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Welcome to another one of Revenue Rewired from Innovation Visual, the podcast where we explore how data and strategy are transforming the way businesses drive growth.
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I'm Tim Butler, the CEO of Innovation Visual, and with each episode I'll be talking to people reshaping the future of Revenue.
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From CROs to CMOs, tech leaders to transformation experts, if you are ready to rewire your business growth, then you're in the right place to be listening.
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Here, for this episode, I'm really happy to be joined by Chris.
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Hello Chris.
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Hi.
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Well, and when we were chatting about this, we thought we wanted to just have a conversation that people would find useful.
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I'm gonna use the word 'AI', such a buzzword at the moment.
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But we're seeing a lot of people in the revenue leadership space that are struggling to take AI adoption to the next level.
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Like actually take AI into their tech stack.
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So, we wanted to talk about that today.
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So, are you ready for a conversation on that?
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Yeah absolutely.
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Let's do it.
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Okay, so you see a lot of different scenarios coming across your desk from new people that we're talking to about potentially working with them, to people that we've worked with for a number of years.
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What are you seeing in terms of actually getting AI, which is a vast area into people's tech stacks?
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Yeah I suppose there's a bit of a broad spectrum in terms of how businesses are using AI, you've got at the one end we have clients and prospects who have kind of come to us with a brief of, "oh I've been told I need to be adopting AI or I'm hearing a lot about AI, so I think I should be adopting AI", and they don't really know where to start or you know that kind of thinking is maybe not aligned that well with business objectives.
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So, you kind of have that end where it's almost not been adopted, through to I suppose those where AI is in use, but maybe it's in use in pockets amongst the teams.
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So, you have kind of internal champions who've just started using it personally, and they may be using it more from a kind of search functionality, maybe research or like content generation.
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So, they're kind of at the copy and paste into ChatGPT side of things.
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Maybe they're using that as almost like a personal assistant, but essentially the broader use of it across the business is maybe not particularly well structured, not being managed that well.
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You know, the direction isn't necessarily being set that well across the business.
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Okay let's talk about setting that direction then.
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If you are the CMO of an organisation and you help set that direction, or is it something that the CEO alone has to do?
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Nobody wants to sort of sit around and wait for others.
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Certainly, I'm Mr Impatient.
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So how do you actually get your organisation on the journey?
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I think the conversation at the top of a business is important because there's a lot of scare mongering around AI, there's you know, a lot of unknowns.
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I think, from a business leader perspective, it's like educate yourself is a good starting point, because I think there's that worry from a lot of people's point of view when you talk about AI, it's about kind of people's jobs being replaced or it's about data privacy.
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Those are often the first things that I think people are thinking of, rather than actually where's the value in this.
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What should we be doing with this, and we need to be thinking about AI as one of the many tools or a collection of tools within the toolkit that a business has.
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Yes, there are things that need to be taken very seriously around the privacy of your customer's data, or you know how it's gonna affect your workforce.
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But if you're not having those conversations, this is gonna essentially be happening without you being at the wheel, and you know technology is moving very fast.
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So, I think by educating yourself as a business leader and making sure you understand the implication of AI is gonna impact your business.
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Yeah, I mean, I know that we're putting on some workshops in the autumn this year specifically for leaders around how they can work with AI into their organisation at a deeper level.
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But what you are saying is you've gotta have a certain amount of self-education there, in order to be able to sort of understand the landscape of AI and where it fits.
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What's your take on AI policies within organisations and where they're sitting at the moment in terms of making things happen?
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A lot of businesses probably haven't really developed much of a policy around certainly the use of AI.
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I'm sure there are plenty of companies that are worrying about how they fit into their Ts and Cs and kind of trying to cover themselves legally, in terms of what AI is doing.
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But in terms of a policy, how it should be used, where it can be used, where it can't be used, I don't think companies have got a good grasp on that at the moment.
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What's your take on it?
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It's interesting because I was at an event with very senior leadership of different businesses just a few weeks ago and I was gobsmacked by the lack of AI policies cause we've been talking about AI policies in like our Leaders Lunches, the people who are sort of in the sphere.
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We've got a sort of, I say a group, it's a large group cause we have different people coming in different lunches, and most of those people over the last I would say 12 to 18 months, because we've been talking about AI and AI policies, most of the people coming to those events and part of that private LinkedIn group, actually do now have AI policies.
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But, when I went into this wider room and we're talking about I spoke to more than a dozen, probably 20 odd different leaders.
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Honestly, I was gobsmacked by the fact that most of the answers were no, but I think we need to have one, or I think somebody's putting something together.
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You know there was only I think one definitive yes, we've got one and we're confident in what it covers.
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If you don't have a policy, you can't manage your staff, your employees properly around AI, cause you can't say you shouldn't do that if there's not a policy that says you can't do it.
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And the other thing which I don't think people are thinking about this, what's their risk from not having a policy?
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So, if you have not got a policy which says, do not drop this kind of data into this type of model or LLM or whatever, it might be, right.
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What happens when your employee takes a spreadsheet of 10,000 customer records which has private data on it and drops it into something that then actually isn't private and you know, a fine for GDPR is based on revenue of your company.
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Yeah.
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And so, I think it's gobsmacking that people haven't, gone hold on a minute...
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there's a massive risk exposure from not actually having this written down and saying what you can and can't do.
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And I think people feel that they, I just think that they're mystified as to what should be there, and they probably think that they need to go to a lawyer, spend lots of money on some fancy policy, when actually in my mind it needs some thinking about in the first place.
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And again, coming back to what you said about being informed, do people actually know what are the limitations and the differences between these things are?
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Agreed.
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And I think there'll be you know Gen Z and Millennial workers now in the workplace whose kind of natural instinct is to be using things like ChatGPT as a search engine.
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You know, and have grown up in a world where data privacy of their own data is hard to come by and therefore, they're actually towards it, probably is looser than the company would want it to be.
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So, their natural inclination is gonna be to use these tools and think about things like data privacy second.
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So those policies do need to be in place.
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Yeah.
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But they also need to be constructed in a way that's gonna encourage the business to adopt the tools effectively.
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Yeah.
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I'm not of the opinion where it's like, you must not use AI.
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I think that's the worst policy in the world.
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That's like saying don't use a computer.
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You're only allowed to use an abacus.
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Don't Google things.
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Yeah, but I think that you need the policy in place to then go to the next level.
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When we're talking about infusing technology and AI into your tech stack, but actually it's adopting AI in your organisation is probably a more realistic conversational part.
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Although, in terms of our remit we tend to look at the sort of the bit which influences revenue.
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Because from policy, it's then into training and adoption and education I would say, and I mean, what have you been seeing on that?
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Again, it's very self-led a lot of the time isn't it, people are either using the tools, or they've been given the tools.
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We have seen some businesses, we've seen some partner businesses, that have been sharing resource out across the business in terms of you know here are structured prompts that will deliver certain value.
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There are companies doing that, but I think there are more companies not doing that.
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Yeah.
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We talked about infusing AI into the tech stack whether you are actively doing that or not as a business.
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A lot of the tools that you are already using are doing it for you now, whether it be Photoshop, whether it be your CRM, like AI is now the buzzword for every SaaS platform in the world.
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And that means those tools are there for your team to use, right next to your customer data whether you like it or not and therefore like that education I think is really important.
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Whether it be a structured approach making sure everybody in the team is going through a you know a certain curriculum if you like of AI training, or whether it's holding, so we obviously have our AI task force, we try and stay ahead of trends and then we make sure we're holding those training sessions internally around that.
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Yeah.
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And I think that's definitely a really good approach.
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Yeah.
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The scale of your business is gonna dictate how you approach that training.
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Yeah, and you touch on the AI task force.
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I think some people call them AI councils, and I think having a group which is mandated to actually look at what's going on in terms of the relevance of different tools and different technologies to a specific business and then drive that forward.
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I think that's the next stage in terms of understanding what training needs to happen and what education needs to happen within the business and what then a roadmap might look like.
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Yeah absolutely.
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And these tools are changing so fast, you know we saw that update a few months ago where suddenly ChatGPT held memory across all of your chats.
