Webinar: Decision making in times of uncertainty

When the ground keeps shifting, how do good boards keep making good calls?

Economic volatility. Regulatory change. Technology disruption. AI reshaping entire industries overnight. Today's directors and leadership teams are being asked to make consequential decisions with less certainty than ever before, and the cost of getting it wrong has never been higher.

This webinar brings together governance experts to unpack what separates confident, effective decision-making from analysis paralysis or reactive guesswork, especially when the data is incomplete and the stakes are real.

Who should attend:

Directors, board chairs, company secretaries, and senior leaders who are responsible for steering their organisation through change, and want sharper tools for the moments that matter most.

Why it matters now:

Uncertainty isn't going away. The organisations that thrive won't be the ones waiting for clarity, they'll be the ones who've built the muscle to decide well without it.

Transcript

SPEAKER_01 0:08

Hi everybody, welcome to our webinar today, titled Decision Making in Times of Uncertainty. Today I am joined by Stephen McCron, Megan Motto, and Stephen Bowman. So welcome to you all, team. My name is Sean McDonald, and I will be your sort of moderator in the background for the next 45 odd minutes. As usual, thank you so much for attending our webinar today. We always appreciate the effort you make to be here for our live uh webinar events. And during the session, if you have any questions, uh please try and use the QA on your toolbar as against chat, because it just enables us to keep a track of things as we're going through. And finally, if you stay through till the end, which of course we hope you'll do, we have a really short one-minute survey at the end of the webinar for you to consider. Your feedback's really important to us because it enables us to consider topics, um, the forward topics for webinars that we have planned. And it also enables us to position the team of speakers that we have for you. So please take a moment to consider that really short one-minute survey as you uh exit the webinar today. Now, for those not too familiar with Board Pro, we are a board software provider, sometimes called a board portal, and we serve about 35,000 users around the globe and across about all about 34 different countries these days. And we enable organizations to run more effective board meetings with less effort and significantly better outcomes for their stakeholders. And as much as we are a board software provider, part of our wider mission here at BoardPro is to make the fundamentals of governance free and easy to implement for all organizations, but especially those organizations with resource constraints. And one of the many ways we do this is by providing access to hundreds of business templates, guides, and resources, which you will find in the resource section of our website, and also our brand new uh community portal, which we released last week. These webinars that we also host once a week are a great way of accessing key governance knowledge without the time, commitment, and costs associated with uh in-person events. So for the next 45 odd minutes, just relax, uh listen, add to the discussion by asking as many questions as you would like using the QA button. A full recording of the webinar along with the slide deck and the other resources, other resources will be sent to you 24 hours after the session today. So let me have our team introduce themselves. And excuse me for my voice, it's really throaty at the moment. So my apologies for that. Um let's start with you first, Stephen Bowman.

SPEAKER_00 3:00

Hi everyone, Steve Bowman here from Conscious Governance. I've been working with boards and CEOs for over 40 years, learned a few things along the way. And one of the reasons I really enjoyed um being on the panel of these webinars is the ability to share some of the ideas and thoughts and things that are actually working with that other boards have shared with me. Stephen McCron over to you, sir.

SPEAKER_02 3:25

Uh Kiora, yes, Steve McCrone, I'm the managing director at AGLX Asia Pacific. I work mostly in and have developed the uh adaptive strategy method, and I'm here to talk to you today about what I think of as the kind of uh currency or fundamental unit of strategy, which is the decision.

SPEAKER_03 3:47

I'll jump in. Um hi, I'm Megan Motto, um, uh currently managing director of Motto Advisory, which is my own advisory business in strategy and governance. Uh my love affair with governance started long ago. I've worked in industry and professional associations for about 25 years. Um, so across the fields of strategy and governance, um, particularly writing a whole range of governance frameworks for how governments make decisions on infrastructure spending originally and and the governance of those decisions, and then moving into I was the CEO of the Governance Institute of Australia for six years. So um I absolutely love the intersection between strategy and governance, and this is exactly what this webinar is presenting. So I'm very excited.

