Jul 3, 2019 | 3 min read

Conversations with Jay Allardyce

Podcast #65: Powering the Next Generation of IoT with Uptake

Jay Allardyce is the Chief Product Officer at Uptake. Our conversation touched on his extensive background in technology and analytics from his time at HP, Vertica and GE prior to joining Uptake. We discuss the evolution of the market around analytic software, some of the key experiences and lessons from working with different types of customers and business problems, and the unique value-add that AI provides when embedded in business process. We discuss the landscape of IoT platforms and the critical elements for success both in terms of the platform as well as in organizations adopting advanced connected solutions, and dive into some of the aspects of the energy industry that make it a unique, and exciting sector for innovation. Lastly, he shares his vision for AI-powered transformation and the unique opportunities across different industries today.   


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Good day, and welcome everyone to another episode of Momenta Podcasts, this is Ed Maguire, Insights Partner at Momenta. Today our guest is Jay Allardyce who is the Chief Product Officer, and Head of Customer Success at Uptake. Jay and I first met last year at IoT Solutions World where he was part of a discussion that we had around energy, and digital transformation, and I thought he was one of the most articulate, thoughtful, insightful folks I’d met in a really-really long time.

Jay, we’re thrilled to have you join us again for a discussion, but first time on the podcast.

Ed, it’s great, and thank you very much for the kind words. It was great meeting up at the IoT World event, and glad to reconnect here.

Let’s start first with some context. I would love to get a sense of your background, could you share some of your personal history, and what are some of the key experiences that have brought you to where you are today in your current role.

I’m a Silicon Valley native, born and raised in and around the Vale. A lot of travel though the last 20-plus years in tech. I grew up not too far from Hewlett-Packard, and it was really a lot of the influence through family members and neighbors and the like, that it all aspired over the years to go work at Hewlett-Packard, to HP Labs and the like. I had that influence early on in my career that tech was an interesting sector or opportunity for me and would always listen to their stories intently about their interactions with both Phil and Dave, and the things that were happening at HP as it grew in its early days.

I think with that it really helped me to cement a perspective of, I would say, entrepreneurial growth in the Valley, and knowing that companies helped to form and grow, and then spawned other great companies. I was always fascinated with that aspect, it’s a foundation of how I saw things. So, over the years for me I’ve always been in tech, started early in my career as a developer, and realized that was not a long-standing suit for me, but I always had this interesting view of inter-connected systems and environments. This goes back to EDI and RosettaNet and spending a lot of time configuring these environments to connect enterprise supply chain systems, and channel systems. I was always enamored with this idea that you could work in this vast deco-system. So, I’d say that was the early foundation.

I spent about 15 years in Hewlett-Packard in a variety of roles across M&A, as well as some internal start-ups, and then software, but I think the most notable for me 10 or 12 years ago was working with some great fellows in HP Labs, helping to bring together this product offering around real-time energy management, out of thinking of HP and what it could bring to the market from a technology-datacenter perspective, to buildings and broader environment. It was all on the simple principle of, ‘Can you create insight from the data that you have access to, to affect the way the business works?’ That has stuck with me I think for the last 10 or 12 years in a variety of roles, with Vertica and some of the big data areas, then spending a number of years at GE.

I think for me it’s been a culmination, and the premise that we do live in a data-driven world, and we’ve seen it really explode in the B2C side of things, and I believe there’s a mountain of opportunity in the industrial and enterprise space when it comes to IoT.

It looks like you’ve been through a couple of transitions as well, working for HP and then Vertica, and the GE. Could you talk about some of the formative experiences which shaped your views of the potential of what data could do, and how you’ve seen the industry evolve over the past couple of decades? Vertica was really part of a whole group of companies that were focused on accelerated data, I call them data warehouse appliances. Then of course GE was focused on tapping the data that comes from industrial machines and applying IT methodologies to the analytics. It would be super-interesting to get a sense of how you’ve seen the market evolve, particularly as it relates to the embrace of industrial data.

