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E23 Can Technology Deliver On Its Hype? ft. Lindsay Tabas

October 2024

65 minutes

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Episode Notes

Is the latest tech really delivering, or is it just overhyped?

In this podcast episode featuring Lindsay Tabas, the discussion centers on whether technology can live up to the immense expectations placed on it.

The conversation explores how to navigate the gap between the hype of new technologies and their real-world applications.

They address the importance of aligning technological innovation with human-centered design and practical business needs, providing insights on how leaders and entrepreneurs can better evaluate tech solutions to drive real value rather than just chasing trends.

Key topics include:

  • The challenges of bridging the gap between tech innovation and user needs
  • How to assess whether technology can truly deliver on its hype
  • Practical insights on leveraging tech for business success
  • The role of human-centered design in tech adoption

Find Lindsay: Podcast: Youtube: LinkedIn:

0:00-10:50 Introduction To Lindsay Tabas

10:50-12:53 Understanding Human-Computer Interactions

12:53-22:02 Low Success Rate of Large Enterprise Software

22:02-25:25 Complexity of AI Integration At Work

25:25-33:06 Training and Education in Technology

33:06-34:46 Data Integration and Standardization Challenges

34:46-38:42 The Risk of Using Excel as a Workaround

38:42-44:27 Interoperability in Enterprise Software

44:27-47:29 The Shadow IT Function

47:29-01:02:06 Inflexibility of Software Systems and the Need For Workarounds

01:02:06-01:04:50 Pragmatic Futurism and Prioritizing the Human Experience

is a global, leadership-strategy consulting company. 3Peak creates the roadmap that aligns behaviours, relationships and Functional Human-Systems™ to achieve your business strategy.

Co-Founder holds a Ph.D. in Neuroscience, and did extensive research in Consciousness, Trauma and Physical, Emotional & Mental Health in various Institutes and Research Centers around Europe.

Co-Founder is one of the most sought after therapists in the world, mastering diverse modalities and opening wellness centers in Istanbul, Santiago, New York and Berlin. Her approaches bridges transpersonal psychology, meditation, bioenergetics, family- and business-constellations and more.

Co-Founder has extensive experience advising Fortune 50 and FTSE 100 C-Suite Executives in leadership, strategy, team dynamics, and organizational change. Before coaching, Mino worked in finance, management consulting, and mergers and acquisitions (M&A).

Transcript

Mino Vlachos: Hello and welcome to the 3Peak master leadership experience. My name is Mino Vlachos and I'm the co founder of 3Peak Coaching and Solutions, where we support executives to master leadership. Our company provides coaching and team workshops. We're joined today by a very special guest, Lindsay Tabas. Lindsay is a media personality and culture critic of the technology industry, host of the podcast Make Sense with Lindsay T. Lady Engineer and founder of Learnproductmarketfit.com, where you can shop for resources to elevate the way we innovate. Today's topic is very important to, I would say, almost all people living in society today. And I really think about it as this is where people meets technology, how we can best use our tools, especially when we think about large organizations, even small businesses, but how we really master the use of technology. So I want to thank Lindsey for making the time to be here today. And I'm going to start with a quick story of something I experienced before I hand it over to Lindsay to hopefully explain a little bit maybe what I was experiencing and how we can do things a bit differently. I used to work at a company and we had a lot of kind of custom coaching that we were doing. And by that, I mean some of it was 45 minutes, an hour and a half, 3 hours, 4 hours. It really depended. And from the top down, there was this mandate that we were going to put like a scheduling tool in. And the person who was running the project, they went around and collected feedback. And I was one of the people that was asked to kind of give feedback. And based on what they were telling me, I really couldn't understand how the technology was going to actually help us. It felt like it was going to make way more work when it was mandated across the business. I also felt like none of the feedback we were giving was actually being taken on board. So by the time the actual technology was kind of rammed through, no one ended up using it. And so it was a pretty expensive project and no one ended up actually adopting it. And it created a lot more kind of chaos and frustration in the organization. And so, Lindsay, I know you talk a lot about kind of enterprise software and what you sometimes call the misery of it. And so I'd like to kind of get your take based on the story I just walked through. Like, what have you seen in terms of implementing enterprise software pros and some of the cons?


