Make Your AI Strategy Actionable (Guest: Vin Vashishta)

Andreas Welsch: 0:21

Today we'll talk about making your AI strategy actionable. And who better to talk to about it than someone who helps leaders do just that. Hey, how are you?

Vin Vashishta: 0:34

I'm good. Thanks for having me on. How are you doing today?

Andreas Welsch: 0:37

Doing all right. Hey, why don't you tell us a little bit about yourself, who you are and what you do.

Vin Vashishta: 0:41

Sure. Vin Vashishta, I've been in technology for over 25 years. Been working in the data science machine learning AI space for the last 10, doing strategy work, AI strategy, data strategy, data, product strategy for just about eight years now because as I started V squared in 2012. And almost immediately realized I could not do any interesting projects unless I got C-level buy-in unless the ROI was there. And I began to see all of the problems that were stacked up behind asking for buy-in. And that's where I got involved in strategy because you can't do this without holistic strategy, which is what we're talking about today. So I've helped clients, Fortune 100 clients, SMEs, startups. It's been interesting over the last five years how interested startups and SMEs are suddenly in doing this in a more rigorous way and doing this in a way that returns value to the business instead of this exploratory pilot way. So that's my story. The only funny part about it was I tried to get into AI in the early nine or early mid nineties. When that first boom happened and no one wanted to hire me, I went to college for machine learning. I think I was gonna graduate and go straight into Microsoft. Yeah, I had to wait almost 15 years for it to come around.

Andreas Welsch: 2:05

See, that's what I like about the industry. There are lots of second chances. Awesome. I've, been following you on LinkedIn for quite some time and I'm sure many of of you in the audience are familiar with you and your content as well. So I'm really, looking forward to learning more about defining an AI strategy from you. And, to those of you in the audience, if you're just joining the stream, drop a comment in the chat. What do you think an AI strategy for your business needs to answer? And if you have any other questions, please do put them in the chat as well. I want to make sure that we have enough time to answer them and keep this interactive.

Vin Vashishta: 2:42

Yeah, take advantage of the free time if you got a question. Definitely throw it into the chat because this is a good opportunity.

Andreas Welsch: 2:49

Exactly. Doesn't get better than. At least not cheaper.

Vin Vashishta: 2:55

I promise it'll be worth every penny.

Andreas Welsch: 2:58

I'm, really curious. I, posted a poll on, LinkedIn earlier this week, and I would love to get your perspective. So I asked the community how should organizations start their AI journey, start with strategy or start with projects. What do you say?

Vin Vashishta: 3:14

I think you should start with opportunity discovery. It's the same place that you should start any sort of strategy planning is, and instead of saying the technology enables these opportunities, so we should do them I take a more traditional strategic approach, which is, here are the opportunities that are presented to the business. Here are the opportunities the business is best suited to go after. Just from a basic core strategy standpoint. Not even thinking about the technology at this. And then from there we have your business strategy and then business model, operating model. And you should have a third piece, which is the technology model. And that explains why the business uses technology to go after the opportunities that it's decided on, that it's discovered, that it's gone through the process of saying, this is what we should be doing, not what we could be. And when you do that, you have strategy and value driving technology versus technology saying, this is possible, so why don't we do it? Yes. That's the big mistake upfront that I think most, clients that I get brought in to help, most businesses that I see struggling with data science and machine learning, just because it's possible, they think it's probably something that should be done, and there's a lot of FOMO. There's a lot of what if the competition gets there first? It's way more important to say these are the best opportunities for the business. Technology agnostic. How do we use data, analytics, AI to be better at capitalizing on those opportunities. And if you use the discovery framework, you end up with a better AI strategy because it's through to the technology model. It's tethered to your business model, your operating model, and core strategy. It isn't trying to change core strategy or drive core strategy, it's just an extension.

