Jeff Standridge:
Hey guys, good to have you back on another episode of the Innovation Junkies Podcast. My name is Jeff Standridge.
Jeff Amerine:
Hey, and this is Jeff Amerine. Glad to be back. Looking forward to today’s episode.
Jeff Standridge:
Should be should be really good. We’ve got a great guest with us today. Jay Goldman. Jay is a New York Times bestselling author of The Decoded Company. We’re going to learn a little more about that. He’s also a member of the Forbes Technology and Harvard Business Review Advisory Councils and co-founder and CEO of Sensei Labs. Jay, welcome to the Innovation Junkies podcast.
Jay Goldman:
Pleasure to be here. Thanks for having me.
Jeff Standridge:
So let’s start with you maybe filling in some of the gaps and telling a little more about Sensei Labs and your role there and where you guys spend most of your efforts.
Jay Goldman:
We make a transformation orchestration platform. We work with large enterprises all over the world to help them orchestrate their most critical initiatives with significant EBITDA impact. And so that might include transformation programs with a transformation management office and a particular transformation mandate. It might include mergers and acquisitions on the acquisition or divestiture side of things. Lots of post-merger integrations might include procurement and cost reduction programs for ESG initiatives, anything that’s a large complex program, typically tens or hundreds of people working on it for four or five plus years, and typically tracking 100 million or more in impact net benefit to the business.
Jeff Standridge:
So predominantly large enterprise organizations is who you’re working with. Gotcha, gotcha.
Jay Goldman:
Exactly.
Jeff Amerine:
What are some of the major challenges that you typically see in, in helping organizations get through these kind of epic transformations or changes? We all know change is difficult. What are the typical challenges you guys help them overcome?
Jay Goldman:
We sometimes come back to an Albert Einstein quote that he’s purported to have said, and I’m gonna probably misquote him here, but the essence of it is that we can’t solve today’s problems with the same thinking that created them. And so when we apply that in a transformation space, essentially you can’t solve today’s transformation needs with the same people, processes, and platforms that created the need for transformation in the first place. And that might be a distinction that you’ll see in a large enterprise having a PMO who are traditionally responsible for the execution of day-to-day business as usual kind of projects versus the creation of a TMO or a transformation management office who we think of as being responsible for business as unusual projects. So where are we disrupting that status quo and intentionally doing things differently? And for us as a software provider and a technology partner that will sometimes look like, in fact, most frequently looks like, the status quo today is a lot of Excel and PowerPoint, and we are trying to manage these very complex transformations that are in a rapid moving state with all kinds of critical success factors and KPIs using the same tools that we’ve traditionally used to manage our normal business as usual sorts of programs. And they work maybe adequately for business as usual, they don’t work at all for business as unusual.
Jeff Standridge:
So tell me about your platforms at Sensei Labs and what kind of functionality and capability that you bring, you know, other than the business as usual capabilities that you’ve already mentioned.
Jay Goldman:
Right. Yeah. So we have a platform called Conductor. Conductor is a transformation orchestration platform. It brings together the strategic portfolio and project portfolio management components that you would expect. So all the things like timelines and Gantt charts and risk and issue tracking, along with real collaboration right at the level of tasks. So you can get down to Kanban boards. You can collaborate on those tasks, both in Conductor or in a Microsoft Teams environment. And KPI and benefits tracking, so defining a data model of KPIs. They are usually financial, but often also non-financial KPIs. So we might be tracking the cost of our program in OPEX and CAPEX. We might be tracking direct and indirect savings. But we might also be tracking our ESG impact from an environmental perspective as we look towards decarbonization. We’re working on a very large program on a 10-year time horizon with one of our very large industrial agriculture customers. And for them, it’s a cost capital and carbon transformation. And so we can start to see more and more of the impact of those environmental changes across our customer base and the need to factor that into the success factors of any large pro.
Jeff Standridge:
Do you find yourself involved in a lot of digital transformation engagements now just given where we are kind of in the world?
Jay Goldman:
Every transformation we’re involved in has a digital component. So we’re finding the term digital transformation is less relevant than it was. I think it also got a little bit sidetracked because IT departments that had been trying to get funding for a long time for kind of business as usual, things like we need to upgrade our version of SAP or we need to migrate our SAP instances or we need to maybe move towards a new version of some other piece of software and they’ve never been able to get funding for it. And then they called it a digital transformation and they got all the funding. And so the term became less relevant for the transformation piece and more we need to do things that are sort of digital, but also we’re finding more and more, and maybe COVID was, and I wouldn’t even say maybe COVID was a significant accelerant here. Every program that we work on with our customers has a very significant digital component to it. So they’re really all digital transformations at this point.