Lindsay Tabas: Yeah, you know, I am so sorry you had to experience that. And unfortunately, that is all too common and it's not surprising at all. One thing that is actually good was that you were even involved in the process. Right. When I first started my career, my first full time job, I was working for a consulting company. We were serving a large utility company. And I went to the manager who I was collecting requirements from, and I said, hey, can I talk to your employees? Because they're the ones that are going to be using this software. And the manager said, no, that's not necessary. They'll use it because it's their job, too. That was the first time I felt this heartbreak, because I really, really value the human part of human computer interaction, which was my field of study. Unfortunately, that type of leadership style, that top down they do what I say they will do is still very prevalent in our modern times. Even though that was 20 years ago. Right. There's still very much a top down. Do what I say. We see this in the return to office conversation these days. That's probably one of my biggest insights for leadership of the future, as Gen Xers and millennials start to take over, is it can no longer be. They'll use it because it's their job to. We see way too many examples of people not using the software and it being a total waste of money. The interesting thing, you felt right away that it felt uneasy. And that's really interesting, because a lot of times employees are so happy to be even asked that they don't even sense whether this is going in the right direction or not. And I think that is a testament to the younger generations and our ability to see what's past. The line of visibility. Line of visibility is a term in service system design, something I studied in graduate school. It says, you know, the front stage is what an end user and customer experience. The backstage is everything in the back of the system that think of back of house, kitchen in a restaurant. The thing is that we started to learn 20 years ago, and very much is true and demonstrated in what you say about feeling uneasy, is that the line of visibility is increasingly more transparent because our younger generations are more cognizant of human computer interactions, of computer to computer interactions, and people to people. People to computer interactions. So I think that's really interesting. I don't have advice as to what you should do with that unease, except to continue to advocate. The other thing in your story that is deeply relatable and common is this idea that you gave all this feedback and none of it was used. I actually call the gap between what people say and the insights to act on the silent killer. Okay, now, the easiest thing and there's plenty of videos on my YouTube. The easiest thing to understand about processing feedback is if one person says it, it's an anecdote. If two people say it, it's a coincidence. And if three people say it, it's a theme to act on. Yet when we go on listening tours to listen to feedback and insights and input, it is very, very easy to, one, attach to negative input, and two, to listen to one loud user and listen to one very opinionated person. And if that opinionated person is also on the decision committee, that becomes a problem. Which is why I highly suggest, if anyone is on a project like this and is responsible for dissecting and interpreting feedback, that you actually create a way, whether it's on a whiteboard or in a document or a spreadsheet, you create a way of tracking all of the insights you get across all the different participants, so you can truly identify the themes that are across at least three or more people and confidently throw out the opinions of the loud people you listen to. So I'm not surprised that the feedback seemed like it was not used at all. And then I'm also not surprised that there was no adoption in enterprise software. Large companies, these large software systems that large companies use, sometimes people don't know what enterprise software means. It's the large software systems that your company uses. We often see that it is incentivized to build and install the thing, and the leader sponsoring the project to build and install the thing and launch it on time, they are not incentivized by behavior change. So whatever you install and in whatever timeline, what we want to see in two years is a reduced time between. A reduced time spent on calendaring. Right? That's the type of behavioral metrics we should be using to incentivize our IT teams and our leadership that are sponsoring this project. When the incentives are aligned towards just getting the project out, we lose budget and time focused on change management and adoption. And I'll tell you one story. I worked with a Fortune 50 company. It was a quick three week consulting project. They had a new leader come in, a CTO who's convinced they needed to replace a major digital asset management system, and they had literally just installed one. Over the past three years, there were people that spent their entire 40 hours a week for at least a year moving digital assets from the old system to this new system. And now this new leader wants to come in and rip out this system, install a new one, because he didn't see that this system that was just three years old, was having an impact, was doing its job. I interviewed 30 employees around the world, and what was abundantly clear is that no one was trained on how to use it. There was one department that was able to finagle a resource to spend 20% of her time offering webinars to teach her coworkers in her department how to use that platform, and they were having success. So this CTO had spent whatever he spent on me to do this work, and whatever he spent on a huge consulting company to collect requirements, assuming that a new system needed to be put in place, when in reality, he could probably have hired one full time resource at $65 to $85,000 a year to work 100% of their time on training this company of over 50,000 global employees. And that kind of brings me to a point I know we'll keep discussing, which is that we often throw technology at solutions before first understanding the human part.


Mino Vlachos: A lot of what you said just deeply, deeply resonates with my experience. Have you seen it work? Like, I don't know if it's just my. Like I'm pessimistic, but have you seen kind of implementation of large enterprise software actually be successful? And if so, is there anything we can understand from those successes, learn from those successes?


Lindsay Tabas: Unfortunately, in my lifetime, I have not seen a project be a success. 55% to 75% of corporate it projects fail. That's. We're spending a lot of money in the hopes, and this is very damning for technology as a solution. Corporations spend a lot of money in the hopes that this new technology will drive efficiency gains, operation gains, and reduce expenses so that they don't have to raise their prices. And then when they do these projects, and 55% to 75% of these projects fail, and they're out, for instance, 2 million to. I've even seen $250 million projects fail, then they have no choice but to go back to, well, we need to increase revenue, so we either need to launch new products or increase our prices, or shrinkflation, reduce the amount we sell, but keep the price the same. And so this is, for me, one of my big passions and one of the reasons I host my own podcast, make sense, where we discuss the impact of technology on our everyday lives, pragmatically and without darken. I have this podcast because I feel very strongly that technology is not living up to its potential. And that is why I put out resources to help others elevate the way we innovate so we can get better outcomes.


