Latest news with #complexity
Gulf Business
09-06-2025
- Business
- Gulf Business
Beyond the risk register: Why future-ready leadership demands strategic discomfort
Image: Supplied The year is 2029, and vertical farming has become a symbol of national resilience in the Middle East. Governments had have poured billions into hydroponic megafarms. Food security indices have climbed and export deals rolled in. The region has also been hailed globally as a pioneer of agricultural innovation – a place where technology had triumphed over land scarcity and climate stress. And then it all collapses. A fungal microbe, exploiting the genetic uniformity of hydroponic crops, mutated in a single facility, sweeps through the region's interconnected systems. Within six weeks, 40 per cent of regional crop output is lost. Emergency imports are then scrambled at record costs. What seemed like a shining example of resilience is exposed as dangerously brittle. The risk was known. The signals were there – just not heard, or not heeded. This isn't a possible 'future' story about agriculture. It spotlights leadership under complexity. From pandemic blindspots to supply chain fragilities and climate volatility to AI backlash, organisations across every sector continue to be surprised by visible and often documented disruptions that were ultimately sidelined. Known, but ignored Why does this keep happening? Not because the risks are invisible but because they're inconvenient, ambiguous, or don't fit the dominant narrative. In environments that reward momentum and performance, there is often little appetite for the slow work of horizon scanning or scenario stress-testing – especially when things appear to be going well. Risks that are uncomfortable or unfamiliar are easily dismissed as fringe. And when success stories dominate, dissenting signals – especially weak ones – struggle to break through. The vertical farming collapse followed this exact pattern. Early warnings were buried in obscure journals, dismissed as edge-case thinking. There was no lack of intelligence. But attention was highly selective. The illusion of the list Many organisations believe that because a risk appears on a register, it is being managed. But listing a risk and engaging with it are two very different things. Take the World Economic Forum's Global Risks Report . Each year, it publishes a heat map identifying the most severe and likely risks facing the world over the next decade. Climate volatility. Biodiversity loss. Emerging infectious diseases. Cybercrime. Water crises. Year after year, these threats are mapped, flagged, and even color-coded – often with 'blobs' so large they're impossible to miss. And yet the most common organisational response is to file these risks under 'context', rather than integrate them into core planning. They are acknowledged, but rarely rehearsed. The problem isn't the heat map. The problem is what happens after. The mere appearance of a threat on a list can create a false sense of preparedness – a box ticked, a risk 'covered'. Risk registers often serve as a checklist – useful for reporting, but misleading when it comes to real readiness. Rarely do leadership teams ask: What would we actually do if this happened tomorrow? And most registers fail to consider how risks interact. A CEO scandal, shifting consumer ethics, a tech system failure, and policy fragmentation – individually manageable, perhaps. Together? Catastrophic. Strategic foresight starts where the risk register ends – not with what's on the list but with how those risks might collide. From risk registers to risk realism So, what does it take to build a future-ready organisation in a time of converging disruption? We propose three shifts: Expand peripheral vision: Build structured capacity to detect early signals from the margins – in scientific literature, startup ecosystems, citizen movements, and niche media. Weak signals are often the earliest indicators of system shifts. Unless you design for it, they won't rise through the usual filters. Institutionalise strategic discomfort: Challenge internal optimism regularly. Build in moments to stress-test assumptions and rehearse disruption. Reward people who challenge prevailing wisdom, not just those who confirm it. Map risk interdependencies: Move beyond lists. Use systems thinking to explore how risks could combine. Model chain reactions and secondary effects. Ask not just 'What could go wrong?', but 'What could go wrong together ?' Future-readiness is a cultural trait Foresight isn't about crystal balls or radical prediction. It's about readiness for uncertainty – and a willingness to engage the uncomfortable. The most resilient organisations aren't those that see the future clearly but those that build the muscles to adapt to futures they can't fully see. That begins with humility, curiosity, and the courage to ask: What might we be missing? This demands a cultural shift. One that values critical inquiry over certainty. Signals over noise. And reflection over reaction. In the aftermath of every high-profile shock – from pandemics to tech crashes – leaders demand tighter regulation, faster protocols, and better reporting. But those alone won't build adaptive capacity. Because in every one of these cases, there were warnings. The failure was not one of ignorance – but of attention. Foresight failed because it asked the system to be uncomfortable – and the system declined. Three questions every board should be asking Which of our success stories might be blinding us to emerging fragilities? What signals are we currently incentivised to ignore? If three of our 'low-impact' risks hit at once – what would break first? If your strategy doesn't create space for doubt, it's not a strategy – it's a narrative. If your risk register doesn't provoke discomfort, it's incomplete. And if your future looks smooth and linear, it's probably fiction. Doris Viljoen is a director at the Institute for Futures Research at Read:
