Alma Kondili - Digital transformation as the foundation for AI transformation

Alma, welcome to the podcast. Thank you so much for joining us today. Anything to add here?

Alma Kondili: Thank you, Tim. It's a pleasure to be part of your podcast. I've listened to some, and they are really great guests. So thank you for allowing me to to join this wonderful platform. I started my journey in a technology and then along the way I became very passionate strategy and then I moved into operation excellence.

I learned very early on the value of organizational change. And then at some point in my career, I read a book that was very influential, The Third Wave, by Steve Casey, who is the former founder of AOL. And he put it into perspective what digital transformation was going to be. And how it was going to transform the economy and our lives.

And that happened sometimes in 2016. I went back to training, which has been a path of my life in design. Thinking, and I had a front row seat to drive digital transformation as I joined DocuSign. So it's been quite a journey and I would love to share, you know, some of the things that I learned, what I am observing right now in this, I call it gold rush into the AI, and, you know, looking forward to a great conversation.

Tim Butara: Awesome. Same here. And yeah, let's get right into it. And to start off, to set the stage, I'm wondering what kind of connection there is actually between digital transformation and AI initiatives.

Alma Kondili: Yeah, that's a great question, because I think there are a lot of similarities and dependencies between the two.

For example, a company that had succeeded in digital transformation have created an end to end business architecture, have embraced platform thinking, have applied data strategy, have achieved a high level of adoption by its employees and customers, and ultimately have seen high ROI in its investment of digital transformation.

And this company, is in a much better place to first and foremost identify the AI initiatives to further, you know, pretty much extend and gain much more ROI in the process because there is this foundation in place. The opposite, you know, is when companies have failed miserably to do this in the right way, have done this, you know, what I call it in asylum mode, have picked and choose easy things to do without ever addressing an end to end transformation and without ever building platforms to create either digital products or digital services.

Even more never, you know, approached a data strategy in the right way. So they, they still, you know, lacking the foundation for, for, for driving AI. So I think this are the two extremes, anything in between, you know, probably it allows AI. But I think the ones that going to be, you know, the leaders are the ones that have done digital transformation in a really successful way.

Tim Butara: But we know that that's quite a minority, right? I mean, I mean only about like, we, we, we've all heard probably, probably most of the people listening to this right now will have heard the figure of 70 percent of DT initiatives. It's actually not succeeding. So why is that relevant for our discussion?

Why do you think that this failure rate is so high and what does this mean for AI initiatives apart from what we've already discussed?

Alma Kondili: Yeah, no, that's a great question. Personally, what I have. observed, and I think the, the, the rate is high, especially among large corporations and enterprises. And a classic example is General Electric.

Its failure of digital transformation literally brought down the whole company and now it's been chopped to pieces and sold. So some things we learned in this particular case is that, and, and that applies to all other companies is first and foremost, Start small, instead of trying to do the whole thing.

Focus on what problems you're trying to solve. Partner with the right technologies and either do a pilot or proof of concept, succeed or fail fast, that needs to happen, you know, regardless, scale, learn from that failure, you know, or as you succeed, you know, scale as fast as you can, and then engage both employees throughout, you know, change management strategies, but also the clients and also share a percentage of the profits with clients to gain the trust and adoption on digital products and services so And then continue this cycle and break the silos and and build platforms and and leverage And and define a cloud strategy.

But what we saw, you know, is that the opposite of all this happened, you know, you know, GE tried to do everything, tried to become and build everything in house and failed miserably. So. I think, why not learn from the failures and try not to imitate them? So with AI, it seems to be a lot of activities all over the place without much orchestration, without much governance.

There is no, what I consider a center of excellence built in some large companies. To share best practices or learn an average solutions across. For example, if you, if you have an LLM and you are successful on applying that LLM, why not leverage it across, you know, other, other businesses? So the role of the COE, for example, in this case, will help to identify who owns the AI.

I mean, I heard different names, but you know, I, I've seen more and more, you know, companies having achieved. AI officer. And that's a great starting point because without proper leadership and ownership, it's very difficult to drive something that important for a company. Some, some are calling it an AI star, regardless of the name, you know, it needs to be defined who owns AI.

And then that person needs to bring together both Business and IT into the conversation and leverage past automation, address the data, bring risk and compliance very early on engaged on those conversations as well. So again, there is a road map how to do it right, but also how to avoid it. You know, all the things that we learned from digital transformation.