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Which at a first glance seems great, 'oh fantastic my assistant is gonna learn with me'.
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Actually, you start getting this confirmation bias because it remembers everything you've ever asked it.
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So, the next time you come and ask it, it holds that information and it kind of starts giving you the answers you want to hear.
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So that change can be so rapid and the way that these companies are rolling out updates and new models, you've got to be actively, know training and pushing forward.
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You can't do a set and forget in terms of policy and training.
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Yeah, what in terms of an AI task force and an AI council, how do you think that should be made up?
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Typically, our audience is sort-of mid-size businesses, and much larger, how would you like if somebody's listening to this who's a leader, and thinks well we don't have an AI task force, an AI leader, that sounds like a sensible thing we should do.
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Yeah.
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How should that be constituented if that's the right word?
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I think you need people from across your business.
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So, we're talking kind of the breadth of business in terms of your different functions.
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But also, the depth in terms of from seniority downwards.
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So, I think you need a good cross section of people across your teams, but you also need to be making use of those people in the team who are natural champions and natural early adopters.
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So, there will be people who just are naturally inclined to try new tools, to read about things, to listen to podcasts to you know, soak up that knowledge.
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They're just so valuable as people within the business, and you know what they're gonna be doing it anyway, whether you involve them in your task force or not.
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So, you might as well harness that energy and have them in the discussion.
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Yeah, Interesting.
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Slightly side note, but I saw a stat that employees that are good at using AI for their job, can get paid up to 56% more than their peers doing the same job.
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So, if you're listening to this and you want to get a pay rise, informing yourself more and more about using AI and being involved in things like a taskforce is obviously a very valuable thing to do as well.
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Yeah absolutely.
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It's fascinating isn't it.
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What people haven't necessarily realised, that aren't adopting AI in their daily workflows, is it's not just about you know image creation, or getting it to write a blog, It's just about like efficiency.
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It's getting AI to do things that are repetitive or involve a lot of data or something you just do very regularly.
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They don't need to be highly complicated tasks that AI is doing for you, but the efficiency gains can be massive.
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Yeah, I think it's interesting cause you're talking about some things there which are at a personal efficiency level, you know using some of these tools, as an employee rather than a system or business level.
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And I think what we've seen is that where people are adopting within their own workflow, they're much more likely then to adopt the wider organisational AI initiatives I would say.
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Yeah, because they get it, don't they.
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They see the value.
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I think as with any tool, an employee or a team that feels forced into using something that they're not interested in aren't gonna see the value from it.
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The moment it clicks, and you know Claude or ChatGPT or whatever it might be, does something for you that you didn't know it could do and you kind of go- huh?
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That's cool and you get it.
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Then I think you start to buy in, and you start to go yeah, I can understand why management is saying we need to be thinking about how we deploy AI.
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Yeah, okay.
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So, let's talk about some use cases for AI deployment.
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You've already mentioned that you know it's the buzzword already within lots of different SaaS things.
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So, let's talk about embracing AI via what you are already using in your tech stack.
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Can you sort of tell us a bit more about how that can happen?
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Yeah, so I think initially a lot of businesses were I guess bolting on AI.
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You know, they were using existing models whether AI did very well out of this, lots of companies started embracing that; whether it was HubSpot or Apple, they kind of just sort of adopted that technology and bolted it onto the side.
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What we've seen more recently is actually you know these tools have now had a few years since OpenAI really kind of burst the AI bubble, to think about how to deploy some of these tools a bit more thoughtfully they're focusing a lot more on it being part of a daily workflow.
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How do we streamline the use of the existing platform on the existing tool by incorporating AI?
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Not just bolting it on, not having a kind of copy and paste relationship with it.
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How can we actually use this tool within here we're seeing that now?
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So, for users that aren't familiar it's gonna be a lot more similar to using something like Microsoft Word, where you have things like your spell check is built in.
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We take that for granted now, AI is starting to do that when we're talking about things like CRMs for example, where suddenly you have this kind of ever present assistant or this functionality that's AI powered, within the platform, it's within the tools, whether it's the WYSIWYG that you are writing copy in, whether it's within the tool functionality for building out an email template.
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AI is now there and therefore that ease of integration within your daily workflow I think is a lot more obvious.
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In some ways AI is becoming less in your face and more subtly part of the wider process.
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Although obviously you always get those little stars appearing wherever a platform wants to tell you there's AI within the tool.
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Yeah.
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The actual functionality is becoming just baked in and I think that's what's quite interesting now because it really is just infused into the technology.
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Yeah, whoever designed the little star icon they should have painted it and charged 0.001 P for one of those and would be rich beyond their wildest dreams now.
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In terms of this embedding of AI within platforms, are there any dangers to that?
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Yeah.
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I mean there are in as much as with any of these AI tools, the source of information sometimes is not always that clear.
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Depending on the platform you're working in if you are dealing with customer data I think there's always an inherent risk of you know "where are we sharing data to?".
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So if we're simply turning AI features on without reading the small print without questioning what's happening to our first party data I think that's a real risk, I think that's something that needs to be part of that policy that we talked about.
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But I think with well-educated staff, with well written policies with processes that consider the use of AI.
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You know and using software that you feel confident about and you've researched.
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I think they all of those things add up to a world where we can use AI safely.
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Okay But it does need to be approached with a bit of common sense.
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Okay.
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Coming back to understanding the what's and the wherefores about things like data and things going there.
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Okay, so what about talk about using AI within tools and how effectively if you've got things like Salesforce or you've got HubSpot those kinds of tools you're seeing those appear the quality, I think is generally getting better.
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Although, there was an AI assistant, which I did yesterday, and it was basically worse than a search function.
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But anyway, that beside, what about, like more custom uses of AI within people's revenue generating technology.
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What's the approach with that?
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So, I think this is where it starts to get really interesting and we've done some work recently with clients that you know, they're really interesting use cases that off the bat maybe weren't the most obvious uses for AI.
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Yeah.
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I think this is where having that discussion around are the pain points, what is it that we're trying to solve, trying to understand a particular process flow from end to end.
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Whether that be part of your sales process, whether it be part of your marketing team, whether it be more customer facing and part of your product.
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Understanding where AI is a good fit for the type of task, whether it's a very repetitive task or volume data task, but also where it's gonna add value, that we don't wanna be doing is saying just "stick AI on it, it'll be fine".
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Yeah, because I've heard people sort-of talk about like AI is the answer now... What was the question again?
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and it's not that way round.
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It's good that you highlight that cause I think if people are looking at adoption, start with where's the need, in terms of as you say repetitive, time-consuming, problematic tasks and look at the process there.
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And I suppose process mapping plays a really important role at this point.
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It does because you need to fully understand.
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We've talked before about how business leaders aren't aware of what's going on within their technology or their processes, and you sometimes have these silos between teams.
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So, understanding that full picture with process mapping and understanding where data is flowing across your business, what processes are interconnected, where teams interact with each other and where they interact with their technology.
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That will start to then highlight, actually, there's an awful lot of manual importing going on over here or someone's doing hours of research over here, and those are things when people start talking about data or research, those are things
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Actually AI is very good at that, and when built in a flow that is very structured and very set, you can harness AI in a way that it's gonna give you very repeatable outcomes.
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So, you know, good example, we've worked recently with a company where they wanted to automate parts of their sales process.
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We spent time with them understanding where is the value gonna be, where are your real problems, where do we want to make sure that we're focusing on letting people do their jobs in a way that only the people can.
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Therefore, how do we put AI in the background of this process to be going off and doing things like research, bringing data back, formatting that data, structuring it in a way that can ultimately put out a very repeatable output, which in this case was a customised pitch deck to allow them to really understand that process from end to end.
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You need to have various different AI agents throughout a flow that can do a set job, nothing more, nothing less.
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Yeah.
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That's why we talk about infusing AI, because this is not a case of "oh I just bring in a chat bot and it will do a thing for me", it's a case of each part of this, is locked down in a way that it's gonna do what it is expected to do.
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But, that kind of the magic if you like, what AI can do is then go off and do that very fast.
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Okay.
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Really good results.
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Okay To go and do the proper work.
