897: How to Enable Enterprise AI Transformation, with Strategy Consultant Diane Hare
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This is episode number 897 with Diane Hare, founder and CEO of BizLove. Today's episode is brought to you by Tranium 2, the latest AI chip from AWS. By Adverity, the conversational analytics platform. And by the Dell AI Factory with NVIDIA.

Welcome to the Super Data Science Podcast, the most listened to podcast in the data science industry. Each week, we bring you fun and inspiring people and ideas exploring the cutting edge of machine learning, AI, and related technologies that are transforming our world for the better. I'm your host, John Krohn. Thanks for joining me today. And now, let's make the complex simple.

Welcome back to the Super Data Science Podcast. Today's a special episode for you with Diane Hare, a world-class leader on enabling AI solutions in enterprises. Diane is founder and CEO of the New York-based strategic consulting firm BizLove, like business love.

which has been mobilizing key stakeholders to deliver on enterprise-wide priorities like AI initiatives at Fortune 100 companies for seven years. Prior to her seven years leading BizLove, Diane spent seven years at EY, the global professional services giant. They have nearly 400,000 employees.

that is formerly known as Ernst & Young. She holds an MBA, and she was captain of a semi-professional women's soccer team in New York City. Today's episode is well-suited to anyone looking to make an impact with AI and automation, which I suspect is about every listener to this podcast. In today's episode, Diane details why people not technical capability are holding back AI's transformative power in organizations.

She also talks about how to prioritize the items on an enterprise AI roadmap, why storytelling is essential for gaining buy-in from stakeholders on an AI initiative, and her top five tips for enabling AI transformation. All right, you ready for this superb episode? Let's go.

Diane Hare, welcome to the Super Data Science Podcast. Thank you. We're filming live from New York. I've done a couple episodes in this studio before. People are watching the video version. You get multiple camera angles, great lighting, great sound. I mean, everyone gets to enjoy the great sound. And I think we just get to have great rapport together in person. So yeah, we're in New York. We're at Neuhaus near Madison Square Park.

And, yeah, Diane, we should fill the audience in a bit on how we know each other. We were introduced by Allie Miller, who is well-known. Allie K. Miller, to disambiguate. She has over one and a half million followers on LinkedIn, so...

A lot of people in data science know who she is because she's focused on AI. I believe her tagline at the time of recording is, number one most listened to voice in AI business. Yep, exactly. And yeah, you've known her for a few years. I've known her for a few years. And we owe Allie thanks. I don't even know if we've really, I haven't really filled her in on this yet, but maybe you have. I think I have. Shout out Allie Miller if she's listening. Yeah.

And yeah, so basically you and I are doing business together now. So I launched earlier this year my own consultancy. It's an AI consultancy called Y-Carrot, which for our machine learning aficionados out there is like Y-Hat, the output of a machine learning model. But the carrot is what that hat on top of the Y is called in computer science. So Y-Carrot.

And then it's spelled like a vegetable so that we can make great use of the carrot emoji on social media.

And yeah, so why care? It's, you know, we're focused on implementing AI solutions so we can help people figure out technically what is the right solution for a particular scenario, you know, given a situation that your organization is in and then we can implement it. So the machine learning model, you know, training the model weights or figuring out whether you should be calling, uh,

You know, an open source LLM that's your own proprietary model running on your own premises or calling a third party LLM, you know, because you're comfortable with getting a state of the art model up quickly. All those kinds of considerations around getting some AI solution implemented are

And then we can also put it in a product. So we can build a user interface for you. We can, yeah, we can. So from end to end, in terms of implementation, YCaret is there for you. And BizLove is the perfect complement, your company, BizLove, I'll let you speak about this more, but you're the perfect complement to us because you have decades of combined experience at BizLove with YCaret.

change enablement, particularly with respect to digital change, change related to data and AI solutions. And so, yeah, you guys bring the enablement and we bring the implementation. Yeah, well said. So a little bit about BizLove. We are storytellers and strategists. So a lot of us come from corporate big four consulting, corporate management, so the Fortune 500 space. And

And then the storytelling elements to our offering, they are a craft that we have learned. I'll get into that a little later. But we use storytelling and strategy as complements to enable organizations through storytelling

Change, disruption, innovation. And when I met John and he was focused on the AI digital data space, I was like, OK, you're my opposite. You love technical implementation. I love enabling an organization and people to receive it.

We know that buyers or I should say clients today, they need an integrated team because they are a leader of the whole thing. So to have two different agencies or consultancies at the table is like double work.

For us, you meet with one party, right, John and I, and we help navigate the integration for the client with the client. And I think that's a dream for folks who want to implement AI across their enterprise. The one party you don't want to miss. And yeah, for people not watching the video version, we are indeed opposites. Diane has great hair. I have no hair. Yeah, in every way imaginable.

And yeah, so to kind of give some context around, probably a lot of listeners are already aware of the kinds of problems with getting AI implemented in an organization. Constantly, you know, from podcast guests, from people at conferences, from people in client meetings, right?

