Finding Patterns: A Practical Way to Introduce Enterprise AI

Michael Reeves: This is Solving for Change, the podcast where you'll hear stories from business leaders and technology industry experts about how they executed bold business transformations in response to shifts in the market or advances in technology. In every episode, we'll explore real world strategies and technologies that fuel successful evolution.

I'd like to welcome Chris Falloon from DELL Technologies to the podcast Solving forChange today. Really excited to have you on. I appreciate you taking the time, especially now in a global role that you have, to come in and be a part ofMOBIA Connect here in Whistler and for the partnership I'd like to thank you as well. And you know now selfishly, I'd like to thank you for being on the podcast today. So, thanks for taking the time to join us and if you don't mind,Chris, it'd be great If you could just take a couple minutes, because we're going to spend a lot of time talking more about you, anecdotes, and what you see as you've been traveling around the world in your role as we get into the discussion.

So maybe if you could just give people an intro on who you are, kind of what you do, a little bit about kind of where you've been going and soon.

Chris Falloon: Happy to, happy to. And listen, thank you for having us here and selfishly on my side being able to work within an hour in my home in Vancouver is an unusual pleasure.

So I really enjoyed the drive up and real happy to see some faces here from when I worked in technology for the first half of my career inVancouver. So thank you for having me. Look, I went down into the into the U.S. Markets and into the global side of EMC and subsequently DELL Technologies in 2014.

It's about 10 years ago. And one of the groups inside of DELLTechnologies that's poorly understood, I guess maybe even inside of our company, is this group called the Transformation Office of which I am part. And the Transformation Office was really pulled together about, about eight or nine years ago to help solve some of the platform complexities that our customers, the frustrations that our customers were seeing trying to bolt together all the things they knew they needed to bolt together to deliver technology services.And so, you know, as an infrastructure vendor, our predominant role in this is world class technology at the bottom of the diagram.

Um, but our role within the transformation office is to help customers align their technology strategy with what the business expects from them in terms of services. Uh, and really navigate the selection of best of breed technologies, navigate the complexities of engineering and platform engineering so that they can figure out how to successfully deploy and achieve the benefits that some of these software packages say they have on the box.

Not always that easy to do. My personal role, I run a group within the Transformation Office called the Practices Organization. I've got four practices globally. Um, we've got a data and AI practice, a telecom transformation practice, um, a cloud and multi cloud practice, and a security practice. Um, and all four of those, uh, really with the mandate I just described, is just to help customers navigate those platform outcomes.

So, um, I get I've got people in 11 countries. I get a good chance to be with DELL's largest partners and our largest customers globally.So it affords me the privilege of being able to take in a fair bit of DELL. Customer specific information, and I'm really glad to be able to share some of that with you.

Michael Reeves: Oh, wonderful. Awesome. Yeah, and that's what I want to spend some time talking about today, because, you know, you're here at the conference, you had a great session this morning, and, you know, there's, we start talking about digital transformation, now you layer in AI Everybody's, you know, a lot of pressure at the executive level in the boardroom and then throughout the organization of any, any, any company now.

Um, and it's, it's around what are we, or how are we going to approach AI and, um. It's, it's, it's really interesting to talk about that because if you, you know, we were talking a bit about social media this morning. If you go and you look at some of the anecdotes online, you think, wow, a lot of people got this figured out.

A lot of companies, like they're really, they've got a lot ofrichness and they've gone quite a ways down the road and had great success, youknow, deploying.

And, you know, I think the reality is that is a very smallsegment of, you know, what is happening within a lot of organizations globally.And that's kind of where I want, where I wanted to start today is I, maybe it'sto, um, massage some of those concerns of a lot of people in companies andsenior executive roles of thinking like, I'm not being successful.

I'm not getting anywhere with AI yet. And, you know, there's alot of fear around that. And so people are just kind of rushing to the table totry and find ways to drive it into an organization. And I think what I'd liketo start with, um, with you just based on your experience, like you travel allaround the world and you talk to a lot of customers, a lot of differentprofiles of industries, et cetera.

And, you know, listening to you this morning, I thought it wasvery, it'd be very helpful for a lot of folks that listen to our podcast justto get, you know, your perspective on what you're seeing and. And I don't wantto, there's lots of ways we can go with the discussion, but really there's kindof maybe three or four, you know, major anecdotes or themes that you're seeingas you've been, you know, traveling or traversing the world, talking todifferent customers or you kind of share some insights there.