Jeff Standridge:
One curiosity that I have, Jeff and I are involved in a number of smaller-scale transformation initiatives with more mid-market types of firms. And one of the things I’ll ask you is how often do you go in where the principles of whatever the organization is that your client think that the transformation is one thing, but you get in there and the transformation is way messier and something more far-reaching in the organization.
Jay Goldman:
Absolutely. And that’s almost every transformation. And I think it starts because you’re trying to solve a problem that is itself somewhat ill-defined. So we think about, and maybe a sort of useful analogy between leading and lagging indicators when we’re talking about the scale and it’s lagging because it comes after the effort for me to do the thing that I’m going to do. But also I can’t directly influence that number. I’m sure we all wish that we could just will the number down on the scale, but it’s not the way that it works. And so when I stand on the scale, I’m seeing the end result of the work and I can’t directly change it. What I can change are the leading indicators that lead me there. So in the weight loss example, that might be, I can consume fewer calories and I can work out and burn more calories. Those are leading indicators that I can pay attention to, and they’re gonna influence my lagging indicator. That’s relevant in the transformation space because lots of transformation programs originate from a lagging metric. So the organization will say, we need to achieve $500 million in net benefit. And that’s a large number for large enterprise. Obviously, if you’re a smaller organization, you might say we need to achieve $50 million or $5 million in net benefit. But that’s the lagging indicator. How we’re gonna get to that is going to be a combination of we need to increase our revenue run rates. So we’re going to find programs to expand our ability to earn money. And we’re going to look for ways to reduce our costs so that we’re going to save money. And between those two, we’re going to get to that target that we’ve set in place. And that’s the, the leading indicators that lead us to that lagging indicator. When we get involved in a lot of transformations at the beginning, they’ve been handed a mandate that looks a lot like a lagging indicator, and they have to figure out how they’re going to create leading indicators and then plans that support them that are going to get to that lagging indicator in the end. And that’s the messy part where it will often change in terms of what we’re going to do and how we’re going to get there. And then even as you’re executing that program, it’s going to change. So if you were going along and you were maybe considering how we’re going to get there through organic growth, and then you discover an acquisition target that fits right into your plans and has an inorganic growth component, which is significant compared to what your plan was, your whole plan has now changed and you need to be able to adapt and pivot to that, but you also need systems and tools that support your ability to adapt and pivot to that.
Jeff Standridge:
Mm-hmm. Gotcha.
Jeff Amerine:
As you’re thinking about the roadmap for your own platform, how has that been influenced by the rise in the democratization of AI and ML and all that’s going on within what we used to call in the dark ages, expert systems. But how are you thinking about that these days?
Jay Goldman:
AI is a fascinating space and I think it’s become the new version of saying this is a digital transformation to get some funding. So it’s not clearly defined when we say AI what we’re specifically talking about and the range of kind of I’m playing with ChatGPT all the way to I’m building my own LLM, or I’m training an extra set of data, or I’m building a layer that sits on top of this. All of those are sort of broadly speaking AI projects, and we are certainly seeing in our customers that the innovation fund, which might have previously gone to something like, I’m going to do a digital transformation is now going to someone who’s saying, I’m doing an AI project. And that’s kind of the magic keyword that unlocks that funding. And we’re seeing quite a lot of data that’s supporting that. Um, even from a 16 Z and large investors like that, who are putting out quite a lot of data at this point saying enterprises are willing to experiment with quite significant budgets if you mentioned the magic keyword AI. Good thing for vendors who are listening to remember when they’re trying to sell their solutions in. I do think there’s a lot of experimentation going on though. And it’s a more rapid adoption rate than I’ve seen in my career in technology, pretty much any other piece of technology that we’ve come across. So this has gone from a year ago when we would say we have some AI components in our platform, we would get sort of passing interest in them to now pretty much every sales conversation our team’s involved in. There’s a question about AI capabilities in Conductor and what that looks like. We recently announced Harmony, which is our transformation specific AI model in Conductor. Harmony is the world’s first transformation AI. It understands through the work that we’ve done in training it, how transformation programs work, how to analyze them, and how to make recommendations about their success. And it uses all the data that exists in Conductor on the execution side on the resource management side, on the KPI and benefits tracking side, to make recommendations that drive real increases in value in transformation programs. And that’s now available in beta to our customers.