As I mentioned, it kind of culminated through a number of experiences, but things that do stand out I definitely would say the experience and exposure with Vertica was very informative, from the viewpoint of your ability to ask a very different set of questions on how to run the business, and then think about the type of data you would need in order to answer those questions, or the inverse, because you have access to the data what questions could you ask, or what could the data tell you? It was the time spent to understand what was happening, as companies from gaming and web, banking, to insurance, healthcare, and retail that were utilizing a lot of these technology platforms, such as Vertica to answer some of those questions, or understanding an insight they’d never had before.

I was always amazed by the fact we’d take the standard view of let’s just dump data into a data lake, and then be able to understand a way of how to bring additional datasets. Again, the whole notion of structured, unstructured, finished structured data to affect the way a business worked. And so spent a lot of time a variety of different use cases with different customers and partners, just to understand, and that I think exposed me tremendously on the thesis of, if you start with this viewpoint of your enterprise, and what does the metadata view of your enterprise look like, then you can think about those fundamental business questions you need to ask, to run the business.

Fast forwarding, I took a lot of that experience with Vertica, and with HP Software more broadly when I went over to GE. It was again on that principle which I think for me also was the viewpoint that you can think about selling formidable technologies for IT buyers or business buyers. I was really much more enamored with, because you’ve access to data, how can it change the business landscape? Going to GE was one of the best experiences I’ve ever had, and I think a lot of people think in life ‘Wait a minute, why don’t you come and work with one of my smaller portfolio companies, and go that route?’ The reason why I went to GE was because as I’ve mentioned before, the story to growth in the Valley of companies spawning other companies, GE was down that path to do the same in the East Bay, just out of the Santa Monica area where you started to see a very large company make a significant bet, and it’s rare to see that happen. But effectively that does spawn an ecosystem of growth.

So, coming into GE to really understand more the industrial space, it was because I had some unfinished business. In my own personal view I’d done an energy startup, I was really enamored with the fact of the way you could change business behavior, and my example back then was, if you could help a CFO understand what they consume, use, and waste, relative to natural resource consumption, could you affect the way they managed their entire energy portfolio?

That really stuck with me, and I think it’s just correlating further that being a part of GE, GE is very much known for being outcome-minded, and I think oftentimes in the tech world we speak about the outcomes, before we even talk with an industrial customer or client, or an OEM, that matter is day-in and day-out and that’s the only thing that matters. With that it really helped to shape a view and perspective and appreciation for the role that not only had been put in place in industries in society at large, but what sort of story and insight can you glean and create because you can access this information and bring that forward. So, to me that really started to create a crystalized view of the value of IoT, versus simply just connecting a bunch of devices and censoring devices are not really getting that value. So, that really formed a view in my mind about this industry that we’re stepping into around industrial IoT, that in my analogy is kind of the first sitting of a 9-inning game. That’s a little bit of the experience there.

How did your experience in energy inform the work that you did at GE? I’d be interested to get your perspective on how energy differs from other industries that may have been earlier to adopt analytic technologies.

In my early exposure at Hewlett-Packard, it was really exciting to see a way to potentially change the way our consumer behaviour thinks about energy use and waste. In Western world’s we have to take it for granted, full-stop, and it’s something that again because we have an abundance of, we assume it’s just available, and that’s not the case for second world, or third world countries in the fact of being challenged with just having availability to supply. With that experience, when I went into GE I was very much interested in going back to help that problem and advance it, and in this case of realizing that we have to live in the notion of an energy value chain, from a generation, to distribution, to consumption, but realizing it’s really becoming a ubiquitous network, and the rise of the prosumer was coming forward, and still is very much with the onset of renewables and solar, and the ability to think about battery storage and other technologies.

With that you realize there’s going to be a massive impact to the grid, or the existing ways in which utilities have operated. So, I’ve long been fascinated with that way of understanding how you disrupt an industry in a right way and in a positive way, using data. I think partly why I was gravitated towards energy is, 1) a very hard problem to solve, 2) an industry that has a significant amount of data, and 3) an industry that knows it needs to reinvent itself, and 4) which is probably the learning coming into, or being a part of it is just it takes a long time. Change is definitely hard, and regulated or unregulated markets, and/or just the habit of the way things have operated in the past. It’s not to say utilities aren’t willing to make that change, I just think there are a number of factors that’s putting effort in the way of how quickly the adopt and transform.