Andreas Welsch: 5:10

The point that really resonates with me is what you mentioned is it a solution looking for a problem, right? We could be using AI, so let's throw it at a problem or let's let's use AI and let's find a problem that we can apply to it. To your point, I feel we still see that quite a lot in businesses today, and it's great to hear from you really think about the opportunity first, what do you really want to accomplish? And then think about your AI and technology strategies and ancillary support.

Vin Vashishta: 5:40

Yeah. And it forces us to justify ourselves. I think that's one of the critical pieces of it, is now we're not assuming there's value now. Now we're making the case. And when you make the case, we talk about buy-in, we talk about stakeholders, keeping them engaged. If they've already decided this is what they need to do, this is what they should be doing, and I come along and say, I got great news for you. I can help you do this in a way that generates more value, where you'll be more productive or in a way that'll cost you less. That's engagement right there. I'm not pitching them my pet project, I'm pitching them their pet project made better with this technology. Now I'm a partner.

Andreas Welsch: 6:23

That's, that's awesome. And I think, also just the last piece you mentioned, partnership is really important in this, too. And seeing on eye level. Now strategy can, be a pretty big term. And I'm sure even if you do work with C-level executives, strategy obviously it's very important. But as you cascade it down in the organization, it's still a big term and it might be a little fuzzier or nebulous. So what would you say is the first thing that the leaders should really consider when working on their AI strategy? I know you already mentioned look for opportunities and then see what other strategies can support it or might need to be adjusted. But what's the first thing leaders should take a look at?

Vin Vashishta: 7:06

I would actually say three things. I think one is the concept of continuous transformation. When you look at where the business is today, they're not going to go from level one maturity to level 30 in one step. So looking at it as continuous, I think that's really important because you are going to start making decisions that amplify and support your goals next year and in three years. And when you start looking at transformation as a continuous process your decision making changes. And I think that's a critical component of defining strategy. Strategy must inform decision making across the enterprise. That's the implementation piece of it, which often gets lost. We have this great strategy, this great thesis of value creation and justification for why we're using AI data analytics. That's awesome. But do people downstream have the capability to make better decisions now than they could before with the presence of this strategy? Did this do anything? Did it actually improve? And I think we overlook that AI data strategy analytics strategy isn't just for the data organization. It lives throughout the entire business, every business unit, and all the way down to frontline employees. They're making decisions about data, about what software to buy, about what functionality to enable, how to spend their time. What literacy really means. These are all decisions that are happening anywhere from mid-level management to the front line, and if strategy doesn't inform decision making, it's useless. It also has to create this alignment. If I'm in two different business units say a supply chain and marketing, if I'm making a decision about buying a piece of technology and that decision is something that could impact marketing, then my strategy needs to be able to inform me that, hey, I need to talk to other business units. Because there could be some impacts. They might already have something. And this happens so frequently where there's a tool that can just be repurposed. Yes. Where all you have to do is buy a couple of seats and you're done. There's no adoption. There's also the concept of, oh could we be centralizing? Oh, so now instead of having five places where data exists, we can keep it to one place. Oh, that'll help the data team too. It'll make it less expend and, so these decisions are what I think is critical for strategy to inform.

Andreas Welsch: 9:45

That's fantastic insight. Yeah. Especially I think in larger organizations like you mentioned, there's a focus on what's right in front of your nose or what's in your area of influence where there could be so many opportunities across the different business units. Fantastic. So hey, why don't we take a quick look at the chat. I see there's a question from Mike Nash. So Mike is asking: When using a discovery framework, how do you weigh up one opportunity over another? Do you use a form of metric or measure?