A very common problem that I'm encountered with is something that actually two weeks ago at the time of filming, I was at the Open Data Science Conference East in Boston and the Crew AI, COO Rob Bailey. So Crew AI, if you're not aware of it, it's an open source Python framework for getting crews, teams of AI agents working on different tasks potentially within your organization or as an individual.

And Rob Bailey had a quote that I love from his keynote at ODSC East, which was that tech isn't the barrier to multi-agent systems. There's so much opportunity in enterprises to be implementing multi-agent systems effectively, but people make it more challenging. And so that's kind of like, I think that kind of frames the problem that, you know, this kind of having like technical solutions, like why care can provide us doing that on our own. It isn't enough.

And we need the kind of expertise that you have and that you will be sharing, Diane, with our listeners today on what they can be doing to actually affect change in their organization and take advantage of the crazy amount of technological capability that we have. And, you know, it's like this episode will come out a few weeks from the time we record it and we'll have like

you know, 20, 30% more technical capabilities that we could be taking advantage of as an enterprise. So true. And many of those enterprises are just going to kind of be adding it to a list. Yeah. Yeah. And I think, you know, we talk about advanced technology and the breakthrough technology, but most enterprises out there are not prepared for even basic technology, right? It's, they don't have clean data. They don't have people, practitioners that know how to use it.

And we want the Ferrari, but we don't even have the highway. We don't have the toll booths. We don't have like, where does the car go? So we help organizations create the highway, create the toll booths, right? The governance, the structure, and then enable their people to drive the car.

And I always laugh because I always listen to, you know, the news and these headlines and it's like emerging tech, this and that. But I work with B2B multinational organizations who have been around for decades. And I know that they are the people and the teams who keep the world running. And they are so far away from being able to receive what that is.

So you are a storyteller. You have lots of expertise in that. And it shows in just that Ferrari example. It just makes things so tangible. Thank you. Hopefully some of that's going to rub off on me in this episode as well as through the process of working together.

So, yeah, so there's this kind of, if you don't mind me kind of getting into a common problem. So, you know, we talked about already this common problem that Crew AI COO Rob Bailey brought up around tech not being a barrier, but people being the barrier. And I don't think that people are the barrier because they're Luddites. And in some scenarios, they are because they're concerned about, oh, they're augmenting my role, they're replacing my role. Parts of what I'm doing are going to be automated.

For the most part in organizations, that is an opportunity for that person as well, for that worker, because it often means that it frees them up for more interesting tasks, less repetitive tasks. Right.

So it is, you know, there are going to be examples where AI solutions do probably completely replace somebody in the job, but that's a minority case. For the most part, it's something that's good. And so I don't think that people are resistant to AI because they're so worried about their job being replaced. I think the thing that makes organizations, people in organizations difficult is

to work with in terms of AI implementations is because it's hard for the whole organization to get aligned on what to prioritize. Yep.

And you already mentioned all kinds of problems there, like if the data aren't well organized, if data are in silos, but some AI solution requires them to not be in silos. There's all kinds of structural problems that you could have. And maybe we can talk about that later in the episode. But fundamentally, one problem that I think every organization has, even if they have those highways built that you described, is that they don't have the infrastructure.

is that they don't know how to prioritize. Where do they invest the time, the money? Yeah. Especially when AI is so fast moving that, you know, you implement a solution and actually if you just waited six months, there'd be better tooling that could have made it way easier, way more effective. So have you wasted all this time?

And so in a relatively recent episode with John Rose, the CTO and chief AI officer of Dell, so that's episode 887 of the show, he talked about ROI, return on investment, as kind of one key metric. And that is one way to go, but it doesn't give you a full picture or there's not much nuance to ROI.

So something that I like to use from a technical perspective is something called a RICE score. Yep. And so RICE scores, R-I-C-E, like white rice. And what that is, so it's reach, which is how many people will be impacted by this solution. Impact, of those people that are impacted, how much does it change things for them? How much does it change their user experience or their experience as an employee, the quality of their output?

Um, confidence C is how confident you are about the solution. And this matters because you, um, you know, if you're not confident about the reach that it will have or the impact that we'll have, then, you know, that reduces how much confidence you should have in that whole solution. Right. Or is it bulletproof or is there failure there? Right. Like how bulletproof is the technology? So.

Exactly. So you take those first three RIC ingredients in RICE, in that acronym, and you multiply those by each other, and then you divide them all by E, by the last letter, which is effort. So that's how much time, how much money, how many person hours are going to be involved in this solution. And so that gives you a way, like you can imagine a spreadsheet where you could spend a day

brainstorming, you know, your executive team in a company or, I mean, it depends on the size of the company, but some mix of executives, technical people, and maybe you have some more insights into how to do this practically. But, you know, you sit in a room for a day and you come up with tons and tons of ideas on whiteboards and then you get them into a spreadsheet and you have four columns, R-I-C-E, and the more reach, the more impact, the more confidence you have in a row and the less effort you

Then you get this overall race score that you can rank the whole table by. Yeah. And the, you know, the solutions that show the most promise bubble towards the top. That doesn't mean you need to dogmatically follow it. Right. Anyway, so that's

So that's kind of this technical solution, but we're actually geared to hear from you mostly. And I've been talking way too much in this episode so far. That's because we're partners. Diane will be talking. We're partners. You can tell. When you look back over the whole episode, I bet you by the end, there will be more Diane time than me. But so far it's been heavyweight on me as I frame these things.