I like what I’m seeing, and the acceptance and acknowledgment across the CEO communities for utilities that acknowledge that this is important not simply to do, but to ensure that it’s a part of the fabric of utilities going forward.

I’d like to come back to that a little bit later, but first I’d like to talk a bit about what you’re doing at Uptake, and can you share a bit about the company and your role there, and some of the areas of focus that are in your wheelhouse.

I’ve been with Uptake for just over a year now, I came in early-on to have a number of different roles, from sales and marketing, through the industry, through applications, and now product and customer success. Part of it is, Uptake is just coming on its fifth year, 2.3 billion in valuation, and as a company it’s been purpose-built with data science and AI machine learning at the core. So, it really spoke to me about again the data-driven view of re-imagining our enterprise software can and should be built and consumed. With that, my impetus to be a part of this, was knowing that a company is in its own essence thinking about the adoption of data science as a first-class citizen, or the kernel of what the product is. And so, that was one of the biggest draws to the opportunity, and as a result we’ve been down a path of thinking about how you democratize data science across not only the industrial space, but the enterprise, but doing it in ways that are creating a massive amount of repeatability.

Why that’s important is, because a lot of when we talk about data science, AI, machine learning, deep learning, a variety of terms to use different toolsets for different use cases, for different outcomes to be solved, oftentimes there is not a lot of repeatability, and therefore teams, enterprises, [inaudible 15:49] large data science seems to really manage. But our essence is creating and prioritizing those capabilities to think about AI through the entire lifecycle, not simply being able to discover, build, train, deploy data science models, but how we think about the access of data, and speeding up that process, because we all argue it’s the tip of the iceberg problem that 90 percent of the problem lies beneath the surface, and oftentimes data science is only as good as the data coming in. So, you spend a lot of time trying to think about the data engineering, the data [inaudible 16:27] really simplified that, and then having the ability to think about data science in a way that effectively are creating optimization engines, and the way we go about affecting outcomes for customers.

What I really love about this is, if you think again about the outcome, if you think about the KPI that a customer cares about, and all this in our industrial space, there are a variety of them. But the traditionals around proven reliability or availability, or throughput, or a variety of others, you can back that into what data analytics do you need, and therefore what data, from what data sources are required and we’ll support that outcome. So, simplifying that method and mindset is absolutely paramount, and we’re strong in belief that it’s all about time to first insight.

So, we’re down a path, we’ve got a significant customer base across a variety of industrial clients, and in that regard,  there is a lot of transferability of the data science models, and the way they work for different assets, as well as different scenarios that we have with customers. So, we continue to forge ahead with the platform that we have, and excited to be bringing a lot of new capabilities, both in the platform and the application level to different industry customers.

I think what’s so interesting about what you’re doing at Uptake is how the AI components, or machine learning as it were, is embedded into the platform, and into the DNA of the company. I’d love to get your perspective on how you work with your customers, and also even before customers, developing the solutions and identifying the business problems that can be optimized, and where the solutions can be improved through the application of advanced analytics and AI technology. What I’m trying to get at is to better understand the role of the main experts and data scientists, as well as the customer facing folks that you have. How you combine all of those elements together to drive outcomes for your customers.

To give you an example, last week we hosted about 30 different customers, super-effective customers in the construction space. There was everything from operators, to OEMs, and very different types of OEMs, and so it was interesting having a very strong cross-section of individuals, and that very question came up. What I shared with them was, first get a sense of where people are in their journey, if we talked the notion of digital transformation, but more importantly their acceptance and willingness of leveraging AI machine learning.

