**The Challenges of Adopting AI and Digital Transformation**

Defining what it means to trust consumers or customers with AI technology is a crucial aspect of its adoption in business. The lack of trust can lead to a disaster for any organization, emphasizing the importance of understanding the needs and concerns of clients.

While isolated cases of successful transformation programs exist, there are not yet many examples of organizations that have successfully implemented AI on a large scale. This is partly due to the fact that executives are often caught off guard by the potential of AI technology, and they may not fully understand how it can be applied in their organization. As a result, they may underestimate the challenges involved in adopting this new technology.

The lack of clear guidance and support from organizations and business leaders has led to a sense of uncertainty among those responsible for implementing AI solutions. This has resulted in a "panic mode" approach, where individuals rush into implementing AI without fully understanding its potential benefits and limitations. The consequences of such an approach can be severe, highlighting the need for organizations to adopt a more strategic and thoughtful approach to AI adoption.

**The Impact of Trust on AI Adoption**

Trust is essential when it comes to adopting AI technology in business. Organizations that fail to establish trust with their customers or consumers risk losing them forever. The impact of losing trust cannot be overstated, as it can lead to a significant decline in sales and revenue for an organization.

On the other hand, organizations that successfully adopt AI technology are likely to experience significant benefits, including improved efficiency, increased productivity, and enhanced customer satisfaction. By understanding the needs and concerns of their customers, organizations can develop effective strategies for implementing AI solutions that meet these needs.

**The Future of AI and Digital Transformation**

While there have been some successes in adopting AI technology, these are largely limited to small businesses that have been able to adapt quickly to new developments in the field. For larger organizations, however, the adoption of AI technology is a much more complex process that requires significant investment of time, money, and resources.

One area that holds significant promise for AI adoption is personalized education at scale. By combining human intelligence with machine intelligence, it may be possible to develop more effective learning strategies that cater to individual needs and preferences. This could have a significant impact on areas such as mental health, aging, and other fields where personalized approaches are essential.

**Thinking with AI**

The author of the text is excited about the potential of thinking with AI technology, rather than simply relying on machines to perform tasks. By collaborating with machine intelligence, humans can unlock new levels of creativity, productivity, and innovation. This approach requires a fundamental shift in how we work and interact with technology, but the potential rewards are significant.

In order to fully realize the benefits of this approach, organizations will need to invest time and resources into developing the skills and expertise needed to work effectively with AI technology. This may involve training programs, workshops, and other initiatives that help employees understand the capabilities and limitations of machine intelligence.

**Common Challenges and Mistakes**

One of the most significant challenges facing organizations as they adopt AI technology is the tendency to underestimate its potential benefits and limitations. By rushing into implementation without fully understanding the needs and concerns of their customers or consumers, organizations can end up with suboptimal solutions that fail to deliver on their promises.

Another common mistake is underestimating the cultural shift required to adopt AI technology effectively. This requires a fundamental change in how we work, interact with technology, and approach problem-solving. By failing to recognize this need for cultural transformation, organizations can struggle to achieve meaningful results from their AI initiatives.

In conclusion, adopting AI technology and digital transformation requires careful consideration of several key factors, including trust, impact, future prospects, thinking with AI, common challenges, and mistakes. By taking a thoughtful and strategic approach, organizations can unlock the full potential of this emerging technology and achieve significant benefits for their customers and stakeholders.