And so that's kind of from a technical perspective how we handle things. But talk about how, from your perspective, from the enablement perspective, you handle this problem of how to prioritize a change, you know, prioritize various changes in an organization, particularly, you know, something like an AI, like AI changes. You know, when you were telling me about the RICE criteria, I was thinking, who in the enterprise...

has that much time to come up with a million use cases to score, right? It's not the C-suite. It's definitely not the senior leadership team. It's probably the data AI team, right, who are responsible for

in tasks with understanding enterprise-wide, where are the use cases? The product people. Product people. Product people. They're the, you know, I think typically in an organization, you would have the product people be owning kind of this like prioritization. The bottoms up assessment. Yeah. And so where I come in and where I really see value is those teams, even if they come up with the best use cases, they can't get them into the enterprise unless they have senior leadership buy-in.

And they have an actual implementation roadmap that makes sense for the orchestra of enterprise priorities that are happening around them.

So whenever you try to push an enterprise-wide priority across an enterprise, it has to be in concert with what is important to that enterprise that quarter. And if you don't integrate it, it feels like this foreign, non-priority, siloed mission, and it doesn't get the adoption or the airtime that it deserves. So if I were to work with you and we had a team in front of us, we were doing a workshop, and it was mostly product data AI training.

Where we would come in at BizLove is to understand how do we get your senior leadership team bought into this, right? We have really good ideas at the table. John and his team would help with the ROI for each use case. We would score them. We'd have our top five. The next step is socialization, alignment, and approval. BizLove is very good at aligning leadership teams around what's important in concert with the rest of their enterprise priorities. We wouldn't work in a silo. We would bring it up and integrate it.

And then we would help understand what's the change impact and how ready is the enterprise to receive what you want to give them.

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Now back to the show.

And a point to make is that we're not doing this podcast episode to just pitch you on like, oh, you should be using Y-carat or Bisla, but these are examples of how you can take

How Diane said there, you know, BizLove has specialization in this and it has this impact. It allows you to make change in your organization, adopt this AI solution. You know, these will be generalizable solutions.

that anyone can use. And if you are a product person in your enterprise and you want to roll out an AI solution, I guarantee you're grappling with how to get it in front of the C-suite or the senior leadership team to get budget approval, resource approval. And so whether you have us at the table with you or not, you're still climbing that ladder trying to get it in front of the decision maker, get it resourced. And so how you do that is a key. Nicely said, as anticipated. Yeah.

from such a great storyteller. Let's talk actually about your storytelling background because it's interesting. You told me about this, one of the first meetings that you and I ever had together, this history. It involves a very well-known nonfiction author. And to provide a bit of context, I think a quote that you gave me when we were preparing for this episode just before recording is you said, to make change in an organization is

You need to have creative, original content that is targeted at the smart, busy people that make up your organization. Yeah. And so, yeah, with that kind of framing in mind, tell us about your storytelling background and how you became the expert you are. Yeah. So storytelling for me is a technique to...

Cut through the noise, right? I think especially in a generative AI content creative world, we see a lot of recycled content. And you know when you hear something original, it sticks, it resonates, you repeat it.

And so when you're a storyteller... If you hear original content, it makes you repeat it. Yeah. If you hear repeated content, you're less likely... You zone out. You zone out, right? Because your brain already recognizes it as knowledge. You already received that. And I think when you're in a corporation and you're checking your email and you see floods of emails coming at you, you're only going to be able to retain 10% of what comes through from your corporate comms function. And so how do we make...

So you mentioned an author. So Simon Sinek came into Ernst & Young when I was working there through a partnership. And it's the reason why I fell in love with storytelling was watching an author, a storyteller come into a story.

a big four consulting firm and have such impact, right? Be able to resonate, cut through the noise, reach the decision maker, shift the way they thought, inspire them to take action. And I realized, oh, it's this during change and transformation. It's more of this.

And so after spending three years with Simon in a room, right, in New York City, kind of tinkering around how do we bring Start With Why to commercial. You spent three years working directly? Not every day, but yes, it was a three-year partnership. And I was one of the founding members of the team. Because he's like, as a business author, he's one of the biggest. Like, he's like, you know, you don't have a tier, I don't think, that's like higher.

Yeah. Yeah. He's a luminary. Like if you think about he's not just an author, he's he's an he inspires. He's one of the greatest orators of our time.

Yeah. So with that partnership, I realized what we needed to do to actually drive change through an organization. And it was strategy and storytelling. At the time I was doing strategy, I wanted this tip of the spear craft to make my strategies go somewhere. Right. And that's storytelling. I learned how to work with the Golden Circles. Why, how, what?

The order matters. How you... You're going to have to tell us about the golden circles. The golden circle. Just one. That sounded like there were three. Okay. So it's like three circles in one. You start in the center with the why, then you work out to the how, and then the outer ring is the what. Most enterprises today go what, how, why. Classic in AI as well. It's the, you know, trying to find a nail after you have a hammer.

