3236: ThoughtSpot and the Rise of AI-Driven Decision Making

[00:00:04] The world of data analytics is undergoing a seismic shift right now. And AI is at the centre of it all, predictably of course. But what does this mean for data professionals, business leaders and organisations all striving to make better data-driven decisions? Well today I'm joined by James Smith, Vice President EMEA at a company called ThoughtSport

[00:00:30] and they are at the forefront of AI-powered analytics. And my guest also has a reputation as somewhat of a dynamic leader in the data space. So today we're going to talk about how AI is reshaping the role of data analysts, while AI could be the new BI, and the importance of cultivating a 2% done mindset. And what the future of autonomous enterprises could look like as a result.

[00:00:58] So if you're interested in looking beyond the hype that surrounds AI and learn more about how it could help you unlock new opportunities for businesses, or why the best companies never stop evolving, and how data-driven decision making is becoming more accessible than ever, this is one of those episodes that you're not going to want to miss. So with that scene perfectly set, it's time for me to introduce you to James from ThoughtSpot.

[00:01:25] So thank you for joining me on the podcast today, James. Can you tell everyone listening a little about who you are and what you do? Yeah, thank you for having me. I'm really excited to be here. So yeah, I run ThoughtSpot in EMEA. I've been in the role for about 10 months, been brought into double our business in the EMEA region and inherited a brilliant team and some brilliant customers I get to work with every single day. My background is analytics.

[00:01:52] I've worked in analytics for 15 years. I was lucky enough to stumble into the industry out of university, but really got hooked on the concept of putting data in the hands of people and seeing the ideas at that spark, see the creativity at that spark. And so I've kind of had a, you know, the North Star of my whole career has been about how do we make data more accessible and put data in the hands of more people.

[00:02:20] And I think with what's happening in the market with AI, there's a, you know, we're making real strides on that. And I think ThoughtSpot is a, for the listeners that don't know, it's a complete intelligence platform and help people use AI to answer analytical questions. And it's a really exciting moment for us in the market. And we feel like we're in a real sweet spot to help people there, help people do that. Well, as I said, it's a pleasure to have you on the podcast.

[00:02:49] We've had four or five different guests from ThoughtSpot over the years. So almost friends of the show. And you talked about AI there. And I think we've even gone a step further than just generative AI. There's a lot of hype around agentic AI this year, especially after Gartner. I think they predicted it was going to be the big trend of 2025. Everyone seems to have jumped on that. But how do you see agentic AI reshaping the role of data analysts specifically?

[00:03:17] And what skills are most valuable in this new era, would you say? Yeah, it's all moving very quickly is what I would say. As I say, I've been in the industry 15 years. I think I've learned more in the last 12 months than I did in the first 14 years. And I think all of our roles are changing with AI and the new world of agentic AI. I think the data analyst role in particular will shift massively because I think a lot of

[00:03:46] organizations have employed a lot of really highly skilled people that are really capable to work with data, but often find themselves just swamped by ad hoc requests that come into the business from the business users. And what agentic AI gives us the capability to do is kind of automate and remove quite a lot of those ad hoc requests and actually free up the data analyst to use some of those skills to work on

[00:04:14] some of the sort of bigger ticket, more needle moving items in the business and harder problems to solve that can't be automated, that take things like critical thinking and creativity and business context to really understand and tackle. And I really think that's where those are the skills that are going to become more important because AI is brilliant at automating things. But, you know, the human elements around problem solving and judgment and critical thinking,

[00:04:43] I think, is going to become really important. And I think that's where the role of a data analyst will evolve because we're already seeing ourselves. We launched a product called Spotter in November, which is an AI analyst. And we're really seeing, you know, what that allows is that gives every person in the business an analyst to help them answer questions, help have conversations with them, help them do their work and make decisions.

[00:05:10] And we're seeing that, you know, that can take away, you know, a large proportion of the ad hoc requests that might have gone to a data analyst. But we don't think that organizations remove those data analysts from their org. We just think they reposition them into actually now we can work on the big problems that we've been wanting to tackle for a while and actually deliver much higher value from the data analysts that we have in the business.

[00:05:37] So, yeah, it's a real, it's changing quickly, but we really feel like it's an important role that's evolving at pace. And another reason I was excited to get you on today was to talk about the 2% done mindset and how that encourages continuous self-improvement. So, can you tell me a little bit more about that mindset and ultimately how businesses could maybe adopt an approach like this to drive a culture of innovation and excellence? I know it's something you're passionate about.