"WEBVTTKind: captionsLanguage: enthis 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 organizations are kind of 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 hi Allison thank you for joining me on the show thanks so much Richie I've been looking forward to this conversation wonderful glad to hear it so uh to begin with um what does digital transformation consist of today oh goodness well um digital transformation has always been a challenge for the organization and um you would think after 30 years of digital transformation uh 30 plus years that we'd be um we'd be good at it but it still presents a challenge and let me tell you this new wave of accelerated AI Innovation is absolutely crushing traditional digital transformation playbooks so um so that is a moving question okay uh so it sounds like edge sword in that if we're bad at is at the moment and things are changing then I guess they're probably not going to get worse but also there's a bit of a challenge just to keep up so can you talk me through a bit more how is um he's mentioned like these new technologies like AI is changing things what's changing absolutely and I also say it's a challenge but for the right firms that can respond to this moment which is very challenging it's also a huge opportunity so if you you know with any don't let a good change or a good a good challenge pass you by um so AI is coming into the organization in a way that is breaking our traditional transformation and and Innovation playbooks so there's a couple factors that are contributing to that and then there's a bunch of different things that we need to approach differently that are 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 what's why is this different and why is it so hard why is what's happening with the acceleration of generative AI just throwing us all um so a key factor of course is the velocity of innovation so and I keep kind of looking back at the calendar just saying wait did that really just happen 19 months ago in March um 2022 when um open a I uh unleash chat GPT to the world and all of a sudden everybody has extremely sophisticated AI at their fingertips so um 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 um within five months um sorry within two months 100 million people had tried chat GPT and to put that in perspective it took five years for hundred million people to use Facebook so um that 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 outlying um different uses in different parts of the organization of AI tools and so this um this sort of sanctioned adoption is really throwing organizations for a loop and then there's two other factors I'll mention briefly one is an a kind of an undertone um of fear although I've seen in many cases some of this being alleviated over time and we can talk about tactics with with that but an undertone of fear of like how is this going to impact me and my role in my job and um the work world I know and then the other piece is that we are being confronted Business Leaders um and people all throughout the organization are being confronted with questions that we were never um 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 it's just these are questions that are brand new and so all of this is completely throwing the traditional way we did Innovation it seems like there are a lot of big changes happening and you're right the the philosophy is incredibly fast so lots of changes happening and so this justes cause problems so maybe um we'll try and figure out like where to start um I suppose strategy is is normally the starting point this so can you talk me through um how do you go about aligning your uh transformation Vision with your business Vision so there's a lot of different angles to cover on this uh so I'll start by the fact that there's a mindset shift that I see is a little bit of a struggle in um among executive teams and um 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 you know we are used to working with two major categories of entities things in the organization traditional software in humans and this is unlike either one so um you know all our business processes and all the ways that we operate are based on traditional software and um 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 this 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 challenges and goals and questions with this software based off of unstructured unstructured data we'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 add can I 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 you know our task level approach is you know we want to um we want to track the distri bution 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 the maybe that's where they start but there's an opportunity to uplevel that discussion and start from a more strategic grounding and say how can generative AI helped me say develop a um a refin my competitive differentiation so all the marketing that I 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 um tasks within that strategic goal where generative AI capabilities are uniquely suited to unlock this new value for act you know for example it might be personalization what kind of personalization at scale or what kind of synthesis of you know um disperate data sources of of large uh unstructured data can I pull from to get insights or you know maybe it's helping my um teams and Executives with our ideation and product um product Innovation and service Innovation uh challenges like all these things can be sort of reset to um unlock something new and and we just don't have we don't have the literacy in the organization we don't have the business processes for this we don't we don't have all these structures to truly Leverage The Unique value yet um 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 okay so I really like the idea of starting with what are your business needs and then figuring out how can technology um help you work towards those needs rather than starting with technology and then figuring out how can we sh on this into the business um and the marketing example is is very interesting about like well do you just use it as a productivity boost to increase your output or do you think about uh other metrics like um customer engagement that sort of thing instead okay all right so um you mentioned a few times that the technology is moving very quickly and It's tricky to keep up so um do you have any advice for how to build a strategy when that that underlying technology is Shifting so fast so one thing that I do is I um um researching various organizational mechanisms that people are putting in place to help to manage these challenges and so I can share um uh several that I'm seeing that are successful um I'll give first some kind of easy easy things that you know one can Implement and then I'll talk more broadly about what um you know uh Innovation models that seem to be um more fruitful in this moment so some of the tactics that can use are um 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 um to stay a breast 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 speed to deliver it's not about getting produ um getting products out the door unless you are at 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 um learning circles within an organization and I like to have um there's different ways you can structure this um one way I really love to do it is essentially attack identify strategic needs that an organization has and pulled together people across the organization to um to attack to to figure out how do we attack that problem or or or seize that opportunity through generative AI um 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 AI changing the marketing or how is AI changing HR and to gather those people across the organization that are interested in that and and and work and collaborate in a very um 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 the the learning and Topping in and then the other one I'll mention to is I call it learning networks where we're taking an active role to um Network in 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 um something more formal like a partnership or collaboration say with a research or an academic Institution or that could happen where you're um 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 you're learn the people in your learning circles also have their own learning Networks you're just amplifying the learning across across the ecosystem but I'll also emphasize that um 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 um how we respond and leverage this this transformation and the shift points to a model of innovation in the organization that's more decentralized and this is exciting and scary for organizations you know often organizational structures have Innovation coming from you know the top or a separate group that's you know creating uh Innovation and trying to filter it across ization this is a moment where we actually need to decentralize organization uh 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 org organizations that are truly building their AI muscle right now are taking that mindset to really equip people to innovate in the like within the organization and what's super important is not just equipping them um to understand how to um act with you know a 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 those that 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 um to see to get visibility to the entire port portfolio that you have of innovation to support what early successes are to you think about how do we scale this or how do we move this to production um and that you are also um you know really mindful of how do we 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 so you have to have that two-way communication oh man uh lots to unpack I've noticed that um what a lot of organizations are doing is they're you know it's actually fascinating because as you dive into these organizations a lot are coming up with um organically with these new ideas and structures and um they're seeing subtraction with it now it's the step for actually um you know organ organization management um science professionals to to Really identify what 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 goo doesn't know about it but also how do we as um you know how do we all learn from this and 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 ing it because this is a learning moment for literally everybody absolutely um I love