Jeff Standridge:
So I want to go back to the messiness issue. You mentioned the word messy. I think I mentioned the word messiness of some transformation initiatives occasionally. Tell us about, you’re involved in a transformation initiative and you run into real cultural roadblocks along the way. Thinking about the quote, culture eats strategy for breakfast, right? So, any thoughts particularly on the role of culture in transformation, both good and bad?
Jay Goldman:
Many, many thoughts on the role of culture in transformation, both good and bad. It is the component that most of the organizations we speak to undervalue the impact it will have on the success of their transformation. And in this case, I would use culture maybe interchangeably with change management, which is a term that comes up a lot there. Change management’s a different term, they don’t have a perfect overlap. If it was a Venn diagram of culture and change management, there, the circles overlap a lot, but they don’t overlap 100%. There are components in the change management piece, which are just things like we need to train people on how to use new systems or we need to teach them about the advantages of new doing something new versus something old. But there’s a significant change component in there as well. And that change component can come down a lot to the culture of an organization. If you’re a fast-moving organization that exists and maybe thrives in a high degree of volatility, uncertainty, change in sort of environment, then you’re more likely to adopt new technologies and new ways of working. And I don’t necessarily mean software platforms here. It could just be new ways of working as well. And when we saw something like COVID, which is an interesting sociological impact looking across large enterprises at the ones who are very able to adapt to everybody is suddenly working from home and we are relying on digital technologies in a much bigger way than we ever have before. How do our ways of working change? How do we figure out how to continue to operate as an organization? How do we thrive in that environment as much as we can? And then how do we continue the things that are working really well in a post pandemic world when we no longer have those same pressures on us?
And it was, I think, interesting because we all experienced the same pressures at the same time. And that’s very rare. Usually when you see those kinds of pressures, they are either specific to one company or maybe to one industry, not across the board to every industry and every company size at the same time. But that is really how culture affects a business. Can it pivot? Can it change on short notice? Can it adopt new platforms and new ways of working? And those are big predictive factors in how successful you’ll be at transformation. If everyone in the organization exists in a state of fear because they are worried about their jobs and that exists when you have scarcity and people are defending their territory, then you will have a much harder time affecting change and therefore getting transformation complete. If you exist in more of a world of abundance as opposed to scarcity where people aren’t focused on protecting their jobs, but are on more of a rising tide floats all boats sort of mentality, then you will have much more of a willingness to adopt change and to proceed with transformation.
Jeff Standridge:
Do you do any readiness to change assessments as part of your transformations? And if so, maybe tell us a little bit about that.
Jay Goldman:
Yeah, we’ve developed a model that we think of as an enterprise orchestration maturity model. And it looks at those sort of three dimensions that I mentioned earlier, people, platforms and processes. And it looks across a set of 10 dimensions that are in those three to assess kind of the readiness of an organization’s ability to enter a constant state of change. And that’s the highest level of maturity is recognition that the rate of change is only going to accelerate and that the companies that succeed the most will be the ones who can exist in a constant state of change. We think less about transformation as a defined project. We’re going to undertake a five-year transformation with a hundred million dollar target, and at the end of it will be transformed. And we think more about the interesting evolution we’re seeing in the most successful organizations we work with where that transformation management office is evolving into a strategic management office. And it’s becoming an ongoing center of excellence for how the organization continues to adapt to a constantly evolving environment and continues to be excellent at bringing that change in and then reacting to it. And so the enterprise orchestration maturity model has an assessment that goes along with it. We spend about half an hour with a team or with an individual, we take them through that and they rate each of those 10 dimensions on a scale of one to four. And then at the end, we can give them a score. We can show them how they compare to some other organizations and we can show them how you can put in place a specific plan to address any of the 10 dimensions on how you want to increase your maturity and readiness around that particular dimension.
Jeff Standridge:
Gotcha. Very good. Very good. Well, let’s shift gears a little bit. Jeff Amerine and I are both in the VC space and a little bit in the PE space as well. This concept of portfolio orchestration. Talk to us a little bit about that and maybe give us some examples of what you’re talking about there.