[00:06:07] Yeah, it is. It is. Yeah, yeah. It's up on our office wall. We have one of our walls in our office. We have one of our office. We have two percent done. And it's just a bit of a, it's a mindset approach. It's a mentality. And it's one I'm very passionate about myself because it encompasses a growth mindset. And I'm a massive advocate of continual learning, you know, continually, you know, the more you learn, the more you realize that you don't know.

[00:06:35] And that's where that sort of that two percent mentality comes from. And it's, you know, from our perspective, like we're really proud of where we are. We think we've got a really great product and a really innovative product. We're delighted with the customers that work with us every single day. But we're only just getting started. And I think, you know, with the world of AI and agentic AI and generative AI and where that's going, I think just the opportunity in front of us is just so immense.

[00:07:05] And as I said, the innovation is moving so quickly. I really think the whole industry is just scratching the surface of where things will go. And so it's just reminding us of that, that, you know, the journey ahead of us is much greater than the journey behind us. And I think organizations can adopt that, you know, going back to, you know, first principle thinking when they're trying to tackle problems

[00:07:30] and making sure that, you know, sort of growth mindsets encourage, give people the space to be creative, give people the time to be creative with data, but also give people the time to develop and learn. And, you know, I'm a big learner from podcasts, a podcast like this one, give people the time that they can, you know, go and listen to content and go and really understand and go deep on a subject. And I think it's, I think with how fast things are moving, it's really important that, you know,

[00:07:59] you create the space for the people that you work with to be creative and have the ability to learn. I think that's a really important part of it. Completely with you. And the percentage also reminds me of the old saying, how do you eat an elephant? And of course, it's one bite at a time, you know, it's just chipping away every day. And from previous conversations with your colleagues, ThoughtSpot does have a reputation for making data more accessible. And of course, data is the lifeblood of all things AI.

[00:08:29] So how do you see AI-powered analytics transforming business decision-making in the months and years ahead? Because everything's moving that way now, right? Yeah, yeah. Yeah, we're talking a lot at the minute about how AI is the new BI and how, you know, I think there's been lots of years where there's been lots of solutions deployed to organizations

[00:08:56] with the nirvana of, you know, 100% self-service of all business users. And unfortunately, we kind of live in a world where still only like, depending on which bit of data you look at, 20, 30, 40% of people use data for decision-making. And I think a lot of people are looking at AI as, okay, is this the technology breakthrough that can actually help us

[00:09:23] make those strides into the vast majority that are still not using data for decision-making? And I think what AI does is it lowers the barrier to entry because it makes the interaction with data a lot more conversational. And I think that's obviously where our platform's pointed is how do we help people have conversations with data? So rather than consuming in, you know, static reports and dashboards that kind of answer your question,

[00:09:49] but kind of just stimulate another 10 questions, how do we make that a much more of an iterative, interactive process? How do we give people different experiences so that they can actually, yeah, have conversations and ask the questions that are pertinent to them? And how do we, you know, how do we get to the point where there is real large-scale self-reliance? Because in doing that, you're completely changing the operating model of how most organizations are set up with data.

[00:10:19] In that most organizations are set up where there'll be a centralized data team servicing data to the wider population. Whereas I think what AI gives you the ability to do is flip that on the head and say, actually, we're going to have large-scale self-service. We're going to have an AI assistant, an AI analyst sat on everyone's shoulder, helping them answer those questions themselves. And actually that operating model becomes a lot more dispersed into the business areas.

[00:10:46] And then where it's going next, you know, coming back to Agentic AI, you know, we have a bit of a vision around sort of an autonomous enterprise where you've got agents helping you solve the problems that you want to solve in your business. You know, I want to increase sales by 20% next quarter. You know, you give an agent that task and they are going to go and look at all the different data sources available to them

[00:11:15] to understand how that might be able to be accomplished and then bring those data points and insights back to you so that you can help make that decision and help, you know, drive your business forward to make the change to drive the 20% that you need. And that's going to happen a lot more autonomously than it is today, where, you know, there's lots and lots and lots of people in the loop of helping to make those decisions.

[00:11:41] So we think the future is a real interesting one about how organisation is going to evolve. You need to not make the same mistakes that we've made in the BI era and really embrace AI as a way to transform and change your operating model and then embrace this new future around an autonomous enterprise. And, of course, with the rise of AI-driven insight, there's a whole set of arguments on how we use AI.