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Okay, I'm gonna take you back to the start of that interesting section there.
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You talked about process mapping.
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Now, no doubt a lot of people are gonna go off and ask ChatGPT to tell them, or maybe old school Google it.
00:23:17.019 --> 00:23:22.348
But can you just explain to the people listening, what does process mapping mean?
00:23:22.348 --> 00:23:27.493
Because I think people are probably concerned that it's more complicated than it really is.
00:23:28.472 --> 00:23:53.032
Okay, so in a nutshell, process mapping is essentially bringing together all of the things that you are currently doing across your business; so it can be everything from which systems, or which people do a certain function, what that function is, what it links to, and bringing that together in a visual map so that you can literally understand in this particular part of our business.
00:23:53.153 --> 00:23:56.032
So maybe it's the handoff of new sales leads.
00:23:56.512 --> 00:24:01.942
These people are responsible for these things, they use these tools, the data flows from here to here.
00:24:02.363 --> 00:24:27.053
This is the sequential process that happens, mapped out visually and the reason is its a really powerful process is because what we tend to find within businesses, is that individuals know their bit of the process and a lot of it's up here, it is in their heads, so they're not necessarily sharing it that well across the business, and generally there isn't somebody that's holistically taking ownership of that process from end to end.
00:24:27.863 --> 00:24:27.942
Yeah.
00:24:27.942 --> 00:24:31.718
Mapping it out you, can start to go well hang on, what happens in this part?
00:24:31.728 --> 00:24:33.048
Where does the data go after that?
00:24:33.048 --> 00:24:33.923
Or who owns that bit?
00:24:33.923 --> 00:24:36.032
Or why does nothing happen here?
00:24:36.502 --> 00:24:36.792
Yeah.
00:24:36.942 --> 00:24:41.313
It's a really, really good way of being able to break a process apart, as well as mapping it out.
00:24:42.022 --> 00:25:05.208
Yeah, thank you for that, and I think it has to be the starting point for any kind of customised AI implementation, solving the problem, is the process map defines the problem if you like in terms of you know, a business is a collection of processes working in harmony, hopefully.
00:25:05.738 --> 00:25:34.228
And it was interesting because yesterday there was a conversation about that prospective company that came to us and said, "oh we'd like you to solve this with AI" and we actually had to go back to them and say: "Well we can't because you don't have a defined process, you've got all of these things going off all over the place and you can't until you've mapped what you want it to be, you can't just throw AI at the problem." Exactly, and sometimes the answer, or at least the first port of call is, change the process.
00:25:35.038 --> 00:25:38.228
Technology is great, but it can't fix everything.
00:25:38.278 --> 00:25:45.722
Ultimately, if people are not engaging with technology the way that we want them to as a business, the technology can only go so far.
00:25:46.788 --> 00:25:47.028
Yeah.
00:25:47.538 --> 00:26:05.008
I do think also there's a thing about being open to changing process, because you've got the technology, so you know it should be something that when you are implementing technology, look at the why are we doing this?
00:26:05.137 --> 00:26:05.438
Yep.
00:26:05.583 --> 00:26:23.522
If your route to getting to the objective is this way the technology can take you in a shorter route, faster route, then subject to you know quality and all of that, you shouldn't be afraid to actually say, well we could re-engineer the process underneath this because we want the objective.
00:26:24.063 --> 00:26:24.992
Yeah, absolutely.
00:26:24.992 --> 00:26:27.613
And I think like you say there, it's asking the why.
00:26:28.093 --> 00:26:36.853
I think we've seen a few businesses recently who it feels a little bit like they're going through a bit of a tick box exercise of, I've been told to use AI, I've just gotta tick that box.
00:26:37.393 --> 00:26:37.692
Yeah.
00:26:37.752 --> 00:26:38.803
That's not the right way to approach it.
00:26:39.548 --> 00:26:39.837
Yeah.
00:26:39.883 --> 00:26:42.938
This really is a genuinely very very helpful tool.
00:26:43.417 --> 00:26:47.597
I don't want to go too much into the hyperbole of saying it's a game changer, but it is a big deal.
00:26:48.018 --> 00:26:48.077
Yeah.
00:26:48.432 --> 00:26:51.767
And to just be ticking a box is not the right way to be thinking about this.
00:26:52.653 --> 00:27:20.048
Yeah, but the flip side of that I would say is we had a really enjoyable conversation last week, with that potential new customer where they'd got this kind of "oh well we thought maybe you could help us with this" and then when we start asking them about problems there was the this whole sort of cascade, then of like "oh well, this is a problem" and we went, have you thought about it if you did this technology?
00:27:20.347 --> 00:27:38.518
And I loved that conversation because I realised the power of having a conversation with us being knowledgeable about what the technology is capable of doing, just open the minds of that company as to "oh wow you could do that".
00:27:40.008 --> 00:27:44.923
And they just thought that was how it had to be, I thought that was a brilliant conversation.
00:27:45.527 --> 00:27:51.356
I agree and it is that moment of asking the right questions of well could you get AI to do this?
00:27:51.477 --> 00:27:59.277
And obviously our response at the time was, yeah absolutely, and that sort of almost jaw dropping moment of oh my god, we spent hours doing that...
00:27:59.688 --> 00:28:00.048
Yeah.
00:28:00.106 --> 00:28:05.676
And that's you know, that's what we're talking about here, it's quality of life for your employees, it is time saved, its value driven.
00:28:05.826 --> 00:28:09.096
You know being able to kind of speed up sales velocity and things.
00:28:09.156 --> 00:28:15.457
All these things are things that we should be thinking about, but asking the right questions of how to get there.
00:28:15.787 --> 00:28:17.106
Yeah, absolutely.
00:28:17.507 --> 00:28:33.366
To kind of bring it right round, you've come back to this sort of self-education bit of asking the right questions and gaining the understanding and we talked about how fast things are moving at the start.
00:28:33.366 --> 00:28:39.541
But we've kind of brought ourselves back around to the start, where you gotta know something in order to get moving.
00:28:39.957 --> 00:28:40.227
Yeah.
00:28:41.071 --> 00:28:42.741
And then do the next actions.
00:28:43.366 --> 00:28:50.356
Yeah absolutely, I think that's the thing, don't just look at AI as this big daunting thing that's gotta happen overnight.
00:28:51.227 --> 00:28:54.892
Start with education, start with policy, and build.
00:28:54.942 --> 00:29:00.376
You know, I think the technology moves really fast so staying ahead of it is always gonna be a challenge.
00:29:00.957 --> 00:29:01.247
Yeah.
00:29:01.307 --> 00:29:04.247
The place to start is now, with what's in front of us.
00:29:04.886 --> 00:29:12.076
Yeah You know, like I said change management, thinking about how we bring different elements of AI into the business.
00:29:12.352 --> 00:29:12.622
Yeah.
00:29:13.521 --> 00:29:29.142
Well, I think that the AI opportunities audit that we do, is just, I love those because they're so exciting when you have a systematic approach to uncovering opportunity, that some of the gains are gigantic.
00:29:29.221 --> 00:29:36.727
If you think about some of the other things, and this is why I think AI and technology is such a strategic game player for the companies that get it right.
00:29:37.386 --> 00:29:55.146
You think about doing something really amazing on like, I don't know some SEO project or some PPC, and picking on those things, it's sort of traditional digital marketing lead generation thing and you do something fantastic, and you get a 20 percent maybe uplift if it was absolutely knockout.
00:29:55.146 --> 00:30:09.021
Because you know you're assuming you're already doing some good stuff over here, anyway 20 percent would be a really big, like wow this is really good, but you could do something with this new AI?
00:30:09.021 --> 00:30:16.517
And when we look at these opportunity audits and when you turn around and go oh, we could save 98% of your time, 98%.
00:30:17.067 --> 00:30:22.616
Imagine what that person can do with that freed up 98% of the time.
00:30:22.616 --> 00:30:25.106
Now they're only spending 2% on that task.
00:30:25.826 --> 00:30:29.576
Oh, that's like wow.
00:30:29.636 --> 00:30:41.727
We haven't seen gains like that since back in the good old days of digital marketing when you know, people were still convincing companies to get a website, cause you'd get ahead if you had a website kind of thing.