Chris Falloon: Sure.Yeah. Look, I, I think, uh, first of all, nobody should feel bad about being,um, feeling like it's moving slowly. I think we're. One of the data points Ishared this morning was that less than 10 percent 7 or 7 or so percent ofcompanies are have been able to get some sort of AI or gen AI based workloadrunning in a production environment at scale.

Um, it's a very small number, and even among those, uh, thoseuse cases are not terribly exciting. They're usually a rather mundane sort ofdata retrieval type workload, and so despite all of the you talked about socialmedia, I mean, uh, We are at the peak of, of peaks in terms of the hype cycle.I've not seen this level of, of frenzy and, you know, since we were selling,um, fiber really, you know, when, when fiber connectivity became became a bigdeal.

Mm-Hmm. , um, I think. Making decisions at the top of a hypecycle is always difficult, uh, in any industry with any problem. But in thisparticular case, we've got, uh, a frenzy that's coming from the board level,uh, of most organizations. There's mandates, uh, to try and solve for, uh, notjust how we use these technologies, but how we implement them into thebusiness.

One of the things I touched on this morning is that not only isit difficult to make the technical choice, it's also difficult to make the usecase choices and it's difficult to write business, uh, cases for some of these,uh, use cases. So, we've got board level members struggling, we've got line ofbusiness leadership, uh, struggling with these answers, we've got data deliveryand data management folks and application developer teams and the underlyinginfrastructure teams.

Everyone's struggling at exactly the same time, which I think,in this particular case, is unique in the tech industry to have everybodystruggling with the same problem at the same time. Now, that's positive, uh, insome ways, which at least we all have to talk, and because we're all facing theproblem at the same time, uh, talking makes sense.

Um, but now we need a framework or a common language becausethese teams tend not to talk to one another. So I spend a lot of time, we as anorganization spend a lot of time trying to create some patterns or some, youknow, some way to talk between the business, the tech deployment platformfolks, and the underlying infrastructure folks.

Michael Reeves: Andyou've got maybe to unpack that a little further, so. You know, this morningyou were talking, really kind of try to categorize things or put some structurearound a lot of this looseness that you're describing that we're talking about.And that is, you know, you've got, you've got an underlying infrastructure thatneeds to be there.

You've got a whole bunch of things around governance and risk.And then you've got data, um, kind of blending across all those things. Andthen you take the other complexities around, you know, multi hybrid, I. T.hybrid cloud, multi cloud, et cetera. And it's interesting. You talked throughthis morning. You've got some, some different models and approaches in terms ofhow you traditionally have a discussion with a customer or somebody that'strying to figure some of this stuff out.

Um, maybe if you don't mind, take a minute and

Chris Falloon: walkus through that. So sure. It was very helpful. Look, I think patternrecognition is, It's something that technology delivery organizations have, uh,it's, it's been important for them to be good at that, you know, um, sincethere was a technology delivery organization, um, understanding how tobucketize the request from the business into something that's usable, um, to atechnology engineer or a platform engineer.

And this is really no different. I think what's missing here isthe maturity, the language and the structure to have these conversations withthe business leaders. Um, one of the things we walked through this morning is,is, uh, sort of a patterns discussion where we start up at the use case layer.And, and, you know, I think DELL's internal use cases now, the requests of ITare in the hundreds, uh, you know, a few hundred use case requests coming tothe technology teams, some with good business cases, some without.

Um, you know, I was, I was in Australia last week with anotherclient. They've got 1600 use cases that have been a potential use casesidentified by the business team. So what that leaves you thinking is, well,it's not useful to build technology for a use case or, or even a handful.What's, what's important is to understand not just the ROI and how to have aconversation around the viability or the, the plausibility of a particulartechnology in the business and what it provides to the business in terms ofreturn.

But how do I start to categorize use cases into meaningfulpatterns so that I can understand the implication it has to, um, to my businessat all layers? What are the implications to the data teams? What are theimplications to the infrastructure, developer, and application teams? If westart to bucketize them, Then we can, uh, we can build technology for largechunks of use cases, which means that if I have a strong ROI for two or three,and I've got 30 that happen to be supported by the underlying architecture, nowI can build a platform that delivers 30 use cases, even though the TCO or theROI is supported by one or two key.

So that type of patterning, um, is something that we, we spenda lot of time talking about. Um, I think, you know, we're, we're seeing some,um, very obvious technology patterns, things like, you know, reg and, and, um,and, you know, graph based databases, um, you know, the retrieval augmented,even small language models is becoming a more common technology pattern.

There's a bit, there's some of these things that are, um, alittle mundane, uh, but, but there are many, many, many use cases that fallinto these really simple technical patterns.