[00:12:06] I've always said that you should use AI to create version one and then version two. That's where you come in. That's where you make it your own. But how can organisations ensure that human intuition, human expertise, still play that critical role in strategic decision making? Because it kind of gets lost in the argument sometime with AI or AI versus humans. And I think the reality is somewhere in between. It's both using them both together. That's where the magic happens. But what do you see here? Yeah, no, I completely agree.

[00:12:35] I think it's using AI for what it's good at and having people really at the centre of it as well and using both the capabilities at the same time. So my particular view on that is AI can't be a black box. I think we're running into problems if AI machines are off making decisions without us. We've all seen the movies that that can create.

[00:13:06] And I think the really importance is when we are using AI to automate things, we are using AI to have conversations with us or deliver us insights that we might go and make a decision on. It's really important to have transparency of how the agent has come to that insight, where that's come from, what data points has been used. And you need to have that transparency and you need to be able to explain it as a person

[00:13:35] if you're going to base that insight for a decision you've made. And it's really important to keep sort of that human in the loop feedback really prominent. And that might be human in the loop feedback when you're building these agents or coaching them or giving them the business context and the terminology you want them to use. But really keeping the human in the loop in the decision making process and framework. And I think use AI for what it's good at. It's great for tasks that are repetitive.

[00:14:05] It's great for things where you've got a desired outcome. Use AI for that. But combine that with the human intuition. Combine that with the business context that come from people. And I think if you get those things working in tandem, that's where you can really leverage the advantages that AI can bring to the table. And you've worked with leading data and analytics companies throughout your career, everywhere from Tableau to ThoughtSpot now, of course.

[00:14:35] And I'm curious, from everything that you've seen throughout your career, what are the common challenges that everyday organizations face when adopting those data-driven strategies that are now paramount? And how can they overcome some of those challenges? I suspect you've seen a few trends and cycles throughout your career. Yeah, I have. And I think it's really shifted in the last 12 months. I think for a good decade, data teams and chief...

[00:15:03] There's been a rise of the chief data officer role, which would indicate the rise of importance of data within organizations. But when you speak to chief data officers and, you know, data functions and teams, so much of their role has been about evangelizing and advocating and communicating the importance of data. And I think there's some organizations that have done that really successfully.

[00:15:28] And there's a lot of organizations that have really struggled to really get the business to lean in and understand the importance. And so the demand for data solutions hasn't necessarily been there. And a lot of it's been data function almost trying to pull the business functions along the journey.

[00:15:52] I think GPT and large language models have completely turned that on its head in that, you know, most people are using some AI to help them be a bit more productive. You know, they might have used, you know, ChatGPT or another tool. And so actually the awareness of what data can do has skyrocketed in the last 12 months as those tools have been more pervasive. And actually the awareness isn't the problem anymore.

[00:16:22] People are really aware of what can be done. But most people are therefore asking their data teams, I just want to be able to ChatGPT my data. And like, have you got a solution for that? And that may sound like a really simple thing, but actually, unless you've invested in, you know, great data foundations, great data management capability, great data quality,

[00:16:45] you're not going to be able to capitalize on the opportunity as much as you might be able to. So I think it's a really interesting moment where all of a sudden the demand's really high and the data team, you know, depending on where you are, could be a little bit overwhelmed. I think it all depends on where your foundation's a bit upon. If you've got decent foundations, you know, you've got a real opportunity to change the operating model,

[00:17:16] disperse self-service in a really large scale and capitalize on a lot of the ease of use that AI brings to the table with making data more conversational. I think that's a real opportunity if you've got your foundations in place. If you haven't got your foundations in place, then you've maybe got a little bit of work to do. But I think it's really important in that aspect to not really understand that there's not a one size fits all approach.

[00:17:43] Like different people in the business will have different levels of data maturity and therefore need different experiences to interact with data. You know, there'll be some people that will just want, you know, alert service to them. There'll be some people that want to have conversations with data. There'll be some people that want to do like deep data science. And I think that's kind of where our approach goes around. I mentioned the complete intelligence platform earlier on in the conversation.

