New Models for Digital Transformation with Alison McCauley Chief Advocacy Officer at Think with AI & Founder of Unblocked Future

hen there's a bunch of different things that we need to approach differently the best practices haven't been established yet. We're still working on this. We're still forming it. But there's some early indications that can give us some hints of what direction to go in.

And then there's a bunch of leadership practices we can do to continue to stay tapped into what's developing. So I'll start, if it makes sense, with why is this different and why is it so hard? Why is what's happening with the acceleration of generative AI just throwing us off? So a key factor, of course, is the velocity of innovation.

and I keep kind of looking back at the calendar just saying, wait, did that really just happen 19 months ago in March 2022 when OpenAI unleashed chat GPT to the world and all of a sudden everybody has extremely sophisticated AI at their fingertips. So, I can't believe it was such a short time.

It feels like so long ago because the innovation is happening so fast and organizations. And even people that are in the space are having trouble keeping up. So that's one of the pieces. Another is the uptake velocity. So within five months sorry, within two months, a hundred million people had tried chat GPT.

And to put that in perspective, it took five years for a hundred million people to use Facebook. So, just that uptake velocity is of course very rapid. And the other piece of it. is that there's all kinds of unsanctioned adoption that's coming into the enterprise, where people are using this, even if it's been, and believe it or not, some organizations are outlawing different uses in different parts of the organization of AI tools.

And so this sort of unsanctioned adoption is really throwing organizations for a loop. And then there's two other factors I'll mention briefly. One is of an undertone of fear. Although I've seen in many cases, some of this being alleviated over time. And we can talk about tactics with that, but an undertone of fear of like, how is this going to impact me and my role and my job and the work world?

I know. And then the other piece is that we are being confronted, business leaders and people all throughout the organization are being confronted with questions that we were never taught how to answer. Where do we want a human? What is the role of a human? Where do we need a human? I guess just these are questions that are brand new.

And so all of this is completely throwing the traditional way we did innovation.

Richie Cotton: It seems like there are a lot of big changes happening and you're right that the philosophy is incredibly fast. So lots of changes happening and so this does cause problems. So maybe we'll try and figure out like where to start. I suppose strategy is normally the starting point for this. So can you talk me through how do you go about aligning your transformation vision with your business vision?

Alison McCauley: So there's a lot of different angles to cover on this. I'll start by the fact that There's a mindset shift that I see is a little bit of a struggle among executive teams and business leaders, is that this is unlike anything we've experienced before. So when we think about strategy and how to approach it, we have to understand we have to have new processes, new approaches and new expectations for what this software can do for us.

What this new technology can do for us. So that's the first piece is really the importance of this mindset shift. And I'll explain it a little bit more. we are used to working with two major categories of entities, things in the organization. Traditional software and humans. And this is unlike either one.

So, all our business processes and all the ways that we operate are based on traditional software. so a lot of people are starting to interact with the quest to leverage generative AI, kind of with the old traditional software mindset and thinking about like, what tasks do I automate?

But there's actually this opportunity to examine your business at a deeper level and set a higher expectation for what you can accomplish to really leverage this software and extract more value of the software by starting from a point of strategic grounding and saying, like, What is my true business need?

What business need do I truly have that I can address with this? Because we are now able to tackle open ended data. Thanks. challenges and goals and questions with this software based off of unstructured data. You've never had that opportunity before. So if you start with this mindset of like, what business needs do I have?

And what strategic goals can I work on that will uniquely leverage generative AI capabilities? And that's the place to start. So for example, for traditional software, we might say, Hey, our task level approach is, we want to track the distribution of, of marketing content, like that's a task, right?

And a lot of people will approach generative AI from, say, a task level of, Hey, okay, now I have these new capabilities. I want to accelerate the creation of marketing content. maybe that's where they start. But there's an opportunity to up level that discussion and start from a more strategic grounding and say, How can generative AI help me, say, refine my competitive differentiation so all the marketing and sales outreach that I develop resonates more with my prospective audiences?