that um idea that everybody needs to learn about Ai and actually see on your point you made about um AI literacy versus AI fluency this is a conversation we have a lot at data Camp so we use AI literacy a lot in our marketing and every couple of months someone 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 for that ter it doesn't play very well there in the audiences so you have to go back to AI literacy but I agree with to have the debate too and like what do we really need to be AI fluent like what does that mean it it depends on your role but I think what's interesting is you everybody needs to understand what are the data implications what kind of how does my data what what does my data need to look like to be ready for AI uh you need to have understanding of responsible AI but does everyone need the Deep technical knowledge no you know so like I think it'll be a really interesting um discussion for all of us to have is how much uh what is what is literacy look like and what does fluency look like and how do we progress people on that Journey okay uh yeah it's wonderful i' love some more details there because um it seems like there is that big difference between okay I'm an AI researcher I need to know absolutely everything and maybe um I'm in a non-technical role and we need the basics so where should that Journey begin for getting that sort of basic AI literacy or fluency what's step one what's the first thing everyone needs to know well I have to so I I speak to thousands of people because I'm on the road giving talks and doing work workshops and so I get um I I'm getting a lot of exposure to people who are not early adopters at all and so I think one of the you know the the answer to that is actually a lot more sort of simple and um foundational level than than um I think those of us who kind of are in this bubble would would think um 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 you know it could be a work capacity or a personal capacity but they're going on to um 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 you know because these tools obviously have been trained on you know the you know entire Corpus of sort of human generated content at this point that they need to have some basic principles in how we communicate with AI to generate an 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 for some clients one onone and um it's always a really I do it because it's so much fun to get to that aha moment with someone which doesn't take long where um they came they may they might say oh I I wrote a letter or I worked on a proposal with they it was like okay it was sort of okay but it still feels like a toy um 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 you know tell AI you know who it is what role it's playing to give it feedback and iterate like we all know these things but um 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 is really um uh getting getting that famili getting to see the AHA getting to see the value getting to see something that can actually help me dayto day in my business uh so that just sound like a great idea showing people what the value of these tools can be uh 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 I do workshops on this and so it's um a pretty straightforward process in different there's two things two things I'll highlight it's a pretty straightforward process where the initial thing is um actually a play stage where you're starting to just get a feel for um how these things work and the fact that they're multimodal so that's something that um a lot of people don't really understand or haven't tried out for themselves and so once you see that um then to uh the next step is really to identify that need that you're trying to address and then and start to work with the tools to um on that particular need and to do it in an inative 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 um also importantly integrating it into your either business process or your day-to-day work and that um habit formation is actually another piece that's very challenging alling for people but the other thing I want to highlight is the importance and to really get people across the organization so to scale this effort one of the most important things is something very basic it's storytelling so I'll give you an example I um I was giving a talk and an engineer approached me after the talk and she was explaining how she had um found a really interesting use case for um generative AI she was working with a group that was doing a custom quote process that was H very complicated it took this group weeks to create these quotes and so she had created a 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 rot 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 and she looked at me funny and said well no one so no one had heard it outside the team there are so many incred inredible stories happening across organizations of breakthroughs in using these tools that we could all learn from and so it's um 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 to you leverage it to um come up with improvements in work and they're generating these incredible stories of change s 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 how 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 okay so um I absolutely agree with you that if no one finds about all the cool things you're doing then yeah it's not going to have any impact so this seems like um there's definitely opportunities in many companies to improve the level of communication between data and AI teams and the commercial colleagues and also with managers um do you have any tips for this in terms of um how data teams and executives are working together and leaders uh yeah how can uh they share the stories of the work they're doing in order to have a big impact so I think we um we really need to focus on this and um 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 uh 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 um the communication between a data organiz a and the rest of the business is because I um am concerned we're we have this sort of impending and brewing I'll call data crisis and that's because um I don't believe that a lot of um Business Leaders across the organization understand that our data isn't ready to be able to really leverage this new technology and and don't understand the work that and 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 um to better leverage this and what it will take from an investment standpoint because there's all this pressure on the business to deliver to leverage this new technology and to deliver something new using it and that pressure is only going to increase but a lot of um 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 the unstructured data and so we need to make sure that we've got good stuff in to work with and um that's a new challenge and it can feel quite insurmountable I mean you might have you know 10 years um of contracts and documents that are essential to your business that you want to be able to leverage but Version Control never happened there's been no content life cycle 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 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 um on data professionals without understanding how much work it really is is going to take to get there and so that's I'm concerned that you know in the next couple quarters there's going to be a lot of organizations that are sort of having this this wakeup call a reckoning day where there's this disconnect so I would encourage people to start communicating early about what the opportunity is and where we need what we need to do to be able to get there and surface those problems and those challenges early uh to perhaps alleviate some of the the challenges of that pressure that's coming okay yeah certainly um a lot of these data governance issues so like how do you find the data what's the quality of the data how does it fit into other workflows these are becoming incredibly important issues for many businesses um 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 right away number number one um 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 we're you know among all the different areas where we could use it where do we want to start and so that's a that's a whole another question of like how do we decide where to where to we're to um lean into first um that gives us some guidance on what start with with the data and I would encourage you know you get that question do I start with the easy stuff the low hanging fruit or um or do I use another another guidepost to to help me understand and I would um 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 um that can help people navigate where it's looking for um use cases that are lower risk still have high hum in the loop so that we can be really we can really be involved in seeing what the results are and refining um the process to get to better results but also offer the potential is if you find something that you can bring to bring to production you can scale it will deliver High economic value back to the business so I look at those you know High High human the loop low risk high potential value as a place to start to order um the the Strategic areas where we want to start and with that you can get an 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 organization needs to be happening right away and ideally those you know you're you're almost putting together a SWAT 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 and um again in the um in the mindset of really using this as a a time to accelerate your speed of learning um um versus necessarily delivering um right away this is an opportunity for the entire organization to learn from some initial collaborations and to shift to start to do the work to shift your your mindset on how you approach it so um this idea of uh a case where you got 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 May a in there let's try and automate things do you have any examples of like real business processes where that's the case so there's a couple places to start um you usually find a a lot of uh 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 use these Technologies for example your employees are going to be often more tolerant than your um your customers with some some you know upfront challenges that you you find in the process so that's one area another one is marketing so marketing is um often so high human in