Mino Vlachos: Yeah. So if a lot of these projects, and I did not know those figures. So I'm just digesting how big the failure rate is.


Lindsay Tabas: It's terrifying. People with full time jobs that are more risk averse than entrepreneurs spent years working on a project, and it is a failure. And unfortunately, in american culture, where we put so much emphasis on our work, it's a huge impact to their identity and their self worth. When something like that happens, it's incredibly frustrating. It builds resentment in your leadership. It's just really unfortunate. And to be honest, it's coming from what I immediately said, which is that top down opinion, top down leadership, and the idea that I know best, whoever's picking this out knows best. And one of my guests talked about how enterprise software is almost leveraged as a means of control, and that is not how it should be used and leveraged.


Mino Vlachos: So is there an alternative? Because technology is also, I really feel wonderful and helpful, and we all use technology on an individual basis, so these large projects are not successful. So then what is an alternative in the working world?


Lindsay Tabas: It's a really interesting question, because it's a question that I pose to my guests all the time. I ask in a snarky way, like, why does enterprise software at work suck? And then what can people do about it? It's very hard. When we talk about systemic problems, it's very hard for the individual to feel empowered, to make changes. The first thing. The first thing is, I do believe that the tools you have at work are probably more helpful than you realize. And spending an hour once a month or once a quarter exploring training guides and tutorials will help you get more use out of it. I think people are so used to being frustrated by technology because admittedly, user interfaces are not great. It's an issue I've been fighting for a long time. User interfaces are not as usable as they should be. They lack a lot of principles from cognitive science and the field of human computer interaction, because that stuff just is nothing popularized on technology teams. It's a lack of education. And that's another passion I have, is that we're not educating our students really well on how to be skilled practitioners in designing and building software. So first and foremost, training. There are a lot more tools and features available to you if you take the time to learn them. I wish all interfaces were just easy to use and you didn't have to train on them. But when it comes to enterprise software, it's trying to meet so many people's different needs that it becomes bloated with features. So just like any problem like acceptance is a part of this, right? Awareness and acceptance a part of the solution and action is taking the time to learn the products. The other thing I believe, especially because I know from having my own podcast for the past two years, is that middle management is really being squeezed since the pandemic. There's top down pressure from leaders to return to the office. There's bottom up pressure from their employees for satisfaction and flexibility. Middle managers have an opportunity to be incredibly influential. One way is by banding together to advocate. So that is one way in which middle manager leaders can work together to get better outcomes for software projects. The other two things that they can do besides banding together is to advocate that their employees are involved in the IT project. Not just to like sign off on a document, but let me see the drawings. Let me show you exactly how I do my job. Now, if someone comes and asks you feedback or insights for a piece of software and you're sitting at a table just looking at each other, no, have them sit behind you at your desk and show them exactly what you do and how you do it now, and point out as you do it the things that are going wrong. There is no replacement for actually seeing it with their own eyes. Better yet, help them do it for you so they can experience it themselves. So the third is the second is banding. Banding first is banding together. Second is advocating for your employees to be involved with it in the process. Third is also helping it to understand what your benchmarks of success are. And I would definitely agree to that with your other leaders, because we may know that the CTO or CIO's bonus is hinged on installing the thing on time, but we can at least make it known to all of our peers in other departments that are executing this project what we need the outcomes to be to be successful. And the fourth thing is, if you yourself are part of advocating for a new piece of software, take a step back. Make sure you understand the human problems and the human solution before advocating for a new piece of software. Every new piece of software complicates your whole enterprise system because of data connections. It creates more cybersecurity threats. It just creates more mistakes, both on the employee experience and customer experience side. If you understand the human problems first, and you can find a human based solution first, it's probably actually a lot simpler. I do have a caveat on that, though. Oftentimes I'll back up if the problem is around an automation. If we think a human solution is Lindsey remembering that every Monday she needs to email some data set. That's not it. That's not the type. But usually some problems could be solved by two teams on different ends of the office, having a meeting once a month, or actually having representatives on each other's teams.


Mino Vlachos: This is what I really love about this conversation is you're really helping us visualize. I feel like technology as a system. So it's like a relationship, right? Where like different. We even talk about different enterprise software and the data, the input output and how it's a language barrier between systems, but they form this constellation of a network of different tools. But then this is what our work is as three peak is like, we look at that from the human side. So humans also form networks, and they all speak different languages, depending on what department they're in, or even their personality style or their identities. And so there's so much complexity within an organization, from the humans to the tools and how all of these interact with one another. It's a lot to sort out. There's a lot of complexity. It's a tough. I don't know. I also, I guess I'm not jealous of like the job of some of these leaders who have to manage so much dynamics within a business.