WIRED
08-06-2025
- Science
- WIRED
A New Law of Nature Attempts to Explain the Complexity of the Universe
Jun 8, 2025 7:00 AM A novel suggestion that complexity increases over time, not just in living organisms but in the nonliving world, promises to rewrite notions of time and evolution. Illustration: Irene Pérez for Quanta Magazine The original version of this story appeared in Quanta Magazine. In 1950 the Italian physicist Enrico Fermi was discussing the possibility of intelligent alien life with his colleagues. If alien civilizations exist, he said, some should surely have had enough time to expand throughout the cosmos. So where are they? Many answers to Fermi's 'paradox' have been proposed: Maybe alien civilizations burn out or destroy themselves before they can become interstellar wanderers. But perhaps the simplest answer is that such civilizations don't appear in the first place: Intelligent life is extremely unlikely, and we pose the question only because we are the supremely rare exception. A new proposal by an interdisciplinary team of researchers challenges that bleak conclusion. They have proposed nothing less than a new law of nature, according to which the complexity of entities in the universe increases over time with an inexorability comparable to the second law of thermodynamics—the law that dictates an inevitable rise in entropy, a measure of disorder. If they're right, complex and intelligent life should be widespread. In this new view, biological evolution appears not as a unique process that gave rise to a qualitatively distinct form of matter—living organisms. Instead, evolution is a special (and perhaps inevitable) case of a more general principle that governs the universe. According to this principle, entities are selected because they are richer in a kind of information that enables them to perform some kind of function. This hypothesis, formulated by the mineralogist Robert Hazen and the astrobiologist Michael Wong of the Carnegie Institution in Washington, DC, along with a team of others, has provoked intense debate. Some researchers have welcomed the idea as part of a grand narrative about fundamental laws of nature. They argue that the basic laws of physics are not 'complete' in the sense of supplying all we need to comprehend natural phenomena; rather, evolution—biological or otherwise—introduces functions and novelties that could not even in principle be predicted from physics alone. 'I'm so glad they've done what they've done,' said Stuart Kauffman, an emeritus complexity theorist at the University of Pennsylvania. 'They've made these questions legitimate.' Michael Wong, an astrobiologist at the Carnegie Institution in Washington, DC. Photograph: Katherine Cain/Carnegie Science Others argue that extending evolutionary ideas about function to non-living systems is an overreach. The quantitative value that measures information in this new approach is not only relative—it changes depending on context—it's impossible to calculate. For this and other reasons, critics have charged that the new theory cannot be tested, and therefore is of little use. The work taps into an expanding debate about how biological evolution fits within the normal framework of science. The theory of Darwinian evolution by natural selection helps us to understand how living things have changed in the past. But unlike most scientific theories, it can't predict much about what is to come. Might embedding it within a meta-law of increasing complexity let us glimpse what the future holds? Making Meaning The story begins in 2003, when the biologist Jack Szostak published a short article in Nature proposing the concept of functional information. Szostak—who six years later would get a Nobel Prize for unrelated work—wanted to quantify the amount of information or complexity that biological molecules like proteins or DNA strands embody. Classical information theory, developed by the telecommunications researcher Claude Shannon in the 1940s and later elaborated by the Russian mathematician Andrey Kolmogorov, offers one answer. Per Kolmogorov, the complexity of a string of symbols (such as binary 1s and 0s) depends on how concisely one can specify that sequence uniquely. For example, consider DNA, which is a chain of four different building blocks called nucleotides. Α strand composed only of one nucleotide, repeating again and again, has much less complexity—and, by extension, encodes less information—than one composed of all four nucleotides in which the sequence seems random (as is more typical in the genome). Jack Szostak proposed a way to quantify information in biological systems. Photograph: HHMI But Szostak pointed out that Kolmogorov's measure of complexity neglects an issue crucial to biology: how biological molecules function. In biology, sometimes many different molecules can do the same job. Consider RNA molecules, some of which have biochemical functions that can easily be defined and measured. (Like DNA, RNA is made up of sequences of nucleotides.) In particular, short strands of RNA called aptamers securely bind to other molecules. Let's say you want to find an RNA aptamer that binds to a particular target molecule. Can lots of aptamers do it, or just one? If only a single aptamer can do the job, then it's unique, just as a long, seemingly