the loop um 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 so um I we often find cases in marketing or in HR and you know a great way to um to sort of get you know build your build your training wheels on this is really to look at your your entire sayate content generation process and marketing and understand where can we use the tools so for example if you say let's let's look at how um how we can attack marketing and and and geni 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 at each what are the um what are the governance practices that we need to put in place to be able to leverage these tools what are the um how does a business process need to change um we're we're in the 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 um a great deal of human oversight um the um opportunity to get return um to get value quickly H is um is massive in that area as well and so um that's just a that's sort of like a often a good place to start hunting for those uh initial ways to leverage the technology and to learn from that and then spread that learning across your organization Okay cool so it seems like yeah HR marketing they're good places to look for these sort of like high person intent of things that can be automated um so you've talked quite a lot about the need to change processes and you've given some ideas of things like forming a 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 um if you've got some sort of large scale transformation program yes so in general one of the key things first is to just understand and and this is the 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 um 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 your your 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 being done you know and then you know scaling it and being done with it this is about it studying it understanding where you want to um 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 you know learn about how you want to develop that process iterate it hone it you know work on it and then once you have something then you extend it so it's a slower it's a slower process than we're used to in you know versus implementing a CRM system or you know shifting your CRM system or whatever it may be um so there's just a lot of emphasis in understanding how do we do this well and refining that uh 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 okay so um I'd like to talk a bit about the role of management in this so if you got this transformation program you're like okay let's try and uh make use of AI or let's try and change our approach to data or whatever then how should my be involved in this I think that's such an interesting question because there's so much that's happening that's Grassroots um but it's really critical if you're going to leverage this technology to have top- down support um we see some really interesting use cases um if you um look at um and this is a this is a fascinating one if you look at what Mna has done where Mna is looking to accelerate their um their product portfolio coming to Market so um they have the covid-19 vaccine um their revenue has um has um plummeted in recent quarters because of you know fewer people are getting getting that That vaccine and so they need to bring new products to Market faster so the CEO who H is a very um really excited about the opportunity this technology decided to um develop a partnership with open Ai and is enabling 3,000 people across the business equipping them with um sophisticated um the sophisticated tools um giving them support and training and um has an expectation that over time employees will use uh use chat GPT 20 plus times a day and that's a lot that a constantly going back and forth and um employees have developed over 750 um gpts or you know uh agents for for different parts of the business and attacking areas like um helping to respond to Regulators which used to take weeks and now takes um 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 late uh a data intensive process that can take a long time and um so these tools are helping people with that so this real decentralization of innovation and a topown 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 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 um 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'll have on the business but the um 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 AI muscle and so um you know a lot of 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 um 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 um correctly how do I do this with a real Framing and lens towards being responsible about how we use AI so you know it it really needs to happen from management but also um the the work is coming from people people on the ground close to customers and business processes yeah so that sort of executive um 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 um in the absence of a chief AI officer who tends to be that top person who is um accountable for everything so what I've noticed is it's somebody who is um really believes in the potential of technology and it can come from different parts of the organization we see CEOs taking on that kind of um that 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 points in the organization and um you know and you see pressure from boards as well to to do that so you know it's um I'd say that it could come from anywhere um but ultimately there needs to be support across the organization to really make a difference and I think we're going to see a over time I think we're going to see this this stratification um from organizations that have this willingness to um to build this muscle and to experiment and go through this it's it's kind of a harrowing time when you think about it because organizations large organizations especially um rely on established best practices and paths and so this is this is a a unique organization that's willing to take on that role in this in this moment um you mentioned agility it has to be an agile organization um often it's an organization Under Pressure um from existential Market threats and um you know has to change and they understand that um that is willing to take on that that challenge and honestly that risk to do something in a new way um but you know those that don't I think over time because this technology is so powerful um I think that over time it will um Drive essentially competitive differentiation if you are able to leverage these Technologies versus not and um the other challenge that firms are facing is that you know we over time um disruptors could be smaller and because they can use these tools and I'm sure you know I'm sure many of your listeners have have heard that um that headline uh when Sam Altman said the next you know maybe the next unicorn will be a um will be a company run by one person you know when we get to a billion dollar company that's run by a single person and the use of AI and of course that's a provocative statement but the um the concept behind it that there are going to be organizations that can do more with less um is something that we are are all facing in our future okay yeah um the idea of like uh these Unicon companies worth a billion dollars but very very few employees that that is very interesting um yeah I think that's more of a pipe dream at least being a billionaire aing person but you know to have a department run by a couple people that used to be run by you know um you know dozens or hundreds I don't know I could see that okay no it is delightfully optimistic and yeah I think uh it's certainly in the right direction even if it is a little bit over hyp maybe but um so I think the other side of this is that a lot of companies have fearce around Ai and this is preventing them from sort of going full Full Speed Ahead so do you have a sense of like what are these um Enterprise feares of AI are and which are real and which are imaginary yeah so I and I think this is I mean the the the undertones of fear are strong and they're at the organizational level and they're at the individual worker level as well so and that's creating all kinds of challenging Dynamics so from an organizational level there's a lot of fear of um doing something um you know making missteps and doing something wrong because it is new and scary and we certainly see a lot of visible missteps um where you know everyone's got their crazy story about the Rogue chat that was you know embedded in in um 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 um to ensure but again that's why it's need to learn over speed to deliver right now um and so that organizational fear is holding people back and um you know they need to be thoughtful about how they execute right now so um that you know that's understandable so what I emphasize is you have to you have to get in and you have to understand this technology and build your muscle but you don't have to yet brace things to production um because everybody is learning right now the individual worker level is a whole other area that's um really important and you know we need to understand how our workforces will change how the needs of our workforces will of what kind of Workforce we need will change over time and how we um help people skill in the direction where we're going to need them to to be and there is a lot of fear and concern around that and um that creates resistance and so there's a lot of work that will need to be done on that there was a um there was a a really interesting story about how even when Microsoft was implementing um AI in their own customer service organization where they've been were able to see incredible results um 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 a lot of the um 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 um how we leverage these tools and how we um we are Workforce transitions with us um it's it's really hard work it is very hard work okay yeah so idea of a rogue chat but and I think that's like many companies like as a as a real nightmare because you don't want to annoy your customers and cause a PR problem things that so you mention and liability like the the liability that's associated with us like we don't have this we don't know yet how we have not been able to catch up to these systems in terms of answering the big questions that's surround them yeah so um you mentioned things like you've got to be very careful about this and got to tell some tell the right stories and things in business do you have any