Okay. So this, this is important. We often go what, how, why? Because it's how, you know, if I met John on the sidewalk in New York, I'd be like, Hey, I'm Diane. He'd be like, what do you do? I'm the founder CEO. Why are you talking to me? We lead with the what instead of me saying like, Hey, John, I'm Diane. Right. I exist to help companies create space so they can transform into their highest potential. Right.

How I do that is through my consultancy. What I deliver, M&As, product launches, AI integration. Most of the times companies start what, how, why, and they don't even mention their why because they've never spent the time to discover it. No idea what their why is.

Right. But if you lead with why, you capture people who are about your cause, who want to be a part of what you're building. And it's almost like a filter for people who aren't your true match or who are supposed to be part of your world. And so I fell in love with that from a corporate task. Right. How do we help companies find their why and how do we help them lead with it to inspire, to motivate action, to drive change, right?

And that's what we do at BizLove is we take that foundational work from the Simon Sinek partnership and we built more on top of it. I think his most famous book is called Start With Why, right? Yes. And it does occur to me now, do you think that you had a natural inclination to work with my consultancy because we're called Why Carrot? I mean, I didn't lead with that, but now that you say so, yes.

For people just listening, it is, in my case, it's just like the letter Y, like Y Combinator, visually similar. A lawyer told us on Friday that he was like, should you be worried about some kind of infringement? I can't imagine why. It's completely different. It's a completely different why. Completely different second word. Completely unrelated.

I did also spend the weekend looking into what Y Combinator means, and it turns out I gave a poor explanation to you guys last week at lunch. So I learned over the weekend, so Y Combinator gets its name from a computer science algorithm, and it's related to... So a Y Combinator algorithm, it can... It's like nested. So it's like this idea of how Y Combinator, the company...

other companies. And so it's like nested. Yeah, it's, I don't actually, I didn't spend enough time on it to understand the technical details, but that's kind of where their thing comes from. Anyway,

Lots of whys in this episode. So yeah, so Simon Sinek, Start With Why, amazing book. Seems like something that maybe people in general, regardless of whether we're talking about in a commercial sense or not, but maybe spending some time, it's on my to-do list to figure out why I'm doing anything I'm doing. I'm sure...

books like that like start with why and the many books that simon sinek has created kind of in that uh ecosystem that he's created i'm sure they help us you know there must be exercises in these kinds of things to help us tease out why we're doing anything we're doing personally professionally yep you can um if you want to find your why you can go to simon sinek start with y.com and you can take a digital course and he can help you facilitate you through finding it

But that's not why he worked with, at the time, us. That's not why he worked with EY. Right. The reason he worked with EY was to help companies find their why. And that's a different process when you're bringing together a whole organization versus a one-on-one person. Very cool. Amazing that Ernst & Young brought him in. That's a huge...

Huge name to bring in and incredible that you got to have all that exposure in that time and develop the storytelling expertise, which is obviously made a big impact on you and the way that you think. Yep.

Lots more for me to dig into there. All right. So now with that context in mind, I'd love for you to be able to get into some particular case studies. And obviously in consulting, there's lots of client privacy. So I suspect in a lot of these cases, you probably won't be able to mention the client name, but you'll be able to, you know, these are real world situations where Diane or her consulting firm, BizLove, have been able to make a big impact on

And so I've noted a few down here from things that we've talked about in the past. And one of them that I love is...

When you upskilled executives on leveraging AI in their work, that seems kind of like an easy stepping stone into further cases. Yeah. So a lot of, I would imagine this is sitting with CEOs and like CHROs, but those that are responsible for learning and development. Right now, they are trying to figure out how to upskill their workforce from executive leadership down to, right,

manufacturing floor, how do we get ready for AI? And the senior leadership team need different curriculum than the frontline workers. Middle managers need different curriculum. And in that personalized learning track, there's also call it masters and intermediate courses. So how do you cut something as vast and complex as AI? We help organizations pinpoint the different curriculum needed for the different employee types that they have.

And we help them package it so that they can actually receive it, whether that's an intensive or virtual sessions, bringing in keynotes or thought leaders to the table to inspire them with then practical exercises to really get them into the gears of applying that to their day job.

We had a leadership offsite earlier this year where we took a global HR team, right? And we not only had to task them with how do we upskill you as the people function, but how do we also help you upskill the organization? So they're like a double whammy because they own learning and development for the whole company, but they also have to prepare themselves. Giving them really clear curriculum related to their organization

the job they're going to wake up to tomorrow? How do you start to use AI from a very tangible tactical? And then from all the way to like workforce planning, how do you think about how it's going to impact a manufacturing function? How do you upskill and reskill blue collar workers? How do you rethink the lab and scientists and how they interact with their experiments and formulas?

For the office employees, how do they get smart with sales, marketing? There's so many different facets and being able to navigate holistically for leaders who are responsible for the whole is really where we've worked and we come in. What are some kinds of generalizable guidance you might have for people, for organizations that are trying to do this? So this could be

I guess really anyone in an organization, but you know, execs might be curious. Oh, I, you know, I would love to have these kinds of curricula developed for the, for the people in my organization that are, you know, to, to change their mind around AI, to understand AI.