So if you start there, And then you start to identify what are the tasks within that strategic goal where generative AI capabilities are uniquely suited to unlock this new value for example, it might be personalization. What kind of personalization at scale or what kind of synthesis of, disparate data sources of large unstructured data can I pull from to get insights?

So. You know, maybe it's helping my teams and executives with their ideation and product innovation and service innovation challenges. Like, all these things can be sort of reset to unlock something new. And, and we just don't have that. We don't have the literacy in the organization.

We don't have the business processes for this. We don't have all these structures to truly leverage the unique value yet that generative AI provides. We do this, experiments have happened that are really exciting, that are, they're showing us the way, but we haven't yet codified the best practices and figured out how to implement them more broadly.

Richie Cotton: Okay, so I really like the idea of starting with what your business needs and then figuring out how can technology help you work towards those needs rather than starting with technology and then figuring out how can we shoehorn this into the business. And the marketing example is very interesting about, well, do you just use it as a productivity boost to increase your output or do you think about other metrics like Customer engagement, that sort of thing instead.

All right. So, you've mentioned a few times that the technology is moving very quickly and it's tricky to keep up. do you have any advice for how to build a strategy when that underlying technology is shifting so fast?

Alison McCauley: So one thing that I do is I researching various organizational mechanisms that people are putting in place to help to manage these challenges. And so I can share several that I'm seeing that are successful. I'll give first some easy things that, you know, one can implement, and then I'll talk more broadly about what innovation models that seem to be more fruitful in this moment.

So, some of the tactics that we can use are first of all, making sure that you're raising fluency across your organization. in AI. And a lot of people say AI literacy, but I like to say fluency because that's ultimately where we want to be. And so that's equipping people across the organization to be able to stay abreast of what's happening.

And so you're creating an organization that's able to absorb more information. I really emphasize that this is a time to accelerate your speed to learn. Versus speak to deliver. It's not about products out the door unless you are at the front edge of this space. It's about making sure that you're activating your organization to learn.

Another tactic that I really like that can happen in multiple levels is creating learning circles. Within an organization and I like to have there's different ways you can structure this one way i've really love to do it is essentially attack identify strategic needs that an organization has And pull together people across the organization to figure out how do we attack that problem or, seize that opportunity through generative A.

I. And to collaborate on that. So it's a learning circle around a specific strategic need. Another area might be people across your organization that are interested in, say, how is A. I. Changing the marketing or how is A. I. Changing H. R. And to gather those people across the organization that are interested in that and work and collaborate in a very structured manner, you're meeting on a regular basis.

You're accountable to your peers to come in with your insights. You have a set agenda. Where you're sharing your insights, you're asking new key questions as a group. So that kind of structure is something that we often neglect to do, but it's a very effective way to start to amplify learning and tapping in.

And then the other one I'll mention too is, I call it learning networks, where we're taking an active role. To network outside of our walls and across the greater ecosystem to tap into the learning that's available there. And so that could either happen through something more formal, like a partnership or collaboration, say, with a researcher, an academic institution, or that could happen where you're really prioritizing.

Having people go out and learn what's happening in the industry, spend time at conferences, be talking to people and putting more emphasis there. And ideally, if the people in your learning circles also have their own learning networks. You're just amplifying the learning across the ecosystem.

But I'll also emphasize the you know, what this is really pointing to and a lot of the challenges that we're facing and how we innovate and how we how we respond and leverage , this transformation in the shift points to a model of innovation in the organization that's more decentralized. And this is exciting and scary for organizations.

 often organizational structures have innovation coming from. You know, the top or a separate group that's, you know, creating innovation and trying to filter across the organization. This is a moment where we actually need to decentralize innovation so that it can happen in a high value and productive way.

All across the organization. And so you're seeing a lot of the that are truly building their AI muscle right now are taking that mindset to really equip people to innovate within the organization. And what's super important is not just equipping them. To understand how to act with, a mindset for responsible AI and to understand what the opportunity is and to understand what this technology can do, but also are supporting what comes out of that kind of effort. So that means that you're establishing structures to tap into that innovation that's happening to evaluate what's working to get visibility to the entire portfolio that you have of innovation to support what you're doing. Early successes are to, you know, think about how do we scale this or how do we move this to production?