Step one was just simply dispelling the myth where there’s the general concern, especially in the industrial space, that the rise of AI equals job loss. My view is it’s often not the case, if you think about the industrial revolutions we’ve gone through, I know not too many folks are interested in going back and digging with their hands when the shovel was introduced, and tongue in cheek as we say that, but what it implied a lot is the ability to automate a lot of routine and maintain tasks that folks may be doing today, that our technology environment can do on behalf.

With that is the natural acknowledgement first around the fear their job might be redundant, and therefore getting people onboard to think about they are possible. And it’s a straight question to oftentimes there’s an opportunity to improve cost structures, and so that usually is the easiest low-hanging fruit, and then just start talking about how you think about the day, and how you operate. I’ll give you the example on the construction environment, we’ve got scenarios where customers are like, ‘I constantly don’t know if I have the right bench of individuals that have the right skillset, and are available at the right time, when I need to go and fix a piece of equipment that I’ve been alerted is down. Oftentimes I’m questioned, do I have the right person, the right tool, the right part at the right time, and can I go fix the unit itself to get it back online, and continue to drive better productivity in its use, and avoid the downtime. That is in essence a way of us just simply looking at the problem set that someone has had, and say, ‘Okay, look back through that and understand what problem we’re trying to solve, and therefore ask the question of, what data do we have access to today?’

Oftentimes it’s a bit of a block, and helping the team to think about that, as I mentioned earlier, ‘What is the metadata of your enterprise?’ ‘How do I think about the data that’s available?’ and, ‘What problem am I really solving?’ So, be clear on the problem, and therefore the use case we think we can improve their pain points, then it gets very easy and very quick to walk through in building that out, defining it, and for us a way to do far less of talking about what can be done with digital transformation, and going through discussions with PowerPoint, let’s show them the art of the possible using their data, and using a way to assess that immediately so we can have a conversation around the data, the insights, the potential for improvements.

That’s usually the best way to go about, or least the way we have found will bridge that gap of uncertainty and concern, to ‘Okay, I think I have something here that I’m very interested in. How do we get started?’

Are there notable differences in working with some of the industries, or different industries that Uptake works with? I know we’ve spoken quite a bit about energy, but I know that Uptake if you think about the platform, it’s pretty horizontal, and I’d love to get your perspective on which industries seem to be moving forward rapidly, and maybe some others where there are either transformational hurdles, or maybe ways of thinking that they’re still working through?

I would say a couple of things. I’ll start first on the energy side, one thing that was interesting over the course of last week, the Edison Electric Institute conversation in Philadelphia, and I must say for the number of interactions from CEOs about, ‘How can I not only leverage the AI machine learning to help streamline my existing operation, but how do I think about it as the way to be the strategic angle of where my business goes?’ has been paramount. I think my only point on the energy side is there’s more and more acceptance and support to try and move forward, which is great. But for us, as you mentioned we do go across a variety of industries, so whether it’s in manufacturing, in auto-manufacturing, or work we’re doing with the US Army, as well as work we’re doing in mining and construction, we touched on a variety of them.

What we found is there’s a fair bit of reusability with different data science engines and models that we’ve deployed, they’ll allow it to be able to move more quickly for the first to insight. So, in the example in the case of auto manufacturing, it is really about managing the uptime and the production of various lines that our customers have and trying to be mindful that it’s not simply looking at the downtime of a given asset, but it’s looking at the entire process itself. What we do is very similar to what we’ve done in other industries, is looking at the asset in their given setting, and not just on an individual basis, but the interconnectedness of those to understand what metric that customer’s carrying to improve. If you think about OEE, one of the biggest things is ensuring that obviously the asset availability and up-time is there, such that the production line can be as effective as it can be, on its command and planned schedule.

So, we take a very similar approach, we look at the failure or failure modes, and areas and issues within a given set of assets and start to look at not only areas of how we predict potential failures, but also how do we create recommendations around ways to improve the operations. So, that’s just a great example of being able to not only do that at an asset in a plant level, but how does that quickly role out to a broader enterprise.