Lindsay Tabas: Right, right. I think I glom onto the complexity because I'm a systems engineer by education and personally I think disposition. I am always looking at the systems of things. It is very messy. I certainly don't envy the leadership either. I do believe there are, I do believe there is a way out and over this problem. And I think it really starts with understanding that technology is a tool that is part of a solution, but not the solution itself.


Mino Vlachos: So I want to now bridge into probably the hot topic of today, where I think a lot of this is coming to the surface of is it a tool? Is it more than a tool? And talk about AI. And I'll give kind of, I'm going to muddy the water by giving my two cent 1st, so that you can either kind of rebut or give your opinion. So what we've looked at when it comes to a lot of technology adoption, at least what I've read, is that when a big kind of society changing technology is introduced, there's usually kind of a quote unquote lost decade of productivity going all the way back to the printing press. So in the beginning, when something comes out, there's so much energy applied towards figuring out how to best use this thing. And there's a lot of kind of societal wide trial and error. The same thing happened with the personal computer. So when it first came out in the kind of more seventies, eighties, there was a decade where actually productivity and efficiency went down because people didn't know how to use this tool and what's the best way to make use of it. I have a feeling that AI is somewhat similar in that. First of all, there's a lot more, I think, chatter about it and talking about it and philosophizing about it than actual adoption. So last time I checked, I think it was like 4% of american workers are actually regularly using AI as part of their job, and yet everyone is talking about it and more. So we don't really know how to fully integrate and use AI. There's also lots of issues like hallucinations and kind of making things up. So it's like a weird thing where we need people to go do the trial and errors, because that's the only way we'll understand how to really best use it and for it to evolve and get better with time. But also, this kind of, this is just my personal belief that the more we kind of overhype and say we have to jam it in and everyone start using it now, or actually decreasing productivity in these kind of large enterprises. So that's just my kind of two cent of where I see AI as really promising huge potential. I'm very interested, but is it really useful? Right now I have some doubt, but I'd love to ask you again, you're such an expert when it comes to humans and tools. Humans, technology. I know you speak a lot about AI and have amazing guests on your podcast. So how do you see this kind of unfolding right now? The kind of AI landscape, and how it fits in with companies, businesses in the landscape?


Lindsay Tabas: I want to first address the productivity claim. So it's interesting. I did not know that there was a decade loss. My first instinct is, I'm sure there were a lot of other things that were influential at the time, so wasn't a direct line, which is funny, because one of the things that I hold against the technology industry is the fact that we've had significant innovation over the past 20 some years, and in this millennia, productivity indicators have been stagnant comparative to the 19 hundreds. So I want to use that point actually to say we might not see the lag in productivity because the lag is already there, right? So we might not see that same lag because productivity was growing at an eight to 10% rate in the 19 hundreds, whereas now it's kind of like 2%, even though we've had all of this innovation, and that is a testament to actually how one enterprise, software and technology isn't doing its job and actually creating a more efficient work environment and operations. It also shows how, as one of my guests, Ronnie Batista, said, he's a design leader and consultant, a chief experience officer. He said last year, humans have a great way of working themselves back into the solution. Right? So I'll give you an example. I was researching yesterday the use of autonomous forklifts in warehouses. Actually, specifically on cargo boats. There's a scene in episode six of Silicon Valley HBO. Jared gets in an autonomous vehicle and goes into a shipping container. And that shipping container gets put on a boat. And on the boat, he finally gets out of the shipping container. He's relieved when he sees a forklift that he's going to have a person and it's actually a robot. And I was like, how real is this? And let me just do a quick aside. I am launching a series of Silicon Valley reaction videos. So if you're listening to this, please follow me on YouTube because those are coming out soon.


Mino Vlachos: Love it.


Lindsay Tabas: Anywho, when I went online and said, how real is this? You know, that was filmed in 20. 1420 1314. How real is this today? 2024. If there are autonomous forklifts, like, there is actually a person whose hundred percent job, like 100% of their time is to manage them and make sure that they, you know, are operating and address any, any bugs. So we have a way of always needing humans in the technology solution. Think about it with self checkout at the grocery store, sometimes there are one, if not two, employees standing there to help everyone go through self checkout. And here is an interesting fact from a project I did in 2003 in college, where we modeled the pacing of people going through a regular checkout and a self checkout. And it's actually faster for you to go through a regular checkout than to do it yourself. The only reason you perceive it to be faster to do it yourself is because you're more actively engaged in the activity. So grocery stores install these self checkouts, and the reality is they'd probably be able to move through these customers a lot quicker by having those two employees running a regular checkout system.


Mino Vlachos: It's just about making us feel better.


Lindsay Tabas: Essentially, we're like two steps forward, two steps back. So let me get back to AI. So this is all to say, like, hype is hype, talk is cheap. I call myself a pragmatic futurist because I have always been so attuned to humans and their rate of adoption. That I understand. Just because a technology is possible doesn't mean people are actually using it, right? So when it comes to AI, particularly AI and the enterprise, let me set a baseline. All of our major financial institutions and banks are built on top of software that they installed in the seventies in a computer language that no one understands these days. Okay. That's where a ton of data is. Okay?