like um 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 well so that is the question in this technology right so we need to and this comes back to data as well too so you know what you know what data are we working with you know is this what is the role of that chatbot um you know what are we allowing it to do um so this is this is what we need to work out in our in a in a business process to understand how can we leverage this technology in a way that delivers value and we've mitigated that risk um and I organizations are still figuring that out and so I think a big piece of it is really understanding that um you need to you need to test well and need to understand that this is a process and um we are you know making sure you're deploying the technology in the right in the right places where you can learn in a lower risk way um before you start to to bring it out um 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 um looked at this and um it there's some generational differences um but um there are um there have been some findings that if an a consumer knows that AI is being used that lowers the trust of an organization but there's also indications that uh younger Generations um don't have a problem with that so it'll be interesting to watch as well what is what is the you know what kind of trust um how does trust play into this and what's the what's the impact of trust and how I'm using using Ai and with my consumers or my customers okay yeah uh definitely you don't lose trust with your customers that's going to be a real disaster for your business um all right so have you seen any success stories where companies have gone all in on this and they've uh had some transformation program they've adopted Ai and they've seen a benefit so you hear isolated cases but this isn't I have not seen this at you know 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 organizations um I I think it's a sort of a surprise to Executives to understand like everyone feels behind everyone feels like they um they're not really sure the right way to move forward everybody is learning right now and um it's a it can be a very uncomfortable time for organizations because they don't there aren't a lot of um of you know there there aren't perfect case studies or examples to see on how to do this um that's not you know that's not something that executives are used to we're used to you know cases that you know have been well documented that we learned a business school you know like there's a case study written up about it that we can study you know study and understand and um and this is throwing Executives so uh so no we are not seeing a lot of examples where an organization has been transformed by Ai 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 do to punch well above their weight so but you know that's not what we're talking about we're talking about the Enterprise this is the challenge ahead so uh yeah uh I think you're right in that a lot of Enterprises they tend to move slower so a lot of sort of theoretical potential at the moment rather than uh realized gains um so what sort of time scale are we looking at for Enterprises to be able to successfully take advantage of this technology 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 and I think this is going to be tough I think we're going to see a few breakouts that uh organizations that have been able to do this really well that are going to um to start turning results around um but I think this is going to take a you know a while for us to really see some real results I we'll see some organizations start really giving us some good case studies I think 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 um in parts of their organization and have you seen any um like common challenges or mistakes that organizations are making while they're trying to get the heads around this 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 as 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 um potential competitive advantage and um and organizations are kind of 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 okay uh yeah just underestimation the challenges is is going to set you up for failure for sure okay all right so um what are you most excited about in the world of AI and digital transformation at the moment so I'm actually um in the midst of writing a book right now where um the focus is on how do we think with AI with with being the operative term um I think there's such an opportunity for us to um Infuse our with these sort of you know superhuman capabilities when we collaborate with when we take our human intelligence and we collaborate with machine intelligence what is possible um I'm really excited about what will happen in terms of um personalized education at scale personalized coaching at scale um there's a lot of different areas that I'm watching in terms of mental health aging um other areas what is possible when we all of us humans have the ability to um to infuse our thinking and our work with machine intelligence that's one area I'm really excited about okay yeah I like the idea of thinking with AI rather than just letting a I think for us uh that kind of collaboration is pretty amazing all right super uh thank you for your I'm melisonthis 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 organizations are kind of 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 hi Allison thank you for joining me on the show thanks so much Richie I've been looking forward to this conversation wonderful glad to hear it so uh to begin with um what does digital transformation consist of today oh goodness well um digital transformation has always been a challenge for the organization and um you would think after 30 years of digital transformation uh 30 plus years that we'd be um we'd be good at it but it still presents a challenge and let me tell you this new wave of accelerated AI Innovation is absolutely crushing traditional digital transformation playbooks so um so that is a moving question okay uh so it sounds like edge sword in that if we're bad at is at the moment and things are changing then I guess they're probably not going to get worse but also there's a bit of a challenge just to keep up so can you talk me through a bit more how is um he's mentioned like these new technologies like AI is changing things what's changing absolutely and I also say it's a challenge but for the right firms that can respond to this moment which is very challenging it's also a huge opportunity so if you you know with any don't let a good change or a good a good challenge pass you by um so AI is coming into the organization in a way that is breaking our traditional transformation and and Innovation playbooks so there's a couple factors that are contributing to that and then there's a bunch of different things that we need to approach differently that are 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 what's why is this different and why is it so hard why is what's happening with the acceleration of generative AI just throwing us all um so a key factor of course is the velocity of innovation so and I keep kind of looking back at the calendar just saying wait did that really just happen 19 months ago in March um 2022 when um open a I uh unleash chat GPT to the world and all of a sudden everybody has extremely sophisticated AI at their fingertips so um 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 um within five months um sorry within two months 100 million people had tried chat GPT and to put that in perspective it took five years for hundred million people to use Facebook so um that 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 outlying um different uses in different parts of the organization of AI tools and so this um this sort of sanctioned adoption is really throwing organizations for a loop and then there's two other factors I'll mention briefly one is an a kind of an undertone um of fear although I've seen in many cases some of this being alleviated over time and we can talk about tactics with with that but an undertone of fear of like how is this going to impact me and my role in my job and um the work world I know and then the other piece is that we are being confronted Business Leaders um and people all throughout the organization are being confronted with questions that we were never um 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 it's just these are questions that are brand new and so all of this is completely throwing the traditional way we did Innovation it seems like there are a lot of big changes happening and you're right the the philosophy is incredibly fast so lots of changes happening and so this justes cause problems so maybe um we'll try and figure out like where to start um I suppose strategy is is normally the starting point this so can you talk me through um how do you go about aligning your uh transformation Vision with your business Vision so there's a lot of different angles to cover on this uh so I'll start by the fact that there's a mindset shift that I see is a little bit of a struggle in um among executive teams and um 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 you know we are used to working with two major categories of entities things in the organization traditional software in humans and this is unlike either one so um you know all our business processes and all the ways that we operate are based on traditional software and um 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 this 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 challenges and goals and questions with this software based off of unstructured unstructured data we'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 add can I 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 you know our task level approach is you know we want to um we want to track the distri bution 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 the maybe that's where they start but there's an opportunity to uplevel that discussion and start from a more strategic grounding and say how can generative AI helped me say develop a um a refin my competitive differentiation so all the marketing that I 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 um tasks within that strategic goal where generative AI capabilities are uniquely suited to unlock this new value for act you know for example it might be personalization what kind of personalization at scale or what kind of synthesis of you know um disperate data