From Data Overload To Decision Advantage: Inside Anticipatory Intelligence with Ansel Stein

[00:00:03] What if your business could start the day with the kind of briefing that was once reserved for presidents and prime ministers? Well today I'm going to take you directly into the Arizona desert because we're going to talk about something that feels increasingly relevant in boardrooms all around the world.

[00:00:22] And that is while many leaders are drowning in dashboards, alert fatigue and endless news cycles, my guest today is from a company called Crisis24 and he's asking a different question. How do you turn all that noise into judgment? Because my guest leads the development of Aya powered by Palantir, an intelligence platform that is built to help executives anticipate disruption

[00:00:51] rather than scramble around firefighting after it's actually happened. And this is not about tracking people or chasing hype. What we're talking about today is structured thinking, disciplined analysis and preventing the so what and doing so in a way that business leaders can actually use. So if you've ever been in the workplace and found yourself overwhelmed by data yet under informed when it comes to how do you make that big decision?

[00:01:18] I'm hoping today's conversation will resonate with you. So I invite you to join me as we explore what intelligence really means in a business context and how anticipatory thinking can actually change strategy. And yeah, yes, it is a tech podcast. We talk about AI, but we'll also focus on why human judgment must still sit firmly in the driver's seat.

[00:01:44] And on that note, it's time for me to officially introduce you to today's guest. So a massive warm welcome to the show. Can you tell everyone listening a little about who you are and what you do?

[00:01:58] Yeah, thank you. My background is actually in like government analysis and diplomacy and spent a spent a lot of years sort of thinking about how organizations navigate uncertainty, geopolitical, economic and kind of operational risks and how those connect and how decisions are made in organizations when the future is not obvious.

[00:02:19] So today, kind of working at crisis 24, IA, we bring together structured analytic techniques, which were developed in academia, but adopted in the government with AI agents to kind of turn. Sometimes we say signals and data into into judgment, usable judgment. And the goal is really to help organizations manage uncertainty, spot opportunity.

[00:02:46] And we really believe that opportunity and risk kind of live in the same space. And so the technology is really focused on understanding that sort of that sort of dynamic and bringing clarity to what what they do. And for people that are listening, hearing about you guys for the first time, hearing about AI for the first time, can you start by explaining in plain terms what it is, what it's built for or who it's built for?

[00:03:13] And the problem is helping leaders solve in these increasingly unpredictable world that we both find ourselves and there's a huge pond between us, too. But we're both seeing the same thing. So in a phrase, one problem we solve is the tyranny of data. So it's a tool built for senior leaders, let's say operating in complex, fast environments. OK, that's, you know, that's a pretty common thing.

[00:03:37] And it's but it's also that people who aren't short on data points but need clarity about what's changing the world, what it means to the decisions. Of course, you know, throwing the remark now we live in unprecedented times or something like that, you know, and let's acknowledge that the international environment is not what it was. It's not as predictable as it was. You know, let's say particularly the Munich Security Conference just happened. That was one of the major themes, right? Predictability or the old order is not the same.

[00:04:06] So I at its core helps leaders move from reacting to events to anticipating how risks and opportunities unfold. And that enables their organizations to act earlier, to act with intent and bring more of their organization into decision making, because it basically takes data in and runs it through our structured analytic techniques and tailors to a specific so what for that organization.

[00:04:35] And that's really like a benefit. We're our partnership with Palantir. The data side of it is really that benefit of structuring, cleaning, presenting data in really powerful ways. But that's not all the product does. It's not enough to just sort of present a data set. Right. And so AIA is not an acronym, but I will say if it was one, it would stand for anticipatory intelligence.

[00:05:03] So getting ahead and then the back end is integrated action. What do you do about it? So, you know, it's not enough to just present this this AIA data picture. You've got to move beyond it. And that's really what the tool enables users to do. And it was that partnership with Palantir that put you on my radar. And you are described as an intelligence platform rather than a typical AIA analytics tool. But what does intelligence actually mean in this context?

[00:05:31] And how is that different from simply, I don't know, collecting more data or monitoring activity, et cetera? Yeah. So to me, intelligence is really defined by relevancy. And it's understanding what signals matter, how they relate to what you're trying to accomplish, and what are the implications for a decision around that.

[00:05:53] So there's a lot of really great AI tools that can kind of tell you what's happening or what's trending. And that's the what. That's in the intelligence context, that's news. What's happening is the news. Intelligence is the so what. And what do we do about it? And so AI supports that process, but judgment is sort of the outcome we're aiming for.