00:30:41.797 --> 00:30:45.277
And it was a competitive differentiator to actually have a website.
00:30:45.277 --> 00:30:50.346
I imagine there's people listening to that go what, you actually had to persuade people to have a website?
00:30:50.346 --> 00:30:51.277
Everybody's got a website.
00:30:51.517 --> 00:31:03.186
But I've been around that long, I had conversations with people about saying if you get a website you'll stand out in your industry because, so few people have a website.
00:31:03.186 --> 00:31:06.426
And they were like but we've got brochures, why would we want a website?
00:31:06.906 --> 00:31:10.977
Yeah, now the game has changed I think that's where we are with AI.
00:31:11.451 --> 00:31:11.662
Yeah.
00:31:11.747 --> 00:31:29.297
We are gonna be at a point in five years at most maybe where everybody's going to be in the game and the winners will have jumped ahead, and the people that don't adopt fast will have fallen by the wayside kind of thing.
00:31:29.862 --> 00:31:45.076
Yeah, absolutely agree, and I think you know now is the time to be doing it because the technology is much more established, you know it's in a position now where it is enough that we can do really amazing things with it, it's moving really fast.
00:31:45.076 --> 00:31:50.057
So, now is the time to make sure you're ahead of the curve and you're really understanding where to enhance it.
00:31:50.162 --> 00:31:50.491
Okay.
00:31:50.821 --> 00:31:52.912
That's great I think we've covered off a lot.
00:31:52.912 --> 00:32:00.442
We probably should put a few bits and pieces in the show notes in terms of links to some of these things, education, development.
00:32:00.541 --> 00:32:12.616
I think that for summary it's like that education informing leadership, so things like having an AI policy, and that policy has got to evolve as you evolve your use of it.
00:32:13.487 --> 00:32:25.396
AI Task Force/AI Council for adoption, then start looking at what AI you've already got access to, and the tools that you've got, and then looking at the wider things.
00:32:25.396 --> 00:32:33.596
So, yeah, I think the AI opportunities audit that you know, and people do those kinds of things if they're well informed enough themselves.
00:32:33.596 --> 00:32:41.906
But, that kind of thing is then the next stage for the more bespoke if you like AI implementations.
00:32:44.561 --> 00:32:54.461
Okay that's really good I've as always enjoyed chatting to you on this and I could chat for ages, but we'll get in trouble with the team for creating too long an episode.
00:32:54.461 --> 00:32:58.541
So, any more gems before we sign off on this one Chris?
00:33:00.261 --> 00:33:30.731
No I just think you know it's again, there will come a time where AI is just one of the names that gets given to a collection of tools, if not part of every tool that we use, I liked your analogy about the policy of saying oh you know don't use a computer is kind of the equivalent, but it's similar as well of the way we're talking about the use of AI and you wouldn't expect somebody in the team to not have a keyboard with their computer, and it's gonna come to a point where AI is part of everything that we do within business.
00:33:31.211 --> 00:33:46.027
So, it is time to be adopting it, to be thinking about it, not as this big standalone thing that we can copy and paste in and out of, it needs to be something that we interact with in the same way we would any of the tools that we use daily.
00:33:47.201 --> 00:33:49.241
Yeah, no that's good.
00:33:49.721 --> 00:33:51.932
Good finishing comment there, I like that.
00:33:54.221 --> 00:33:58.576
Well, thanks to you Chris for another interesting conversation, really great.
00:33:58.876 --> 00:34:03.586
And thanks to you all for listening, this has been another Revenue Rewired episode.
00:34:03.586 --> 00:34:13.536
So, if you found this conversation useful, which I hope you did please do follow us, like, subscribe all of that stuff, whatever platform you choose to listen to us on.
00:34:13.536 --> 00:34:15.277
And we're on all of the popular ones.
00:34:15.496 --> 00:34:21.492
To find out more about Innovation Visual or AI adoption, in fact, just go to innovationvisual.com.
00:34:21.811 --> 00:34:26.871
And, until next time keep rewiring the way you look at your revenue systems.
00:34:26.871 --> 00:34:29.661
So, thank you very much Chris and thank you all for listening.
00:00:02.842 --> 00:00:13.913
Welcome to another one of Revenue Rewired from Innovation Visual, the podcast where we explore how data and strategy are transforming the way businesses drive growth.
00:00:14.303 --> 00:00:22.667
I'm Tim Butler, the CEO of Innovation Visual, and with each episode I'll be talking to people reshaping the future of Revenue.
00:00:22.717 --> 00:00:32.118
From CROs to CMOs, tech leaders to transformation experts, if you are ready to rewire your business growth, then you're in the right place to be listening.
00:00:32.118 --> 00:00:36.368
Here, for this episode, I'm really happy to be joined by Chris.
00:00:36.398 --> 00:00:37.148
Hello Chris.
00:00:39.463 --> 00:00:39.912
Hi.
00:00:39.972 --> 00:00:47.167
Well, and when we were chatting about this, we thought we wanted to just have a conversation that people would find useful.
00:00:47.537 --> 00:00:51.468
I'm gonna use the word 'AI', such a buzzword at the moment.
00:00:51.497 --> 00:01:02.137
But we're seeing a lot of people in the revenue leadership space that are struggling to take AI adoption to the next level.
00:01:02.137 --> 00:01:06.158
Like actually take AI into their tech stack.
00:01:06.457 --> 00:01:09.337
So, we wanted to talk about that today.
00:01:09.337 --> 00:01:11.537
So, are you ready for a conversation on that?
00:01:11.927 --> 00:01:12.867
Yeah absolutely.
00:01:13.048 --> 00:01:13.528
Let's do it.
00:01:14.347 --> 00:01:26.168
Okay, so you see a lot of different scenarios coming across your desk from new people that we're talking to about potentially working with them, to people that we've worked with for a number of years.
00:01:26.707 --> 00:01:35.028
What are you seeing in terms of actually getting AI, which is a vast area into people's tech stacks?
00:01:35.968 --> 00:02:01.578
Yeah I suppose there's a bit of a broad spectrum in terms of how businesses are using AI, you've got at the one end we have clients and prospects who have kind of come to us with a brief of, "oh I've been told I need to be adopting AI or I'm hearing a lot about AI, so I think I should be adopting AI", and they don't really know where to start or you know that kind of thinking is maybe not aligned that well with business objectives.
00:02:01.608 --> 00:02:12.048
So, you kind of have that end where it's almost not been adopted, through to I suppose those where AI is in use, but maybe it's in use in pockets amongst the teams.
00:02:12.048 --> 00:02:22.937
So, you have kind of internal champions who've just started using it personally, and they may be using it more from a kind of search functionality, maybe research or like content generation.
00:02:23.342 --> 00:02:27.092
So, they're kind of at the copy and paste into ChatGPT side of things.
00:02:27.092 --> 00:02:37.532
Maybe they're using that as almost like a personal assistant, but essentially the broader use of it across the business is maybe not particularly well structured, not being managed that well.
00:02:37.532 --> 00:02:41.282
You know, the direction isn't necessarily being set that well across the business.
00:02:43.103 --> 00:02:45.907
Okay let's talk about setting that direction then.
00:02:46.008 --> 00:02:55.638
If you are the CMO of an organisation and you help set that direction, or is it something that the CEO alone has to do?
00:02:55.687 --> 00:02:58.207
Nobody wants to sort of sit around and wait for others.
00:02:58.207 --> 00:02:59.962
Certainly, I'm Mr Impatient.
00:03:00.022 --> 00:03:04.592
So how do you actually get your organisation on the journey?
00:03:06.052 --> 00:03:13.883
I think the conversation at the top of a business is important because there's a lot of scare mongering around AI, there's you know, a lot of unknowns.
00:03:14.362 --> 00:03:29.943
I think, from a business leader perspective, it's like educate yourself is a good starting point, because I think there's that worry from a lot of people's point of view when you talk about AI, it's about kind of people's jobs being replaced or it's about data privacy.
00:03:30.302 --> 00:03:36.513
Those are often the first things that I think people are thinking of, rather than actually where's the value in this.