Vin Vashishta: 10:19

I think that's just traditional strategy. Whatever the business is currently using for KPIs should be the beginning of where we trace value back to. So I don't want to introduce top level new metrics to begin with and new KPIs to begin with. I want to dovetail into whatever the business is already using because this is hard enough. I don't want them to change overnight. I'm going to provide decision support systems. I'm going to begin to evolve KPIs and improve them. Decisions with outcomes using KPIs and get to the point where KPIs are causal. But I'm not going to do that tomorrow. I'm not going to try to do that as step one. So no matter what the business is using right now, I'm just going to dovetail with that. And my message is always, so you're discovering opportunities this way. Is this how we should be discovering opportunities? Maybe it is. Maybe you're right and I'm gonna help you prove. Now we're gonna have data to support these KPIs that you're already using. And part of the discovery framework and discovering opportunities is discovering opportunities to improve the numbers, the data points that we use to measure success and what we're watching. that helps us make better decisions. And so opportunity discovery isn't just products, how we're gonna use data to generate revenue. It's also how are we gonna use data to optimize your business model and your operating model. And so that becomes part of the Discovery framework.

Andreas Welsch: 11:56

Fantastic. Thanks, thanks for sharing that. I'm, looking at the chat again. It seems that also covers Cynthia's question, who asked: Do you have a standard way of identifying the value of a new initiative? So not just increased revenue or hard savings, but cost avoidance, increased productivity, and so on. So maybe if you can expand a little bit on that or if you have an example.

Vin Vashishta: 12:19

Opportunity scoping is number one, it's critical. Number two, it's badly done. Product management is used to traditional software products. Data products are a completely new type of asset, and so we have to measure the value of a dataset differently. We have to measure the value of research artifacts differently because they can be reused multiple times. And so when you create software, you create it for an internal use. And it doesn't generalize. You have to make significant modifications to it in order for it to be used again. But a data set can be used to train. Five models, a hundred models, the value of that dataset. When you try to capture that, you know the, what is the ROI of capturing this particular dataset. You have what's right in front of you, but then you have everything else that it opens up later. So this concept of assessing the complete and total picture of ROI very, difficult. So what I come into this doing is I'm trying to figure out what are the most valuable processes that we have right now? When you look at the value stream connected to the workflow, what are the most valuable processes? Those are going to create the most valuable data sets. And so that's where I want to begin. Are we capturing data about that? And so KPIs that I'll capture, things like what percentage of our workflow is producing data, and I want to start with the highest value Generat. Areas of the workflow. And that's not just internally, but that's also customer products. How much of their workflow is being captured? How much data do we have? And those are the beginnings. If you have nothing if you're starting at zero and you don't have any metrics to capture value, that's where I begin. And then you can extend forward from there.

Andreas Welsch: 14:17

Building on that when you say you look at, processes is there a bias to look more at the the processes that impact your top line? So from lead to cash if you will, or is it more at the tail end of this part-Finance. How, can I optimize my business? Where do you typically see opportunities or where do you see a bigger impact if you get your strategy right?

Vin Vashishta: 14:48

Products. I want the team to generate revenue as fast as possible. At the very earliest stage. And yes, process mining, process discovery, process mapping, process mining those are the earliest. I go from transparent to opaque. We want transparent. Most of all the business does right now is opaque and so we need to start gathering data about that. And so I think when you ask where do we begin, where is that? If you're at zero, where's the bias for your first few initiatives? Yes. And I like to sit down and find people's biggest. because it's not so much ROI that gains trust and that gets those coalitions built and gets you advocates for next initiative. Next initiative. At the very early beginning, if I can take pain away from someone. I have a friend for life. That person will go to bat for me and every time I need a recommendation, some sort of social proof, I can go to them and say, Hey, can I get a quote from you? Can I send somebody over to talk to you about what this did for you? And so that's not the highest ROI at that level. But when you look at it from a long-term perspective, the allies are sometimes almost more valuable because, now, I can justify more initiatives. I can get to those highest value use cases now. Because I have someone who's proven that I'm okay to touch this really important thing. It's okay. I'm not gonna break it. I'm not gonna mess with the golden goose or I'm not gonna do anything bad. This will actually help. And I have proof now and that's really the hesitation, is if I try to go straight for the highest value use case. In some organizations, everyone says yes, that's awesome. We've scoped a very high value use case. It's connected to either customer value or some sort of internal high value. High need for automation use case, high need for scaling, and those are good criteria if I need. If scale is a problem, that's usually a good high value use case, but more times than not, I can't touch that because no one trusts that this data person or this new data team can touch this product because oh, what if you

Andreas Welsch: 17:10

And I, remember seeing that as well in my roles or early projects, right? It's building that clout, that reputation. And, to your point that's social proof. Just goes to show that technology is just this tiny little piece in this.