random sequence of letters is unique. Szostak said that this aptamer would have a lot of what he called 'functional information.' Illustration: Irene Pérez for Quanta Magazine If many different aptamers can perform the same task, the functional information is much smaller. So we can calculate the functional information of a molecule by asking how many other molecules of the same size can do the same task just as well. Szostak went on to show that in a case like this, functional information can be measured experimentally. He made a bunch of RNA aptamers and used chemical methods to identify and isolate the ones that would bind to a chosen target molecule. He then mutated the winners a little to seek even better binders and repeated the process. The better an aptamer gets at binding, the less likely it is that another RNA molecule chosen at random will do just as well: The functional information of the winners in each round should rise. Szostak found that the functional information of the best-performing aptamers got ever closer to the maximum value predicted theoretically. Selected for Function Hazen came across Szostak's idea while thinking about the origin of life—an issue that drew him in as a mineralogist, because chemical reactions taking place on minerals have long been suspected to have played a key role in getting life started. 'I concluded that talking about life versus nonlife is a false dichotomy,' Hazen said. 'I felt there had to be some kind of continuum—there has to be something that's driving this process from simpler to more complex systems.' Functional information, he thought, promised a way to get at the 'increasing complexity of all kinds of evolving systems.' In 2007 Hazen collaborated with Szostak to write a computer simulation involving algorithms that evolve via mutations. Their function, in this case, was not to bind to a target molecule, but to carry out computations. Again they found that the functional information increased spontaneously over time as the system evolved. There the idea languished for years. Hazen could not see how to take it any further until Wong accepted a fellowship at the Carnegie Institution in 2021. Wong had a background in planetary atmospheres, but he and Hazen discovered they were thinking about the same questions. 'From the very first moment that we sat down and talked about ideas, it was unbelievable,' Hazen said. Robert Hazen, a mineralogist at the Carnegie Institution in Washington, DC. Photograph: Courtesy of Robert Hazen 'I had got disillusioned with the state of the art of looking for life on other worlds,' Wong said. 'I thought it was too narrowly constrained to life as we know it here on Earth, but life elsewhere may take a completely different evolutionary trajectory. So how do we abstract far enough away from life on Earth that we'd be able to notice life elsewhere even if it had different chemical specifics, but not so far that we'd be including all kinds of self-organizing structures like hurricanes?' The pair soon realized that they needed expertise from a whole other set of disciplines. 'We needed people who came at this problem from very different points of view, so that we all had checks and balances on each other's prejudices,' Hazen said. 'This is not a mineralogical problem; it's not a physics problem, or a philosophical problem. It's all of those things.' They suspected that functional information was the key to understanding how complex systems like living organisms arise through evolutionary processes happening over time. 'We all assumed the second law of thermodynamics supplies the arrow of time,' Hazen said. 'But it seems like there's a much more idiosyncratic pathway that the universe takes. We think it's because of selection for function—a very orderly process that leads to ordered states. That's not part of the second law, although it's not inconsistent with it either.' Looked at this way, the concept of functional information allowed the team to think about the development of complex systems that don't seem related to life at all. At first glance, it doesn't seem a promising idea. In biology, function makes sense. But what does 'function' mean for a rock? All it really implies, Hazen said, is that some selective process favors one entity over lots of other potential combinations. A huge number of different minerals can form from silicon, oxygen, aluminum, calcium, and so on. But only a few are found in any given environment. The most stable minerals turn out to be the most common. But sometimes less stable minerals persist because there isn't enough energy available to convert them to more stable phases. 'Information itself might be a vital parameter of the cosmos, similar to mass, charge, and energy.' This might seem trivial, like saying that some objects exist while other ones don't, even if they could in theory. But Hazen and Wong have shown that, even for minerals, functional information has increased over the course of Earth's history. Minerals evolve toward greater complexity (though not in the Darwinian sense). Hazen and colleagues speculate that complex forms of carbon such as graphene might form in the hydrocarbon-rich environment of Saturn's moon Titan—another example of an increase in functional information that doesn't involve life. It's the same with chemical elements. The first moments after the Big Bang were filled with undifferentiated energy. As things cooled, quarks formed and then condensed into protons and neutrons. These gathered into the nuclei of hydrogen, helium, and lithium atoms. Only once stars formed and nuclear fusion happened within them did more complex elements like carbon and oxygen form. And only when some stars had exhausted their fusion fuel did their collapse and explosion in supernovas create heavier elements such as heavy metals. Steadily, the elements increased in nuclear complexity. Wong said their work implies three main conclusions. First, biology is just one example of evolution. 