So it's kind of, you know, you need almost, you know, a centralized way to at least start and, and have a centralized way to understand what's happening and where, where the investments are going and what, what, what are the outcomes as well.

Tim Butara: Yeah, that makes a lot of sense. So basically experience with digital transformation will basically translate into experience into more experience in a similar way vein with AI and will help more responsible and more successful implementations of these new technologies.

Alma Kondili: That is a great. Yeah, that is a great summary. Absolutely. I agree more. Yeah.

Tim Butara: So we, we talked a little bit about the reasons for the massive failure rate of DT initiatives. Would you say that, that one of these reasons is also a misunderstanding or not understanding the full scope of digital transformation?

Alma Kondili: Yeah, I think I would start with the strategy. And this is probably where a lot of companies, you know, have really kind of failed since from the beginning. First and foremost, again, who, who, who needs to articulate that strategy? And probably I will get that there a little bit more of. Of, you know, what, what are some of the key components of, of the digital transformation leaders?

But as we started with the strategy, you know, what we need to define what is the North star? Why is this important to the company? And this is all part of, of defining that scope. What are we trying to address? What, what, what we're trying to fix. You know, if, if, if this is like a burning platform, for example, how are we going to transform the business?

And what is the risk if we don't do anything? You know, it's, so it's, it's, so all of this needs to be really validated and kind of articulated and what kind of disruptions are happening, happening in our sectors. I mean, we really need to, to put this into the context because no, not one company X along is part of, you know, it's, it's, it's, it's X into a sector let's say financial services, for example, or retail or, you know, you name it, I mean, pharma, you are in that sector, there are disruptions happening there, for example, so you have to be constantly seeing what others are doing and and how are they being transformed and and also look internally and and see can you become a disruptor and how can you become a disruptor leveraging, you know, either digital transformation or enabling and if you have done that successfully enabling now the AI so all of this needs to be in the context of business and business outcomes. Unless that is the North Star, you know, shooting in the dark is, is, is going to fail miserably.

And you're going to see a lot of investment being, being wasted in that process as well. My view of digital transformation is almost like a symphony. There is a lot of orchestration that needs to happen, but you really need to have a great conductor and that conductor really need to know, you know, what the symphony is from the beginning to end.

And there is a lot of. Agility. And there is a lot of things that, you know, it will be discovered and probably in the process of doing this right, more disruptions or more, you know, great initiatives will come across, but we have to start with, with something that is meaningful to our business.

Tim Butara: So who should be the conductors in order for, for maximum possible success?

Alma Kondili: Yeah, that's a great question. So I had the pleasure, you know, to be involved with Isaac Sacolick. He's the author of Digital Trailblazer and he has an amazing follow up. He does every Friday, you know stand up. I'm lucky to be invited in that stand up, but collectively there was probably 10 of us that were invited by him to provide, you know, a variety of, of skillset that, that we feel as, as digital trailblazers are, are important.

And we came up, you know, with an extensive 50, but I will focus on five that are really, you know, core competencies, I believe. It starts with leadership, and this is, you know, it should pretty much answer the question of leadership style, qualities, or practices. Personal competencies. I mean, you need to be competent to do this.

So identifying what should be the personality skills, the knowledge, the behaviors that are common on, of digital trail blazers, the people skills. I mean, at the end of the day, again, if you're going to be a leader, you know, you need to have the people to follow you, but also you have to engage to have a certain style to engage.

So how a digital trailblazer approaching those practices when collaborating with teams, partnering with other leaders, or supporting individuals is super important. It's always the part of being digital fluent, so to speak, recognizing the types of skill digital trailblazers rely on to translate opportunities into solutions and deliver business outcomes.

And then from a transformation, you know, It's specifying the types of practices a digital trailblazer will likely apply to challenge the status quo, grow supporters, handle detractors, and lead change management practices.

Tim Butara: And we know that one, one very important part in both the digital transformation and AI initiatives is data, right?

So can we talk a little bit more about this role of data and what the key considerations and challenges with data are that are relevant here the most?

Alma Kondili: Yeah, I think and I tried to touch a little bit prior to one of the core dependencies on a successful digital transformation is the designing of a data strategy and the implementation of a data strategy.

What does that mean? Because it sounds like a great thing to have. What that means is that. Pretty much there is a data in strategy in place that all the systems slash platforms somewhere somehow are interconnected. And there is a data flow, but also there is, there is a consistent way of, you know, capturing and controlling that data across the platforms and those controls and, you know, based on risk and compliance are in place.