SPEAKER_02 4:30

Take it away, Steve. It's all yours. Great, thank you very much. Okay, so I'll just um start by saying is decision making is a pretty uh broad topic, and um there's a lot of people interested in it, both from um people who are making decisions to you know behavioral economics, cognitive psychology, and there's plenty of armchair experts kind of chime in as well. Um, we're not gonna cover the whole uh gambit today, but what I'm gonna do is focus on decision making from a systems perspective and hopefully um give boards um a bit of a stare on how to make better decisions faster. Um, so I'll start with the features of the world. So the world is becoming more complex and interconnected. Um, you can show that um, you know, scientifically, but you can also feel that that's happening. Most of us in our careers will have a sense um that things are far more complex now than they have ever been. And I'm sorry to say, but that's just going to continue um to be the case as technology, human systems, and society develops. However, humans as a as a species um have evolved to seek certainty and avoid uncertainty. In the past, this was an advantage to us because in a slow moving world, in a world where um the world as experienced by your grandparents was the world as experienced by you, then certainty was an advantage, and uncertainty um was dangerous and risky. In today's world where it's changing very quickly, seeking certainty and avoiding uncertainty is actually a real problem for a lot of um people and a lot of boards when they're making decisions. The other point I'll make, and this is probably the most important one, is decision making, uh leadership, um, you know, board training tends to train the individual, and yet humans have evolved to make decisions in small groups with people we know well and trust. So we're kind of the way that we get taught to make decisions is at odds with how we make decisions. The fact that boards exist is evidence that we need, when we're faced with tough decisions or high consequence environments, we need a group of people that we know and trust around us. And you can see that in almost any field from sport to the military to business, um, where time and time again we self-organize into small groups. Um as we as we progress in our careers, then all of those things start to become more important. We're making less decisions, but those decisions are more complex. Jump to the next uh slide. Right. So, what does this mean to us? Is um Diego Espinoza uh sums it up pretty well, saying, you know, our relationship with uncertainty is pretty fundamental. But actually, what I see and what um a lot of boards get frustrated by is we tend to outsource um big decisions or tough decisions or complex decisions to what I call the certainty merchants. And these are uh people who claim expertise or um offer analysis, and and when we're working in an area where expertise and analysis is not a valid approach, we can get really tied up in knots. Decisions become slow, they become hard, and you know, more information doesn't necessarily make a better decision. And that sense is a uh sense there's a um a frustration. Uh Megan and Steve, you must uh add to this.

SPEAKER_03 7:44

Yeah, look, I'll I'll jump in here. I always talk about in at the helm of any organization, there's there's three key teams. You know, there's team executive, which work in the business every day and a CEO on the C-suite. There's team board, which obviously then govern the organization. And then there's you know team organization, which is the collective of those two teams. And one of the great frustrations, I think, for executive and for boards and for creating psychological safety in a in a boardroom is understanding the different decision-making styles of the individuals, but then understanding the decision-making frameworks and dynamics of the team itself. So it's really frustrating, for examples, to executives who you know might say, well, we've done all the research, we've dug deep into the detail, we've seen all the analytical reports, so we know what the right decision is, but the board has got a much lighter touch approach because they're not working daily in the business day to day. So you see that frustration occur where the decision-making frameworks that the board applies are not, A, they're not openly uh discussed. We were talking about this earlier, Stephen, Stevens, I should say, Stephen Square. Well, how do I how do I refer to you? Um, that we were saying about the methodology for how we make decisions is not normally a discussion in the boardroom. So you jump straight into the decision making of the op of the strategic or operational decision without having a conversation about how you're going to do it and the framework that you're using. And that leap means that no one ever really knows the rules of the game with regards to, well, how much information is required for this decision? What comfort are we going to have with this level of uncertainty? Are we going to have a high level of comfort with the uncertainty surrounding this decision and proceed anyway? Or do we need a higher level of certainty? So those frameworks are not really discussed. And it's a little bit like having a discussion around the principles via which you're going to make a decision before you jump to the solutioning and the decision itself. So it really does cause a lot of frustration.

SPEAKER_00 9:44

I think one of the what I've found with many boards is they've got a fundamental mismatch between what they think a decision is and and uh how that intersects intersects with the notion of choice. And so often we find boards try and spend an awful lot of time getting an awful lot of information to make the right correct decision. Because if we don't make the right correct decision, by definition, it's the wrong decision. And because we all know the decisions, they're set in stone forever and ever and ever, we've got to make sure we get it right rather than wrong, and therefore you miss the opportunity, you miss the potential leverage that you could get from it. And one of the things always to for us to remember is that everything is just a choice. So what are the what are the two or three options that we've got? Then we'll decide which one of those is likely to work. Then we need to monitor whether or not it is working and change it if it does. And it gets to the very heart of what Steve McCrone does so much work on, which is this notion of adaptive strategy. But I just can I'd I'd really encourage people to start looking at everything as a choice rather than what they think is a decision, which is set in stone and has to be the right decision never to be re-looked at. Steve, back to you.