So this could be for executives. This could be for the CHROs. This could be for technical people who are trying to get AI solutions implemented because they see all the potential. They know all the what. And they're trying to find the why. So, yeah, it could be from this case study or just from your experience in general. What are some kinds of things that people can do

to upskill effectively? So I would think about it a couple twofolds. If you're the person upskilling yourself and you're trying to figure out where do I go, what's the right curriculum, I would think about if you had more time in your day, where would you put it? How would you help your enterprise grow, succeed? Identify what you would do with extra time and wherever you need to dig in from a I need to get really smart fast or whatever,

I just want to have a conversation and use ChatGPT to prompt you and go back and forth or to help you distill resources to learn, right? It's a shortcut to kind of serve you with what you need once you identify what it is. So you need to do your own discovery process and then leverage AI to do your shortcut discovery diligence. So that's if you're like Diane and John and you want to get smart and be prepared for the future.

If you're doing it for a team or your company, it's different. Speak for yourself, Diana. I'm not interested in the future. Yeah, okay. Sorry, I just interrupted you. So if you're a leader and you need to do that for your team, you also need to tell them, I did this with my company. When AI first came out, and we're writers, storytellers, I was like, no one phone it in. You're not allowed to leverage AI to write.

Now I've opened up my guardrails. You can leverage it for discovery input, but the creation of writing needs to be sourced from you. Clients ask me all the time, do you use ChatGPT to write? And I tell them there's transparency there. I used to not let my team touch it. Now they use it as a discovery tool and an input tool to create their own stories.

Why does that matter? Because the human element is why people turn to BizLove. It's what they love. It's the novelty behind our team. They're not ready to hear us say, we use ChatGPT to write all of your deliverables. I really don't think that that would be a world where our craft could come to life. So what do we do with that, right? So I'm a leader of a consultancy. They're my team. They need to get smart with AI. I need to be smart with where I leverage AI alongside their skill set.

What am I comfortable with? What is my company comfortable with? I think we put so much pressure on our teams to prompt better. You have to get better at prompting. It's like, yes, and you have to understand where that data is coming from and how to leverage the prompt to use it in the way that it's smart right now.

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make smarter decisions, collaborate more easily, and cut reporting time in half. What questions will you ask? To learn more, check out the show notes or visit www.adverity.com. That's A-D-V-E-R-I-T-Y dot com. Yeah, and think critically about the outputs as well. Yeah. Like, I think the way that you have it balanced there, you know, the way that you are currently advising your team members to get inspiration from

from a Gen A model. And I think that that can be great because

It can, you know, some of the ideas that come out, you know, you're just like, no, that's a bad idea, bad idea. But, oh, I forgot that one. And that is actually really important in this case. Yes. So super useful for that for sure for brainstorming. But then I like how you have people, you know, drafting their prose from scratch because that forces you, writing in prose forces you to think critically about

about your thoughts and to make sure that you, you know, if you if you get used to Gen-AI models producing outputs and you're just shipping those to a client and most of the time it works well, it could mean that you get less and less vigilant about checking. And then some things can go off to them where it's like, actually,

That is nothing to do with the scope of what the client's expecting. Right. Either you're going to look dumb or it's going to look like you copy-pasted from somewhere. Right. Yeah. But obviously, if you write it from scratch, you're familiar with the client, with their circumstances. Everything that you ship out to the client is going to be high quality. Yeah.

And, yeah, it'll also help you refine your own thinking and maybe come up with new ideas for the client as well. That's the – when you're a creator, like, you need to create, right? That's what we are novel about is the creation process. And I think every company and every creator understands what that is when you create something from scratch.

versus short-cutting it through ChatGBT, you feel the disconnect. Nice. All right. So yeah, that's upskilling execs. I also, I was curious if you have, so you've kind of given guidance on framing, like kind of the strategy around upskilling. I was curious because I think you might have told me in the past that

And I could be misremembering this, but I think you might've told me in the past about some interesting implementations of upskilling as well. Like maybe I'm blending different work that you've done in the past, but I think like maybe even like creating custom

You know, like creating a custom platform for delivering upskilling. Am I am I hallucinating this? No, no, you're right. We have a software we use to create experiences for learning that distills a story into almost think of it as a digital room that you just keep opening doors and finding different pathways to explore different topics.

in a choose your own adventure. So we do, we have built learning platforms and curriculum experiences from scratch. And that's a really like high touch experience

experience we can also and we also leverage a lot of companies have those software tools internally and then you just plug in the content so it depends on the need nice any particular software providers that do that that you recommend we use ciros ciros ciros c-e-r-o-s nice yeah

Lovely. I'm so glad that I was worried that I asked that question and something about I couldn't read your facial expression as I was asking the question. I thought you were going to come out. No.