Then I’d say one of the things we’re working heavily on is with the US Army in the Federal space, a number of opportunities; but a lot of it has been publicly known working with the US Army around the Bradley fighting vehicles, and helping to make sure we have operational readiness for our wartime heroes and fighters, to make sure the equipment that they use is available and ready for dispatch and deployment when it matters most. So, for us, the ability to look at the various use cases we find in different industries, we have a backdrop of 55 or so data scientists to allow us to share a lot of that knowledge across, when applicable, and being able to understand more. In some cases, we have to be segmented in the way we work, especially with military; but the principles and mindsets of how we approach our data science model in the commercial side, helped us dramatically to glean insights and ways to evolve these models more quickly for the benefit of our customers.

Are there some effective best practices or approaches you’ve seen your customers employ from an organizational standpoint? Any customers that are doing things right in a way that’s aligned to the organization which has led to success which stands out to you?

Interestingly enough one of the more recent customers to Codelco, out of Chile in Latin America, a very large copper mining operation, and great to be partnering with them. I would say first and foremost it was the curiosity around what AI could do for them in managing their mining operations. Then second to that was the value and view of what are the most critical assets you would care about, being able to model and understand for performance, versus simply doing that for all the assets in your enterprise.

One thing we’ve been very mindful of is, as we saw through the hype-cycles of Cleantech where there’s a mass amount of venture, couple of dollars going into Cleantech, and then we saw the trough of despair and concerns of what are the returns on the products that are being built; you see a lot of that with IOT, there’s massive investments and claims to the amounts of money that IoT as a market will bear, and I think there’s a lot of truth to that, but there’s also a lot of learning. One of those which I think is paramount is sometimes many organizations have said, ‘I’m in IoT, I’ve now connected half of my operations’, and my simple question is, ‘For what? What is the outcome you care about? What KPIs do you fundamentally care about? Can you get the right type of data, not simply looking at machine data? Can you bring together a variety of datasets that help to affect the KPI you most care about? And, is it even baseline and track and managed?’ And if we can’t simply go through that, then going down a path of connecting a variety of spare assets really is not going to drive the value that people once hoped it would.

I think best practice is where I’ve seen customer who’s great even its willing to try to create the right beachhead within the organization, such that it has the right level of support, and then be able to use this as a way to showcase where and how it could role across organizations. The best practice is looking at the right type of organization to carry this forward, and then certainly having seen a little leadership that believes in the case; because as you and I both know, moving quickly in the industrial space is not something we’ve seen, but it is continuing to pick up more and more, and really having that senior leadership support is tremendous to see the success of the number of these programs become digital transformation initiatives.

I’d love to get your thoughts on the market around platforms. You were at GE, and if you go back a few years, that was at the time when GE and Cisco, and a few others, were very excited about the potential, creating some forecasts or expectations that we would see a really rapid ramp. It was still very early stages in the adoption of platforms, and I think what’s interesting now as we get into a later stage, I wouldn’t necessarily call it platform 2.0, but interestingly PTC was saying their biggest challenge isn’t demand, it’s finding enough skilled people to help implement their projects.

If you look at GE, I think Predix’s vision was incredibly compelling, and GE’s going through some changes, mostly not related to the work they were doing in GE digital, but I think they laid a lot of interesting groundwork. I’d like to get your perspective as Uptake is moving into a new era of platforms, and certainly establishing a leadership position in the market, how you look at this market around platforms. And the last point I’ll make in my extended comments here is, two years ago there was an estimate there were 400 platforms in the IoT space, and of course that is pretty characteristic of an early market where a lot of those platforms evolve into applications. I would love to get your thoughts on the evolution and dynamics of the market, and where you guys see yourself in relation to some of the other players in the market.

Your point about the compelling nature that GE laid out the vision, which is similar to what the leadership team did, was absolutely a reason why I joined as well, because I saw the vision and direction, and absolutely believed and bought into it. I think in many regards GE paved the way for the industry we see now in helping to create that believership. So, I think a lot is credited to them in making that happen, and as with any market, you end up spawning new companies as a result of that market momentum believership, and so it’s a natural course of what we’ve seeing.

