page. It's not like you've got the one superstar and everybody turns to him or her and says what do you think after that? We start with a. Our personal belief is that in our Ignition framework you need to establish not only a governance and use policy that everybody gets tuned up on and trained on. Great. Now that we've got that policy, let's actually train the teams on using the tools through the lens of that policy. And we found that that definitely mitigates a lot of risk when it comes to how my teams and how or our clients teams are using AI. Once that's done, we tend to identify a champion in each department. Now we focus in SG&A sales general administrative in particular because that's something that every company does, every company hires, every company runs payroll, every company markets and sells. So by doing it that way, we don't need to possess that internal domain knowledge or industry domain knowledge because we rely on the people in your company that already have that. And now they're able to start to do something that we call think in AI. And that's basically creating a new reflex, a new default behavior of before they take an action, they at least consider can AI support this action being done better, being done at a higher quality and being done faster. And typically you may have that department lead, but they may not be the AI enthusiast in that department. In that case we'll get the department lead and somebody that in that department has raised their hand and said, hey listen, like I'm interested, I'm, I'm using it, I'd like to be involved. So once we have those domain experts or department level experts in your company and perhaps being supported by the internal AI enthusiast, we will start to identify specific use cases within their departments that are low hanging fruit things that nobody likes doing. Everybody has to do it. It's part of the deliverable, the fulfillment for that department. But it ends up being not high leverage and, and a lot of times it may be. Well, we've got this spreadsheet that we've built over the past five years and if somebody enters this, it updates this. Those things are certainly helpful as better than guesswork in any company. However, it may not be the most effective way to get that insight, to update that report or whatever it is. So we show them how to identify those and then we introduce either some proprietary prompting frameworks that we teach that honestly I don't see being taught anywhere else and I'm surprised because they're so easy and powerful. Sometimes that's supported by custom GPTs or in Claude it might be a project, or in Gemini it's a gym. And the combination of those two, I'll give you a good example. We were working with a construction company in Orange county, does about $50 million a year. They had seven departments represented in our training and we came back and circled back with them about 30 days later and just kind of hey guys, what have you been doing? Where are you stuck? And how is that impacting your role? And across just those seven individuals 30 days later, collectively they were saving about 300 hours per month of bandwidth that could now be focused on higher leverage activities, strategic activities, addressing the backlog of projects that are sitting on the whiteboard. And then once we do that, it's kind of a cycle, we'll repeat that cycle, but we'll go deeper and deeper and deeper into the different departments of the company so that the results end up compounding. It's a pretty fantastic thing to witness.

B (9:09)

That's amazing. Thank you. I love you going through and breaking out your ignition process. And just to highlight a couple things, yes, we see people doing a lot of shadow usage out there right now and it's like I said, it's double edged sword. It's great. I'm glad they're leaning into it. But you know, without a policy, which we've created, of course, and we do have an IT governance council that helps manage that, but it is hard to kind of keep that lassoed in a little bit because there are some gotchas, as you know, and you explain those. But yeah, starting with that exec team, making sure everybody's understanding everybody's on the same page and then really leaning in and looking to see where we can start to adopt this technology. Chris, last question of the day. If you could briefly share from your experience, what are the top two to three AI use cases delivering the highest ROI right now in operations or go to market strategy?

A (9:56)

You know, marketing is low hanging. Fruit marketers just by design tend to be very interested in new technologies. They're already comfortable with CRO tools, SEO tools. So the bridge from their day to day role into using this new tool is very easy for them. And then with marketing, obviously there's a lot of content, there's a lot of data comparison when it comes to creative that you're using and things like that. Those are tasks that AI generates. Generative AI is specifically fantastic at. So marketing is a great place to start. Obviously finance, not arithmetic, is not an opinion. So it makes it very easy if we've got specific ways that we run reports or that we analyze, you know, last quarter's data and things like that, rather than having an analyst or analysts spend days or maybe even weeks before they're able to present their findings using generative AI safely and with the frameworks that we teach, they're able to get these insights, let's say instantly, especially compared to the old way of doing things. So I would say marketing, finance for sure. Sales, operations, hr, huge opportunities in hr. We talk to HR leaders all the time, who, when they tell us what their existing processes are, look, they have to do it there's no two ways around it, but the way they're doing it is the old way of doing things. It's not introducing something that is able to help pre screen candidates, help match them up, help review their personality tests and identify if they're going to be a good fit for the role, if they're going to have friction with other people in the department based on those other people's personality tests or performance indexes. So those would be areas where I think every company could feel safe. Starting now, our number one advice for every company is we kind of look at using AI in this 108010 framework. The first 10% of you working with the models for any task is you as the human being. Very clear on what's my ideal output from the this session with ChatGPT, hit the enter button. Next 80%, that's the models doing the work and doing the work accurately and quickly. But before you copy and paste that output and send it to the boss or send it to the investors or send it to the board, we encourage that final 10% of you working with AI being giving it a review, taking a look at what is this output? Is this how I would have said this, or is this the information that I would have highlighted? Look, that's important for people to understand. It's not like a Google search where I enter something in, I get some results and it's like, okay, let me go to work. The intention behind best practice with using generative AI is that there is a human in the loop at the beginning and at the end. And you may find that you did such a good job on that first 10% that the last 10% happens really quickly, like, hey, this is good, let's ship it. But more often than not, we find that there are human nuance that wants to get introduced into that output before it does go into production or gets presented to the public or the board. So those would be the areas where I would suggest any company at least feel comfortable doing that, introducing AI or at least exploring the introduction of AI. And guys, it's not that hard. I always tell executives you've already done the hard part of learning AI. And what I mean by that is that all the career experience, all the lessons learned that you've had in your career, that was the hard part. AI can give you answers quickly, but if you don't know how to craft that first 10% based on all the experience you've got, it's unlikely that you're going to be getting ideal output in your sessions. But when you leverage your existing experience, that makes it so much easier for you to be clear in your instructions, to be clear on what you want as your output, and to be able to fully review and approve any of the output that comes from these models. And that goes from a customer service agent all the way up to the CEO or president of the company.

B (13:32)

Thank you Chris. I appreciate that. I love how you highlighted several examples verticals, departments that could easily step into this. As you said, low hanging fruit, you know, marketing, finance, sales, operations, HR and marketing. Obviously there's a lot of content analytics and SEO. Definitely a perfect use case just like the rest of me mentioned. But I like that in that 10, 80, 10 which that first 10% is you of course having some good prompt engineering skills obviously helps have AI do that 80% make sure that last 10% that you're reviewing that. And I like that point you made. Human in the loop. And I think that's so, so important. Chris, I really appreciate having you on today and I look forward to speaking with you real soon.

A (14:12)

Thanks everybody.

B (14:13)

Bye for now.