00:03:36.823 --> 00:03:46.032
What should we be doing with this, and we need to be thinking about AI as one of the many tools or a collection of tools within the toolkit that a business has.
00:03:46.798 --> 00:03:55.603
Yes, there are things that need to be taken very seriously around the privacy of your customer's data, or you know how it's gonna affect your workforce.
00:03:56.223 --> 00:04:05.073
But if you're not having those conversations, this is gonna essentially be happening without you being at the wheel, and you know technology is moving very fast.
00:04:05.453 --> 00:04:13.407
So, I think by educating yourself as a business leader and making sure you understand the implication of AI is gonna impact your business.
00:04:13.487 --> 00:04:25.237
Yeah, I mean, I know that we're putting on some workshops in the autumn this year specifically for leaders around how they can work with AI into their organisation at a deeper level.
00:04:25.588 --> 00:04:37.177
But what you are saying is you've gotta have a certain amount of self-education there, in order to be able to sort of understand the landscape of AI and where it fits.
00:04:37.697 --> 00:04:46.658
What's your take on AI policies within organisations and where they're sitting at the moment in terms of making things happen?
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A lot of businesses probably haven't really developed much of a policy around certainly the use of AI.
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I'm sure there are plenty of companies that are worrying about how they fit into their Ts and Cs and kind of trying to cover themselves legally, in terms of what AI is doing.
00:05:03.062 --> 00:05:12.187
But in terms of a policy, how it should be used, where it can be used, where it can't be used, I don't think companies have got a good grasp on that at the moment.
00:05:13.267 --> 00:05:13.942
What's your take on it?
00:05:16.108 --> 00:05:38.208
It's interesting because I was at an event with very senior leadership of different businesses just a few weeks ago and I was gobsmacked by the lack of AI policies cause we've been talking about AI policies in like our Leaders Lunches, the people who are sort of in the sphere.
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We've got a sort of, I say a group, it's a large group cause we have different people coming in different lunches, and most of those people over the last I would say 12 to 18 months, because we've been talking about AI and AI policies, most of the people coming to those events and part of that private LinkedIn group, actually do now have AI policies.
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But, when I went into this wider room and we're talking about I spoke to more than a dozen, probably 20 odd different leaders.
00:06:06.877 --> 00:06:16.637
Honestly, I was gobsmacked by the fact that most of the answers were no, but I think we need to have one, or I think somebody's putting something together.
00:06:16.798 --> 00:06:25.028
You know there was only I think one definitive yes, we've got one and we're confident in what it covers.
00:06:25.538 --> 00:06:38.718
If you don't have a policy, you can't manage your staff, your employees properly around AI, cause you can't say you shouldn't do that if there's not a policy that says you can't do it.
00:06:39.137 --> 00:06:46.627
And the other thing which I don't think people are thinking about this, what's their risk from not having a policy?
00:06:47.158 --> 00:06:58.642
So, if you have not got a policy which says, do not drop this kind of data into this type of model or LLM or whatever, it might be, right.
00:06:59.673 --> 00:07:21.002
What happens when your employee takes a spreadsheet of 10,000 customer records which has private data on it and drops it into something that then actually isn't private and you know, a fine for GDPR is based on revenue of your company.
00:07:21.093 --> 00:07:21.382
Yeah.
00:07:21.512 --> 00:07:25.593
And so, I think it's gobsmacking that people haven't, gone hold on a minute...
00:07:26.103 --> 00:07:31.742
there's a massive risk exposure from not actually having this written down and saying what you can and can't do.
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And I think people feel that they, I just think that they're mystified as to what should be there, and they probably think that they need to go to a lawyer, spend lots of money on some fancy policy, when actually in my mind it needs some thinking about in the first place.
00:07:51.358 --> 00:08:00.574
And again, coming back to what you said about being informed, do people actually know what are the limitations and the differences between these things are?
00:08:01.483 --> 00:08:01.903
Agreed.
00:08:01.903 --> 00:08:13.942
And I think there'll be you know Gen Z and Millennial workers now in the workplace whose kind of natural instinct is to be using things like ChatGPT as a search engine.
00:08:15.322 --> 00:08:25.122
You know, and have grown up in a world where data privacy of their own data is hard to come by and therefore, they're actually towards it, probably is looser than the company would want it to be.
00:08:25.572 --> 00:08:30.463
So, their natural inclination is gonna be to use these tools and think about things like data privacy second.
00:08:30.523 --> 00:08:32.423
So those policies do need to be in place.
00:08:32.998 --> 00:08:33.357
Yeah.
00:08:33.413 --> 00:08:38.062
But they also need to be constructed in a way that's gonna encourage the business to adopt the tools effectively.
00:08:38.817 --> 00:08:39.148
Yeah.
00:08:39.447 --> 00:08:44.118
I'm not of the opinion where it's like, you must not use AI.
00:08:44.118 --> 00:08:46.227
I think that's the worst policy in the world.
00:08:46.467 --> 00:08:48.567
That's like saying don't use a computer.
00:08:48.567 --> 00:08:50.337
You're only allowed to use an abacus.
00:08:50.427 --> 00:08:51.927
Don't Google things.
00:08:52.452 --> 00:08:57.743
Yeah, but I think that you need the policy in place to then go to the next level.
00:08:57.802 --> 00:09:11.727
When we're talking about infusing technology and AI into your tech stack, but actually it's adopting AI in your organisation is probably a more realistic conversational part.
00:09:11.727 --> 00:09:17.768
Although, in terms of our remit we tend to look at the sort of the bit which influences revenue.
00:09:18.077 --> 00:09:27.217
Because from policy, it's then into training and adoption and education I would say, and I mean, what have you been seeing on that?
00:09:28.743 --> 00:09:35.043
Again, it's very self-led a lot of the time isn't it, people are either using the tools, or they've been given the tools.
00:09:35.543 --> 00:09:44.913
We have seen some businesses, we've seen some partner businesses, that have been sharing resource out across the business in terms of you know here are structured prompts that will deliver certain value.
00:09:45.312 --> 00:09:49.452
There are companies doing that, but I think there are more companies not doing that.
00:09:50.403 --> 00:09:50.692
Yeah.
00:09:50.743 --> 00:09:56.533
We talked about infusing AI into the tech stack whether you are actively doing that or not as a business.
00:09:57.268 --> 00:10:07.227
A lot of the tools that you are already using are doing it for you now, whether it be Photoshop, whether it be your CRM, like AI is now the buzzword for every SaaS platform in the world.
00:10:07.947 --> 00:10:19.107
And that means those tools are there for your team to use, right next to your customer data whether you like it or not and therefore like that education I think is really important.
00:10:19.798 --> 00:10:37.148
Whether it be a structured approach making sure everybody in the team is going through a you know a certain curriculum if you like of AI training, or whether it's holding, so we obviously have our AI task force, we try and stay ahead of trends and then we make sure we're holding those training sessions internally around that.
00:10:37.863 --> 00:10:38.153
Yeah.
00:10:38.317 --> 00:10:40.327
And I think that's definitely a really good approach.
00:10:40.947 --> 00:10:41.278
Yeah.
00:10:41.378 --> 00:10:45.908
The scale of your business is gonna dictate how you approach that training.
00:10:46.918 --> 00:10:50.038
Yeah, and you touch on the AI task force.
00:10:50.097 --> 00:11:10.222
I think some people call them AI councils, and I think having a group which is mandated to actually look at what's going on in terms of the relevance of different tools and different technologies to a specific business and then drive that forward.
00:11:10.222 --> 00:11:21.253
I think that's the next stage in terms of understanding what training needs to happen and what education needs to happen within the business and what then a roadmap might look like.
00:11:22.357 --> 00:11:23.317
Yeah absolutely.
00:11:24.097 --> 00:11:31.957
And these tools are changing so fast, you know we saw that update a few months ago where suddenly ChatGPT held memory across all of your chats.
00:11:32.437 --> 00:11:36.957
Which at a first glance seems great, 'oh fantastic my assistant is gonna learn with me'.
00:11:37.663 --> 00:11:42.192
Actually, you start getting this confirmation bias because it remembers everything you've ever asked it.