Michael Reeves: And Iappreciate that. A lot of richness in there. Um, the other thing that we, youknow, we talked about a lot this morning and, uh, you've touched on it a bit,but we get down to the people layer.

Um, And I'd be curious just to get your insights in terms of,you know, what you're seeing again as you've traveled around and talked todifferent customers around how they're dealing with the people side.

Chris Falloon: Yeah.Well, I mean, it should not surprise you maybe, uh, that the most difficultpart of IT transformation for the last decade has been people.

Uh, and process, uh, the technology almost is never theproblem. Um, not to say it's not a problem, just it's the easiest one to solve.Um, and if, if we have weaknesses in, in our people in process in IT or in ourtech delivery, um, environments, it becomes exacerbated by the conversationsaround AI and generative AI workloads because they're just that much moredemanding.

And making mistakes is that much more expensive. And, and so,poorly deployed assets or poorly managed people, um, um, people, resources ofany kind, uh, really have, uh, drastic effects on overall cost. And so, it'scritical to get your, your operational house in order, um, to deploy these. Andit doesn't matter whether it's on prem or off.

Your, your, those mistakes can be made in any premise. Uh, lotsof idle capacity sitting out there, costing people money, not generating realvalue. And that's a, that's a real concern.

Michael Reeves: Yeah,no, uh, agreed there. Um, it's gonna move on a little bit. That's great. I'mtrying to kind of this together in a fashion that gets us to from problemstatement to kind of what you're seeing to, you know, what, uh, um, what you'veseen in terms of success.

And, uh, um, you know, we talked a little bit about that now.Um, and you get it, you've talked about it a bit already. Now we startconsidering use case cause everybody always goes to, you know, give me the bigbang. And you talked a bit about that this morning and, and, and, uh, You know,and as you mentioned, like every company has some sort of backlog ofinitiatives that they feel would be good candidates and I know everybody istrying to figure out like what is the best approach?

How do we start? Like what is that? You know, that launch pointin terms of a use case. So again, I'd just like to lean into you maybe a littlebit more. And I know you said there is no answer. There's no one answer forthis. But, um, you know, everyone's kind of looking for an approach orapproaches to try and get to a point where they can get to a use case.

Chris Falloon: Yeah,look, it's easy to say things like there's no killer happen. And, you know, Ithink there are. Uh, there really isn't, um, a single use case that's obvious.However, when you start to look at industry specific use cases, there aredefinitely patterns that are emerging where there's some no brainer initiativesthat takes such big cuts at, at the cost of operations of an organization outor create such significant competitive advantages.

Those are, uh, the boards understand those by now. The boardswill understand those so implicitly that there are plans in place to go aheadand launch those already. It's really the it's the first wave of ones wereunsure of that customers are really struggling with. And I think the, you know,the technical plausibility and, and, uh, and the return are both difficult toarticulate, right?

People are immature in the technology. So, uh, I think they,they think about um, getting it stood up in a, in a non production environmentis much different than running something at scale. Uh, and the maturity aroundcost, we talked about cost this morning, but the maturity around cost and costto serve and TCO is very immature um, in these AI use cases.

And so, It becomes difficult for a marketing lead or a financeleader, someone to articulate an AI use case, a business case, ratherinternally, that's coherent enough to say yes to, because there just isn't thematurity, um, to, to make those assumptions. Um, the last thing I would say, I,I did say this morning, which is if you, if you're.

As most customers are confronted by hundreds of use cases, theones that are the most, uh, technically intriguing or intellectually, uh,stimulating may not be the ones that actually provide the obvious payback and,uh, just because the ones that are, are technically curious to us, um, tend tobe the ones that are the most difficult.

Um, What we've seen in terms of production, AI use cases thatare being rolled out at scale, they're the most mundane and uninterestingthings, uh, you know, in most organizations. Risk and compliance work, um, youknow, sort of the back office type work that you might assume Um, that youmight assume someone has already maybe outsourced, uh, to a body shop or anoffshore partner.

Mm hmm. Many of those now can be, can be looked at and, and uh,reassessed as, as something where, you know, a combination of humans andmachines can probably take a ton more cost out of those environments. Mm

Michael Reeves: hmm.That was, uh, thank you. There was a, there was a lot in there done back. Um,and, and if you look at, you know, the, the whole discussion around, you know,continuing on the theme of use case, um, and I, I, I've seen this anecdotally,not just within our company, um, because we're trying to do a lot with, uh,with AI, less so Gen AI.