Mino Vlachos: Yeah.


Lindsay Tabas: So what I'm trying, what I want to say is the systems are so old, you're scaring me now. Putting AI on top of it is not solving a problem. That's like just putting makeup on an ugly face, like so. So I address the productivity, I address the chatter. The reality is far, far more complex people. It was a lot of hype in 2023. There was a ton of hype in 2023. With the release of chat GPT, we have seen the tools become a little more integrated. For instance, you and I are recording on Riverside. They have AI tools to help us cut and clip up our videos, go into LinkedIn. They want to give you an AI assistant to write your messages and write your posts. So we're starting to see it creep. In certain places in the enterprise, there are particularly challenging issues. The first being in kind of an extension of these old systems, is that the data that the enterprise is sitting on is not good data. Okay. And here's another example. I did a consulting project for a data science company that serves top 100 power companies, and they're crunching massive amounts of data in enterprise systems like Oracle or SAP, customer data, usage data, etcetera. Now, this company was a services company. Every project was bespoke. They wanted to become more of a product company in order to be a product company, where you can offer one product to multiple customers and you can scale as a product company more than you can scale as a service company. The first thing we need to do is look at the top 100 of their customers and say how many are using the same software system? Because if they're using the same software system, then we can write our own solution to integrate with that software solution. We know what the data fields are. We know what the data type should be. It provides a consistent baseline. I asked that question, and they went off and looked at past projects where their different clients were using the same system, and the data sets were not equal. They were not the same. They were using fields in different ways to mean different things. So to simplify it, I might call in my system street address, one street address, two, you might call it. Address line one. Address line two. When you start doing those things, computers are confused. So AI doesn't have great data to crunch in the enterprise to be useful, that possibly AI could fill out the missing data and normalize the data so that we can do something with it. But that is a long project. That is a long way to go, right? And we, and we got here because humans are subjective. And for the past 40, 50 years, we've been installing technology without listening to humans and taking the input, the subjectivity input.


Mino Vlachos: You just remind me of, I used to work in management consulting, and one of my first projects, I was doing a project in one of the biggest pharmaceutical companies. And at that point, I was right out of university. I was kind of like naive and like, business is awesome. So I go into this pharmaceutical company, and I was doing mergers and acquisitions. So the project was to carve out and sell a piece of this business. And I was in charge of like the kind of HR work stream. So my task, I thought it was, I thought I got like the easiest project of all time. It was just, can you get a list of employees who work in this business unit? The address, what their job is, and the kind of financial part, what's their salary? And I was like, okay, so that'll take me what, like five minutes, right? I go into some database, I hit download. It took me four months of working, like round the clock. And this is also like in the, I was at Ey. So we're doing a lot of, like, working all night kind of thing. So I was probably working like 80, 90 hours a week for four months to get those four fields, basically name, address, their salary. Like that. Was it because the systems didn't talk to each other. They actually had no centralized database. There was none of that. And it blew my mind. How can such a huge company that's like a brand name, we all have their products, like, in our medicine cabinet right now, and you cannot get a list of employees.


Lindsay Tabas: It's insane, right? And, you know, one of the things, as you know, a culture critic of the technology industry, that I can tell you, I started my career in Silicon Valley in 2005. The professor, I did research for my two years in graduate school, Professor Bob Glushko, he sat, there were on, there were these data, there are these groups that talked about how to standardize data formats, and they were leaders from all of the big companies, Oracle, Microsoft, SAP, eventually Google in there trying to agree on the standards for different computer languages, CSS, HTML, C, ShaRp, trying to agree on the standards of document formats, XML, if you've ever heard that. And it sounded so promising, right? Because up until that point, at the time, I knew that all of these systems had proprietary data formats because that was the competitive advantage. That was the moat. If you were already using Oracle, it was going to be very hard to move to SAP. So that puts you in the Oracle family. And it says, we see it with Microsoft too, right? Like, you're not going to get slack. You're going to use teams because you're a Microsoft company, right? And so there was this hope in 2005, which was a very formative time. As we look back, it very much informs where we are today. There was this hope that companies could somehow come to an agreement for the best interest of their customers and to see the technology really succeed. It's at the same time that this field of is called service oriented architecture. We don't really use that term anymore, but we see, like, web services APIs trying to connect things. We were in the valley. We were talking about that stuff in 2005, and there was so much hope again that we could get these huge enterprise software systems to work together. It is now 2024. I interviewed Professor Bob Glushko on my podcast. I said, what happened to those standards boards that you were on? He said, I figured out that they're just a bunch of rich guys that want to play golf together. Womp, womp, right? Then I have a digital transformation leader from slalom Consulting, Berman painter, on my podcast, and he wants to talk about componentizing software systems for more flexibility. And imagine Legos, right? You know, you can rebuild, you can build a bunch of different sets with the same lego. Legos, right? You can build a woman, you can build a man, you can build a house, you can build an office, a picnic table, whatever. And so this dream of being able to do that with enterprise software, and I'm like, that sounds familiar. I love that idea. And I love that people are still trying to pursue it, but we've been trying to pursue it for 20 years, you know? And so when you talk about AI again, being deployed intelligently in the enterprise. Whoa, whoa, whoa, whoa, whoa. An analogy would be like, I volunteered in Haiti once. My, I was with some engineers without Borders. We were installing compostable toilets. I came away thinking, man, if we wanted to fix the wastewater and sewage in most of Haiti, we would have to airlift all of the citizens up off the island and install the piping, right? This is totally impractical. It will never happen. It is really the same thing with enterprise software. And the technology systems that we use. I would not go to Haiti with a flush toilet and try to install it when there is no piping, there is no sewage plant. Enterprise software is like bringing a flushable toilet to Haiti, is bringing AI into the enterprise. It's like, at a macro level, that's how I. At a macro level now, different enterprises are smarter. Capital one is a great example that's come up in my podcast. Like, they've done innovation really, really well. I was a former ING direct customer. I've watched as they have slowly integrated that into 360 checking, and I've seen over the past 15 years, the software from ing direct slowly get absorbed into the capital one experience and then, you know, replaced. And they've done a wonderful job of that. But at the macro level, most of our companies, even our health insurance companies and our hospitals, these things that, like, cost so much money, this is what they look like.