And that you are also really mindful of how do we make sure that we've got feedback loops in place so we are surfacing problems and challenges that are coming up and we are addressing them with new policies and guidelines. And so you have to have that two communication.

Richie Cotton: Oh man lots to unpack there.

Alison McCauley: I've noticed that what a lot of organizations are doing is it's actually fascinating because as you dive into these organizations, a lot are coming up with organically with these new ideas and structures and they're seeing some traction with it. But now it's the step for actually you know, organization management science professionals to really.

Identify what is working particularly well and making sure that we're sharing that knowledge, not only within the organizations, because a lot of times that knowledge isn't shared within an organization. So something interesting might be happening here and another division doesn't know about it or another geo doesn't know about it.

But also, how do we, all learn from this? And that's a lot of what I'm trying to do in this moment, is making sure that we're surfacing what's working and we're sharing it. Because this is a learning moment for literally everybody.

Richie Cotton: Absolutely. I love that idea that everybody needs to learn about AI. And actually, on your point you made about AI literacy versus AI fluency, this is a conversation we have a lot at DataCamp. So,

Alison McCauley: Oh, tell me more.

Richie Cotton: we use AI literacy a lot in our marketing, and every couple of months someone will say, I'm not sure that's quite the right term.

What we really want is for people to be fluent in AI. And we try AI fluency and nobody Googles flat out. It's, it doesn't play very well with any audiences. So you have to go back to AI literacy, but I agree with

Alison McCauley: think it's important to have the debate, too, and like, what do we really need to be AI fluent? Like, what does that mean? It depends on your role, But I think what's interesting is you, everybody needs to understand what are the data implications? does my data need to look like to be ready for AI?

 You need to have understanding of responsible AI, but does everyone need the deep technical knowledge? No, you know? So I, I think it'll be a really interesting discussion for all of us to have. is what does literacy look like and what does fluency look like? And how do we progress people on that journey?

Richie Cotton: Yeah, that's wonderful. I'd love some more details there because um, seems like there is that big difference between, okay, I'm an AI researcher. I need to know absolutely everything. And maybe I'm in a non technical role and I need the basics. So where should that journey begin for, for AI?

Getting that sort of basic AI literacy or fluency, like what's step one, what's the first thing everyone needs to know.

Alison McCauley: so I, I speak to thousands of people because I'm on the road giving talks and doing work, workshops. And so I get I'm getting a lot of exposure to people who are not early adopters at all. And so I think the answer to that is actually a lot more sort of simple and foundational level than, I think those of us who kind of are in this bubble with, with think there's a big gap between what people are hearing is the potential of this technology.

And what a lot of people are seeing when they start to play with it, which usually happens sort of in a, it could be a work capacity or a personal capacity, but they're going on to a free version of a tool and they're interacting with it. And often what they get back feels to them like AI, like a generic answer.

And what they're not understanding is that, because these tools obviously have been trained on, the, entire corpus of sort of human generated content at this point, that they need to have some basic principles. And how we communicate with AI to generate and extract good value.

So really it starts there with that first aha to get to value, to glimpse that value. And I do, I do executive coaching as well for some clients, one on one. And I do it because it's so much fun to get to that aha moment with someone, which doesn't take long. Where they might say, Oh, I wrote a letter or I worked on a proposal with AI.

It was like, okay, it was sort of okay, but it still feels like a toy to a moment where they're actually using AI as a thought partner in their strategic work. And it doesn't take that long to get there. They just need to learn some basic fundamentals. to, tell AI, who it is, what role it's playing, to give it feedback, and iterate.

Like, we all know these things, but it is very helpful to get that framework to understand how to work with these tools in a more productive value so they serve you better. So, that's the first step, actually, is really getting to see the, aha, getting to see the value, getting to see something that can actually help me day to day in my business.

Richie Cotton: So that does sound like a great idea, showing people what the value of these tools can be in order to encourage them to adopt them. Do you have any advice on how you might systematically give people that aha moment across a large organization?