[00:06:19] And that's the sort of the crisis 24 part of the equation through it and the experience of kind of our intelligence organization in bringing that into the machine that runs. And when I was doing a little research on you, one of the standout concepts is AIAS president's brief. So how did that come about? What's the story behind that?

[00:06:41] And how does condensing these complex global signals into short briefings change the way executives maybe make decisions day to day? Yeah, I mean, the idea is in government, you know, there's this huge apparatus to support senior decision making, you know, from collection to analysis and presentation through.

[00:07:04] And so watching how senior government leaders sort of consume information, they don't you know, they don't have time for a long reports. They don't have time for shallow analysis. They need rigor and timeliness. I heard one time, for example, that this is a long time back, but vice president, then vice president Dick Cheney's daily schedule was in two minute intervals. So if you were visiting, you know, you were the president of Finland or whatever, you got two minutes to make your case. Right.

[00:07:31] So this thing of how valuable time is and how efficient you have to be in supporting it. And so we modeled AIAS president's brief off of the president's daily brief, which the U.S. president gets, you know, kind of every day. Its goal is to sort of distill complex global events, short focused articles, as you said, kind of highlight the so what, what matters and all of that. What we what we do, what the tool does is produce state level intelligence.

[00:07:56] The output is really good, but it does it customized to our clients area of focus. So it's all hinged on what does my business need to do? What decisions am I facing and how can I approach those? And it's produced in, you know, a fraction of the time, but also a fraction of the cost that it would take a government to produce it. Right. You know, no business on earth has the luxury of having a couple hundred thousand people devoted to producing a daily briefing. Right. It's just it's just not.

[00:08:25] And technology has transformed. It's it's wonderful through. But you still have to have that rigor to get to the the so what why why still from a business leader? Why should we give our time to reading this every day? It has to be hinged on on relevancy and inaction ability. And you draw on intelligence tradecraft that has traditionally been used at the highest levels of government.

[00:08:49] So how do you adapt that discipline for business leaders without importing the secrecy, the surveillance culture that many people will associate with intelligence work? Yeah. So it's funny. I love I love this question because he does sort of intelligence tradecraft. Let me talk about analytic tradecraft because analytic tradecraft is actually really about transparency. It's not about secrecy.

[00:09:11] It's about, you know, testing your assumptions in a transparent way, being clear about how you're weighing the source information that goes into it and avoiding cognitive biases. And, you know, the sort of like, well, I know this is right. So you read everything through to sort of prove that prove that one point. So, again, transparency, not secrecy. Right. You should be willing to say test my test my assumptions. So when we were building IA, we anchored it on two public standards. These are intelligence community directives.

[00:09:40] They're called ICD 203 and 206. They're U.S. government public documents. Anybody can Google them and see them. Those two things are really powerful because they define analytic rigor and sourcing discipline. How much work should you put into your assessment and be clear about it? And how should be clear about why you actually think that? So, in other words, there should be an according to or judging from statement at the end of every, you know, I'm saying the sky is blue, according to me looking out the window.

[00:10:08] Right. So the person knows, OK, this is this is the basis of it. So we hold ourselves to those standards because they're transparent and you can be accountable to them. Right. That's that's the first part of it. And the second is that the goal is sort of better reasoning amid uncertainty and not watching not watching people or collecting this, that or the other. It's the sort of the rigor of the argument and and and the linkage of the data to the question. And that's the power of it.

[00:10:36] And there will be people listening that are curious, but also cautious about AI because they worry about constant tracking loss of privacy. So when you explain to someone with those concerns, especially when it's linked to the Palantir partnership, how do you describe what the platform does, what their data uses and how clearly those boundaries it operates within? Yeah. So, you know, we really direct about this. We don't track individuals. We don't monitor employees. We don't scrape private data.

[00:11:05] The machine does not need any of that actually to do it. President's Brief works with primarily open source information. These are, you know, articles published in journals, news reports, government assessments, think tanks, et cetera. That is the vast bulk of the reasoning. And then that's complemented with commercial data. This could be, you know, port flows. It could be trade numbers, et cetera, et cetera. And then client approved inputs. That's how you get to the relevancy, what the company wants the machine to think about in addition.