No idea what you're talking about, but great. Yeah, that's really cool. That's helpful and practical for all of us. All right. So that was the first case study on upskilling execs. Another cool case study that you guys have around change management with respect to AI is getting scientists in a company to adopt a new tool so that they could be more efficient in a lab.

And so this isn't so much about upskilling. It's not so much about learning. It's about transforming, I guess, people's perspective of how a new tool, you know, even though they've gotten used to doing something some a particular way. And yes, there's going to be a learning curve or there's going to be maybe, you know, some things that aren't as great as the previous tool. But overall, you know, we've done testing. We know that overall, this is going to be great for your efficiency and

And therefore for the firm as well. Tell us about that. Yeah. Yeah. So I think when you look at the lab space in organizations, if it's a science technology company where they have chemicals, molecules, right, they're creating something from scratch. Their brilliance is something they don't want to put into an AI tool to create access to anyone who wants it.

And so getting this type of population to adopt something that's going to democratize their brilliance is hard. And I love this use case because it's it's like humanity at its severest as far as not wanting to change and not wanting to give its secrets. So how do you inspire these scientists to want to put their formulas, their sequences, their discoveries into a tool for other scientists to learn from?

And the only way to really do that is, and I say this to John, like there's two ways to make people receive change. It's to incentivize them or to inspire them. And you would have to believe the people who choose science as their profession, they care more about breakthroughs than they care about their own ego. So really wrapping that tool adoption into an inspirational cause, into a fight worth fighting is,

is how storytelling enables the change to happen. Diane, you're never going to win a Nobel Prize with that kind of thinking. So that's the easy answer for you. Then there's the strategy side of...

Being in the lab space, watching the scientists work across its table, knowing where the tool needs to sit so that it doesn't, you know, throw their flow off. Or there's a lot of safety and like health code regulations we need to watch out for. So where do they need to be when they put that information in? Do they need to wash their hands? They need to take their gloves off. Like there's all of that user adoption, user experience that we have done.

Just watching scientists in the lab and knowing how to interact. Also creating two-way communication for that scientist to give feedback. What's working? What's not working? What do they wish they had? You don't usually see the tech team and the scientists having conversations, but you can create that bridge and make sure there's a two-way conversation happening. So that's one example of something that we would have to help implement. Mm-hmm. Mm-hmm.

I this this might, I don't know, be too, too deep or too detailed of a question. But yeah, so there's the example there of, you know, making sure that I guess from the beginning of, you know, when this idea of a new tool, you know, leveraging, say, cutting edge LLM approaches, being able to somehow encode parts of what a scientist knows about something they're doing in a lab or an engineer knows something.

It could be any kind of subject matter expert, really, for the purposes of this, for generalizing this case study. And so I guess from the very beginning, you want to have the subject matter experts that are going to be augmented or parts of their work are going to be replaced. There's going to be some kind of democratization of their intelligence across the org. In those kinds of scenarios, it sounds like it's key to be working with the technologists, like the data scientists, the machine learning engineers, and

Who are devising or implementing the solution from the beginning so that they feel like they're part of it, so they don't feel like they're just kind of being pushed out. Yeah. Yeah. And I think the yeah, the adoption happens before the tool is ready to implement. So can you bring them to the table early? It doesn't have to be the whole team, but can you pinpoint your champion, your early adopters and have them part of the process early?

And to why they were doing that, you're right. It's the speed of innovation. If they can figure out what sequences don't work, don't result in breakthroughs, those other scientists don't have to try them. So it's all about speed of innovation, speed to market, speed to patient. And that's the point of helping them understand how this actually helps the cause. I think sometimes companies don't translate that all the way to make them want to be a part of it.

Also, they have something to gain. Even their own brilliance could go faster if they leverage it. For sure. If there's some part of their job that can be...

augmented or automated, then they're freed up for having their own internal brain cycles spent on something more valuable higher up the food chain. Yep. Cool. Do you have any interesting stories of really obstinate subject matter experts and how they came around? So that's a good question. It's something I learned from Simon Sinek about

Not focusing on the naysayers and only focusing on your early adopters. So there's this thing called law of innovation, right? It's called Lodi, but it's this whole concept of focus on your 18% and let them tip the hole versus you focusing your resources on everyone. So you spend more time up front figuring out who are your real early adopters, giving them the tools, celebrating them and ignoring the rest.

and letting them create the tipping point. And I think that's really important for any change practitioner to not get discouraged by people who don't want to receive the change because they'll come along when they're ready. It's more about focusing on those that are ready today and then celebrating equipping them for it. Nice, nice. I'm glad to ask that question. That was a really interesting response. Practical one as well. And as a third case study here,

you had a situation where you integrated two enterprises' digital portfolios. And you're going to need to explain what a digital portfolio is to me first. It's kind of interesting how actually from the beginning with working with you at BizLove, you talked about how BizLove specializes in digital and data enablement. And I kind of like have a vague understanding of what that means. I guess like, you know, compared to like on paper or the analog, you do things online.

analog. But yeah, digital portfolio. I don't really know what that is. I'd love to understand what that is. And then you say you integrate two enterprises' digital portfolios to bring a new cutting-edge product to market. And I imagine in that situation...