00:11:42.643 --> 00:11:47.202
So, the next time you come and ask it, it holds that information and it kind of starts giving you the answers you want to hear.
00:11:48.163 --> 00:11:59.851
So that change can be so rapid and the way that these companies are rolling out updates and new models, you've got to be actively, know training and pushing forward.
00:12:00.121 --> 00:12:04.022
You can't do a set and forget in terms of policy and training.
00:12:04.702 --> 00:12:11.753
Yeah, what in terms of an AI task force and an AI council, how do you think that should be made up?
00:12:11.832 --> 00:12:27.153
Typically, our audience is sort-of mid-size businesses, and much larger, how would you like if somebody's listening to this who's a leader, and thinks well we don't have an AI task force, an AI leader, that sounds like a sensible thing we should do.
00:12:27.371 --> 00:12:27.662
Yeah.
00:12:27.878 --> 00:12:31.207
How should that be constituented if that's the right word?
00:12:31.258 --> 00:12:33.837
I think you need people from across your business.
00:12:33.927 --> 00:12:38.368
So, we're talking kind of the breadth of business in terms of your different functions.
00:12:38.398 --> 00:12:42.148
But also, the depth in terms of from seniority downwards.
00:12:42.508 --> 00:12:52.827
So, I think you need a good cross section of people across your teams, but you also need to be making use of those people in the team who are natural champions and natural early adopters.
00:12:53.488 --> 00:13:02.388
So, there will be people who just are naturally inclined to try new tools, to read about things, to listen to podcasts to you know, soak up that knowledge.
00:13:02.687 --> 00:13:09.097
They're just so valuable as people within the business, and you know what they're gonna be doing it anyway, whether you involve them in your task force or not.
00:13:09.307 --> 00:13:12.217
So, you might as well harness that energy and have them in the discussion.
00:13:13.033 --> 00:13:14.863
Yeah, Interesting.
00:13:14.863 --> 00:13:29.437
Slightly side note, but I saw a stat that employees that are good at using AI for their job, can get paid up to 56% more than their peers doing the same job.
00:13:29.437 --> 00:13:41.572
So, if you're listening to this and you want to get a pay rise, informing yourself more and more about using AI and being involved in things like a taskforce is obviously a very valuable thing to do as well.
00:13:42.222 --> 00:13:42.923
Yeah absolutely.
00:13:42.923 --> 00:13:43.822
It's fascinating isn't it.
00:13:43.873 --> 00:13:56.743
What people haven't necessarily realised, that aren't adopting AI in their daily workflows, is it's not just about you know image creation, or getting it to write a blog, It's just about like efficiency.
00:13:56.793 --> 00:14:03.753
It's getting AI to do things that are repetitive or involve a lot of data or something you just do very regularly.
00:14:04.413 --> 00:14:10.863
They don't need to be highly complicated tasks that AI is doing for you, but the efficiency gains can be massive.
00:14:11.753 --> 00:14:26.663
Yeah, I think it's interesting cause you're talking about some things there which are at a personal efficiency level, you know using some of these tools, as an employee rather than a system or business level.
00:14:28.072 --> 00:14:42.977
And I think what we've seen is that where people are adopting within their own workflow, they're much more likely then to adopt the wider organisational AI initiatives I would say.
00:14:43.923 --> 00:14:45.182
Yeah, because they get it, don't they.
00:14:45.182 --> 00:14:46.442
They see the value.
00:14:46.893 --> 00:14:57.932
I think as with any tool, an employee or a team that feels forced into using something that they're not interested in aren't gonna see the value from it.
00:14:58.592 --> 00:15:08.182
The moment it clicks, and you know Claude or ChatGPT or whatever it might be, does something for you that you didn't know it could do and you kind of go- huh?
00:15:08.913 --> 00:15:10.952
That's cool and you get it.
00:15:11.202 --> 00:15:18.942
Then I think you start to buy in, and you start to go yeah, I can understand why management is saying we need to be thinking about how we deploy AI.
00:15:19.743 --> 00:15:20.942
Yeah, okay.
00:15:21.393 --> 00:15:28.182
So, let's talk about some use cases for AI deployment.
00:15:28.273 --> 00:15:34.212
You've already mentioned that you know it's the buzzword already within lots of different SaaS things.
00:15:34.423 --> 00:15:40.523
So, let's talk about embracing AI via what you are already using in your tech stack.
00:15:40.523 --> 00:15:43.312
Can you sort of tell us a bit more about how that can happen?
00:15:44.508 --> 00:15:51.363
Yeah, so I think initially a lot of businesses were I guess bolting on AI.
00:15:51.423 --> 00:16:03.327
You know, they were using existing models whether AI did very well out of this, lots of companies started embracing that; whether it was HubSpot or Apple, they kind of just sort of adopted that technology and bolted it onto the side.
00:16:03.687 --> 00:16:20.562
What we've seen more recently is actually you know these tools have now had a few years since OpenAI really kind of burst the AI bubble, to think about how to deploy some of these tools a bit more thoughtfully they're focusing a lot more on it being part of a daily workflow.
00:16:20.592 --> 00:16:26.322
How do we streamline the use of the existing platform on the existing tool by incorporating AI?
00:16:26.413 --> 00:16:29.653
Not just bolting it on, not having a kind of copy and paste relationship with it.
00:16:30.582 --> 00:16:34.394
How can we actually use this tool within here we're seeing that now?
00:16:34.399 --> 00:16:42.868
So, for users that aren't familiar it's gonna be a lot more similar to using something like Microsoft Word, where you have things like your spell check is built in.
00:16:42.957 --> 00:17:03.802
We take that for granted now, AI is starting to do that when we're talking about things like CRMs for example, where suddenly you have this kind of ever present assistant or this functionality that's AI powered, within the platform, it's within the tools, whether it's the WYSIWYG that you are writing copy in, whether it's within the tool functionality for building out an email template.
00:17:04.748 --> 00:17:12.827
AI is now there and therefore that ease of integration within your daily workflow I think is a lot more obvious.
00:17:13.688 --> 00:17:19.357
In some ways AI is becoming less in your face and more subtly part of the wider process.
00:17:20.167 --> 00:17:25.958
Although obviously you always get those little stars appearing wherever a platform wants to tell you there's AI within the tool.
00:17:26.073 --> 00:17:26.363
Yeah.
00:17:26.417 --> 00:17:33.728
The actual functionality is becoming just baked in and I think that's what's quite interesting now because it really is just infused into the technology.
00:17:34.528 --> 00:17:44.983
Yeah, whoever designed the little star icon they should have painted it and charged 0.001 P for one of those and would be rich beyond their wildest dreams now.
00:17:45.453 --> 00:17:52.023
In terms of this embedding of AI within platforms, are there any dangers to that?
00:17:53.593 --> 00:17:54.012
Yeah.
00:17:54.192 --> 00:18:04.133
I mean there are in as much as with any of these AI tools, the source of information sometimes is not always that clear.
00:18:04.928 --> 00:18:12.748
Depending on the platform you're working in if you are dealing with customer data I think there's always an inherent risk of you know "where are we sharing data to?".
00:18:13.198 --> 00:18:25.788
So if we're simply turning AI features on without reading the small print without questioning what's happening to our first party data I think that's a real risk, I think that's something that needs to be part of that policy that we talked about.
00:18:26.688 --> 00:18:33.528
But I think with well-educated staff, with well written policies with processes that consider the use of AI.
00:18:34.188 --> 00:18:39.397
You know and using software that you feel confident about and you've researched.
00:18:39.758 --> 00:18:44.137
I think they all of those things add up to a world where we can use AI safely.
00:18:45.098 --> 00:18:47.917
Okay But it does need to be approached with a bit of common sense.
00:18:48.123 --> 00:18:48.482
Okay.
00:18:48.962 --> 00:18:56.262
Coming back to understanding the what's and the wherefores about things like data and things going there.
00:18:56.413 --> 00:19:15.028
Okay, so what about talk about using AI within tools and how effectively if you've got things like Salesforce or you've got HubSpot those kinds of tools you're seeing those appear the quality, I think is generally getting better.