SPEAKER_03 10:56

And so, Stephen, just to jump in there, I think that's a really useful lens, particularly because actually most choices aren't made in a vacuum, particularly in a complex world. So it's actually a choice about trade-offs. You see this in government policy. It's all about trade-offs. So, you know, understanding the pros and cons and the trade-offs involved in a decision, in a choice between different options and um thinking of it through that lens. I think it's it's incredibly important.

SPEAKER_02 11:22

Cool. So um a point I'll make here is decision making uh as a group or as a board or a team or executive um team or project team is actually a teachable skill. And I don't often see boards actually training uh to make decisions um or how to uh get a shared understanding of context, but rather they learn as they go and then they crash into communication coordination, they crash into uh untested assumptions, and they sort of lack a situational awareness. So one of the um kind of key takeaways from this is to encourage boards to train in decision making as a board, as a team, and then get better at that before they're facing these high consequence or highly complex uh situations. And I'm gonna explain that uh through the lens of Kinevin, which is on the next slide. Now I'm not gonna uh spend a whole lot of time on this. Kinevin is a very searchable uh term. Uh it's a lovely Welsh word that means a place of your kind of multiple or your spiritual belonging. Um and it's quite a nice um name for a framework that is really a decision support framework. So Kinevin won't make a decision for you. You can't plug information in and a decision drops out. But rather, what Kinevin does is it forces you to understand context before you start deciding or finding what tools you're going to use to decide. So the key point here is context is the most important thing when faced with a decision. And I'm going to go through this quite quickly. Um, some contexts are fairly clear. So if the chain falls off your bike or your shoelaces come undone, the context of that situation is fairly clear. And the uh way in which you fix or solve for that is fairly clear. So for most organizations, they tend to either outsource, automate, or push these um clear decisions down to a very low cost or automated uh system. As we increase the number of variables, we get what we could think of as complicated decisions. And these are decisions where the future is known or knowable, but we have to put a lot of effort into analysis or find some experts or come up with some options and select which one we think is best for us. And this is really where you can map uh cause and effect, you can map the trade-offs. And what most organizations do is really spend a lot of time in this space because it's the space where they have the most expertise. It's the space where they have a unique competitive advantage, it's a space where they can um outperform those in their markets. So complicated decisions are the domain of expertise and analysis, and that's really um the go-to for most uh boards. Um, traditional, or what I call traditional, so you know, 70 years of management training has really emphasized, and particularly with uh Advent AI and big data, emphasized the analytical approach to decision making. However, at the fundamental level, the analytical approach assumes that somewhere out there the right answer is available. We just need to spend the resources or the time to find it. But that's not always the case. So if you ask a question like, how will AI ultimately affect my business? Well, the answer's not sitting out there somewhere to be found. Right? The only way we can find that answer is by using AI, by trying to create sources of value, by learning, by skinning our needs, by getting it right, by getting it wrong. And now we're starting to describe a situation that is uh fundamentally complex. In complexity, the answer to the question is not known until you start engaging in the system with which you're in. So it's if you think about any any sports analogy, the score of the next game that um is played is not known until the end of the game. We can't know that in advance. We might have a a uh a probabilistic view and and we might be able to establish some odds on that, but it's not absolutely guaranteed. So, in complex situations, we start to think about how we can probe or interact or experiment and explore the situation and then lean into value as it arises. Most of the big decisions that boards make, I would suggest, tend to be more complex than complicated. One of the features of Kinnevin is the confused domain, and that's where we're unable to make a decision because we can't decide how to act, or there's not enough information how to act. A lot of organizations get locked into the confused domain because some people think it's clear, we've done this before, just do it again. Others think it's more complicated. They say, Well, this is a little different. Maybe we need to go and get some expertise or find the data. And others are saying, actually, none of us really know. I think we need to experiment and explore and have a look. And what happens is those three kind of sources of energy cancel each other out or get confused, and we end up either doing nothing or we have three parts of the organization doing separate things for the same reason. I'm going to stay away from chaos, although it is important, because for most organizations, they have a pretty good system of business continuity or crisis management, maybe or maybe not, but we don't really see chaos as a um a decision-making process where we have to kind of labor over it. In chaos, you simply don't have time, which actually, um, when you look at examples, um, often frees organizations up to be a little bit more experimental and a little bit more focused on outcomes. So Kinevin actually allows us to understand the context so that we can make what we call a contextually appropriate, or I use the word authentic, decision based on the context that we're in. We might make a decision to shift context, um, and there's a whole lot more kind of depth to Kinevin in that. But the main point here is before we go reaching for our tools or our decision-making process, we need to have a conversation about context. And if we've got a shared understanding of how we act in each context, then decisions become faster and more appropriate.