'There is a more universal description that drives the evolution of complex systems.' Illustration: Irene Pérez for Quanta Magazine Second, he said, there might be 'an arrow in time that describes this increasing complexity,' similar to the way the second law of thermodynamics, which describes the increase in entropy, is thought to create a preferred direction of time. Finally, Wong said, 'information itself might be a vital parameter of the cosmos, similar to mass, charge and energy.' In the work Hazen and Szostak conducted on evolution using artificial-life algorithms, the increase in functional information was not always gradual. Sometimes it would happen in sudden jumps. That echoes what is seen in biological evolution. Biologists have long recognized transitions where the complexity of organisms increases abruptly. One such transition was the appearance of organisms with cellular nuclei (around 1.8 billion to 2.7 billion years ago). Then there was the transition to multicellular organisms (around 2 billion to 1.6 billion years ago), the abrupt diversification of body forms in the Cambrian explosion (540 million years ago), and the appearance of central nervous systems (around 600 million to 520 million years ago). The arrival of humans was arguably another major and rapid evolutionary transition. Evolutionary biologists have tended to view each of these transitions as a contingent event. But within the functional-information framework, it seems possible that such jumps in evolutionary processes (whether biological or not) are inevitable. In these jumps, Wong pictures the evolving objects as accessing an entirely new landscape of possibilities and ways to become organized, as if penetrating to the 'next floor up.' Crucially, what matters—the criteria for selection, on which continued evolution depends—also changes, plotting a wholly novel course. On the next floor up, possibilities await that could not have been guessed before you reached it. For example, during the origin of life it might initially have mattered that proto-biological molecules would persist for a long time—that they'd be stable. But once such molecules became organized into groups that could catalyze one another's formation—what Kauffman has called autocatalytic cycles—the molecules themselves could be short-lived, so long as the cycles persisted. Now it was dynamical, not thermodynamic, stability that mattered. Ricard Solé of the Santa Fe Institute thinks such jumps might be equivalent to phase transitions in physics, such as the freezing of water or the magnetization of iron: They are collective processes with universal features, and they mean that everything changes, everywhere, all at once. In other words, in this view there's a kind of physics of evolution—and it's a kind of physics we know about already. The Biosphere Creates Its Own Possibilities The tricky thing about functional information is that, unlike a measure such as size or mass, it is contextual: It depends on what we want the object to do, and what environment it is in. For instance, the functional information for an RNA aptamer binding to a particular molecule will generally be quite different from the information for binding to a different molecule. Yet finding new uses for existing components is precisely what evolution does. Feathers did not evolve for flight, for example. This repurposing reflects how biological evolution is jerry-rigged, making use of what's available. Kauffman argues that biological evolution is thus constantly creating not just new types of organisms but new possibilities for organisms, ones that not only did not exist at an earlier stage of evolution but could not possibly have existed. From the soup of single-celled organisms that constituted life on Earth 3 billion years ago, no elephant could have suddenly emerged—this required a whole host of preceding, contingent but specific innovations. However, there is no theoretical limit to the number of uses an object has. This means that the appearance of new functions in evolution can't be predicted—and yet some new functions can dictate the very rules of how the system evolves subsequently. 'The biosphere is creating its own possibilities,' Kauffman said. 'Not only do we not know what will happen, we don't even know what can happen.' Photosynthesis was such a profound development; so were eukaryotes, nervous systems and language. As the microbiologist Carl Woese and the physicist Nigel Goldenfeld put it in 2011, 'We need an additional set of rules describing the evolution of the original rules. But this upper level of rules itself needs to evolve. Thus, we end up with an infinite hierarchy.' The physicist Paul Davies of Arizona State University agrees that biological evolution 'generates its own extended possibility space which cannot be reliably predicted or captured via any deterministic process from prior states. So life evolves partly into the unknown.' 