[00:11:35] And there's really clear governance about how that data is used. I often say one of my mantras is we're actually in the trust business first and foremost, because the moment you break trust with somebody, you know, your analysis is shot. They're never going to believe that you're credible if you sort of violate that. And so when we stand up an AIA instance with a client, one of the first things we do is have a really deliberate discussion about what's the data mix that they want to inform their decisions.

[00:12:03] And sometimes it's we want to lean into proprietary data because we're trying to understand how tariffs are affecting our production in countries A, B, and C. Okay. Sometimes it's we're going to do our own assessment. It's going to be in column A, and we want the I assessment to be in column B because we want our assessment to be checked. And we want to, you know, transparently present that through.

[00:12:26] So the power of it is actually in those outputs and you don't, you don't need, in fact, you would be overloading the system and wasting time to collect PII or any other type of data to go into it. It's at that strategic level that's sort of above and beyond that. And most intriguing, I think, is Crisis24. You position anticipation as the goal rather than reaction, which is incredibly cool.

[00:12:52] But can you walk me through how this anticipatory intelligence works in practice? And maybe if you're able, we'd have to name any names, but share an example of where Foresight helped an organization prepare for disruption or prepare for an opportunity. Yeah. So, I mean, the core concept with anticipatory intelligence is that individual events may not matter on their own, but they become meaningful in combination and in looking ahead.

[00:13:19] And so we try and structure our assessments around those signals and explore them. As an example, AYA produces a standing political instability assessment on every country around the world and a lot of subnational. And that looks ahead a year. So 12-month planning ahead. So you can start thinking through, these are the signals we should be monitoring.

[00:13:43] Let's say we source, you know, an important ingredient to our manufacturing in this company. We want to monitor how things are going because if there's an uptick in instability, we might want to diversify our supplier. Or we actually might be an alternative producer of that and we might be spotting opportunity through it.

[00:14:02] So in practice, being anticipatory and structuring the analysis that way gives organizations time to, you know, adjust supply chains, maybe rethink investments. Think about risk mitigation factors. Let's say you're running big international events. You want to know what do we need to have in place on the ground to make sure everybody's safe, accounted for, and enjoying the event to go through. And so there isn't a disruption. I want to be really clear, though.

[00:14:31] There's no crystal ball. Nobody has a crystal ball, right? It doesn't exist. So it's not actually about predicting outcomes. It's about being less surprised and thinking through options and then being intentional about it. An example is the president's brief might present an article. Some event is happening and what it goes through. It also will supply you with a scenarios analysis of how that might impact, you know, I don't know, your factories in a country in the next six months. So there's a new tariff coming out.

[00:15:01] It's not going to hit at this. And scenarios analysis will help you think through what's most likely, least likely, and most disruptive. And then you can have a more robust conversation around what do we do about it? You know, I talked about what and so what before. What a business does, what we want to prepare a business to do is to talk about how do we react to what's happening? How do we get ahead of it?

[00:15:24] And really leverage that human wisdom in the business, not argue over what is actually happening, how we interpret it. That's a really powerful transformation because you're taking intelligence from a supporting function and purveyor of information to an informer of decisions. And ultimately, that's where you get at confidence. How do you have more confidence in what we've done? Well, there's the rigor to the machine. It's anticipatory. It's looking ahead.

[00:15:52] Instead, we've had a robust conversation. And that, you know, that flip there is going from the tech world to the human world. How are we making humans function better inside of organizations? It's because they're coming to the table more informed. They have more continuity of information. And they're thinking through, you know, like I said, a scenario or alternative futures assessment or something like that to really get to a point of, yeah, preparing for difficulty.

[00:16:20] And what I love about what you just said there, it's not just about the technology. It's about the human fact as well. Human judgment still matters, especially in moments of crisis, possibly even more important. So how do you support decision makers without replacing human responsibility, context and accountability? Because it's a big topic right now, isn't it? Yeah, no, it totally is. And at the end of the day, like no machine, I or any other makes the decision. Humans make decisions, right? So you can't lose sight of that. Yeah.

[00:16:50] And what we try and do is like strengthen decision making, making by servicing options, talking about tradeoffs and making uncertainties kind of more clear for providing clarity. So like a key difference I want to sort of mention here is couldn't I just use a commercial LLM to do this? And I think if, first of all, it's happening in every company around the world and we should all be afraid of that, right?

[00:17:17] The amount of sort of scribbing that's going on, you know, you don't want to rely on the cliff notes for the, you know, for your master's thesis on Shakespeare, right? You want to, you know, it matters. It's important enough to provide the rigor. So, you know, our tool is informed with the experience with, dare I even say, wisdom of, you know, Christ24 is the largest private sector intelligence company in the world.