It's particularly complicated because you have two different company cultures. Yeah. No, thanks for the tee up. And so the space was medical technology, so highly regulated, complex patient data. And I think when you think about two digital footprints, you have to think about all of the complexity of where is it safe? Do they need to be integrated today? Can we integrate them later?

And while we were bringing these two companies together, there was also they were on different stacks, different apps, different Google, Microsoft, completely different. And on top of that, they were very different cultures, geography, backgrounds, expertise. So trying to figure out how to map this digital portfolio together to enable their product, bring the best of both companies into this offering was

was highly strategic, but also very technical. And I think what's important, and now that I have John at the table with me, he understands how to have that technical conversation. Otherwise, BizLove relies on the technical people within the company to help spar and understand what makes most sense, how to move things forward.

And so what I love about our partnership is having an advisor who can see that and understand that and speak that language is a game changer. You're still going to have to explain to your technical advisor what a digital portfolio is. It's like, what is the systems, the apps, the data flow? Where is it coming from? What does it run on? It's all of it. Oh, I see. I see. I see. Yeah, you were explaining it there when you were talking about stacks. Yeah. Yeah.

Google Office Suite versus Microsoft Office Suite. Yeah. Yeah. We experienced that same culture clash ourselves. Yeah. Or like, where does it, does the data come from the source or does it come from the environment and how does it flow and what is the real value proposition and how does that compare to the competitors and their digital footprints, right? Everything today is digital. Every conversation.

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So, yeah, so provide us with some kind of generalizable takeaways from how, you know, especially in this kind of situation where you have two different cultures. And this could actually happen not just with two different enterprises, but you can have two different, two very different cultures from different teams within an organization. And so give us some kind of generalizable takeaways for how our listeners can, you know, be in a situation like that and succeed at integrating knowledge, teams, and

digital portfolios and, you know, succeeding at building something new, maybe involving AI. Yeah. So culture is, and you said it when we opened the podcast around like technology isn't the barrier. When you're dealing with any kind of what, it's not about the what. It's about the dynamics, the power controls, the

how you source ideas and how you bring them to the table, how you communicate, how you reference. It's all the intangibles in an organization that come into play when you're dealing with culture. So back to why biz love. Biz love is not world-class and anything, but...

but people. It's not about the what. It's about who we work with, who we serve, who we enable. And knowing that culture is such a big play in enabling the who, we always work with intangibles. And every time we serve a client, they look different. The intangibles are never the same. And they're always changing in the moment.

So being able to pinpoint, understand, quickly comprehend, quickly translate. These are all intangible skills that BizLove has and brings to help navigate and create the bridge between differences and different parties. And I feel like I'm not doing it justice when you try to describe it, because when you try to tell someone about intangibles in words, it does it half justice.

But you know it when you feel it and you receive that when you work with BizLove. So, well, yeah. And so fundamentally in terms of generalizability, your point is that you need to listen a lot. You need to be, you need to understand people's motivations, you know, get away from just the technology that you're trying to get to or that you're working with. Yeah. It's about the why, once again.

And it's about, yeah, understanding people. Yeah. I like when you said understanding, you know, their fears, their needs, their wants. It's like, what's the foundation they're sitting on? How do you plan for that? How do you design for that? Nice. All right. So I have one kind of big topic area. I don't know how big or how long it's going to be. That's really up to you. But I kind of have one kind of technical area to finish off with now that we have those, you know, we've kind of, we've framed topics.

the common problems that we have in organizations around adoption of AI or any other new technology. We've talked about, you know, you talked about particular solutions, including case studies of situations where you have made it, you've been really successful making a big impact with AI and you've provided some here and there, you know, takeaways that any listener can have. I was wondering if you have

a particular set of tips, a particular list of tips that would be helpful for our listeners to be able to, to, to be able to come away from this podcast episode and more effectively enable change in their organization to more effectively be able to, you know, not get stuck on the what and, uh, and, and, and be able to accelerate change, uh, and success in their organization. Yeah. So I have, um,

Five. Perfect. First one is top-down, bottoms-up, which means if you're going to push change through your organization, you have to not only get your senior leaders aligned, you need to enable those at the front lines. And your job is often to be the bridge between the two. So I say top-down, bottoms-up from an enterprise lens. The other thing is bold claims and proof points.

When you're cutting through the noise and you're a storyteller and you say big claims, you have to back them up with data and proof points because not everyone is going to come with you, believe you, understand you. So you need to make sure you're expressing yourself so all different populations can believe and understand you.

The other one is what I said. You have two levers to pull when you're trying to drive change. We're into number two now? Three. Oh, we're into number three. So first one, top down, bottoms up. Yeah, yeah. Bold claims and proof points. Is number two. Gotcha. Yep. The next one is you have two levers to pull when you're trying to drive change. You can either inspire or incentivize. Often companies double down on incentivize and they forget they inspire. Yeah.