SPEAKER_00 17:24

Stephen, I've got a question for you. What have you seen in your travels around the world how organizations have codified this or or put it into practice um and and so that they could actually focus on the different styles and then from there can choose the different tools? What have you seen them do?

SPEAKER_02 17:43

Yeah, I think actually um most of what I've seen isn't uh that organizations codify it in a in a in a in the real sense, but they gain a shared understanding and it becomes part of the culture. So um a lot of engineering firms um, you know, you always uh say engineers are very good at at complicated analytical problem solving, but actually uh engineers that are in touch with the real world often see that the thing that they've designed on the uh whiteboard isn't the thing that works in reality. So they actually become very, very good, um, almost sort of streetwise good at understanding uh complexity, but they don't ever um they don't write it down, they don't codify it in a way that it can be shared. So for a lot of those organizations, what we help them do is actually gain that shared understanding. They already know it. Humans are very, very good at managing complexity. Um it's just that we're not very good at um doing that in a in a business sense.

SPEAKER_03 18:40

Yeah, I I worked with um I used to work with engineering firms almost exclusively um when I was at Consult Australia. And it's really interesting that you say that, Stephen, because um you're absolutely right. They're really good at two things. That uh I mean, well, they're good at many things, um, complicated systems, very, very good at because they are experts in their field, but they're really good at process and understanding process and they and they're good at codification in a general sense, albeit that they might not um orchestrate it in terms of a diagram or a framework, but they do use a lot of decision-making frameworks which include both divergent and convergent thinking frameworks. And so that is one of the ways to deal with complex systems because you're allowing the time for divergent thinking and convergent thinking to coincide uh as you get to a pathway. Just a comment that I'd make on this is um it's a really interesting aspect of the way that you reach the boardroom is often through your expertise. And so often boards are full of experts in their individual fields that are far more uh comfortable with complicated thinking matters. So, you know, deciding whether or not to go to space is complex. Building a spacecraft is actually just complicated. So it's um you end up with a board that are really full of these complicated people. Add in the layer of personal liability for directors and therefore their innate conservatism when it comes to their risk appetite when it comes to some of these big decisions, that also comes to play. So one of the biggest frustrations that I see is boards trying to solve complex problems using complicated methodologies. And uh and so and getting the board into a different mindset, a different frame of mind to come to complex problem decision making is actually quite challenging because individually they tend to get a seat at the table by being an expert who is an expert in complicated systems.

SPEAKER_02 20:39

Very good. I've just seen a question from uh Rob Warner, um, who's asking a good question and also proving the point never ask a question you don't already know the answer to. Um, because I know Rob knows the answer to this. Um in an adaptive strategy, what is the uh difference between a genuine adaptation and a drift? So um we use Kinevin in an adaptive strategy to say actually let's use the complex um uh complexity-friendly decision-making tools or strategy tools to kind of um test or um probe the uh business as usual or the status quo and look for areas where we can create value. Now, the difference between doing that and kind of messing around or strategic drift is um first and foremost, we need to understand, we need to establish a very clear uh sense of direction and which direction are we heading towards or which direction are we heading away from? And once you do that, you can establish our main effort. And that's once you've got those two things, then people have the guardrails or the constraints they need to then use those complexity informed approaches uh in an appropriate way.

SPEAKER_03 21:45

Also very helpful. Um I know working with government uh um, you know, in government public policy space quite a lot. This is something that governments are getting a lot better at because most public sector problems are wicked problems, they're multi stakeholder, complex. Problems with lots of trade-offs, trade-offs not just with a policy outcome, but an electability outcome and a and a you know public sentiment outcome and all the rest of it. So it's good to see, you know, certainly, you know, my perspective seeing in the Australian context, governments over about the last eight, 10 years have started sort of these safe to fail experimentation prototypes. We're going to run a trial, we're going to run a test run. And that is, you know, classic complex decision-making technique where you look for breaking down the problem into safe to fail experimentation spaces.

SPEAKER_00 22:37

I think also just coming to mind is there's a bit of legislation in Australia. I think there's something similar in New Zealand and other countries called the business judgment rule, which actually gives you permission to look at the complex side of things and try things out. And if it didn't work out, but you could show that this is what any reasonable person would have gone through to actually decide whether or not we'd go down this route and you tested it, then oh well, that's just business if it didn't work out. So I think many boards don't understand the business judgment uh um legislation around that. And it's really quite useful. It gives you a uh to some extent a safe hard at harbour to try things out.

SPEAKER_02 23:17

That's a really good point. Um because oftentimes a constraint on this will be oh, we're a heavily legislated organization or we can't take those types of risks. And oftentimes, I think you've just shown, uh Stephen, that it's not true.