sources of of large uh unstructured data can I pull from to get insights or you know maybe it's helping my um teams and Executives with our ideation and product um product Innovation and service Innovation uh challenges like all these things can be sort of reset to um unlock something new and and we just don't have we don't have the literacy in the organization we don't have the business processes for this we don't we don't have all these structures to truly Leverage The Unique value yet um 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 okay so I really like the idea of starting with what are your business needs and then figuring out how can technology um help you work towards those needs rather than starting with technology and then figuring out how can we sh on this into the business um and the marketing example is is very interesting about like well do you just use it as a productivity boost to increase your output or do you think about uh other metrics like um customer engagement that sort of thing instead okay all right so um you mentioned a few times that the technology is moving very quickly and It's tricky to keep up so um do you have any advice for how to build a strategy when that that underlying technology is Shifting so fast so one thing that I do is I um um researching various organizational mechanisms that people are putting in place to help to manage these challenges and so I can share um uh several that I'm seeing that are successful um I'll give first some kind of easy easy things that you know one can Implement and then I'll talk more broadly about what um you know uh Innovation models that seem to be um more fruitful in this moment so some of the tactics that can use are um 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 um to stay a breast 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 speed to deliver it's not about getting produ um getting products out the door unless you are at 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 um learning circles within an organization and I like to have um there's different ways you can structure this um one way I really love to do it is essentially attack identify strategic needs that an organization has and pulled together people across the organization to um to attack to to figure out how do we attack that problem or or or seize that opportunity through generative AI um 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 AI changing the marketing or how is AI changing HR and to gather those people across the organization that are interested in that and and and work and collaborate in a very um 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 the the learning and Topping in and then the other one I'll mention to is I call it learning networks where we're taking an active role to um Network in 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 um something more formal like a partnership or collaboration say with a research or an academic Institution or that could happen where you're um 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 you're learn the people in your learning circles also have their own learning Networks you're just amplifying the learning across across the ecosystem but I'll also emphasize that um 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 um how we respond and leverage this this transformation and the shift points to a model of innovation in the organization that's more decentralized and this is exciting and scary for organizations you know often organizational structures have Innovation coming from you know the top or a separate group that's you know creating uh Innovation and trying to filter it across ization this is a moment where we actually need to decentralize organization uh 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 org organizations that are truly building their AI muscle right now are taking that mindset to really equip people to innovate in the like within the organization and what's super important is not just equipping them um to understand how to um act with you know a 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 those that 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 um to see to get visibility to the entire port portfolio that you have of innovation to support what early successes are to you think about how do we scale this or how do we move this to production um and that you are also um you know really mindful of how do we 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 so you have to have that two-way communication oh man uh lots to unpack I've noticed that um what a lot of organizations are doing is they're you know it's actually fascinating because as you dive into these organizations a lot are coming up with um organically with these new ideas and structures and um they're seeing subtraction with it now it's the step for actually um you know organ organization management um science professionals to to Really identify what 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 goo doesn't know about it but also how do we as um you know how do we all learn from this and 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 ing it because this is a learning moment for literally everybody absolutely um I love that um idea that everybody needs to learn about Ai and actually see on your point you made about um AI literacy versus AI fluency this is a conversation we have a lot at data Camp so we use AI literacy a lot in our marketing and every couple of months someone 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 for that ter it doesn't play very well there in the audiences so you have to go back to AI literacy but I agree with to have the debate too and like what do we really need to be AI fluent like what does that mean it it depends on your role but I think what's interesting is you everybody needs to understand what are the data implications what kind of how does my data what what does my data need to look like to be ready for AI uh you need to have understanding of responsible AI but does everyone need the Deep technical knowledge no you know so like I think it'll be a really interesting um discussion for all of us to have is how much uh what is what is literacy look like and what does fluency look like and how do we progress people on that Journey okay uh yeah it's wonderful i' love some more details there because um it seems like there is that big difference between okay I'm an AI researcher I need to know absolutely everything and maybe um I'm in a non-technical role and we need the basics so where should that Journey begin for getting that sort of basic AI literacy or fluency what's step one what's the first thing everyone needs to know well I have to so I I speak to thousands of people because I'm on the road giving talks and doing work workshops and so I get um I I'm getting a lot of exposure to people who are not early adopters at all and so I think one of the you know the the answer to that is actually a lot more sort of simple and um foundational level than than um I think those of us who kind of are in this bubble would would think um 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 you know it could be a work capacity or a personal capacity but they're going on to um 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 you know because these tools obviously have been trained on you know the you know entire Corpus of sort of human generated content at this point that they need to have some basic principles in how we communicate with AI to generate an 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 for some clients one onone and um it's always a really I do it because it's so much fun to get to that aha moment with someone which doesn't take long where um they came they may they might say oh I I wrote a letter or I worked on a proposal with they it was like okay it was sort of okay but it still feels like a toy um 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 you know tell AI you know who it is what role it's playing to give it feedback and iterate like we all know these things but um 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 is really um uh getting getting that famili getting to see the AHA getting to see the value getting to see something that can actually help me dayto day in my business uh so that just sound like a great idea showing people what the value of these tools can be uh 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 I do workshops on this and so it's um a pretty straightforward process in different there's two things two things I'll highlight it's a pretty straightforward process where the initial thing is um actually a play stage where you're starting to just get a feel for um how these things work and the fact that they're multimodal so that's something that um a lot of people don't really understand or haven't tried out for themselves and so once you see that um then to uh the next step is really to identify that need that you're trying to address and then and start to work with the tools to um on that particular need and to do it in an inative 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 um also importantly integrating it into your either business process or your day-to-day work and that um habit formation is actually another piece that's very challenging alling for people but the other thing I want to highlight is the importance and to really get people across the organization so to scale this effort one of the most important things is something very basic it's storytelling so I'll give you an example I um I was giving a talk and an engineer approached me after the talk and she was explaining how she had um found a really interesting use case for um generative AI she was working with a group that was doing a custom quote process that was H very complicated it took this group weeks to create these quotes and so she had created a 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 rot 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 and she looked at me funny and said well no one so no one had heard it outside the team there are so many incred inredible stories happening across organizations of breakthroughs in using these tools that we could all learn from and so it's um 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 to you leverage it to um come up with improvements in work and they're generating these incredible stories of change s 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 how 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 