Mino Vlachos: Wow.


Lindsay Tabas: So, yeah, AI is also going through a trough of disillusionment right now, after all of the hype. The final thing to mention, just as, like, a footnote, if you aren't aware of it, is that AI takes five x as much electricity as a regular algorithm does. I think it's really unfortunate on Google that they are automatically generating AI results. It's so unnecessary. It's a waste of electricity. I wish they would turn it off.


Mino Vlachos: Yeah.


Lindsay Tabas: We don't have a power grid to support that. We don't have a power grid to support further ev adoption. So those are things that are going to bring down the reality, bring down AI into reality for us. And it's why so many conversations in my podcast I have, the first segment is crystal ball. What does the future hold? And I pitch predictions to my guests and say yay or nay. And all of 2023, I had the prediction, AI won't replace you, but someone using AI will. I don't think most people need to be worried right now about AI replacing them. I think they can take steps to learn how to use the tools to speed up what they're already doing, but I don't think it's a fear that needs to consume.


Mino Vlachos: Yeah. So I want to bring one other topic, which we were talking about before this podcast, which I had never heard of, but the moment you used the phrase, I was, like, really hooked. And that is the concept of a shadow it system. Can you explain to us what that is and then the effects of having a shadow IT system running within an organization?


Lindsay Tabas: Yeah, I laugh because this is actually fairly new to me. Over the past six months. And it's a laugh because I love learning. And so this new concept, or this, it's not a new concept, but it's the connection of a bunch of concepts together. That system I love. Okay, so shadow it is represents kind of all of the data and technology that is not approved, permissioned, monitored or supported by your company's IT department. The second you export a CSV from one of your enterprise software systems and create your own Excel macro sheet, and that becomes a dashboard that you share around that becomes shadow it. Why? Because it has sensitive, secure company data in it. Potentially you might be making changes to it and relying on that data over what's actually in the software system. So thinking about, again, you using this macro thing, making edits to the data there, but not in the enterprise software. That's why enterprise software data sucks, right? It's not accurate because you are using this macro based Excel spreadsheet you created, or people are using software tools, including AI, and copy pasting sensitive company information into an AI tool like chat, GPT, or Gemini by Google. And now that proprietary information is now absorbed by that large language model by the AI, and it's the right of that AI to use that data for other people. So this is all falls into shadow. It creates a lot of problems, but it's an interesting, like, circle of cause and effect that is a loop de loop that would make anyone seasick. So your IT department spends 30% to 50% of their budget on shadow it. So if you are wondering why that AI project's not rolling out or why the benchmark is so low for most it projects, just install it on time, right? Like so low. It's because almost half, up to half of your resources are being, being used to compensate for your excel addiction, for your excel crutch. But here's the thing. The reason so many people within a company use Excel, not just because they know how to use it and are comfortable with the spreadsheets, it's because those software systems that your company rolled out are inflexible to new use cases, and the employees can't do what they want to do with the software system, so they end up exporting to CSV to get the job done. Or they submit a request to it, but it has so much backlog from everyone else that just went and created a spreadsheet, spreadsheet on their own, that they can't pull the data for you, they can't create the report for you, they can't do it in a timely manner. So then you're impatient. You need to get your job done. You have your own deadlines. You're going to export the spreadsheet. So it's this loop de loop roller coaster that is really. It's a very big problem in organizations. And that's why I say that one of the best things leaders can do and any employee can do, is take the time to train yourself on the systems that you do use and have access to, because perhaps you don't need to export to excel. And let me say. Let me talk a little bit more about why that is a problem. We hear about security data breaches all the time. So much so that it seems okay for companies to not even email you and let you know. It's like, it's terrible, and it's a real problem. I have an episode with a dear friend of mine, Juma Kedada, on Cybersecurity 101. I encourage you to watch it. But a simple phishing email, which we all get tons of and we're trained to recognize, but there's always just that one person that didn't realize it. A simple phishing email can take all those Excel spreadsheets off your computer. Done. And that's how data breaches happen. Okay. It's. Yeah, it's cringeworthy. I see your eyes, like, it is horrifying and cringeworthy because, like, that's how easy it is for data to get lost. And this. This is a problem. Every company, there was a story on Ars Technica about Formula one using Excel to manage 80,000 car parts. And Formula one is known for being a very high tech company and using their brand image. And the CEO was horrible. The new CEO was horrified to find out that they were managing car parts in this Excel spreadsheet. And I was like, I'm not surprised. Right?