To your comment about the platform, absolutely, the enamored view that everyone is a platform, whether it’s in technology, or outside of technology, communication, media, you go down the list, everyone says they have a platform. But in the technology sense of it all obviously it means different things to different folks. One of the things we are very strong on is the viewpoint that because of AI and machine learning, we believe there is the nitro notion of a system of intelligence. And that is mindful that as a CIO, or an enterprise leader, in a large enterprise you’ve largely made very significant capital investments in your technology decisions. And what we acknowledge is, many of those environments, whether it’s a European environment, it’s your own environment, HRM, those are great systems of record in doing what they’ve been designed to do.

What we find is, many organizations spend tremendous dollars trying to extend and configure those environments to do things they were not intended to do. The power of AI and machine learning is, again, if you think of a data-driven approach first, you think about the outcome you care about, and where does that data live, and how do you access it within your own four walls to take advantage of driving a business outcome you care about? If it is about improving cost structure, you think about where that information might lie in the European environment, or a historian, or SEMA station environment, that oftentimes in those individual natures they’re incomplete.

In this view of a platform I would determine a view that you have a level of interconnectedness into existing environments, such that you can take advantage of the two-power of machine learning and AI when it comes down to affecting an outcome and delivering an insight that is financially backed. So, with that, as the market evolves we’ve been very mindful, the idea of trying to be everything to everybody is not the pattern and focus, so we’ve centered ourselves squarely in focusing on democratizing data science with the use of AI machine learning capabilities, but being mindful that you have to think about it from raw data to how you digitalize and ultimately consume those insights.

With that, I think the ecosystem is going to definitely go through a form of consolidation in a good way, in the sense that there’s a number of technology companies that have touted that they’re a platform, but they’ve been very good in a given industry, manufacturing, energy, construction, and they hit certain limits depending on where they are in their investment cycle and growth, that they may not have the opportunity to scale even further. I do see there’s an opportunity for consolidation, and I do think especially in this notion of thinking about data science, and data science first type insights, that there are a number of generalized toolsets that help for the deployability of data science models, but they lack a lot of context.

The biggest thing we know in the industrial space is, you have to understand the asset, you have to understand the context around the asset, its failure modes, you’ve to understand the behaviour of the assets, what’s managed and oriented around those in the past. So, what we find is, there’s a lot around generalized tools that are more aligned with our existing cloud vendors, Google, Microsoft, and Amazon, and that’s great, and I think here too are very specific needs of interest, and then there’s some very specialized platform tools that really are more applications for a given industry.

As you look to scale the business, you’ve alluded to a number of common business problems you’ve been able to extrapolate in similar approaches across different industries, but then there’s this need for highly specialized knowledge of machines that may be specific to an industry, or even a sub-sector, or even a specific type of company. Some of that lends itself to replication, and some of it demands bespoke specialization.

How do you think about the way you turn your business to be able accelerate adoption for new customers and new used cased, as much as possible whilst still keeping that flexibility of the ability to customize your technology for new situations, and scenarios?

The very important thing of all this is, certainly data scientists that are very well versed in the discipline can manage very large datasets and come up with very distinct models. But it’s not enough in the viewpoint that the expertise from an industrial perspective needs to wait in terms of understanding and interpreting the value. Over the time period of tech, we not only need investment from the data science space, but also with the subject matter experts in a variety of different industries. One of the things I value is this notion that as much as we would talk about care programming, peer reviews, and things that really help each other from a development perspective get better at what we’re ultimately releasing in the market, the same applies when you’re putting a subject matter expert who’s had 20-years in a given industry, working close to the data, and being able to interpret that. What we’re finding as a result is, you’re not on anyone’s path of having the higher large pools in either direction, but how do you thread that knowledgebase together with some of those folks.

So, that’s one effort of it.

Then the other effort of it is the constant knowledge sharing we have of what teams have seen with certain industry used cases, and challenges, and the tight-knit community of how the data science group has been established. The uptake which I think has been absolutely tremendous over the years to help evolve that, and then it’s also the way you think about the technology, and that is knowing you have very consumable capabilities, or what we call engines that can be applied to different use cases depending on the customer, the type of asset situation, but the ability to deploy that application is very consistent and repeatable, such that again we haven’t built a one-off for a given customer, and we have the ability to leverage that.