Vin Vashishta: 17:25

It's culture whole thing. So yeah, it really is. I think what's unique with where businesses are right now and probably will be for the next three years is it's interesting. The need requires people who are technical strategists that understand data science and machine learning at a practitioner level, but also understand strategy at a practitioner and a planning level. And when you don't have one person or a group that you have people in that group who are both, that have that hybrid capability set, it doesn't. I can't explain how valuable technical strategists are right now in these early to mid phases of data and AI maturity. There are people who are in the data science field right now who are looking for a next step. Some of'em are looking to product management, and that's a great gateway into strategy. Some of them are looking at going straight into the strategist role. These are career paths and data scientists don't have career paths. They just keep putting stuff in front of your data scientist title, and eventually the data scientist leaves because there's no opportunity for growth. And if you open up roles like product management, if you open up roles like strategists, your technical strategists, your AI strategist, and data strategists, if you open those career paths to them, that's huge when it comes to retention. You don't lose your best people.

Andreas Welsch: 18:55

I think that's actually a good segue. I'll pick another question from the audience. Carly says: Hey I'm, hoping to learn more about CoEs and whether the recommendations are to centralize machine learning teams or to embed them on product development teams to be closer to stakeholders.

Vin Vashishta: 19:11

It's an arc. And this is and I think we've seen, I'm not gonna name the company, but there was a big tech company that recently completed their arc. Phase one is everything's decentralized, everything's chaos. You have platforms all over the place. You have people all over the place. You have stakeholders all over the place. There's nothing. There's nothing that's under control. There's nothing that's really supporting or uniform, and so you have to centralize. These are essentials. You have to go through the center of excellence model. You have to create a single process for data analytics, data science, and eventually research. You have to create a single set of tools so that you can. Instead of having 18 different capability sets scattered throughout the organization, really you have to build the central, the center of excellence so that your business learns how to do data science. It becomes reproducible. You have ways of getting things into production, integrating with existing product lines, developing and deploying new product lines. All of these things have to happen. And if you have eight groups that are separated, it's way less efficient. But once that happens, and this is the transition that most people aren't aware of, then you decentralize, you take all of your resources and you put them in either use internal user facing, internal user supporting, or product teams, external product, customer facing teams, and you have an innovation team. Because if you lose that, decentralization has that massive threat. When you centralize innovation, initiatives get prioritized because you know the techies control it. Hey, we're gonna do all this cool stuff, and so you end up with a lot of innovation initiatives. Create the need to have a framework for monetizing innovation, keeping innovation connected to business value. And so that's critical in the CoE so that when you distribute, now, those innovation initiatives are still included because each team and each product team knows how to monetize innovation. And so that gets continued.

Andreas Welsch: 21:30

That's an awesome answer. I think that ties it nicely together. And, also the, evolution. I've been seeing a lot more questions about that lately, too. When is a good time to evolve your CoE or to transition it into the business if, you can, to that point. Thanks for, answering.

Vin Vashishta: 21:46

I think when you've got a track record of success if it was gonna be one thing that I would say is when you have a history of success of internal initiatives that do cost savings and improved productivity, and you have a track record of success delivering incremental new features to existing product lines, and you figured out how to deliver new product. When you've got those running, the processes are documented, repeatable, then you can start saying, okay, now what can we start decentralizing?

Andreas Welsch: 22:17

Good, point around that decentralization and making sure that it also stays connected, too. Maybe to a nucleus that remains so you know what is going on in the different areas so that you can support if needed. I know early on in this episode, you mentioned there are different kinds of strategies that are influenced or that you should look at. How many dimensions of strategy are there and, how do you make your AI strategy actionable? Or how do bring all of that to together and get it to a point where the rubber meets the road?