Jay Goldman:
The last year, year and a half has been a really interesting time to be involved in the private equity space. We work with large-cap and mid-cap funds today on their value creation plans that they’re executing across their hold periods. And so whether that’s the very large caps, KKR, Sinvin, BainCap, and Advent are all running value creation plans on Conductor today, orchestrating the realization of that value, or whether we’re talking more mid-cap funds who might be looking across a larger portfolio of smaller assets cases in both of those cases, Conductor is a really great solution to what they’re looking to achieve. And I say it’s an interesting time because the shift in interest rates has translated in the private equity space to lots of things. It’s harder to close investment rounds with LPs. It’s harder to do a bunch of things more sort of upstream. But in the value creation space, the pressure that it’s created is a need to move from value creation almost more as a checkbox that you needed to be able to say that you had in place. But really most of the return that you could deliver to your LPs was essentially arbitrage of interest rates. The interest rate was so low that they couldn’t do better by leaving their money in various other forms of investments. And so by putting their money with you, if you could achieve a one or 2% lift, you were ahead of the interest rate that they were going to achieve elsewhere. The interest rates going up has meant that they have lots of other choices that can return a pretty decent return on that same money. And so now you actually have to do value creation in order to be able to demonstrate that you have a durable advantage to other places that money could sit. And LPs are getting much more demanding around your value creation capabilities before they’ll place an investment into a fund. And so that’s resulting in funds building, I don’t want to say for the first time ever, but maybe for the first time in a long time, real value creation and operations teams who have real expertise and who can provide real value to the portfolio companies in helping them achieve their value creation targets across a whole period. So when we talked to funds two years ago, they might have had a value creation partner or an ops partner, and that person would join some of those deal conversations, but was really there more than anything else because the other funds also had a person. And so we needed to be able to say, no, we do value creation as well. And now what we’re seeing is that they’ve hired real value creation teams that are building out a team who are capable of being able to do that.
Jay Goldman:
As that evolves as a capability in those funds, where the real advantage comes is the best practices and the knowledge that they can bring into a PortCo to help them achieve value more quickly and more predictably. So if you build a model on a five-year hold period, which would be fairly standard in the industry, but your holds are actually six or seven years, then you’re gonna tank your IRR on all of those investments because you have all kinds of opportunity costs on where that money could have been elsewhere and all your models are actually broken if that’s the case. If you can take that five-year model and actually deliver the value in four years or three years, then you’re significantly ahead of the game. And if you can do it more predictably, so even if you stay at a five-year hold, but we are able to more reliably build value in our portfolio companies so that at exit, we’re recognizing more return on our investment, then that’s a significant advantage for that fund. So Conductor comes in really in two ways. One is that we have a smart templating system called Fast Starts. Fast Starts can be at any level of execution in Conductor. So it could be an entire value creation plan that starts from a Fast Start, but it could also be specific tactics. If a company is gonna pursue maybe a bolt-on acquisition strategy, and they’re gonna go and acquire other assets, each one of those is gonna turn into a post-merger integration. And so you might deploy your PEs post-merger integration fast start. And that’s giving that portfolio company who maybe doesn’t have a lot of experience with how to successfully complete a post-merger integration, a really big start or a fast start to how they are going to successfully integrate those companies. And that’s particularly important if you are pursuing a strategy of acquiring a platform and then adding on to that platform after that platform initial investment may not have a corp dev team, may not have a lot of experience in how to do post-merger integration successfully. So now you can bring a platform into play and you can deploy with them a fast start template around how to effectively do that. We partner with a lot of large professional service partners. Our oldest partnership was with Carney or AT Carney as they used to be called. Carney uses Conductor as their orchestration platform on a global basis. And with them, we’ve built a number of fast starts around their solution delivery sets, M&A being one of them. And they find that maybe about 80%, even in some cases, maybe 90% of the configuration for a given client is already done in the fast start. And the remaining piece is that kind of 20% of there are specifics about this particular client in this particular industry that need to be specifically configured for them, but it’s a big running start to be able to get them there. So that’s sort of the first way that Conductor can help. The second is that portfolio orchestration view where today you might have deployed a portfolio monitoring solution, I-level, Cobalt, something like that. And you’re using that to be able to get to consolidated financials. And it can be very effective for that, especially because it gets you to a standardized reporting format and then gives you some dashboards and that kind of thing. But that is really only the view on the financial side and lots of the mid-cap funds and even smaller cap funds that we talked to haven’t deployed those solutions. So right now they’re getting very mixed reporting from their portfolio companies might be a bunch of emails that are coming in, a bunch of different Excel formats that are coming in, and they’re trying to make sense across that so they can do their own reporting. Conductor lets you standardize that so that you’re getting a single view and a dashboard across your portfolio. You can break that dashboard down into different views. Maybe you want to look at it at a fund level if you have multiple funds, or you want to be able to slice and dice on geographies or industries or even exit strategies. So all of that data gets pulled into Conductor and tagged in an appropriate way where you can slice and dice it into the different dashboards that you need. You can give access internally for members of the funds own teams. You could even give access to investors if you want to open up dashboards for them and conductors, no code workflow capabilities can make it really easy to manage internal workflows in a fund, even at the level of something like due diligence. So if you want to build out DD checklists that you’re going to follow, you want all the files to remain in a consistent place as you go through the DD process on an internal side. You may be accessing the data room of a company, but you want to pull that data down and store it in a consistent way internally. All of that can be done in Conductor as well.