Um, And if you start to look at some of the customers that wehave, some services and consulting that we do to help them move down that path,you know, to your point, um, you know, operational items seem to be the, thepath of least resistance or where people are able to build a business case andcreate some sort of financial construct that says, you know, this is how thiswill pay for itself in reduction of time, cost, you know, people, effort, etcetera.

Um, I find that really interesting that, uh, you know, there's,there's an evolution, I hope, that's going to start to move from here as, youknow, when an organization gets a couple of use cases under their belt, theystart to pick up some, uh, a little bit of moxie around what they're doing andhow they're going, how they're You know, it's going to start to take it furtherinside the organization and develop their own framework or approach to how, youknow, they want to build out on their strategy around AI to start to execute atsome sort of scale or velocity.

And I, I don't know if you have any further comments aroundthat, but I just, I just wanted to echo and share some of the themes that youjust hit on there.

Chris Falloon: Ithink just I'll, you know, the, we talked a bit about patterns and, and, youknow, putting these use cases into sort of a structured format that makes iteasy to talk about them.

I think one of the things that That I've seen that really helpsthink this through is this, this paradigm, um, or this continuum rather betweenanswers to actions. And if, if a use case is providing answers, meaning you'requerying your information or you're looking for a coherent response fromcorporate data, maybe, or a, or a live, you know, a corporate library, etcetera, um, or public based information, or you want to talk to your databasein a way that allows you to use natural language.

Those use cases are, uh, are data, data retrieval oriented and,and they're much, much simpler than an actions based, uh, use case. As soon aswe introduce data science, uh, as soon as we introduce decision making, uh,then the risk profile changes massively and the talent changes massively. Uh,required to, uh, to build the decision models, um, and tweak them andconstantly be tweaking them.

It's very difficult for most organizations, not just from atalent perspective, but from a strategy perspective to get to that end of thespectrum. So I think it's likely that the use cases that are most commonlydeployed in production for the first wave, maybe even the second wave, are onthe answers side of that answers to actions continuum.

Michael Reeves: Youhit on one other thing that I wanted to, uh, to kind of chat about, and thisis, this is one. It's, it's. It's, I think it's almost palpable in everydiscussion when you start going into a discussion with the company around AI.And that is the, the data piece, security, governance, compliance, because Isee so many people.

Companies get stuck there and it really gets in the way oftrying to Move further with anything else around trying to plan for you know,develop a strategy look at use cases, etc You touched on it a bit there I don'tknow if you have any other comments or observations because I I find that A lotof companies, I can say honestly, we've been guilty of this earlier on, and wewould just stop and say, Okay, I guess we can't do this.

Um, and I see, You know, companies like DELL Technologiestrying to help customers get their arms around that and solve for that. It's afundamental problem. Um, I almost call it more of an infrastructure relatedproblem because it's, it's, you know, the data is a core element, like it's,it's the foundational piece to what you want to build from.

So I don't know if you have any comments around that.

Chris Falloon: Imean, we just talked about the predominant use cases of answers, you know, thatcategory of answer based use cases, the, uh, you know, the, the main ingredientin an answer. In most cases is your data, um, and data readiness, data platformreadiness, not just the availability of it itself, but how it's managed, howit's, uh, how it's, uh, generated, uh, where it's stored, how available is it,how controlled is it, um, data platform management is, is probably the least,uh, in terms of technology service readiness in most organizations, the dataplatform is by far, um, the biggest gap, I think, in most organizations for theidea of deploying AI use or ML use cases at scale.

The business case for, for data platform transformation hasbeen very difficult in most organizations. Data is always, almost always anafterthought. Data platforms are not inexpensive. Um, the software is notcheap. Um, and the talent to operate them and write, you know, sort of, youknow, platform strategies is not always inside of every organization.

So we end up with massive data sprawl. We end up with massivedata platform, uh, incoherency, copies upon copies upon copies of corporatedata. Um, and with all of that comes risk and, uh, and security. Um, I, wetalked a bit this morning about, you know, sort of this idea of minimum viableplatform or starting small and allowing your platform decisions to evolve withyour business.

That's a core principle of every platform, whether it's a cloudplatform, uh, an AI platform. But it's definitely the right strategy for datagovern or, you know, sort of data platform transformation is to pick a desiredend state, pick a north star of where you want to be from a data coherency,data management standpoint, and then allow yourself to only make decisions thatget you closer and closer to that end state.

And, you know, it's so expensive, as I say, to transform data.It's not going to happen overnight. Um, maybe the last thing said there isthat. You know, we've never had the business case to, to, um, get our armsaround the data problem in an organization holistically because the ROI hasnever been there. It might be, in some organizations that these AI use casesare finally the, the, the business case that drives us to make coherentinvestments in data management.