Vin Vashishta: 22:54

I think actionable, again, it's back to, it informs decision making, and if you look at what decisions must be made across the organization, this is an extension of process mapping. and your process discovery value stream mapping, you're also mapping decision chains, and when you map decision chains, you begin to understand how your decision support, your internal platform is going to be created. When you understand what decisions are being made about data science, machine learning, just data in general across the organization, you have a better understanding of what your strategy needs to cover. What are the business needs around. What will they become in order to support those opportunities that you've decided the business should be pursuing? What will be the implications of those changes? And you can see how just massive this thing gets, how fast this gets to just mind blowing proportions. And so I think the biggest components of your business model, your operating model, and there is now this third construct, which is a technology model. Because technology is an integral part of how the business creates value and how it delivers value to customers. And so it needs to be that. Third foundational column in core strategy, and then technology can branch out to cover continuous transformation. You can cover your cloud strategy, your AI strategy, your data strategy, your analytics strategy, your 5G strategy. It can cover your IoT strategy. You can cover your quantum machine learning strategy. Whatever comes this concept of the technology model connects it. And so that's where you begin to have this umbrella for of the complexity. It is just as complicated as your operating model and just as complicated as your business model. And so it needs that new pillar.

Andreas Welsch: 24:55

Looking at these different kinds of strategies and parts of the business that are impacted who, should be involved in developing a company's AI strategy? Is it just the data team or just the AI team, or just the head of AI, head of AI CoE? Who do you work with most? Who's, involved in, developing that strategy?

Vin Vashishta: 25:14

Obviously I should own it. You should bring me in and have me take over and I'll help you. I'll take care of this. I think the best person to own the implementation is whoever's in charge. Your C level data leader, your organizational data leader, and even in a startup down to where your, one data scientist essentially is your CDO. That person, even at that seed stage owns it. They own implementation and so they are a strategic leader, but they also lead strategy. This is an important concept that. Data leaders and data organizational leaders don't, bring into the process is that you are going to own the implementation. But when it comes to planning and creating the AI strategy, that's going to happen in collaboration with everyone else because they have expert knowledge about what they need, what customers need. They are your connection to value. And so if the AI strategy doesn't have that, that tethering to value creation, if it doesn't support their decision making, you failed. If it lives in a silo and no one knows what it is, are they supposed to use it? You can almost put a KPI around this. What percentage of the business would be able to tell you what the AI strategy is? What percentage of the business can answer the question? Why do we use data? If that's a low percentage, you're in trouble. It's a bad strategy. It's not effective.

Andreas Welsch: 26:57

Can you maybe summarize the, top three takeaways for, our audience today? I see we're getting close to the end of the show but before we wrap up Sure.

Vin Vashishta: 27:06

Takeaway zero, hire me. Take away one would be that your AI strategy must be actionable. It must inform decision making throughout the firm. It must be pushed down even to the front and it must be value centric. It starts with the opportunities that the business has identified and moves forward from there. And that sort of ties into the second point, which is technology must never lead. Strategy, business needs and value must always dictate what technology should be used to create that value in the best way possible. And then really that the concept of your center of excellence put everything together. As quickly as you can. You're going to be taking resources away from other teams. You are going to be taking infrastructure and ownership of infrastructure and software away from other teams. That's hard. You have to have buy-in. That's why strategy is so critical and connecting it to core business strategy. Cause that's the only way you're gonna justify it. So that centralization, but whoever runs your co oe be ready. At some point you will be out of a job. Because it's going to decentralize. And that's the goal. That's where we're going at a high level of maturity, but you don't get there tomorrow.

Andreas Welsch: 28:25

And it's it's great to go in with eyes wide open. So great to have you call it out specifically as well. Hey it's, been a pleasure having you on. Thank you so much for joining us and for sharing your expertise. For those of you in the audience for learning with us. Thank you for your time.

Vin Vashishta: 28:40

See ya.