SPEAKER_03 23:34

Particularly if you apply a materiality lens, I think that's um also very important to do because it may well be that some experimentation is highly material. You know, getting it wrong will cost the business reputationally or financially. But if you look, and that's why I say safe to fail experimentation, so looking for materiality thresholds that you can be comfortable with.

SPEAKER_02 23:58

Perfect. Um, so the next slide really just talks about the uh authentic approach to each of the um contextual domains. Um I'm not gonna kind of read it out, but when it's clear, you should have a um a fairly good or well-worked process. The role of the board there is to um make sure that the safeguards are in place, make sure that the process is in place, and make sure it's being followed. Um the key to success in this space is less is more. Um, the the fold at the bottom of Kinevin between clear and um chaos is what we call the catastrophic fold. So if a system has too many rules or is too rigid, then when it fails, it will fail catastrophically. And once it starts to fail, it's very hard to recover. So we say actually we want to have rules in place, we want to have you know really hard or you know, firm boundaries in place, but actually, less is more there. What tends to happen in organizations is we allow experts to use their judgment, which is where we have the complex, oh sorry, complicated um domain, and that's the domain of this you know, analytical, expert-led um options analysis, scenario analysis um approach to decision making. And it's totally valid when the outcome is known or knowable, or we have enough confidence or data to understand what's going to happen if we do X or what's going to happen if we do Y. If we don't know that, then we're in the complex domain. And I think to Megan's point, a lot of organizations haven't really developed the uh uh, I call it the muscular response or the muscle memory to operate in the complex domain. In complexity, what we want to do is centralize that coherence or that um understanding of success. Where are we moving? What are we working on in terms of main effort? What are the boundaries and or constraints in terms of um our principles or our ethics or our um things we don't want to do or do want to do? And then you allow people, you distribute decision making to the people who are actually facing the issue or people who are in touch with the system they're in, and allow them to experiment and learn, allow them to do um safe to fail. So it means that if it goes wrong, we can both manage the consequences or withdraw without um, you know, risking catastrophe, and then stay in touch with the results of that, and then the role of the leaders in the business and potentially the role of the board is to support the things that are starting to create value or lean into the things that um we're learning are going to be useful to us in the future. So we stay away from the experience, we let people, we let people um do what they do, and then we support the things that are working well and we mitigate or shift away from the things that aren't. So we're really shifting the power there or the energy um to a lower part of the organization, and that's often um very difficult for people to do in an organization that is heavily bureaucratically constrained. Although I can't.

SPEAKER_00 26:52

What I've seen, Steve, what I've seen some boards do is um when they're getting a report on progress on a particular complex uh issue, um, the really good boards will ask a question similar to, well, what have we learned from this? And what does it mean for us? And I've seen it in the policy space where they've been looking at uh you know policy breaches. What have we learned from this? And what do we put in place to make it even stronger? And it's that sort of continual learning curiosity of the board that can actually help unlock the powerful lessons that we've learned rather than say, oops, yeah, that didn't work out.

SPEAKER_02 27:30

So I mean that's a really good point. And one of the things that we do with clients is teach them how to debrief. So once the decision's been made, actually let's go back and say, well, what did we know at the time? What were we expecting to see, and then what happened? What the purpose of a debrief, we could that's a lead indicator of success, not a lag indicator, because if you're debriefing, then you're learning and your next decision is going to be better, or at least should be um you know better than the last one. Um, so you know, boards that are uh habitualized to operating in complexity, debriefing while really starting to understand the reality of where they are, will outperform organizations that are stuck in this um kind of ROI analytical um benefits realization, that sort of slow process.

SPEAKER_00 28:16

Um of the best techniques that I've seen boards use in that space is um they look at decisions that were made three or four years ago that were complex, and they do a three or four year post-decision debrief of what were the assumptions, what did we get, what what were we close on, what changed, and what have we learnt from that? So that notion of you know assumption revisiting assumptions, not to point the figure at the people who made those assumptions, but to see what have we learned, how close did we get, what changed, what have we learned from that for the next set of complex decisions? Megan.

SPEAKER_03 28:53

I was going to say, and just to add to that, it's that the analogy is a little bit morbid, and probably we shouldn't be thinking about decisions or projects as you know, something that dies. But one of the best techniques that I've seen, particularly in project management, that can then lend itself to decision making in the complexity space is not just having a post-mortem, which is your debriefing at the conclusion of the time period of the decision, but actually to run a pre-mortem, which is an exercise that you go through at the very beginning of that decision-making process to say, if it all went horribly wrong, what would that look like? And so go in with that sort of like horribly wrong, because it helps you to define the boundaries that um Stephen McCrone is talking about at the onset. So the principles or the boundaries that you're going to stick to, how do you develop them? It's actually a really useful exercise to go to to run a pre-mortem, pretending the whole thing's gone horribly wrong and died at the end, and saying, well, what would we not want to do? Or what would we want to do to make sure that that scenario doesn't happen? And that helps you to set where your guide rails might be. So that's a very useful process as well.