[00:18:09] We've just launched like an analyst studio product, which is designed to allow analysts to do that deep analysis alongside the business users who are going to be more conversational with data. And I think it's about having a suite of tools, understanding there's not a one size fits all approach, understanding the different maturity levels of your data within the organization. And really trying to capitalise where you have got that maturity trying to capitalise,

[00:18:35] where you haven't got that maturity really trying to, you know, plug some gaps so that you can at least take some steps forward. And I'm curious from everything you see here in the present day and one eye on the future. Are there any trends in AI and data analytics that you might be watching closely? Maybe they excite you. Last thing you're thinking about before you go to bed at night, when you jump out of bed in the morning. And when you get all those ideas and everything that you're watching, how it's evolving, how should businesses prepare for what's coming next too?

[00:19:05] So I'm incredibly buoyant and incredibly optimistic and excited about the future. And I say that as a non-technical person myself, like I am passionate about the impact that data can have, but about as technical as I get is a sum formula in Excel. I can't write code. I'm not technically skilled, but I do want to use data to drive my business forward.

[00:19:29] And what I've seen recently is just the step forward in the capability that is aimed at someone like me. And I say someone like me because I think I'm in kind of like that majority of business users that aren't technical, that have some degree of data literacy, do know what questions they want to ask of their business, do know where they want to take their business,

[00:19:54] and do know that data has a role to play, but doesn't necessarily have, haven't had the technical skills to use the platforms that have been available to date. I use an example here. I recently had a flight across to San Francisco and I'm not great at long haul flights. You know, I'm not great at being locked up in an aeroplane for 11 hours.

[00:20:18] But I spent eight hours on that flight analysing data and I was able to do that with spotter AI analyst. Without having to go and ask another person, I was able to just be in like the flow of analysis. I was able to ask questions, get answers, find things, make decisions. And like the flight just absolutely flew by.

[00:20:43] Awful pun, but I was able to just stay in the zone of analysis and not get to a point where I got to the limit of the question that I could ask. It was like limitless curiosity. You know, someone giving me a great data set that I've not seen before and I just explored it for hours and hours and hours. And that really excites me because if you work in an organisation that, you know, has 10,000 people,

[00:21:09] imagine 10,000 people being able to ask limitless questions of their data and the impact that that can have on all of our lives and all of the businesses and organisations that we work within. And I don't think that was the case six months ago. I don't think that we had that sort of unlimited flexibility six months ago. And I think it is just going to get easier and easier and easier is the thing.

[00:21:33] Again, I think we're just scratching the surface on how easy this is going to be because as autonomous agents start to come into it, you're going to be able to set off a bunch of agents with a bunch of different tasks and they're going to be doing that while you're sleeping. They're going to be doing that while you're commuting. They're going to be doing that work while you're in another meeting. And then they're going to be coming back to you with the best bits.

[00:21:56] And it's going to be really interesting to see how that sort of human interaction with agents works and, you know, and what sort of world that creates. And but I'm really buoyant about the world that it does create and where it can take us. And so, yeah, it was a really freeing experience for me on that flight. And I'm I'm really encouraged by the fact that hopefully, you know,

[00:22:21] hundreds and thousands of people will be able to have that freeing experience with data in the in the not too distant future. Love that exciting times ahead. Sounds like you were more productive than watching the Joker 2 or June 2. That was on my way back. And obviously, before you came on the podcast, I always do a little research of my guess. I do a little cheeky Google, see what it brings up. And beyond work, I was also reading that you're passionate about golf and collecting rare gins.

[00:22:49] So I've got to ask if we put all that into the AI melting pot, any surprising connections between those interests and your working data and AI? Do they converge at all? I definitely think they do. So, yeah, I collect gins. I've got 250 odd gins at home. I've tasted them all. I haven't finished all of them, but I've definitely at least tasted all of them. And yeah, very keen golfer when I can find the time.

[00:23:19] I actually think both of the processes around gin making and golf do lend themselves to, you know, data and data analysis. Gin in itself is actually a really cool mix of creativity and science. It goes into the different flavourings of gin. And obviously, the scientific bit being more data-driven, the creativity bit being more human-driven.

[00:23:45] And so those worlds come together when you see a massive distiller, you know, using those two faculties together. And then the output of a brilliant tasting gin and tonic is a bit of a wonder to behold. And golf is – there's tons of data on golf and you can go really, really deep. I myself, when I'm really keen and trying to go through a stage of improvement in my golf game,

[00:24:12] I get really deep into collecting all of my stats, all of my – every shot I hit. And Mats Fitzpatrick is a very famous English golfer who every shot he's ever hit in his career, he's written down and collected into a spreadsheet and a database that he then analyses into how he makes himself better. And, you know, that's what we're trying to do in our business.