okay so um I absolutely agree with you that if no one finds about all the cool things you're doing then yeah it's not going to have any impact so this seems like um there's definitely opportunities in many companies to improve the level of communication between data and AI teams and the commercial colleagues and also with managers um do you have any tips for this in terms of um how data teams and executives are working together and leaders uh yeah how can uh they share the stories of the work they're doing in order to have a big impact so I think we um we really need to focus on this and um 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 uh 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 um the communication between a data organiz a and the rest of the business is because I um am concerned we're we have this sort of impending and brewing I'll call data crisis and that's because um I don't believe that a lot of um Business Leaders across the organization understand that our data isn't ready to be able to really leverage this new technology and and don't understand the work that and 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 um to better leverage this and what it will take from an investment standpoint because there's all this pressure on the business to deliver to leverage this new technology and to deliver something new using it and that pressure is only going to increase but a lot of um 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 the unstructured data and so we need to make sure that we've got good stuff in to work with and um that's a new challenge and it can feel quite insurmountable I mean you might have you know 10 years um of contracts and documents that are essential to your business that you want to be able to leverage but Version Control never happened there's been no content life cycle 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 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 um on data professionals without understanding how much work it really is is going to take to get there and so that's I'm concerned that you know in the next couple quarters there's going to be a lot of organizations that are sort of having this this wakeup call a reckoning day where there's this disconnect so I would encourage people to start communicating early about what the opportunity is and where we need what we need to do to be able to get there and surface those problems and those challenges early uh to perhaps alleviate some of the the challenges of that pressure that's coming okay yeah certainly um a lot of these data governance issues so like how do you find the data what's the quality of the data how does it fit into other workflows these are becoming incredibly important issues for many businesses um 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 right away number number one um 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 we're you know among all the different areas where we could use it where do we want to start and so that's a that's a whole another question of like how do we decide where to where to we're to um lean into first um that gives us some guidance on what start with with the data and I would encourage you know you get that question do I start with the easy stuff the low hanging fruit or um or do I use another another guidepost to to help me understand and I would um 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 um that can help people navigate where it's looking for um use cases that are lower risk still have high hum in the loop so that we can be really we can really be involved in seeing what the results are and refining um the process to get to better results but also offer the potential is if you find something that you can bring to bring to production you can scale it will deliver High economic value back to the business so I look at those you know High High human the loop low risk high potential value as a place to start to order um the the Strategic areas where we want to start and with that you can get an 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 organization needs to be happening right away and ideally those you know you're you're almost putting together a SWAT 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 and um again in the um in the mindset of really using this as a a time to accelerate your speed of learning um um versus necessarily delivering um right away this is an opportunity for the entire organization to learn from some initial collaborations and to shift to start to do the work to shift your your mindset on how you approach it so um this idea of uh a case where you got 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 May a in there let's try and automate things do you have any examples of like real business processes where that's the case so there's a couple places to start um you usually find a a lot of uh 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 use these Technologies for example your employees are going to be often more tolerant than your um your customers with some some you know upfront challenges that you you find in the process so that's one area another one is marketing so marketing is um often so high human in the loop um 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 so um I we often find cases in marketing or in HR and you know a great way to um to sort of get you know build your build your training wheels on this is really to look at your your entire sayate content generation process and marketing and understand where can we use the tools so for example if you say let's let's look at how um how we can attack marketing and and and geni 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 at each what are the um what are the governance practices that we need to put in place to be able to leverage these tools what are the um how does a business process need to change um we're we're in the 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 um a great deal of human oversight um the um opportunity to get return um to get value quickly H is um is massive in that area as well and so um that's just a that's sort of like a often a good place to start hunting for those uh initial ways to leverage the technology and to learn from that and then spread that learning across your organization Okay cool so it seems like yeah HR marketing they're good places to look for these sort of like high person intent of things that can be automated um so you've talked quite a lot about the need to change processes and you've given some ideas of things like forming a 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 um if you've got some sort of large scale transformation program yes so in general one of the key things first is to just understand and and this is the 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 um 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 your your 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 being done you know and then you know scaling it and being done with it this is about it studying it understanding where you want to um 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 you know learn about how you want to develop that process iterate it hone it you know work on it and then once you have something then you extend it so it's a slower it's a slower process than we're used to in you know versus implementing a CRM system or you know shifting your CRM system or whatever it may be um so there's just a lot of emphasis in understanding how do we do this well and refining that uh 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 okay so um I'd like to talk a bit about the role of management in this so if you got this transformation program you're like okay let's try and uh make use of AI or let's try and change our approach to data or whatever then how should my be involved in this I think that's such an interesting question because there's so much that's happening that's Grassroots um but it's really critical if you're going to leverage this technology to have top- down support um we see some really interesting use cases um if you um look at um and this is a this is a fascinating one if you look at what Mna has done where Mna is looking to accelerate their um their product portfolio coming to Market so um they have the covid-19 vaccine um their revenue has um has um plummeted in recent quarters because of you know fewer people are getting getting that That vaccine and so they need to bring new products to Market faster so the CEO who H is a very um really excited about the opportunity this technology decided to um develop a partnership with open Ai and is enabling 3,000 people across the business equipping them with um sophisticated um the sophisticated tools um giving them support and training and um has an expectation that over time employees will use uh use chat GPT 20 plus times a day and that's a lot that a constantly going back and forth and um employees have developed over 750 um gpts or you know uh agents for for different parts of the business and attacking areas like um helping to respond to Regulators which used to take weeks and now takes um 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 late uh a data intensive process that can take a long time and um so these tools are helping people with that so this real decentralization of innovation and a topown 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 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 um 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'll have on the business but the um 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 AI muscle and so um you know a lot of 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 um 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 um correctly how do I do this with a real Framing and lens towards being responsible about how we use AI so you know it it really needs to happen from management but also um the the work is coming from people people on the ground close to customers and business processes yeah so that sort of executive um 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 um in the absence of a chief AI officer who tends to be that top person who is um accountable for everything so what I've noticed is it's somebody who is um really believes in the potential of technology and it can come from different parts of the organization we see CEOs taking on that kind of um that 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 points in the