00:19:15.147 --> 00:19:21.258
Although, there was an AI assistant, which I did yesterday, and it was basically worse than a search function.
00:19:21.288 --> 00:19:32.663
But anyway, that beside, what about, like more custom uses of AI within people's revenue generating technology.
00:19:32.712 --> 00:19:34.212
What's the approach with that?
00:19:35.952 --> 00:19:48.347
So, I think this is where it starts to get really interesting and we've done some work recently with clients that you know, they're really interesting use cases that off the bat maybe weren't the most obvious uses for AI.
00:19:48.887 --> 00:19:49.178
Yeah.
00:19:49.488 --> 00:20:01.393
I think this is where having that discussion around are the pain points, what is it that we're trying to solve, trying to understand a particular process flow from end to end.
00:20:01.393 --> 00:20:07.643
Whether that be part of your sales process, whether it be part of your marketing team, whether it be more customer facing and part of your product.
00:20:08.363 --> 00:20:23.532
Understanding where AI is a good fit for the type of task, whether it's a very repetitive task or volume data task, but also where it's gonna add value, that we don't wanna be doing is saying just "stick AI on it, it'll be fine".
00:20:23.604 --> 00:20:29.334
Yeah, because I've heard people sort-of talk about like AI is the answer now... What was the question again?
00:20:29.334 --> 00:20:30.829
and it's not that way round.
00:20:30.950 --> 00:20:49.605
It's good that you highlight that cause I think if people are looking at adoption, start with where's the need, in terms of as you say repetitive, time-consuming, problematic tasks and look at the process there.
00:20:49.934 --> 00:20:53.505
And I suppose process mapping plays a really important role at this point.
00:20:54.703 --> 00:20:57.189
It does because you need to fully understand.
00:20:57.469 --> 00:21:06.439
We've talked before about how business leaders aren't aware of what's going on within their technology or their processes, and you sometimes have these silos between teams.
00:21:06.439 --> 00:21:19.739
So, understanding that full picture with process mapping and understanding where data is flowing across your business, what processes are interconnected, where teams interact with each other and where they interact with their technology.
00:21:20.288 --> 00:21:35.588
That will start to then highlight, actually, there's an awful lot of manual importing going on over here or someone's doing hours of research over here, and those are things when people start talking about data or research, those are things
00:21:35.588 --> 00:21:49.509
Actually AI is very good at that, and when built in a flow that is very structured and very set, you can harness AI in a way that it's gonna give you very repeatable outcomes.
00:21:49.913 --> 00:21:56.124
So, you know, good example, we've worked recently with a company where they wanted to automate parts of their sales process.
00:21:56.213 --> 00:22:08.003
We spent time with them understanding where is the value gonna be, where are your real problems, where do we want to make sure that we're focusing on letting people do their jobs in a way that only the people can.
00:22:08.739 --> 00:22:29.199
Therefore, how do we put AI in the background of this process to be going off and doing things like research, bringing data back, formatting that data, structuring it in a way that can ultimately put out a very repeatable output, which in this case was a customised pitch deck to allow them to really understand that process from end to end.
00:22:29.759 --> 00:22:36.909
You need to have various different AI agents throughout a flow that can do a set job, nothing more, nothing less.
00:22:37.694 --> 00:22:37.984
Yeah.
00:22:38.153 --> 00:22:51.229
That's why we talk about infusing AI, because this is not a case of "oh I just bring in a chat bot and it will do a thing for me", it's a case of each part of this, is locked down in a way that it's gonna do what it is expected to do.
00:22:51.709 --> 00:22:56.568
But, that kind of the magic if you like, what AI can do is then go off and do that very fast.
00:22:57.229 --> 00:22:57.519
Okay.
00:22:57.544 --> 00:22:58.413
Really good results.
00:22:58.773 --> 00:23:00.248
Okay To go and do the proper work.
00:23:01.138 --> 00:23:06.638
Okay, I'm gonna take you back to the start of that interesting section there.
00:23:06.969 --> 00:23:09.338
You talked about process mapping.
00:23:09.669 --> 00:23:16.449
Now, no doubt a lot of people are gonna go off and ask ChatGPT to tell them, or maybe old school Google it.
00:23:17.019 --> 00:23:22.348
But can you just explain to the people listening, what does process mapping mean?
00:23:22.348 --> 00:23:27.493
Because I think people are probably concerned that it's more complicated than it really is.
00:23:28.472 --> 00:23:53.032
Okay, so in a nutshell, process mapping is essentially bringing together all of the things that you are currently doing across your business; so it can be everything from which systems, or which people do a certain function, what that function is, what it links to, and bringing that together in a visual map so that you can literally understand in this particular part of our business.
00:23:53.153 --> 00:23:56.032
So maybe it's the handoff of new sales leads.
00:23:56.512 --> 00:24:01.942
These people are responsible for these things, they use these tools, the data flows from here to here.
00:24:02.363 --> 00:24:27.053
This is the sequential process that happens, mapped out visually and the reason is its a really powerful process is because what we tend to find within businesses, is that individuals know their bit of the process and a lot of it's up here, it is in their heads, so they're not necessarily sharing it that well across the business, and generally there isn't somebody that's holistically taking ownership of that process from end to end.
00:24:27.863 --> 00:24:27.942
Yeah.
00:24:27.942 --> 00:24:31.718
Mapping it out you, can start to go well hang on, what happens in this part?
00:24:31.728 --> 00:24:33.048
Where does the data go after that?
00:24:33.048 --> 00:24:33.923
Or who owns that bit?
00:24:33.923 --> 00:24:36.032
Or why does nothing happen here?
00:24:36.502 --> 00:24:36.792
Yeah.
00:24:36.942 --> 00:24:41.313
It's a really, really good way of being able to break a process apart, as well as mapping it out.
00:24:42.022 --> 00:25:05.208
Yeah, thank you for that, and I think it has to be the starting point for any kind of customised AI implementation, solving the problem, is the process map defines the problem if you like in terms of you know, a business is a collection of processes working in harmony, hopefully.
00:25:05.738 --> 00:25:34.228
And it was interesting because yesterday there was a conversation about that prospective company that came to us and said, "oh we'd like you to solve this with AI" and we actually had to go back to them and say: "Well we can't because you don't have a defined process, you've got all of these things going off all over the place and you can't until you've mapped what you want it to be, you can't just throw AI at the problem." Exactly, and sometimes the answer, or at least the first port of call is, change the process.
00:25:35.038 --> 00:25:38.228
Technology is great, but it can't fix everything.
00:25:38.278 --> 00:25:45.722
Ultimately, if people are not engaging with technology the way that we want them to as a business, the technology can only go so far.
00:25:46.788 --> 00:25:47.028
Yeah.
00:25:47.538 --> 00:26:05.008
I do think also there's a thing about being open to changing process, because you've got the technology, so you know it should be something that when you are implementing technology, look at the why are we doing this?
00:26:05.137 --> 00:26:05.438
Yep.
00:26:05.583 --> 00:26:23.522
If your route to getting to the objective is this way the technology can take you in a shorter route, faster route, then subject to you know quality and all of that, you shouldn't be afraid to actually say, well we could re-engineer the process underneath this because we want the objective.
00:26:24.063 --> 00:26:24.992
Yeah, absolutely.
00:26:24.992 --> 00:26:27.613
And I think like you say there, it's asking the why.
00:26:28.093 --> 00:26:36.853
I think we've seen a few businesses recently who it feels a little bit like they're going through a bit of a tick box exercise of, I've been told to use AI, I've just gotta tick that box.
00:26:37.393 --> 00:26:37.692
Yeah.
00:26:37.752 --> 00:26:38.803
That's not the right way to approach it.
00:26:39.548 --> 00:26:39.837
Yeah.
00:26:39.883 --> 00:26:42.938
This really is a genuinely very very helpful tool.
00:26:43.417 --> 00:26:47.597
I don't want to go too much into the hyperbole of saying it's a game changer, but it is a big deal.
00:26:48.018 --> 00:26:48.077
Yeah.
00:26:48.432 --> 00:26:51.767
And to just be ticking a box is not the right way to be thinking about this.