So, and that also also enforces data quality, but also there is there is this interconnectivity of data in place. That enables, you know, to have real time reporting and look at trends and make decisions based on the data. But unless we design the entry point of the data and how the data flows and, you know, all of that, it's very difficult to end up with great, you know, reporting because, you know, there is this classic thing, garbage in, garbage out.

Unless, again, unless we have this really rigorous controls in place and, and the best way to, to, to design that is, is by designing this end to end business architecture, so to speak. Then it becomes so much easier, you know, to have this data. So now what we're seeing For example, I'll focus in financial services because that's where I'm a little bit more comfortable and I have a little bit more visibility, but in financial services, for example, the PII, which is personal data, it's, there isn't, you know, it needs to be completely scrapped out and that leads you with synthetic data and that is.

Again, you have to have. all the data, you know, captured in such a way. And in order to have a way extract and kind of, or mask out all the PII data to become a synthetic data. That's what you need to have in place in order to even start thinking about AI and running LLMs against the data. Because the moment you touch PII.

Your firm is done, your brand is done, you know, you can't ever, you know, cross that line. And that's, and when we talk about the guardrails, those are some of the guardrails that need to be in place when it comes to data privacy. And now, for example, there is, you know, in Europe, there is a great deal of work that's been done to come up with legislation against, you know, to, to, to Pretty much govern AI.

The same has not happened yet in the U. S., so in, in the space of AI, we kind of, you know, almost acting based on everything that we know, at least, you know, the fundamentals that at least we know we never should touch. Probably even more guardrails or even more compliance will, will come and, and, and will need to be applied.

But at least for now, we just have to, to go with, with the things that, that we know. And that's why, again, the data is what I call the golden source, unless the data is clean and, and, you know, and, and there is, The right data in place and it's captured in a way that you can extract it. It's, it's the only way that's going to make successful AI.

So that's, that's a core dependency between a digital transformation downright and the AI implementation right there.

Tim Butara: Yeah, I think that that's a, that's a great example of the interconnectedness between the two, because even before AI, there was a lot of talk about data, the importance of doing data, right?

A lot of talk about, you know, the, this, this discrepancy between personalization and privacy on, on the kind of different ends of the spectrum. And it's even more pronounced now with AI, right? As, as, as you pointed out. So definitely important point of discussion here. But now I want to focus on, on something that's basically on the other end of the spectrum, but I think that it's also very important and that's company culture.

How important would you say that that's during any kind of transformation? So either digital or an AI transformation?

Alma Kondili: Yeah, it's one of the things that I've become more and more passionate because I have seen what, what the culture does. And Draper, you know, years ago said that culture, it's strategy for breakfast.

And it, it's surprising a very strong statement, but also very true rings true at any given time. What we know is that the only constant in the business right now is change. And what we have experienced in the last 20 something years is that that speed of change has accelerated. And I have observed two types of companies, the ones that are rigid, structured, layered with management and by design, they are stagnant.

They are encouraging siloed thinking. And then you had a flat organization, which is so much more agile. You know, it's, it's built on cross functional and. You know, very collaborative, but it's by design. So this two structures are, are, are, you know, these are, these are the models that we have right now. The first is resistant to change and is designed to resist any change to preserve, you know, the legacy.

And the second, it embraces change. It's fluid, you know, it's kind of. It's, it's built to experiment. It's built to experiment and fail and fail fast or succeed and succeed fast. So the companies that will survive in the future. will need by design to become flat. So they are by design resilient to change.

But what is even more important is that we need to start building this continuous learning culture because what we've seen, for example, due to during the digital transformation, there was a lot of upscaling that needed to happen. So for people to be able to run a digital transformation successfully or adopt successfully.

So the same goes for AI. So when people say, oh, AI is taking away jobs. And there is, there is some truth to that. AI is taking away jobs of the people that are not even learning AI. So it's, and the ones that are learning AI probably will prosper in the next, you know, years to come. So it's all about approaching this right and, and designing, you know, the structures companies that are almost like living organisms.

To, to become again, resilient to change, but also embracing continuous learning culture. So how do we get there? You know, that's, that's the other. So, you know, first and foremost, you need the sponsorship and the leadership, but there is, you know, a proven, you know, methods and, and change management seems to be one of them.