So, I think across those three areas it helps tremendously for us to continue to evolve, think about a contribution model, but realize the value of an SME, along with the data science to really create something that is unique.

I’d like to ask something a little bit more broad which I think is not necessarily related to… we’ve been a bit in the weeds here but, what are you most optimistic about as we look forward over the next five to ten years, and what are some issues or areas that keep you up at night?

Looking ahead, although I don’t have a crystal ball, I do like the odds of what I see in the market, in the sense that there is absolutely resounding acceptance, and interest, and curiosity for what AI and machine learning can do for organizations. So, I do see the fact that it is not to be in any way an appendage to how you think about your business, just as we’ve talked before, that digital transformation is not an off-shoot of how you run your business, it is effectively part of the fabric of running your business.

We have a very strong notion, and I believe you will see the notion of operational AI, especially in the industrial space, where it’s embedded in the way that you work. The thing is we see that in the consumer world today, and many don’t know it or don’t acknowledge it, or just don’t care to acknowledge it; but the ability where we will see this embedded in how things evolve, and the way that industries work, and my hope is that organizations because of the access to data that they have, that they have fundamentally reimagined the way businesses work, meaning they can become more data-driven, they can be more akin to making quicker decisions and changes because of that fabric and landscape that’s available to them. So, that’s a view in the outlook, and I think as a result of that more and more companies are going to find the value hidden in a lot of the projects that they’ve done from an IoT perspective, that I think they’ve scratched their heads in recent times of maybe you’ve not seen that value, because you’re now connecting it to simply where a financial or business outcome you care about.

The thing I would say that worries me a bit, especially in the industrial space is you find this definite knowledgebase, the skillset that is at that point of retirement. It really moves the industrial leaders to think about the cultural change in their workforce and using this is a way as a carrot to attract new talent, that might just be simply borne into what we’re talking about is just the way that they work. That is something that organizations have to take and embrace even further. So, it’s not necessarily keeping me up at night, but I definitely find it’s probably a topic of many that I have which is rather consistent with a lot of senior executives in different industries.

Beyond that, I think the opportunity here is again where we can ultimately bring a lot of context to the value of artificial intelligence and machine learning and get out of the hype cycle of the buzzwords. We’ve all seen it, whether it’s big data, cloud, security, AI machine learning, it’s the kitchen sink statement and we’ve acknowledged that, but I think more context that can be brought to it in the setting of where customers work, and the value and outcomes that they care about, is going to help to drive this industry even fasted. So, I’m just glad to be a part of helping that as well.

That’s a great perspective Jay. The final question that I always like to ask is a recommendation of a book or resource that you might be able to provide for our listeners.

Jay, this has been a fascinating conversation, and it’s always your insights are probing and valuable, and I think our listeners are going to get a huge amount of value out of it. I always like to ask our guests if there’s a recommendation that you could share of a book or a resource that you find valuable?

One that I’ve had to dust off and read and go through, which never gets old, is from Marty Cagan ‘The Silicon Valley Product Group’, it’s all about building products that customers love. What drew me to bring it off the shelf and look at it again in this light is, again thinking through how redefine how enterprise software is build, more-so from the lens of how its consumed, or should be consumed, to be what AI machine learning can bring. It gave me a great perspective to come back to some things that I find are very sound principles and approaches when you think of this product management in general. So, again, inspiring to re-read and rehash some things that I’ve gone through in the past that have helped to inform me, and shape some of my thoughts.

Fantastic, as always, it’s a pleasure to talk to you again, this has been Jay Allardyce who is the Chief Product Officer, and Head of Customer Success at Uptake, and again this has been Ed Maguire, Insights partner at Momenta Partners, with another episode of our momenta podcast.

And thanks again for joining us Jay.

Ed, thank you very much, it’s great to chat with you again.


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