Mino Vlachos: Yeah. I have to say, when I was an employee, I was guilty of many things that you're talking about. So I'm like, oh, wow. I never understood the repercussions of some of my actions. And I am a big, like, it's not good, but I was a big, like, I'll just do it in Excel, guy. And I'm also reflecting that I feel like every management consultant has had a project that always ends up like, you're mapping the systems and you're trying to figure out where things sit, and inevitably, you somehow discover that some business critical operation is being run by one dude in, like, Bermuda who's about to retire in Excel. And if that goes down, like, somehow it will cripple the entire enterprise. And so everyone has a version of that story. So this is so pronounced in the business world.


Lindsay Tabas: Yeah. I do want to give you an example. This is what escaped me of how using Excel is not just creates more of a mess inside your company, it can have real damages beyond, actually, beyond identity theft and stuff for your customers. I followed a story where a man died on a construction site because the delivery of parts and assembly of parts was not in line with the structural engineers guidance, and it wasn't in line because they were using Excel instead of project management software. So the project management software, you can create dependencies between, like, step two can't happen until step one happens. But in an excel sheet, you can just drag row one below row two and there are no repercussions, there are no alerts, there are no notifications. And if the structural engineer isn't there to see it and then someone dies, like, that is real. I think we perceive identity theft differently than we perceive physical injuries and death because it's the same way we perceive a broken arm better than depression. Right? So we are not thinking about the repercussions of our actions the same way. And so that story was like another level of appalling. And it's why you should learn to use the tools that you have, even if they're not perfectly suited to you. People's lives and personal security depend on it. Your company is actually losing out on a lot of efficiency in the long run by you using the wrong tool for the solution.


Mino Vlachos: Wow, now I'm really rethinking my Excel choices.


Lindsay Tabas: I feel bad smiling, sobering, right?


Mino Vlachos: No, I know, but it's really useful to know and I'm happy we're really discussing this because I didn't know this and people need to know this. I'm now seriously rethinking, like, even within my own company, like everything you're mentioning, like, I wish I could sit here and be like, we're doing amazing, but we're doing it. Like, even amongst, I run a small consulting company, we are doing the very things you're talking about. And now I'm going to get off this call and go and whip everyone into action because we're not doing critical things, like you said around physical safety. But still, we use a CRM system. We choose to use HubSpot. And like I, even my co founder, he still uses like a Google sheet instead of using HubSpot. So this is like amongst the founders of a company, we can't even align on one use of technology and we're very small, yet alone, like a giant company where there's thousands of people. It's very difficult to align people and to, of course, like you said, have one piece of technology, meet everyone's needs. But I'm really understanding through this conversation the repercussions it could be, again, physical, financial security. Like there's so many places where when we don't attend to this stuff, it can really cause harm.


Lindsay Tabas: Yeah, I'll give you another example. I was going to guess you probably use a Google spreadsheet for some sort of project management too, or status updates or something like that. So one of the things I do for clients is take them from a service company to a product, or a productized service company. And I do that first by figuring out where the efficiencies are and how you can optimize your back end operations. You can't really deliver a standardized product or experience to your customers if you don't have a standardized backend. This consulting company was 70 people, and they had not agreed on a project management system. They had not agreed on an order for their document management. So what kind of inefficiencies were they experiencing? Creating bespoke proposals over and over and again, creating bespoke client deliverables over and over and over again. They were not able to cover their operational expenses with their revenue because of the inefficiency, the finger cuffs there is that people were so spread thin creating all of this bespoke stuff, there was never an opportunity to standardize everything. So it kept going. Again, it's just a circle that can make anyone seasick. And so I will tell you for your company, because you are small right now, since humans are subjective, every human you add to your company will add a new opinion and level of subjectivity. You need to implement the standards and build the consensus from the very beginning so that you can constrain the amount of variation and humans from that human subjectivity when you add new employees.