Alison McCauley: I do workshops on this and so it's a pretty straightforward process there's two things, two things I'll highlight. It's a pretty straightforward process where the initial thing is actually a play stage where you're starting to just get a feel for how these things work and the fact that they're multimodal.

So that's something that a lot of people don't really understand or haven't tried out for themselves. And so once you see that. Then to the next step is really to identify that need that you're trying to address. and then start to work with the tools to on that particular need and to do it in an iterative way.

And then once you find an aha or a breakthrough, then to figure out how do I make that a repeatable value? And how do I scale that and make sure that you're also Importantly, integrating it into your either business process or your day to day work. And that habit formation is actually another piece that's very challenging for people.

But the other thing I want to highlight is the importance And to really get people across the organization to just scale this effort, one of the most important things is something very basic. It's storytelling. So I'll give you an example. I was giving a talk and an engineer approached me after the talk and she was explaining how she had found a really interesting use case.

for generative AI. She was working with a group that was doing a custom quote process that was very complicated. It took this group weeks to create these quotes. And so she had created a process in a bot that worked with clients and was able to bring this process down to a couple of hours. And that's, super exciting.

So I asked her, how is the team whose job it was to work on these custom quotes? What do they think of it? And she said they loved it because now they're able to use their time to do strategic business development with these clients instead of doing this rote process of chasing down all the details for custom quotes.

And I asked her who had heard that story in the organization. It's a relatively large organization. She looked at me funny and said, no one. So no one had heard it outside the team. There are so many incredible stories. happening across organizations of breakthroughs in using these tools that we could all learn from.

And so there's such an opportunity for organizations to find who, what I call, are their passionistas for this technology, where they're taking it In and of themselves you leverage it to come up with improvements in work and they're generating these incredible stories of change, find those passionistas, nurture them into change agents, tell their stories.

And it's also an incredible opportunity to talk about. The challenges that people are encountering in this process and how they're thinking about overcoming them. How are they thinking about transparency and trust? How are they thinking about data privacy and security? what are they doing to combat those challenges and to share that?

And by the way, if in the process of finding the stories, you find some problems, That's an opportunity to figure out how to address them and then tell that story about how a problem was surfaced and how it was addressed. And so there's all this opportunity to really use storytelling. to raise the understanding across an organization, ultimately the literacy and hopefully the fluency of how we can take advantage and leverage this opportunity in, in, in this technology.

Richie Cotton: Okay, so, I absolutely agree with you that if no one finds out about all the cool things you're doing, then yeah, it's not going to have any impact. So this seems like There's definitely opportunities in many companies to improve the level of communication between data and AI teams and their commercial colleagues and also with managers.

Do you have any tips for this? 

Alison McCauley: So I think we we really need to focus on this. And there's a lot of mechanisms we can do to actually share these stories. And so there are a lot of the traditional change management mechanisms where we're communicating them across every single channel we can. And we're also celebrating the successes.

But the reason I think this is so important in the communication between a data organization and the rest of the business is because I am concerned where we have this sort of impending and brewing, I'll call data crisis. And that's because I don't believe that a lot of business leaders across the organization understand that our data isn't ready to be able to really leverage this new technology and don't understand the work that in the investment that needs to be involved in doing that.

And I think it's really important for data professionals to proactively. surface the challenges that are ahead and a path to better leverage this and what it will take from an investment standpoint, because there's all this pressure on the business to leverage this new technology and to deliver something new using it, and that pressure is only going to increase.

lot of these leaders don't understand the big challenge, especially of making sure that our unstructured data that we've never had the opportunity to deal with in this way is ready for AI. And often, you know, I see a lot of organizations where there's been no effort in the past to curate this.

unstructured data and so need to make sure that we've got good stuff in to work with and that's a new challenge and it can feel quite insurmountable. I mean you might have 10 years of contracts and documents that are essential to your business that you want to be able to leverage. But version control never happened.

There has been no content lifecycle management ever in this because it wasn't, we didn't have a way to really use it. And so that's the thing where we need to have these new practices and it might be easier to implement them going forward. Right. But also to understand what do we do with all that data we have in the past in order to make it ready to leverage?