So I can focus a little bit more, some of the things that I've seen to work really well, you know, in, in the AI and, and in the past years. So identifying, for example, role models, designating, as I said before, someone that owns AI or is the, the champion of AI in the company, ensure that the people are, that are involved can dedicate.

You know, a portion of their time to the AI initiatives and what happens sometimes that I've observed is the people do their daily job in addition, you know, to be given initiatives and it's impossible to do, you know, very well and successfully what what you were hired to do and then engage fully to run another initiative.

So you either, you know, kind of be, reduce their responsibilities in order to focus and, and focus successfully on this initiatives or, or completely move into this initiatives. I mean, that conversation, again, is thinking about the resources in a very, you know, agile way, but also making that investment.

Map relevant resources, you know, identify those resources, current tools, align incentives. You know, sometimes you're going to change the incentive. You know, if people are going to take more or they're going to completely switch and, and, and do something else that needs to be recognized what we fail to do.

And, and I've seen this in, in, in digital transformation and it's again, in AI, we need to set measures of success. What does it mean to have a successful AI initiatives? You know, how are we going to measure? On a on a weekly basis, for example, because things move fast that we either succeeding or we're failing.

So we really need to have a clear definition there set some KPIs like what the cost, the speed, the resources, the work efficiency, how many people, you know, are using it, for example, and consider the deployment strategy. You know what, okay. How many users in each stage who will benefit the most? What are the likely obstacles?

Identify skills and knowledge gaps in the organization. We need to have, you know, and it's always, it's part of a well run change management. What is the readiness of the company? And we did the same. For example, when, when a company started digital transformation, a digital transformation readiness was, was run to measure where the company stood, what the culture, you know, what was the existing, you know, a sentiment and behaviors, the same need to happen for AI, unless we know where we stand, you know, you can't start to move or coach or communicate and try to change behaviors because you don't have a baseline.

Literally, I think these are some of the things that I have observed and they are pretty much similar to, to, to the digital transformation and to the AI. But I think, and more and more I'm, I'm, I'm, I'm hearing change management becoming a central focus point because we're finally realizing that in order to be successful and.

In order to implement correctly, but also adopt correctly the AI initiatives, we're going to need change management applied across the company.

Tim Butara: That's a great point right there at the end. And, and so Alma taking everything into account based on everything that we discussed today and to kind of summarize our discussion what would be your, your top tips or your top words of advice to business leaders that are maybe struggling with their digital transformation and their AI programs?

Alma Kondili: Yeah, that's that's a really great question and I'm happy and I hope this is the first and foremost people find some value in what I shared, but I think I would focus in some key areas. Enable platform thinking because that is key to productivity at scale to the collaboration and deployment. Think end to end.

Because that brings balance alignment, incorporate design thinking, you know, into, into that end to end again, try to create disruptive innovation as you get engaged communication or as part of, of the organizational change, engagement, training, adoption, built and sustained momentum, consistent, quantitative and qualitative, you know, measurements.

And a talent strategy, I think it should be central, and that applies both to internal and external, meaning internally, you know, build and retain top teams, have the incentive alignment, upskill across, you know, different I think that's important. everyone. Lines of business and capture what is considered to be the tacit knowledge.

There are people that been probably working on a company for years and years. Sit with them and get that information, you know, recorded through video, you know, leverage AI to capture. But we need that, you know, that, that, that knowledge in order to transform our business. But also from an external perspective, develop a talent pipeline.

I mean, the war on talent, it's only going to get pretty much worse. So partner with universities, data hackathons, AI hackathons. Try to be plugged in and think on the long term, not just immediately, you know. And so that's why I call this a talent pipeline. But there is also a governance, think about the ethical and the safe AI, the data quality, the privacy, the access, the model, explainability, interpretability, the monitoring, integration to technology, software, you know, and I'm pretty much Those are my key components.

Tim Butara: Those are definitely some great tips and tricks to finish the episode on Alma. Thank you so much for a great discussion. Just before we wrap up the call, if anybody listening right now would like to connect with you or learn more, more insights from you, where would you point them to?

Alma Kondili: I am in LinkedIn, so reach out and I am very happy to connect and jump on a call and you know, always open to new connectivity, so to speak.

Thank you so much, Tim. This has been a great conversation and look forward to, to hear from you all.

Tim Butara: Likewise, Alma. I really enjoyed speaking with you today. Thanks for joining us again. Thank you. And to our listeners, that's all for this episode. Have a great day, everyone. And stay safe.

Outro:
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