What If Robots Really Did Run Your Business? An Interview with Katie King.

Speaker 1 (00:06):

Oh, can Katie, can you hear me? I can hear you. Yes. Okay. I think Joe just went on, on his producer mute. All right. All right. So we'll go into, we'll do the episode intro. I'll do a little bit of background on you and a background on the topic. All right, Katie. Welcome to the podcast. We're so happy to have you.

Speaker 2 (00:27):

Thank you so much, Kim greetings from London.

Speaker 1 (00:31):

Yes. We're. We're excited to talk to you today and hear everything about AI. I know in our prep conversations, we had a lot of good back and forth on, you know, where this conversation could go. And, you know, what's the most up to date information on AI, but I think one of the first things we sort of landed on was let's redefine what AI is or more specifically what isn't AI.

Speaker 2 (00:57):

It's a great place to start. Really we're talking about a family of data science, a type of technology that's been around since the 1950s, but it's really about intelligence. The actual computers demonstrating intelligent human behavior. They're not sentient, they're not creative necessarily, but they can be taught to think like intelligent humans, which is so powerful and so exciting. What they're not are the big, scary robot who's coming to take your job in marketing, in HR, in finance, that's the myth. And that's what we need to kinda share with people.

Speaker 1 (01:44):

I think that it's funny to people in the industry this misconception, but it is a very real misconception. So it's good to call that out. Now in your book, which is great. It covers a lot of ground around AI, but you claim and I'm paraphrasing this a little bit. There's no business over time that will not be an AI business in some form, which is a very, I think, very strong, provocative statement. Can you expand on what you mean by that?

Speaker 2 (02:13):

Definitely. I think I wrote the first book in 2019 and this latest book came out in January of this year. And I'm able to say that now I couldn't have said that two or three years ago. And what I really mean by that is every business function, every geography, every industry sector is going to be touched by AI and will be using AI in some form of another. So therefore they will be a business that's taking advantage of an AI, an AI infused business, for example, using it to do some of their marketing and sales, using it to sift the CVS in their HR. And they could be banks. It could be retailers, it could be construction companies of a tiny size up to, you know, a multinational organization.

Speaker 1 (03:09):

That's a lot. So, and you talked about this, what's the difference though? You said two or three years ago, you couldn't have said that. What is the difference, between AI today versus two to three years ago? Five years ago? What do today's business leaders need to understand about leveraging AI for their business? You talk about this in your book a little bit, but is it really that accessible for businesses of all sizes to say that it will be part of all business?

Speaker 2 (03:35):

Yes, it is accessible. I think five years ago, this was still a sci-fi concept. It was in our lives, you know, but we weren't really thinking about it. You had the early adopters exploring it and leading the charge and they were viewed as the very forward thinking ones, but everyone is not everyone. It would be wrong for me to imply that it's being deployed at scale across every company, in every country and in every industry sector, but it is accessible. That could be as simple as using a point-to-point marketing tool, something like FRA or ConEd or one of the many, or it could be MailChimp and using some AI or brand watch within your marketing. So it is much more accessible. And if we just stop there for a minute and think about AI in our lives, we are coping with COVID thanks to AI in healthcare.

Speaker 2 (04:38):

So had the AI not helped the scientists to speed up the discovery for a vaccine. You know, we wouldn't be here now. Well, we would be because we could still be doing this virtually, but you know, it saved a lot of lives. It's enabled us to go back to offices and so on. So it's in our lives for sure. When I wrote the first book, people didn't have a clue what I was talking about and there was huge skepticism, whereas now, more and more banks, small PR companies, all kinds of organizations are starting to understand it and take small steps in some instances, right up to major scale implementations.

Speaker 1 (05:22):

It's a great point. You're kind of further myth busting AI to say it, it really is plainly in our lives every day. We're not maybe seeing the application of AI, in our day to day. Uh, but it's there. Absolutely. I think it's very important to think about it and you're talking about scale. What does it mean to scale AI and you talk a lot about pragmatism in your book, pragmatism around AI, which is all about our, what if so, what ethos here? So we would love to hear more about that. What are some examples of pragmatism and deploying and scaling AI for business, and more specifically thinking about our workforce today, how do businesses upscale or, or higher for AI?

Speaker 2 (06:13):

That's a great question. And it's so relevant. And as you say, that's very much, my style is that sort of integrity and being very pragmatic. So we've got a meeting of minds there. I think the biggest consideration for scaling it is having the right talent to support it. And that's gonna look very different from business to business. And depending on the actual AI project itself, if you're simply adding AI capabilities to your existing CRM database, for example, then that might be really simple hosting a training session with your team, showing them what the capabilities are, what the tools are, or it might be something much bigger. It might be adopting a solution that requires much more from your people. So in that case, you might bring people in from outside, it might be about integrating it in, you might be a major retailer and you might be having to integrate some AI into your whole customer journey and all the different touch points within that.

Speaker 2 (07:14):

That might require you to outsource that and find the right provider and so on. It's actually in the first book, but I still use it. So the one that came out in 2019 is a scorecard for success. And it's quite complicated in some respects, but you can gamify it and you can break AI down into 10 core areas, which really is all about how do I get start started and how do I scale? And it covers everything from the right mindset, having the right approach to ethics, thinking about doing a proof of concept and much, much more. Maybe we'll cover a little bit more about that as we go along, but it is about small iterations showing that it works having a clear business goal, and then beginning to roll it out across the organization.