00:26:52.653 --> 00:27:20.048
Yeah, but the flip side of that I would say is we had a really enjoyable conversation last week, with that potential new customer where they'd got this kind of "oh well we thought maybe you could help us with this" and then when we start asking them about problems there was the this whole sort of cascade, then of like "oh well, this is a problem" and we went, have you thought about it if you did this technology?
00:27:20.347 --> 00:27:38.518
And I loved that conversation because I realised the power of having a conversation with us being knowledgeable about what the technology is capable of doing, just open the minds of that company as to "oh wow you could do that".
00:27:40.008 --> 00:27:44.923
And they just thought that was how it had to be, I thought that was a brilliant conversation.
00:27:45.527 --> 00:27:51.356
I agree and it is that moment of asking the right questions of well could you get AI to do this?
00:27:51.477 --> 00:27:59.277
And obviously our response at the time was, yeah absolutely, and that sort of almost jaw dropping moment of oh my god, we spent hours doing that...
00:27:59.688 --> 00:28:00.048
Yeah.
00:28:00.106 --> 00:28:05.676
And that's you know, that's what we're talking about here, it's quality of life for your employees, it is time saved, its value driven.
00:28:05.826 --> 00:28:09.096
You know being able to kind of speed up sales velocity and things.
00:28:09.156 --> 00:28:15.457
All these things are things that we should be thinking about, but asking the right questions of how to get there.
00:28:15.787 --> 00:28:17.106
Yeah, absolutely.
00:28:17.507 --> 00:28:33.366
To kind of bring it right round, you've come back to this sort of self-education bit of asking the right questions and gaining the understanding and we talked about how fast things are moving at the start.
00:28:33.366 --> 00:28:39.541
But we've kind of brought ourselves back around to the start, where you gotta know something in order to get moving.
00:28:39.957 --> 00:28:40.227
Yeah.
00:28:41.071 --> 00:28:42.741
And then do the next actions.
00:28:43.366 --> 00:28:50.356
Yeah absolutely, I think that's the thing, don't just look at AI as this big daunting thing that's gotta happen overnight.
00:28:51.227 --> 00:28:54.892
Start with education, start with policy, and build.
00:28:54.942 --> 00:29:00.376
You know, I think the technology moves really fast so staying ahead of it is always gonna be a challenge.
00:29:00.957 --> 00:29:01.247
Yeah.
00:29:01.307 --> 00:29:04.247
The place to start is now, with what's in front of us.
00:29:04.886 --> 00:29:12.076
Yeah You know, like I said change management, thinking about how we bring different elements of AI into the business.
00:29:12.352 --> 00:29:12.622
Yeah.
00:29:13.521 --> 00:29:29.142
Well, I think that the AI opportunities audit that we do, is just, I love those because they're so exciting when you have a systematic approach to uncovering opportunity, that some of the gains are gigantic.
00:29:29.221 --> 00:29:36.727
If you think about some of the other things, and this is why I think AI and technology is such a strategic game player for the companies that get it right.
00:29:37.386 --> 00:29:55.146
You think about doing something really amazing on like, I don't know some SEO project or some PPC, and picking on those things, it's sort of traditional digital marketing lead generation thing and you do something fantastic, and you get a 20 percent maybe uplift if it was absolutely knockout.
00:29:55.146 --> 00:30:09.021
Because you know you're assuming you're already doing some good stuff over here, anyway 20 percent would be a really big, like wow this is really good, but you could do something with this new AI?
00:30:09.021 --> 00:30:16.517
And when we look at these opportunity audits and when you turn around and go oh, we could save 98% of your time, 98%.
00:30:17.067 --> 00:30:22.616
Imagine what that person can do with that freed up 98% of the time.
00:30:22.616 --> 00:30:25.106
Now they're only spending 2% on that task.
00:30:25.826 --> 00:30:29.576
Oh, that's like wow.
00:30:29.636 --> 00:30:41.727
We haven't seen gains like that since back in the good old days of digital marketing when you know, people were still convincing companies to get a website, cause you'd get ahead if you had a website kind of thing.
00:30:41.797 --> 00:30:45.277
And it was a competitive differentiator to actually have a website.
00:30:45.277 --> 00:30:50.346
I imagine there's people listening to that go what, you actually had to persuade people to have a website?
00:30:50.346 --> 00:30:51.277
Everybody's got a website.
00:30:51.517 --> 00:31:03.186
But I've been around that long, I had conversations with people about saying if you get a website you'll stand out in your industry because, so few people have a website.
00:31:03.186 --> 00:31:06.426
And they were like but we've got brochures, why would we want a website?
00:31:06.906 --> 00:31:10.977
Yeah, now the game has changed I think that's where we are with AI.
00:31:11.451 --> 00:31:11.662
Yeah.
00:31:11.747 --> 00:31:29.297
We are gonna be at a point in five years at most maybe where everybody's going to be in the game and the winners will have jumped ahead, and the people that don't adopt fast will have fallen by the wayside kind of thing.
00:31:29.862 --> 00:31:45.076
Yeah, absolutely agree, and I think you know now is the time to be doing it because the technology is much more established, you know it's in a position now where it is enough that we can do really amazing things with it, it's moving really fast.
00:31:45.076 --> 00:31:50.057
So, now is the time to make sure you're ahead of the curve and you're really understanding where to enhance it.
00:31:50.162 --> 00:31:50.491
Okay.
00:31:50.821 --> 00:31:52.912
That's great I think we've covered off a lot.
00:31:52.912 --> 00:32:00.442
We probably should put a few bits and pieces in the show notes in terms of links to some of these things, education, development.
00:32:00.541 --> 00:32:12.616
I think that for summary it's like that education informing leadership, so things like having an AI policy, and that policy has got to evolve as you evolve your use of it.
00:32:13.487 --> 00:32:25.396
AI Task Force/AI Council for adoption, then start looking at what AI you've already got access to, and the tools that you've got, and then looking at the wider things.
00:32:25.396 --> 00:32:33.596
So, yeah, I think the AI opportunities audit that you know, and people do those kinds of things if they're well informed enough themselves.
00:32:33.596 --> 00:32:41.906
But, that kind of thing is then the next stage for the more bespoke if you like AI implementations.
00:32:44.561 --> 00:32:54.461
Okay that's really good I've as always enjoyed chatting to you on this and I could chat for ages, but we'll get in trouble with the team for creating too long an episode.
00:32:54.461 --> 00:32:58.541
So, any more gems before we sign off on this one Chris?
00:33:00.261 --> 00:33:30.731
No I just think you know it's again, there will come a time where AI is just one of the names that gets given to a collection of tools, if not part of every tool that we use, I liked your analogy about the policy of saying oh you know don't use a computer is kind of the equivalent, but it's similar as well of the way we're talking about the use of AI and you wouldn't expect somebody in the team to not have a keyboard with their computer, and it's gonna come to a point where AI is part of everything that we do within business.
00:33:31.211 --> 00:33:46.027
So, it is time to be adopting it, to be thinking about it, not as this big standalone thing that we can copy and paste in and out of, it needs to be something that we interact with in the same way we would any of the tools that we use daily.
00:33:47.201 --> 00:33:49.241
Yeah, no that's good.
00:33:49.721 --> 00:33:51.932
Good finishing comment there, I like that.
00:33:54.221 --> 00:33:58.576
Well, thanks to you Chris for another interesting conversation, really great.
00:33:58.876 --> 00:34:03.586
And thanks to you all for listening, this has been another Revenue Rewired episode.
00:34:03.586 --> 00:34:13.536
So, if you found this conversation useful, which I hope you did please do follow us, like, subscribe all of that stuff, whatever platform you choose to listen to us on.
00:34:13.536 --> 00:34:15.277
And we're on all of the popular ones.
00:34:15.496 --> 00:34:21.492
To find out more about Innovation Visual or AI adoption, in fact, just go to innovationvisual.com.
00:34:21.811 --> 00:34:26.871
And, until next time keep rewiring the way you look at your revenue systems.
00:34:26.871 --> 00:34:29.661
So, thank you very much Chris and thank you all for listening.