organization and um you know and you see pressure from boards as well to to do that so you know it's um I'd say that it could come from anywhere um but ultimately there needs to be support across the organization to really make a difference and I think we're going to see a over time I think we're going to see this this stratification um from organizations that have this willingness to um to build this muscle and to experiment and go through this it's it's kind of a harrowing time when you think about it because organizations large organizations especially um rely on established best practices and paths and so this is this is a a unique organization that's willing to take on that role in this in this moment um you mentioned agility it has to be an agile organization um often it's an organization Under Pressure um from existential Market threats and um you know has to change and they understand that um that is willing to take on that that challenge and honestly that risk to do something in a new way um but you know those that don't I think over time because this technology is so powerful um I think that over time it will um Drive essentially competitive differentiation if you are able to leverage these Technologies versus not and um the other challenge that firms are facing is that you know we over time um disruptors could be smaller and because they can use these tools and I'm sure you know I'm sure many of your listeners have have heard that um that headline uh when Sam Altman said the next you know maybe the next unicorn will be a um will be a company run by one person you know when we get to a billion dollar company that's run by a single person and the use of AI and of course that's a provocative statement but the um the concept behind it that there are going to be organizations that can do more with less um is something that we are are all facing in our future okay yeah um the idea of like uh these Unicon companies worth a billion dollars but very very few employees that that is very interesting um yeah I think that's more of a pipe dream at least being a billionaire aing person but you know to have a department run by a couple people that used to be run by you know um you know dozens or hundreds I don't know I could see that okay no it is delightfully optimistic and yeah I think uh it's certainly in the right direction even if it is a little bit over hyp maybe but um so I think the other side of this is that a lot of companies have fearce around Ai and this is preventing them from sort of going full Full Speed Ahead so do you have a sense of like what are these um Enterprise feares of AI are and which are real and which are imaginary yeah so I and I think this is I mean the the the undertones of fear are strong and they're at the organizational level and they're at the individual worker level as well so and that's creating all kinds of challenging Dynamics so from an organizational level there's a lot of fear of um doing something um you know making missteps and doing something wrong because it is new and scary and we certainly see a lot of visible missteps um where you know everyone's got their crazy story about the Rogue chat that was you know embedded in in um 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 um to ensure but again that's why it's need to learn over speed to deliver right now um and so that organizational fear is holding people back and um you know they need to be thoughtful about how they execute right now so um that you know that's understandable so what I emphasize is you have to you have to get in and you have to understand this technology and build your muscle but you don't have to yet brace things to production um because everybody is learning right now the individual worker level is a whole other area that's um really important and you know we need to understand how our workforces will change how the needs of our workforces will of what kind of Workforce we need will change over time and how we um help people skill in the direction where we're going to need them to to be and there is a lot of fear and concern around that and um that creates resistance and so there's a lot of work that will need to be done on that there was a um there was a a really interesting story about how even when Microsoft was implementing um AI in their own customer service organization where they've been were able to see incredible results um 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 a lot of the um 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 um how we leverage these tools and how we um we are Workforce transitions with us um it's it's really hard work it is very hard work okay yeah so idea of a rogue chat but and I think that's like many companies like as a as a real nightmare because you don't want to annoy your customers and cause a PR problem things that so you mention and liability like the the liability that's associated with us like we don't have this we don't know yet how we have not been able to catch up to these systems in terms of answering the big questions that's surround them yeah so um you mentioned things like you've got to be very careful about this and got to tell some tell the right stories and things in business do you have any like um 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 well so that is the question in this technology right so we need to and this comes back to data as well too so you know what you know what data are we working with you know is this what is the role of that chatbot um you know what are we allowing it to do um so this is this is what we need to work out in our in a in a business process to understand how can we leverage this technology in a way that delivers value and we've mitigated that risk um and I organizations are still figuring that out and so I think a big piece of it is really understanding that um you need to you need to test well and need to understand that this is a process and um we are you know making sure you're deploying the technology in the right in the right places where you can learn in a lower risk way um before you start to to bring it out um 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 um looked at this and um it there's some generational differences um but um there are um there have been some findings that if an a consumer knows that AI is being used that lowers the trust of an organization but there's also indications that uh younger Generations um don't have a problem with that so it'll be interesting to watch as well what is what is the you know what kind of trust um how does trust play into this and what's the what's the impact of trust and how I'm using using Ai and with my consumers or my customers okay yeah uh definitely you don't lose trust with your customers that's going to be a real disaster for your business um all right so have you seen any success stories where companies have gone all in on this and they've uh had some transformation program they've adopted Ai and they've seen a benefit so you hear isolated cases but this isn't I have not seen this at you know 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 organizations um I I think it's a sort of a surprise to Executives to understand like everyone feels behind everyone feels like they um they're not really sure the right way to move forward everybody is learning right now and um it's a it can be a very uncomfortable time for organizations because they don't there aren't a lot of um of you know there there aren't perfect case studies or examples to see on how to do this um that's not you know that's not something that executives are used to we're used to you know cases that you know have been well documented that we learned a business school you know like there's a case study written up about it that we can study you know study and understand and um and this is throwing Executives so uh so no we are not seeing a lot of examples where an organization has been transformed by Ai 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 do to punch well above their weight so but you know that's not what we're talking about we're talking about the Enterprise this is the challenge ahead so uh yeah uh I think you're right in that a lot of Enterprises they tend to move slower so a lot of sort of theoretical potential at the moment rather than uh realized gains um so what sort of time scale are we looking at for Enterprises to be able to successfully take advantage of this technology 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 and I think this is going to be tough I think we're going to see a few breakouts that uh organizations that have been able to do this really well that are going to um to start turning results around um but I think this is going to take a you know a while for us to really see some real results I we'll see some organizations start really giving us some good case studies I think 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 um in parts of their organization and have you seen any um like common challenges or mistakes that organizations are making while they're trying to get the heads around this 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 as 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 um potential competitive advantage and um and organizations are kind of 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 okay uh yeah just underestimation the challenges is is going to set you up for failure for sure okay all right so um what are you most excited about in the world of AI and digital transformation at the moment so I'm actually um in the midst of writing a book right now where um the focus is on how do we think with AI with with being the operative term um I think there's such an opportunity for us to um Infuse our with these sort of you know superhuman capabilities when we collaborate with when we take our human intelligence and we collaborate with machine intelligence what is possible um I'm really excited about what will happen in terms of um personalized education at scale personalized coaching at scale um there's a lot of different areas that I'm watching in terms of mental health aging um other areas what is possible when we all of us humans have the ability to um to infuse our thinking and our work with machine intelligence that's one area I'm really excited about okay yeah I like the idea of thinking with AI rather than just letting a I think for us uh that kind of collaboration is pretty amazing all right super uh thank you for your I'm melison\n"