And I just don't think that business leaders understand the gap that's coming, and they're going to be putting a lot of demands on our on data professionals without understanding how much work it really is going to take to get there. And so I'm concerned that, in the next couple quarters, there's gonna be a lot of organizations that are sort of having this wake up call, a reckoning day where there's this disconnect.

So I would encourage people to start communicating early about what the opportunity is and what we need to do to be able to get there and surface those problems and those challenges early, to perhaps alleviate some of the, challenges of that pressure that's coming.

Richie Cotton: Okay, yeah, certainly a lot of these data governance issues are like, how do you find the data? What's the quality of the data? Yeah. How does it fit into other workflows? These are becoming incredibly important issues for many businesses. So suppose you're undergoing some digital transformation program. Where do, like, improving all these data governance processes fit into this larger program? Like, when do you have to worry about this? How do you go about improving the situation?

Alison McCauley: Okay, right away. Number one. So I think it actually comes in conjunction with that exploration of like, where are we going to use this? Where are we going to use this technology? So that's understanding the strategic need. It's understanding among all the different areas where we could use it.

Where do we want to start? And that's a whole nother question of like, how do we decide where to lean into first? That gives us some guidance on what to start with, with the data. And you know, you get that question, do I start with. The easy stuff, the low hanging fruit, or do I use another, guidepost to help me understand?

And I would encourage as much as possible to really go for where that high value, those high value use cases will be. I use a very simple framework that can help people navigate where it's looking for use cases. That our lower risk still have high human in the loop so that we can really be involved in seeing what the results are and refining the process to get to better results.

But also. Offer the potential is if you find something that you can bring to production, you can scale, it will deliver high economic value back to the business. So I look at those, high human loop, low risk, high potential value as a place to start to order the strategic areas.

Where we want to start and with that you can get usually an area of the business to attack or to start working with and that kind of communication between the data organization and the rest of the organization needs to be happening right away and ideally you're almost putting together a SWOT team around a specific strategic need or challenge and using that as a use case to refine these processes and learn from them and gathering the stories along the way that you then share out.

again, in the mindset of really using this as a time to accelerate your speed of learning. Versus necessarily delivering right away. This is an opportunity for the entire organization to learn from some initial collaborations and to start to do the work, to shift your mindset on how you approach it.

Richie Cotton: So, this idea of a case where you've got a high human in the loop, I think you call it. So this is something that's like lots of people working on this and low risk. So this seems like the ideal case for like, okay, let's get some AI in there. Let's try and automate things. Do you have any examples of real business processes where that's the case?

Alison McCauley: So there's a couple of places to start, you usually find a lot of examples. HR is one because you can start to work on these processes with internal employees. So internal support, for example, is a great way to start to understand how can we use these technologies. For example, your employees are going to be often more tolerant than your customers with some, upfront challenges that you, you find in the process.

So that's one area. Another one is marketing. So marketing is often so high human in the loop. And it doesn't necessarily need to be a process with a lot of people, but it needs to be one that still has a good deal of human oversight so that you can get that learning on where to use this tool versus not.

we often find cases in HR and, a great way to Build your training wheels on this is really to look at your entire state content generation process and marketing and understand where can we use the tools. So, for example, if you say, let's look at how we can attack marketing and gen AI.

If you put together learning circles around each area of content generation. And use that learning circle to go out and research what are the tools that are being used? What are the pros and cons of each? what are the governance practices that we need to put in place to be able to leverage these tools?

 how does a business process need to change? where do we want to be using these tools versus not? Like, there's a whole bunch of learning that can happen. But you're still overseeing it with a great deal of human oversight. The opportunity to get return to get value quickly is massive in that area as well.

And so, that's just a, that's sort of like a often a good place to start hunting for those initial Ways to leverage the technology and to learn from that and spread that learning across the organization.

Richie Cotton: Okay, cool. So it seems like, yeah, HR, marketing, they're good places to look for these sort of like high person intensive things that can be automated. So you've talked quite a lot about the need to change processes and you've given some ideas of things like forming sort of task group to come up with ideas of how to do things.