[00:17:43] So we've tried to bring that experience and expertise into the programming of it and the prompts of it. It's not just trained off the internet, if I could put it crudely like that. And because our expertise, because of that expertise and that experience, we can also help companies implement it. Because it's not really about buying a tool. It's about bringing a tool in and how do you stand it up to accomplish the goals that you want in your company and that tailoring of it, the flexibility.

[00:18:12] So maybe in summary, you know, accountability, context, responsibility, sort of always stay with people. I think what we do is we really provide them a more solid grounding to do that. Better information, better consideration of options and that continuity across the decision-making team of, okay, we've looked at the intelligence. Now let's have the conversation about what to do with it.

[00:18:36] So to give everyone listening an actionable takeaway, as AI-driven intelligence inevitably becomes more common in businesses, what do you think leaders need to be getting right to deliver real value while also maintaining trust with employees, customers, and indeed the wider public? Any tips here on what they need to be preparing for, what they need to be doing?

[00:18:56] So I think the risk is sort of adopting something, adopting AI without sort of strategic intent and boundaries and governance and sort of transparency with the workforce and externally too. We're bringing this in because we want to accomplish A, B, and C. And being really explicit about what AI is used for. So in the case of AI, hey, we're bringing this in because we think it accelerates the decision-making cycle. It also gives us more confidence.

[00:19:24] It gives us rigor that we can use to communicate internally, externally to our board, et cetera, et cetera. And it helps us – it helps our humans focus their gray matter on where the humans really need to, right? That human is accountable for the decision. They're better informed decisions. So I think – I do think the organizations that get this right, that sort of pair AI with transparency, with discipline, with trust, are the ones that are actually going to sort of perform better.

[00:19:53] If you don't do it intentionally, that's where you end up. The reality is we're all sneaking on to the commercial LLMs, you know, not just for dinner recommendation and a recipe. It's also to like, oh, how can I make – you know, how can I make this speech better or something like that? So I think it's – you know, businesses need to accept they're moving on and there's that responsibility to do it in an accountable way, you know, kind of to the workforce and to the best of the companies – to best advance the company's ends.

[00:20:23] And we have covered a lot today, but there's only so much we can cover in a 25-minute podcast. So for anyone listening wanting to dig a little bit deeper, connect with you or your team or just find out more information about how it works, who you're working with, where would you like me to point everyone? To our website, please. And that is aia.crisis24.com. Aia is spelled A-I-I-A.

[00:20:48] And there's some perspectives on there, on that website, you know, sort of about risk, about anticipatory intelligence and kind of navigating uncertainty. And it's easy to get in touch with us off of that. Well, Crisis24 is widely regarded as a global leader in integrated risk management. And you provide intelligence, technology, and proactive solutions that better help organizations anticipate, prepare for, and respond to threats.

[00:21:17] And that sounds so much better than firefighting and reacting after the fact. And you're also trusted by so many Fortune 500 companies and high-profile individuals. So for those reasons alone, I'll urge anyone listening to check out the links. I'll put them in the show notes. They'll also be on the blog post associated with this at techtalksnetwork.com. But more than anything, thank you for taking the time to sit down with me today, talk about it in a language that everyone can understand, and dispel some of the fear and myths there as well.

[00:21:47] So thank you. Thank you, Neil. It's been great. Really appreciate it. If there is one takeaway from today's conversation, I think it's that data alone does not create confidence. But clarity does. And what struck me about Ansel Stein's perspective here is the emphasis on rigor and transparency. And intelligence, as he describes it, is not secrecy or surveillance.

[00:22:12] It's actually in a business context about structured reasoning, tested assumptions, and a clear link between signals and decisions. And in a world where everyone has access to AI tools, I think that discipline might be what actually separates reactive companies from better prepared ones. And we also heard today something equally important.

[00:22:35] And I think tools like AI should support leadership, not replace it. So if you're curious about how anticipatory intelligence could fit into your organization, please check out the links in the show notes to learn more about Ansell and his team at Crisis24.

[00:23:05] And as always, I'd love to hear your thoughts. Are you reacting to events in the workplace? Or are you trying to anticipate them? And how are you doing that? As always, techtalksnetwork.com. Let me know your thoughts. But that's it for today. So join me again tomorrow for another conversation. And I'll speak with you all then. Bye for now.