SPEAKER_02 30:01

Yeah, no, I totally agree. Uh, it's one that um we use a reasonable amount with clients. Actually, Gary Klein updated that method recently to include it's gone horribly right, you know, what happened.

SPEAKER_03 30:12

Yes, yes.

SPEAKER_02 30:13

Um, so he's he's kind of uh uh helped out the people who who a lot of people don't use that method because they don't they don't like the negativity of it. Yeah. Um, but it's actually a function of um a human psychology that we pay attention to stories about risk far more than we pay attention to stories about success. Um so it does have a lot of validity for that reason and is a very useful way, as you say, um, to kind of take the rough edges off a plan or to kind of increase situational awareness. You say, you know, are we looking for these things? Are we looking for the things that will be the weak signals of it going horribly wrong? And if you're interesting, like you're in danger.

SPEAKER_00 30:50

An interesting statistics just to keep in mind is that apparently around 64% of all businesses fail because they grew too quickly, it went horribly right.

SPEAKER_02 31:01

That's right. Yeah, totally. Um I figured I'd talk to you guys about AI and decision making, it's a very topical. Actually, interestingly, if you look at it from a systems perspective, AI doesn't change anything. Um, because it's still, you know, complicated still systems are still complicated and complex systems are still complex. AI does not change uh the context of the system that you're in. Although it can be a very, very useful tool if you're in the complicated domain or the ordered domains we call them, which are clear and complicated, because it's just so much faster than humans about seeing patterns in large volumes of material. Um, it is still prone to um uh some issues which we all very well know about. But this graph comes from the work that uh Apple commissioned called The Illusion of Thinking. And what it shows is AI agents, and they did this with various agents. This is just a um uh uh basically a transcription of the of the of the graph. It's the shape of the graph that matters, not the the height or whatever else. And different AI um uh tools will have different uh uh they won't they'll have the same shape, just at different levels of scale. So one of the keys here, or the key points here, is um low complexity and some medium complexity tasks, AI is very, very useful. And we kind of know this. Um, and this is where um, so one of the clients I'm actually just about to go and talk to today is uh a law firm that's implementing um you know one of the big legal AI tools. And we use this here to say actually when you're faced with a situation where you have lots and lots of um historical information, a lot of precedent, um, a lot of um cases where we've done the same thing in more or less the same way. So, for an example, might be a lease agreement, um, the sort of thing that a junior person might be asked to do. Well, you can put all those into your AI engine, ask it to produce a lease agreement, and it will come up uh up with a fairly accurate result. You still have to check it, obviously. You still have to put your name to it. But it's actually a very useful uh way of using AI. As the complexity increases, the accuracy of AI falls off a cliff and the and the steepness of that curve is not um overrepresented there. So, what that means in tasks where there isn't a lot of precedent, where things are changing quickly, where the link between cause and effect is not really knowable in advance, where um you know there's multiple competing hypotheses, there's contradiction in the system, etc., AI actually is um woefully inadequate. And the um some of the examples they use, the engines just wouldn't produce a result. They just got stuck in a loop and couldn't um burnt up all your tokens and couldn't really come up with a meaningful result. So this this graph here is actually quite interesting because I know a lot of boards are using or thinking about AI in terms of decision support around efficiency and effectiveness, and you can kind of see that in the Goldilocks zone there between low and medium. Um, but if you're a a a new grad or a junior person and you're worried about AI taking your job, then I don't think there's actually that much to panic about because humans are very, very good in the highly complex environment. We can navigate complex systems in small groups exceptionally well. We're very good at that sort of thing. We can use AI to support decision making, but AI is not, or should at least shouldn't, replace human decision making in that complex space. And organizations that use AI to replace human decision making, even when there's a medium level of complexity, are opening themselves up to a huge amount of risk. It will give you a very inaccurate and poor answer most of the time. So we call that the AI sandwich. You have a human at the start. Why are we using this? What are we expecting to get? The AI in the middle, and then a human at the end. Is this result what I expected, what I need, is it accurate? Is it working for the thing I need to do? There you go. Megan, Steve, jump in.

SPEAKER_00 35:06

Oh, very useful. We we we you've got some really good stuff coming up. Keep going, Steve, because we don't want to run. We don't want to run out.