Is there a systematic way you can go about changing processes if you've got some sort of large scale transformation program? Um,

Alison McCauley: Yeah, so in general, one of the key things first is to just understand, and this is a challenge, is because we're used to using traditional software and how that works with our business processes. So you use the same approach where you're mapping out your business process and you're looking for the opportunity for for improvement here.

But the key thing to have in mind when you're. Trying to leverage this technology in the business process is to understand that you're, learning through this process. And so you need to have this mindset of experimentation and iteration during the process. This isn't just implementing something and then, scaling it and being done with it.

This is about studying it, understanding where you want to leverage this technology, testing it, evaluating it. So that feedback loop and the iteration around it, that needs to happen. And so it's very heavy in that process. And again, it's speed to learn over speed to deliver. So, learn about how you want to develop that process.

Iterate it, hone it, work on it, and then once you have something, then you extend it. So it's a slower process than we're used to versus implementing a CRM system or, shifting your CRM system or whatever it may be. So there's just a lot of emphasis in understanding how do we do this well and refining that.

Richie Cotton: That sounds like a very agile approach. You know, you do, you try something and then you get feedback and then maybe you try something else afterwards. So, I'd like to talk a bit about the role of management in this. if you've got this transformation program, you're like, okay, let's try and make use of AI or let's try and change our approach to data or whatever.

Then, how should management be involved in this?

Alison McCauley: I think that's such an interesting question because there's so much that's happening that's grassroots. But it's really critical if you're going to leverage this technology to have top down support. We see some really interesting use cases. this is a fascinating one.

If you look at what Moderna has done, Where Moderna is looking to accelerate their product portfolio coming to market. So, they have the COVID 19 vaccine. Their revenue has plummeted in recent quarters because of, fewer people are getting, getting that, that vaccine. And so they need to bring new products to market faster.

the CEO who is really excited about the opportunity of this technology, decided to develop a partnership with OpenAI and is enabling 3, 000 people across the business, equipping them with these sophisticated tools giving them support and training, and has an expectation that over time, employees will use.

chat GPT 20 plus times a day. And that's a lot. That is a constantly going back and forth. And employees have developed over 750 GPTs or, agents for, different parts of the business and attacking Areas like helping to respond to regulators, which used to take weeks and now it takes hours or helping to identify the right dosage of a drug in a clinical trial.

If you don't have the right dosage, a clinical trial could shut down quite quickly. And so to get the right dosage is very a data intensive process that can take a long time. And so these tools are helping people with that. So this real decentralization of innovation and a top down willingness to open that up and support people in that discovery process to see how this can meet a strategic goal is essential.

to move from this sort of playing around stage to actually getting value for the business. And these are going to be interesting ones to watch. Like we don't know we, they don't have a lot of, they haven't been doing that very long. So we don't know what kind of impact that will have on the business.

But structurally the kinds of moves that have been made I think are really setting up. Organizations that do things like that to really build their A. I. Muscle. And so, a lot of it is we just don't have the best practices yet. We need to discover them. And so that willingness to discover needs to come from top down and the connection needs to be made by having that collaboration across the organization.

To really make this a priority and then connect to the stories and the work that's happening and support the good work and educate and help people understand how do I do this correctly? How do I do this with a real framing and lens towards being responsible about how we use AI? it really needs to happen from management, but also the work is coming from people, people on the ground close to customers and business processes.

Richie Cotton: Yeah, so that sort of executive support of these projects is incredibly important. Now, I suppose in theory, if you're pushing heavily for AI, the person in charge of this ought to be the chief AI officer, but most companies don't have that yet. So I'm wondering in the absence of a chief AI officer, who tends to be that top person who is accountable for everything?

Alison McCauley: So what I've noticed is it's somebody who really believes in the potential of technology and it can come from different parts of the organization. We see CEOs taking on that role. We also see people do it within an organization. So it could be within a marketing organization, for example, that someone wants to really leverage these tools.

So you see it at different levels. Going to the organization and you see pressure from boards as well to do that. So, I'd say that it could come from anywhere. But ultimately there needs to be support across the organization to really make a difference. And I think we're going to see over time, I think we're going to see this, stratification from organizations that have this willingness.

 to build this muscle and to experiment and go through this, it's kind of a harrowing time when you think about it because organizations, large organizations especially rely on established best practices. And pass. And so this is a unique organization that's willing to take on that role in this moment.