Speaker 1 (08:09):

You make it sound so simple. And I think we've all been in this situation where we've seen businesses implement technology, whether as you say, it's a point-to-point solution, or it's a major multi-year implementation and it fails. Digital transformation failures are prevalent in this industry. And we talk a lot about that on the podcast, AI in particular, I think we've seen lots of fits and starts and, you quote P WC from their annual AI prediction survey, and they state AI is hard to many AI investments end up as pretty shiny objects that don't pay off. Most companies have yet to adapt talent strategies, which you spoke to organizational strategies, business strategies, development, methodologies, and risk mitigation for a world that moves at AI speed. That sounds hard. What other people and process elements of AI do you think leaders need to consider to make an investment payoff beyond obviously, uh, the talent?

Speaker 2 (09:07):

Yeah, such an important question. I think the need is absolutely critical. The most essential, if you don't have a valid, legitimate need for AI-what problems do we have that that need to be solved, then it can become that pretty shiny object. So it's being clear on your goals, what you're trying to accomplish. So AI might not actually be the right answer. There might be some business analytics perhaps for a different type of project that you need. So what we're really TA saying here is let's not embark on a vanity AI project, let's work out what is it that needs to be adjusted that needs to either help reduce some kind of, I call it the three DS, the dirty, dull, and dangerous-can we increase revenue? Can we reduce some of the boring monotonous tasks that we need to do?

So that need is an important one. The next one I would say really is data. This AI we're talking about is viable. Now, it hasn't been viable for 70 years because we didn't have enough data or the right kind of data. Right. And because it's now cost effective, we've got cheaper processing power. So today AI is really a data fuel technology. So people need to have a clear understanding of the data they need, what they're gonna get from it, how they can ethically collect it and manage it. And if they don't do that, then they're getting into difficult situations I could carry on with agility and culture and much more. I thought we we'd sort of start with, with those two.

Speaker 1 (10:57):

I think it makes sense foundationally, and as you mentioned, this maturity model that you can use in your book where you can break it up and kind of understand where you are in each of those areas, I think is very important. And as you say, AI might not be the right solution for a business problem. Um, just because it is sexy. absolutely.

Absolutely. So in the first book, for example, people were being mandated by their CEOs, open up your shopping bag and buy some AI. This is flavor of the month. And then I think the case studies that I researched to the companies and the vendors for the last couple of years for this second book, there was much more pragmatism, much more realism, much more of a desire to invest in things that were going to make a difference rather than just having that shiny tag that would help with evaluation, for example.

Speaker 1 (11:56):

Right? No, that's excellent to hear. One of the things that we have been working with our clients on for many, many years and to your earlier point is really starting to pay off is just improving that customer journey through AI, we call it journey science, but so many brands are chasing personalization as a key capability. I'm curious, have you seen some recent, real innovative approaches where AI has been key in driving personalization, given its maturity at this point?

Speaker 2 (12:27):

Yeah. Again, another great question. I've got a whole chapter on personalization in the books that it's this paradox of personalization, which is so interesting. Think about not what AI isn't, AI isn't this big shiny robot taking our jobs. If we, as a bank, as a retailer, as a PR agency, as a law firm, if we can get big data insights on our clients, on our customers, we can give them a very personalized, granular service. And that is absolutely crucial. So I think what we're starting to see is AI baked into the background of our experience to a point where we don't even realize it's happening. So I've got lots of examples of brands in all different sectors using it. So, we're typically thinking of the product recommendations from the Netflix and the Spotifys, but we're starting to see personalization extend to out of house marketing with some companies using data from their consumers devices to give them a personalized advert on display signage in their shops, or it might be a chat bot, where we're seeing a brand take that to the next level with a voice bot and tailoring that experience.

Speaker 2 (13:49):

It might be voiced by a metrics where we're seeing all different sort of possibilities come out there, for example matching people to the right agent or putting a senior citizen into a priority queue. So that kind of ability to identify V I P customers fast track their service is exploding on the personalization side. And I think it's taking an organization from treating us customers in a very homogenous way to being able to give us what we need when we want it, but hopefully in a non-creepy way.

Speaker 1 (14:29):

right. That's always the trick, isn't it? Absolutely. The non-creepy

Speaker 2 (14:32):

way. Absolutely.

Speaker 1 (14:34):

Yes. Well this has been a fascinating conversation. I'm wondering if you could give a business leader one piece of advice around AI that they could act on today, what would it be?

Speaker 2 (14:47):

Don't delay. I genuinely mean that-I think people do need to move fast because they will get left behind. So that's an important one. So move fast, but do your due diligence, be careful not to jump straight in and make a major investment, do some small iterations based around a need and based around your business plan, that's probably three in one I'm being greedy, but, , that would be my advice.

Speaker 1 (15:21):

No, that's excellent advice as, as we will know. Well, thank you so much, Katie, for your time today and for your expertise. And we will definitely make sure that we have your book and everywhere we can purchase it in the show notes. Thanks again, and have a great rest of your day.

Speaker 2 (15:37):

Thank you, Kim. Thanks for a great interview.