SPEAKER_02 35:14

Okay. Um, this last slide, I mean, you can read it at your leisure. This is one I prepared um based on the work that we do uh at AGLX. And really what it shows is if you have decisions that are in the clear uh or complicated domain, we call these the ordered domains. So the future's known or knowable. We're just gonna kind of work it out. And we have a lot of kind of tools and techniques that we use there, but the the key uh things to pay attention to is what is the objective in that space? Well, the objective in the clear space is to reduce cost and um risk to the business and sorry, reduce the cost of managing risk or um reduce the cost of uh production or reduce the um you know increase the marginal efficiency. And again, go back to AI, that's actually uh quite a good use in that space. Um, in the complicated, this is where most organizations you know really hit their sweet spot. This is where they have a uh competitive advantage, a defensible competitive advantage. We want to build value, we want to stop loss if that's the case, reduce marginal cost and increase efficiency again. In the complex, actually, we want to survive and innovate. So, in complex challenges, are ones where because the future is not known or knowable, and because the value proposition can't be stated in direct or categorical terms, it is the domain of exploration. And we really want to uh increase the bandwidth by which we explore and effectively um increase the surface area for luck or serendipity to occur, and then recognize that when we see it. So decisions in that space should be more about how much can we afford to spend to learn about the situation so that we can make a um a better call. So, a good example would be a major IT expenditure that most organizations face at some point. And of course, the vendors want you to spend millions of dollars on this big shiny thing, and it's gonna take six months, and we're gonna get piles and piles of people coming in and spending a lot of time reworking your systems. So rather than making a kind of um a really tough decision on, you know, can we afford that? Have we got the capital? Are we gonna get the outcome? We might ask a different question. We might say, What's a small thing I can do to show that this is a good idea? Or what are a bunch of small things we can do to avoid having to buy this giant thing? Right? We're reducing the time and energy needed to move forward. And that's really where some of these complexity-friendly or or complex um systems approaches come in. So uh the strategy is obviously a pretty self-serving one for me. Um, Flow Learning Lab, that's one that we use to train teams of people who are facing high consequence decisions. It's a compressed learning lab to let people know um, you know, how to work together, how to make decisions. Uh, red teaming, which is uh includes the um debriefing, it includes the um uh pre-mortem uh stuff that uh Megan introduced, human sensor networks, so that's about distributed cognition. What are the people at the front line seeing that can give us indication of what the system is or how the system is orientating, and then how can we take advantage of that? Um of the things that we recommend in the in the um crisis management space is to have crisis response and learning teams combined. This comes from Dave Snowden, who developed Kinevan, uh his experience, because in a crisis, you'll have a whole lot of novelty, you'll have a whole lot of new things, and then we can bring that into the complex domain and say, okay, how can we create the conditions or experiment in a way where we can um you know utilize the novelty and the innovation that came out of that crisis? So it's a nice way of saying never waste a good crisis. Um in my view, organizations are very good at the clear and the complicated. Um, most organizations are center the pants, street smarts, learn as you go, very kind of ad hoc in the complex. The larger the organization, frankly, the worse they become, um, because they have a um a greater uh uh propensity to shift towards complicated analytical spaces. If you're taking on a large organization, get really good at the complex domain techniques because you'll be faster, more adaptive, you'll make better decisions quicker. If you are a large organization, then you need to be able to uh distribute decision making so that you don't end up becoming uh static or strategically vulnerable.

SPEAKER_00 39:35

I'm sorry, go Megan, I'll come in.

SPEAKER_03 39:38

No, that's right. I was just gonna say just a jump in on that. Um, if complicated is the domain of expertise and complex is the domain of judgment, um, which I think is is very true. Um the the two key ingredients for having a small team work in that space in the first place can be competing with each other, but it's about psychological safety and trust, and it's about diversity and inclusion, about having diverse mindsets. Interestingly, homogeneous boards that are all made up of people that look and feel and think just like me tend to have very high psychological safety because I know everyone, they all think like me, it's really easy. We jump to, we jump to decisions really quickly because we all think the same. So the more diverse your board, the the more difficult it is and the more proactive that you have to be about creating psychological safety and trust within that room. But that's where you get the best result because that's when all of the different individual decision-making modes can build together to hide to think differently about these complex system problems.