Because there is a, there's a clear ROI in doing it correctlyand there's a, there's a, um. clear, clear danger in not, uh, following, youknow, the governance and risk pieces alone are almost a non, as you say,they're a, um, a showstopper.

Michael Reeves: Yeah,that's great. Good context. Thank you for that. Just a couple other things tocover off here.

It's been great discussion. Thank you. So, uh, this would bemore. So Chris's opinion, um, what, what does success look like for anorganization that's trying to get down the path of whether it's a use case,setting up the environment and the platform? Um, what, what would success looklike either as a starting point, just to kind of, you know, Get going, or maybeit's maturity.

Um, I'll let you decide which way you want to go with that.It's going to be getting more, more opinion.

Chris Falloon: Yeah,that's a really interesting question. Um, and of course it would be opinion.Uh, I think, look, I think there's, um, I think success at this early stage iskeeping yourself from falling into some predictable, um, mistakes.

Some early mistakes that will, um, Um, I think really causedsome regret, um, you know, midway through next year, the year after as youstart to realize that you've either. Build technology silos because you haven'tthought about this horizontally. Or you've selected tools that are onlyavailable in one premise.

Maybe you've doubled down on a tool set that's available inGoogle or Amazon or on prem and they're not available anywhere else. Therefore,you've got some, you've made some, um, early bets that didn't, that didn'tallow you to be flexible. Um, but I, those are all, you know, sort of avoidingthe key, uh, pitfalls might be, might be the number one, uh, pointer ofsuccess.

But I, that's. Not really success. I would say the, you know,in terms of delivering outcomes under severe pressure from the board, I thinkwe've got a lot of technology organizations that are Desperate to standsomething up to show that they kind of a know what they're doing and be thatthey can keep their jobs because they're, you know, there's a lot of folks thatare demanding that they make this pivot.

And what we do when we when we have unrequited demand fromsenior leadership, we often sort of build something because we can notfollowing a North Star or not following some sort of plan. And so, uh, thosethree things are related. I think if we can identify the potential pitfalls,build ourselves a high level North Star for our data platform strategy, ourcloud platform strategy, and our AI, and those North Stars can be as simple asWe're not going to choose tools that lock us into a premise.

We're not going to have data silos that, that create a biggerproblem than we already have. And we're not going to launch things that wedon't know how to control, right? We're not going to put ourselves in a placeof increased risk. If you just followed North stars that had that type of, ofobjectivity and granularity, then you can start to make principled decisions.

You can build something that not only shows that your techteams can deliver. on an initial use case or use cases. But you can alsoarticulate the AVA vision as, as simple as it might be. What you're buildingtoday is extensible and follows the business. And that's, I think that if youcan build something now and show that it has, that it's future proof, that's,uh, that's as good as you're going to get in the short term.

Michael Reeves:That's great. You actually took on my last question by answering the previousone. Um, a lot of good ideas in there and, uh, it's great. I mean, to me, oneof the things I think. I hope that our teams are trying to do, and that is,like, customers need help. And they need knowledge and experience and theredoes, as you say, there's no, there's no recipe or, or, um, prescription that'savailable yet in terms of here's the approach you want to use.

Um, and there may never be, but some, you know, references orreference material or architectures and things to lean on. So yeah. Um. It'sgreat and I really appreciate you taking the time to share your experience fromall your travels and your customer meetings. I just find that there's so muchwealth of knowledge you can share with all the interactions that you have.

Again, I want to thank you for taking some time to join us heretoday. Thank you for the partnership as well. I'm excited about where we'regoing to go with the partnership with you folks. Thank you. You know, there's,there's so many interesting areas that we can help our customers together. AndAI certainly seems to be a galvanizing point for so many of our customers thesedays.

And I'm excited about what that means for all of us. And, uh,um, just in closing, I always ask, there may not be, um, is, is there a waypeople can get ahold of you or, or is there, um, some social media content youwould direct them toward? Sure.

Chris Falloon: Yeah,look, I'm happy to have my contact information dropped into the show noteshere.

That's, uh, that's probably the easiest way and happy to takean email. I, you know, the partnership. Um, with you guys, um, you know, is, isone of many, but I, this, uh, I'm really pleased to see the outturn of, offolks here, many of whom were, uh, in my Rolodex from early in my career,including a couple of folks inside your organization.

Very happy to work with you guys and, um, happy to have anyonereach out. I'll, I'll, uh, I'll direct you to, uh, someone in our business thatcan help.

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