Mino Vlachos: Absolutely. And so this is where, and this is where we work in, like the leadership part of this is, it's tough because what we're talking about is, well, it doesn't really work if you're too top down, right? So you need to have some access to the end user and understand what really works for them. But then, similarly, what I've seen the last ten years in the more leadership development space is there's a lot of this, which is great because it's needed, but this more emotional, intelligent leader, the empathetic leader, somewhere in that, I think the message been construed of we should all just be the very kind of hands off cool guy, like anything goes. And what's lost in that is standardization is unification. And a lot of what we're supporting clients with is how do you have some emotional intelligence to have empathy? And also you need to set direction as a leader. It cannot just be an assortment of people hanging out, each making their own decision, because then you're not a company. You're like a collective doing like a cool guy hangout. And companies can't run that way when.


Lindsay Tabas: It comes to leadership. What I've heard, along with the compassionate and the EQ, is figuring out how you can lead yourself when you're in a system of bureaucracy. And that would say, go and export that CSV and get the job done, and then don't worry about all the things that are holding you back kind of thing. And I wrote down in my notebook, just right now, I wrote compromise. It's a compromise. So the top down leadership of saying you will use it because I say so. That doesn't work. You will use it because it's in the long term best interest of our team and our company, because not using it causes x, y, z problems. Here, let me give you a half day to learn how to use it. That I think is the leadership of the future. Right? It's explaining why this is the rule or why this needs to be the rule, not just stating the rule and saying, do as I say. Let people in to understand the problem so they can be part of the solution.


Mino Vlachos: I love it. So using some of that empathy to get into the shoes of the end user, using more kind of consensus to build the thing, but then setting direction and saying, this is what's good for all of us, we're in the same boat with each other. And then giving resources so we can actually train people and have them adopt the thing instead of having a good solution, but no one uses it because we never actually train them.


Lindsay Tabas: Yeah. And I also want to emphasize compromise, because your enterprise software systems you use at work are not going to change overnight. They're not going to become immediately flexible to use new use cases. They're still going to be onerous and clunky, and we're going to have to wait for the next batch of systems to sunrise at your company. In the meantime, please find a compromise for how you do your business process now and how you can more integrate the tools of record that your company has so we can free up it to install those new sunrise systems that might be more usable and more flexible to what you need it to do. So part of the solution, right?


Mino Vlachos: I love it. So, Lindsay, I love just kind of talking with you because as I've mentioned, our company deals a lot in the human side of the systems, and you're really at this, like, amazing intersection where people and technology and tools meet, which is so unique, and I find it fascinating. We've talked about many things around enterprise software and how it often goes wrong and some of the wasted resources. What are some alternatives? We've kind of walked through some of the mindsets of it when the corporate side incentivized them to just install and just deliver the problems that that may cause. We talked a little bit about AI, the shadow it system. So I want to give you an opportunity just to, as we close out, give your final thoughts and share anything you'd like to leave our viewers and listeners with. What comes to mind in terms of final thoughts.


Lindsay Tabas: One, pragmatic futurism. Right. There's still reason to be optimistic, and there's better choices we can always continue to make around how we use technology in our personal and professional lives. The other is that, and it's what I'm passionate about, is that we can do better as problem solvers. We need to elevate the way we innovate in order for the human experience to be positioned, like in front in the future. And we can only do that by involving the human in the solution. Okay. Technology is not a solution. It is a tool. Understand the hard human part first, and our solutions, the whole solution, will have the human experience front and center. So it comes with each of you every single day when proposing solutions and solving problems to focus. It's hard. Our egos don't do it. Our egos don't do it naturally. We think of what we think, not what other people think when it comes to a solution. Right? Put the human first as you think of the solution. Then involve the technology, and the human experience will be part of the future.


Mino Vlachos: I love it. I usually offer my own, like, final thoughts, but actually, I don't know that I have any this episode, because you've shared so much rich information with us and it's been really brilliant conversation. So I suggest if anyone is more interested in this intersection of the human and the technology, the tool, please check out Lindsay's podcast. And I also would so, which is the Make Sense podcast? And then I also suggest going to her website, learnproductmarketfit.com, where there's a lot of good resources for people when it comes to innovation. So I want to just thank you so much, Lindsey, for sharing really brilliant, insightful things. And I really am hoping that through this and all the other work you're doing, that we continue to bring these to the light. That's what we're passionate about. Anytime there's any shadow system of any sort of let's bring it to the light, let's bring awareness to it. And thank you so much for joining us today.


Lindsay Tabas: Thank you so much for having me. I really enjoyed having this conversation.


Mino Vlachos: Thank you. There'll be links for all the things we mentioned and a way to get in touch with Lindsay in the show notes for everyone. So thank you. And we're going to close the episode. Thank you for watching and listen.