SPEAKER_00 40:44

That's a good point. Going back to Rob Warner, who's asked yet another very good question. Um, he's talking about pre-mortems, but I'm going to expand it out a little bit more about what are some examples of creating governance culture that allows this type of generative thinking. And there's a couple of things that come to mind immediately. The first one is build it into your annual board calendar, that you're going to do a post-mortem of things that have been in the past, that you're going to look at pre-mortems. So you can actually build these into your annual board calendar. If it's in the calendar, it'll get done. If it's not in the calendar, people forget. So that's a very simple thing that you can do. The second thing that you can do is actually have a look at the templates that you're using for board discussion or decision papers and embed into them some of this thinking. Have, you know, what are some of the pre-mortem questions that we should be asking here? What are some of the uh what are some of the leading indicators that will indicate whether or not we're on the right stage, rather than boring us all with a whole lot of activity reports, which is still 90% of all the board reports I certainly see, but what can we do to actually look at some of the leading indicators and how can we stress test them out? So one of the best ways of creating the culture is not just to do it, but to actually structure it in through both the annual board calendar, your board committee calendars, um, and also through and how you structure your board reporting and the templates it using that give space for this to be actually discussed and looked at.

SPEAKER_03 42:22

And can I just add to that your list, Stephen, is your strategy workshops, your annual strategy workshops because strategy, we always talk about three-year strategy or five-year strategy. And yes, it's a continuum, but oftentimes when we're doing our strategic planning workshop for the next three-year strategy, it's like the previous strategy never existed. We just jump into thinking about the forward three years without actually having a contemplation of what decisions did we make around our strategy in the last three years? What really worked? What can we learn from that?

SPEAKER_00 42:48

So And the annual strategy, sorry, we're on a roll here, Steve. Um the annual the annual review of your strategy is a fantastic time to do this. So uh really briefly, there are five questions that you should ask when you look at doing an annual review of your strategy. And the five questions are really simple. Number one, what worked with strategy one, two, three, and four? The second question, you can probably guess, what didn't work and what have we learned from it? The third question is, what did we miss? Because we've always missed something. What have we missed? The fourth question is, well, what do we take out because we've done it or no longer relevant? And the fifth question is, what do we put in because something new has occurred? That's a fantastic way of actually starting to review in a very simple way what your strategy is like. And again, it gets to the heart of that adaptive strategy, Steve.

SPEAKER_01 43:37

Stephen, you've got uh one minute to talk about induction insights, if you like.

SPEAKER_00 43:42

Okay, so one of the things that we find is uh very difficult for most boards is when they're inducting new directors into how we make decisions, how we do things around here, what actually works, what's the cadence of the board. And so the notion of you know a one-month induction program with a whole lot of papers and good luck really should be thrown out the window because they just don't work well. So, what we've developed up in uh collaboration with BoardPro, something called Induction Insights, and one of its very modules in there is decision making. And then another module is in there. What do you do when you've got incomplete information or uncertain information? And this is for new directors coming on. You can also use it for existing directors. There you go, less than a minute, Sean.

SPEAKER_01 44:27

Thank you, sir. So we're at the end, I think. We've got another minute left. So let me race through the uh the last couple of slides. So please feel free to connect with uh our presenters today, Megan, Stephen, and Stephen. That worked nicely. I'll work, uh I'm sure they'll look forward to your connection. If you'd like to be put in touch with any of the panelists, please indicate uh your interest on the survey as you exit the webinar. We've got some exciting news coming your way. We are coming to cities near you in October and November for our Aussie and Kiwi viewers. That is, uh, introducing our governance with impact roadshow. It's a full day exploring how and what good governance looks like in the new AI world. So we'd love to see you there. And finally, put a face to the name. Early bird tickets are open now. Uh, you save, I think it is 35% off the ticket price. And all you need to do is use the code EarlyBird at the checkout to lock in that price. And if you're looking for past webinars to hone your knowledge, then you'll find our library of webinars in our new community portal. And we invite you to join there and be part of the growing governance community. Only here you'll find the slide deck, the transcript, and the resources from the webinar. So follow the link on the screen, which is community.boardpro.com. Failing that, uh, seven days after the webinar, you can find all you need to know on YouTube and no need to explain the link to get there, I'm sure. So you shall receive an email from me tomorrow, being Friday, which will include a recording of today's webinar, of course, the transcript and the presentation slides. They'll also be hosted uh in the community portal, as I mentioned. And of course, if you're considering board management software for your organization, we would love to hear from you, of course. Better still, uh, why not try our free 30-day trial? It's really simple and straightforward, no credit cards required, and it's really simple to get started. So thank you again, everybody, for your attendance. I hope you enjoyed the session with Stephen, Stephen and Megan. And it comes off the tongue quite nicely, doesn't it? Thank you again, everybody, for the great conversation. I look forward to seeing you in our next webinar. Have a great day.