You mentioned agility. It has to be an agile organization. Often it's an organization under pressure from existential market threats and has to change and they understand that. That is willing to take on that, challenge and honestly, that risk to do something in a new way. But, because this technology is so powerful I think that over time it will drive, essentially competitive differentiation if you are able to leverage these technologies versus not.

And the other challenge that firms are facing is that, over time disruptors could be smaller and because they can use these tools. And I'm sure many of your listeners have, heard that headline that when Sam Altman said you know, maybe the next unicorn Will be a company run by one person.

You know, when do we get to a billion dollar company that's run by a single person and the use of A. I. And of course, that's a provocative statement. But the concept behind it that there are going to be organizations that can do more with less is something that we are all facing in our future.

Richie Cotton: Okay, yeah the idea of like, These unicorn companies worth a billion dollars, but very, very few employees. That is very interesting. Yeah, I think that's more of a pipe dream at least being a billionaire on a single person company for a lot of people, but

Alison McCauley: But, you know, to have a department run by a couple people that used to be run by, dozens or hundreds. I don't know. I could see that.

Richie Cotton: No, it is delightfully optimistic and yeah, I think it's certainly in the right direction even if it is a little bit overhyped maybe, but I think the other side of this is that a lot of companies have fears around AI and this is probably Preventing them from sort of going full speed ahead.

So do you have a sense of like, what are these enterprise phase of AI are and which are real and which are imaginary?

Alison McCauley: Yeah. So and I think the undertones of fear are strong and they're at the organizational level and they're the individual worker level as well. and that's creating all kinds of challenging dynamics. Thanks. So from an organizational level, there's a lot of fear of making missteps and doing something wrong because it is new and scary and we certainly see a lot of visible missteps.

Where, everyone's got their crazy story about the rogue chatbox that was, you know, embedded in, in you know, a customer service app. And, you know, over time, we'll get better at being able to control that and we'll have the best practices around it to ensure. But again, that's why it's speed to learn over speed to deliver right now.

And so that organizational fear is holding people back. And They need to be thoughtful about how they execute right now. So, that's understandable. what I emphasize is you have to get in and you have to understand this technology and build your muscle, but you don't have to yet race things to production because everybody is learning right now.

The individual worker level is a whole other area. It's really important and, we need to understand how our workforces will change, how the needs of what kind of workforce we need will change over time and how we help people skill in the direction where we're gonna need them to be.

And there is a lot of fear and concern around that. And that creates resistance. so there's a lot of work that will need to be done on that. there was a really interesting story about how even when Microsoft was implementing AI in their own customer service organization, where they were able to see incredible results.

They encountered some resistance, you know, and this is, you know, an organization that, you know, they were using their own tools and they knew, you know, they, they knew a lot about this. And so that is natural. That is normal. And we have to work through it. And so we have to do this work with empathy.

We have to do a lot of communication. We have to use our tactics around storytelling, and we need to do this in a collaborative way to understand how we leverage these tools and how our workforce transitions with us. It's really hard work. It is very hard work.

Richie Cotton: Okay. Yeah. So, you mentioned the idea of a rogue chatbot and I think that's like many companies, it's a real nightmare cause you don't want to annoy your customers and cause a PR problem and things like that. So you mentioned

Alison McCauley: And liability, like, the new liability that's associated with us. Like, we don't have this, we don't know yet We have not been able to catch up to these systems in terms of answering the big questions that surround them.

Richie Cotton: Yeah. So, you mentioned things like you've got to be very careful about this and you got to tell some tell the right stories and things in business. Do you have any like, practical ideas on how you can avoid that rogue chatbot? Like, suppose like your CEO goes, Okay, we need a better AI chatbot. Now, how do you make sure it doesn't go rogue?