So focus on storytelling to tell the broader impact, right? To describe the ROI in a way that's inspirational. Right. So I guess like, so if you're being incentivized, there's something like, you know, there's some end of year bonus that you're chasing. And so, you know, you're, you're...

you're not necessarily like inherently motivated by some challenge or opportunity. You're being extrinsically like, yeah, incentivized to go in some direction. But if you're inspired, then, you know, you kind of naturally are like, wow, this is a huge opportunity. This is the moment of my career. And you're not even worried about some specific bonus because you know that if you succeed at this,

You're going to be recognized. You know, there's going to be a big impact in the firm and it's a great opportunity for you. Yeah. The impact you have is bigger than you. That's when you are inspired, you're serving someone else. You're serving a bigger cause and it taps into the discretionary effort, the nights, the weekends, the long hours, the

Not because you have to, but because you want to. And then the next one is number four. Focus on the 18 percent. Focus on the early adopters, not the naysayers. And then the last one is it's easy and simple, but it's they call you a leader because you have the courage to go first.

When you're in change programs, you are usually the outlier. You are the person pushing against compliancy, you know, the way we've always done business. You're crazy. We can't change. And so they call you a leader because you go first. You are going to have to put yourself into a vulnerable, courageous space and just know that going into it. Nice.

I didn't write those down. Can you recap the five for us quickly? Sure. First one, top-down, bottoms-up. Yeah, yeah. Second, bold claims and proof points. Yeah, yeah. Third, two levers, inspire or incentivize. Mm-hmm.

Four, focus on early adopters. You're 18%. Then the fifth one is they call you a leader because you go first. So have the courage to go first. Awesome. Thank you for that. Thank you for preparing those. You're welcome. I had no idea what you had in store. It was like an idea I had for the episode. And thank you for pulling those together for us.

Diane, it's been awesome having you on the show. Something that I should have mentioned to you, and I usually do before I get on the air with guests, but I forgot to do this time, is to prepare them. I ask guests for a book recommendation at the end of the episode. And obviously we have Simon Sinek stuff, but this could be, you know, it doesn't necessarily have to be related to the episode topic in any way. It could be some fiction book you recently read that you loved. You're nodding your head, so maybe something's come to mind.

I think if you are in a place of transition, right, like what's working for you is no longer working for you and you have to disrupt your own life, you have to change your own pattern. The book Untethered Soul is a really good one. It teaches you a lot about your subconscious. Cool. Yeah. That sounds interesting. And so that's nonfiction, I guess? Yes. Yeah. Cool.

Awesome. Thank you so much, Diane. For people who want to hear more of your thoughts after the episode or contact you, where can they find you online? How can they reach out? Yeah. So you can find our consulting practice at www.bizlove.com. You can find me on LinkedIn, Diane Hare, D-I-A-N-E.

H-A-R-E. Very simple. Like a rabbit. Like the rabbit, not the human hair. And yeah, I mean, I'm on Instagram. Our company's on Instagram. You can find us under those names as well. Nice. Yeah. And of course, uh...

We're working together now. And so you can also be reaching out to me. You know, I always announce at the end of the episode, so this is coming up soon. You know, you can get my social media in the show notes. And yeah, we'd be happy to hear from you if there's any kind of AI stuff from strategy all the way through to implementation. We've got you. Thanks to this partnership with BizLove and the enablement that you guys provide.

So thank you for taking the time and sharing all your secret insights, at least an hour of them with our audience. Yeah, it was a pleasure. Thank you for having me. Yeah, for sure. Thanks, Diane. And yeah, maybe we'll check in again at some point in the future and maybe a couple of years or something. We'll have amazing case studies of joint collaboration on transformative AI projects that we just can't wait to share on air.

Thanks, Diane. Thanks, John. Such a fun time with Diane in New York in person. In today's episode, Diane covered how tech isn't the barrier to gaining operational efficiencies with AI. People are. How relative return on investment or frameworks like Rice Scoring can be used to prioritize an AI roadmap.

How to gain buy-in for a big organizational change, you need to tell creative, original narratives that target the smart, busy people that make up your organization. As always, you can get all the show notes, including the transcript for this episode, the video recording, any materials mentioned on the show, the URLs for Diane's social media profiles, as well as my own at superdatascience.com slash 897.

Thanks to everyone on the Super Data Science podcast team, our podcast manager, Sonia Braievich, media editor, Mario Pombo, our partnerships team, which is Nathan Daly and Natalie Zheisky, our researcher, Serge Massis, our writer, Dr. Zahra Karchei, and yes, our great founder, Kirill Aramanko. Thanks to all of them for producing another superb episode for us today, for enabling that superb team to

To create this free podcast for you, we are very grateful to our sponsors. You can support this show by checking out our sponsors links, which are in the show notes. And if you'd ever like to sponsor an episode yourself, you can do that. Just head to johnkrone.com slash podcast to learn more. Otherwise, you can help us out by sharing the episode with people who would like to listen to it.

review the episode wherever you listen to it or watch it, subscribe, edit videos into shorts on YouTube. But the most important is that I just keep, I just hope you'll keep coming back and keep on listening. I'm so grateful to have you out there listening. And I hope I can continue to make episodes you love for years and years to come. Until next time, keep on rocking it out there. And I'm looking forward to enjoying another round of the Super Data Science Podcast with you very soon.