[00:24:40] We're trying to use the data points that we have to analyse our business and make the decisions that will improve us and move us in the right direction. And I think that can be applied to gin making, to golf and probably to a whole bunch of other stuff we do in our lives. So, yeah, no, I definitely see the sort of similarities between those interests and what I do in my professional life. Oh, I love it. Well, my next challenge to you now then is to create your own gin based on all the data you've got, those 250 rare gins and everything that you've learnt about it.

[00:25:11] And make me first in line for a sample bottle of that and we can have that over our next conversation. But thank you for sharing your insights with me today and having a bit of fun with me. I'm going to see if we can do something for you now because some of the biggest names in business, VC funding and tech have either been guests or listened to this podcast or maybe through the six degrees of separation, a friend of a friend may just get to hear this. So who would you like to have a breakfast and lunch with and why?

[00:25:41] Just in case that person gets to hear about it. Who would it be and why? Yeah. So as I said, massive podcast listener. Listen to your podcast whilst going around the golf course, actually. But yeah, one of the podcasts that I subscribe to and a bit of religious listener to is a podcast called Grit. And there's a presenter on there called Jubin Mazagan. He's had some of the most fascinating guests in software.

[00:26:08] He started interviewing a lot of chief revenue officers, now interviews a lot of CEOs of software companies and actually companies outside of software, just successful companies. And it's all around this concept of like, what does it take to be successful? And Grit being the overarching attribute that every successful person has to some degree or the other. But yeah, I just find that particular podcast really fascinating.

[00:26:38] And I'd love to meet Jubin to pick his brains on a whole bunch of stuff that have come up over the episodes. So yeah, if there's anyone, it would probably be him. Oh, fantastic. Let's see what we can manifest and make happen now. Throw that straight out into the universe. And now I know you listen while you're on the golf course. I might throw in the occasional thought every now and again just to raise a smile.

[00:27:01] But before I let you go, for anyone listening just wanting to find out more information about ThoughtSpot, find you fascinating, want to learn more about you or the work that you're doing, where would you like to point everyone? Yeah, so me personally, I'm pretty active on LinkedIn. So you can find me on LinkedIn. That's probably the best place to get a hold of me. ThoughtSpot in general, our website, ThoughtSpot.com. You can have a free trial of our software.

[00:27:31] You can book in a demonstration. We've just a couple of weeks ago had a whole week around Genitive AI and Agentec AI. So there's a whole bunch of great content there to go and listen to and educate yourself on. So yeah, those would be the places I'd point you. Awesome. Well, I'll make sure these links added to everything so people listening can find you and find out more information.

[00:27:56] We've covered a lot today from how Agentec AI is helping to shape the future of data analysts, evolving their roles into a more strategic business partner, but also the importance of that 2% done mindset and how that's pivotal for businesses when encouraging a culture of self-excellence. Throw that into the mix with AI and great things can happen. But thank you for starting this conversation today. Really appreciate your time, James. Yeah, thanks. I really appreciate you having me on.

[00:28:25] So as AI continues to redefine the role of data in business, one thing is clear. AI is becoming the new BI and we're only 2% done. So thank you to James for sharing his fantastic insights today on how AI is elevating data professionals into strategic business partners and why curiosity and innovation should be embedded in company culture

[00:28:51] and how ThoughtSpot is helping organizations turn data into action and do that seamlessly and intelligently. So a huge thank you to James Smith for his insightful discussion today. But now, time for me to put the microphone in front of you. I want to hear from you. How is AI changing the way you work with data? Don't just passively listen, please. You've got so much more to add, I know.

[00:29:19] So hit me up on LinkedIn, X, Instagram, just at Neil C. Hughes. Easiest guy in the world to find. Don't just hit that follow button. Send me a little message. I'd love to hear from you. So thank you as always for choosing to tune into this podcast. I know you've got a lot of choice out there and a lot of people vying for your attention and time. So it means a lot that you've not only listened but managed to get this far. So thank you for listening as always. And hey, if you enjoyed yourself, why not join me again tomorrow?

[00:29:48] We'll do it all again. How many other podcasts can weave AI, rare gins, golf and business intelligence all into one conversation? Hopefully that will be the hook that will keep you coming back for more. Seriously, though, I will speak with you all tomorrow. Thanks again. Bye for now.