Alison McCauley: Well, so that is the question in this technology, right? and this comes back to data as well, too, so what data are we working with? what is the role of that chatbot? what are we allowing it to do? this is what we need to work out in a business process to understand how can we leverage this technology in a way that delivers value and we've mitigated that risk.

organizations are still figuring that out, and so I think a big piece of it is really understanding that you need to test well, and you need to understand that this is a process, and we are, you know, making sure you're deploying the technology in the right places where you can learn in a lower risk way.

Before you start to bring it out everywhere. And it's interesting, too, to see the role of the intersection of trust and AI. So there's been various studies that have looked at this and there's some generational differences. But there are there have been some findings that if a consumer knows that AI is being used, That lowers the trust of an organization, but there's also indications that younger generations don't have a problem with that.

So it'll be interesting to watch as well. how does trust play into this and what's the impact of trust and how I'm using, AI and with my consumers or my customers?

Richie Cotton: yeah, definitely. You don't lose trust with your customers. That's gonna be a real disaster for your business. Alright, so Have you seen any success stories where companies have? gone all in on this and they've had some transformation problem, they've adopted AI and they've seen a benefit.

Alison McCauley: So you hear isolated cases, but this isn't, I have not seen this at scale, in production, broadly in organizations yet. We're just not seeing that yet. And so, you know, we've got, you know, we've got lots ahead here. And think it's a surprise to executives to understand, like, everyone feels behind.

Everyone feels like they're not really sure the right way to move forward. Everybody is learning right now. And it can be a very uncomfortable time for organizations because there, there aren't perfect case studies or examples to see on how to do this.

 that's not something that executives are used to. We're used to, you know, cases that, have been well documented, that we learned at business school, you know, like there's a case study written up about it that we can study and understand. and this is throwing executives.

so no, we are not seeing a lot of examples where an organization has been transformed by A. I. And is operating in that level, except if we look at very small businesses. So there are certainly a high number of small businesses. that are really, in a very agile way, leveraging this technology to punch well above their weight.

but, that's not what we're talking about. We're talking about the enterprise. This is the challenge ahead.

Richie Cotton: So, yeah I think you're right in that a lot of enterprises, they tend to move slower. So it's a lot of sort of theoretical potential at the moment rather than realized gains. So, what sort of timescale are we looking at for enterprises to be able to successfully take advantage of this technology?

Alison McCauley: Honestly, I think it's going to take a long time. We over, we always underestimate how long it takes for people to be able to adopt this new technology. I think this is going to be tough. I think we're going to see a few breakouts. The organizations that have been able to do this really well, they are going to start turning results around.

But I think this is going to take a while for us to really see some real results. will see some organizations start really giving us some good case studies, I think, in the next couple quarters. But most organizations, they're going to take a year or more to really understand even how to use this in parts of their organization.

Richie Cotton: And, have you seen any common challenges or mistakes that organizations are making while they're trying to get their heads around this?

Alison McCauley: They're underestimating how challenging it is. to get people to understand how to extract value from this technology. And they're underestimating the people aspects of this. This is not just a technology change. This is a cultural change. This is a mind shift. This is a new, entirely new area of potential competitive advantage.

And and organizations are reacting in almost a panic mode. This needs to be something that you dive into and think about really strategically and map out a future that this is a key piece of and then understand and invest in really turning your organization around to equip them to leverage this technology.

Richie Cotton: Okay yeah, just underestimation of the challenges is, is gonna set you up for failure, for sure. Okay. All right, so, what are you most excited about in the world of AI? and digital transformation at the moment.

Alison McCauley: So I'm actually in the midst of writing a book right now where the focus is on how do we think with AI with with being the operative term? I think there's such an opportunity for us to Infuse our work with these sort of, superhuman capabilities when we collaborate with me when we take our human intelligence and we collaborate with machine intelligence.

What is possible? I'm really excited about what will happen in terms of personalized education at scale, personalized coaching at scale. There's a lot of different areas that I'm watching in terms of mental health, aging other areas. What is possible when we, all of us humans, have the ability to infuse our thinking and our work with machine intelligence?

That's one area I'm really excited about. 

Richie Cotton: Okay, yeah, I like the idea of thinking with AI rather than just letting AI think for us. That kind of collaboration is pretty amazing. Alright, super. Thank you for your time, Allison.