Jeff Standridge:
Got it. Got it.
Jeff Amerine:
Are there thoughts about extending the platform to also include some intelligent deal sourcing front end kind of vetting, scouting type of functions as well?
Jay Goldman:
Not to date, certainly an area of potential expansion for us. We’re very regimented about sticking to the things that we know we are exceptionally good at and where we can make the biggest impact for our customers. There are quite a lot of deal sourcing and intelligence tools out there. Conductor is in the CRM. So we are very careful at sort of drawing a line to say, if you’re in a CRM like Salesforce, or if you’re in a more sort of specific CRM, like DealCloud, you should continue to use those tools. They’re very good at what they do, but there’s a point in that process where you may want to hand that off into Conductor and we can do that through integrations or we can just do it through a manual process. But that’s really what we excel at. Doesn’t rule out down the road that we might choose to expand into those other areas, but we’re very regimented about sticking to what we are very good at.
Jeff Amerine:
Makes sense.
Jeff Standridge:
Jay, you’re also the author of New York Times bestselling book called The Decoded Company. Tell us just a little bit about that book.
Jay Goldman:
It’s actually the 10th anniversary of Decoded, which is kind of remarkable. It came out in February of 2014. So we’ve just passed the 10th anniversary. Um, decoded was written because at the time we were part of the leadership of an organization called Click Health, which, uh, Sensei Labs is actually a spin-out from Click Health. So we took an internal platform from Click and created Conductor out of that. Uh, but Click has been an incredible rocket ship growth story. It grows 30 to 40% year over year and has done that since it was founded in 1997.
Today, the business is well over 1500 people globally, offices in 10 different cities, hundreds of millions a year in revenue. Click is a professional service provider in the life science space, particularly pharma and biotech as a commercialization partner and more, but primarily a commercialization partner and marketing partner for life science companies. So we would get asked a lot about how we were able to sustain a growth rate like that. 30 to 40% growth isn’t that difficult when you’re a very small startup and the numbers are small, but laws and percentages mean that gets harder and harder to do as the business gets bigger. And so we would get asked a lot about how we were able to do that. And also how we were able to consistently attract such a high level of talent to come and work in the organization. And so myself, along with two of Clicks co-founders, Leram Siegel and Aaron Goldstein, decided we would write a book about it. We brought in a friend of ours, Rahaf Harfoush. Rahaf at the time was working with the World Economic Forum on their pioneer program. And so she took a leave of absence to come and work with us on Decoded.
And we told the story of bringing together data and technology to build a highly talent-centric organization. And we got very, very lucky with our timing. We happened to write a book about talent centricity and about culture and about big data at the time where all of those were becoming very hot topics. The intersection of them ended up propelling the book to become a New York Times bestseller, which was fantastic. It gave us a chance to travel around the world and speak about the book in all kinds of very interesting places.
We got to speak at Harvard, and we spoke at NASA’s headquarters, Google’s offices around the world. We gave a TEDx talk and very consistently wherever we went, there would be a lineup of people when we came off stage who wanted to know where they could build the software platform that we talked about called Genome at Click. And so we realized quite quickly, there’s a really unmet market need here for us to turn this into a platform. And so we signed up our first customer and that was the birth of Conductor. And so that was 2015 when we signed up that first customer. It led to the creation of kind of a division within Click that ultimately became known as Sensei Labs. And then we ended up deciding that we had proven enough of that model and had enough success with our customers to pursue outside investment. We raised our Series A in December, 2020, spun the business out in January, 21 and have been on our path as a separate business ever since.
Jeff Standridge:
Very good, very good. Jake, tell our listeners where they can find you and best connect with you.
Jay Goldman:
I am at Jay Goldman, J A Y G O L D M A N on pretty much every social platform. LinkedIn’s probably the easiest place to find me, but you can also find me on Twitter and other places like that. Sensei Labs is also on most of those same places and sensei labs.com S E N S E I L A B S.com if you’d like to find us there and learn more about what we do.
Jeff Standridge:
Very good. Well, Jay, we appreciate you for spending the time with us and our listeners today.
Jay Goldman:
My pleasure.
Jeff Amerine:
Thanks for coming on. It was great.
Jeff Standridge:
Yeah, very good. This has been another episode of the Innovation Junkies Podcast. Thank you for joining.