'An increase in complexity provides the future potential to find new strategies unavailable to simpler organisms.' Mathematically, a 'phase space' is a way of describing all possible configurations of a physical system, whether it's as comparatively simple as an idealized pendulum or as complicated as all the atoms comprising the Earth. Davies and his co-workers have recently suggested that evolution in an expanding accessible phase space might be formally equivalent to the 'incompleteness theorems' devised by the mathematician Kurt Gödel. Gödel showed that any system of axioms in mathematics permits the formulation of statements that can't be shown to be true or false. We can only decide such statements by adding new axioms. Davies and colleagues say that, as with Gödel's theorem, the key factor that makes biological evolution open-ended and prevents us from being able to express it in a self-contained and all-encompassing phase space is that it is self-referential: The appearance of new actors in the space feeds back on those already there to create new possibilities for action. This isn't the case for physical systems, which, even if they have, say, millions of stars in a galaxy, are not self-referential. 'An increase in complexity provides the future potential to find new strategies unavailable to simpler organisms,' said Marcus Heisler, a plant developmental biologist at the University of Sydney and co-author of the incompleteness paper. This connection between biological evolution and the issue of noncomputability, Davies said, 'goes right to the heart of what makes life so magical.' Is biology special, then, among evolutionary processes in having an open-endedness generated by self-reference? Hazen thinks that in fact once complex cognition is added to the mix—once the components of the system can reason, choose, and run experiments 'in their heads'—the potential for macro-micro feedback and open-ended growth is even greater. 'Technological applications take us way beyond Darwinism,' he said. A watch gets made faster if the watchmaker is not blind. Back to the Bench If Hazen and colleagues are right that evolution involving any kind of selection inevitably increases functional information—in effect, complexity—does this mean that life itself, and perhaps consciousness and higher intelligence, is inevitable in the universe? That would run counter to what some biologists have thought. The eminent evolutionary biologist Ernst Mayr believed that the search for extraterrestrial intelligence was doomed because the appearance of humanlike intelligence is 'utterly improbable.' After all, he said, if intelligence at a level that leads to cultures and civilizations were so adaptively useful in Darwinian evolution, how come it only arose once across the entire tree of life? Mayr's evolutionary point possibly vanishes in the jump to humanlike complexity and intelligence, whereupon the whole playing field is utterly transformed. Humans attained planetary dominance so rapidly (for better or worse) that the question of when it will happen again becomes moot. Illustration: Irene Pérez for Quanta Magazine But what about the chances of such a jump happening in the first place? If the new 'law of increasing functional information' is right, it looks as though life, once it exists, is bound to get more complex by leaps and bounds. It doesn't have to rely on some highly improbable chance event. What's more, such an increase in complexity seems to imply the appearance of new causal laws in nature that, while not incompatible with the fundamental laws of physics governing the smallest component parts, effectively take over from them in determining what happens next. Arguably we see this already in biology: Galileo's (apocryphal) experiment of dropping two masses from the Leaning Tower of Pisa no longer has predictive power when the masses are not cannonballs but living birds. Together with the chemist Lee Cronin of the University of Glasgow, Sara Walker of Arizona State University has devised an alternative set of ideas to describe how complexity arises, called assembly theory. In place of functional information, assembly theory relies on a number called the assembly index, which measures the minimum number of steps required to make an object from its constituent ingredients. 'Laws for living systems must be somewhat different than what we have in physics now,' Walker said, 'but that does not mean that there are no laws.' But she doubts that the putative law of functional information can be rigorously tested in the lab. 'I am not sure how one could say [the theory] is right or wrong, since there is no way to test it objectively,' she said. 'What would the experiment look for? How would it be controlled? I would love to see an example, but I remain skeptical until some metrology is done in this area.' Hazen acknowledges that, for most physical objects, it is impossible to calculate functional information even in principle. Even for a single living cell, he admits, there's no way of quantifying it. But he argues that this is not a sticking point, because we can still understand it conceptually and get an approximate quantitative sense of it. Similarly, we can't calculate the exact dynamics of the asteroid belt because the gravitational problem is too complicated—but we can still describe it approximately enough to navigate spacecraft through it. Wong sees a potential application of their ideas in astrobiology. One of the curious aspects of living organisms on Earth is that they tend to make a far smaller subset of organic molecules than they could make given the basic ingredients. That's because natural selection has picked out some favored compounds. There's much more glucose in living cells, for example, than you'd expect if molecules were simply being made either randomly or according to their thermodynamic stability. So one potential signature of lifelike entities on other worlds might be similar signs of selection outside what chemical thermodynamics or kinetics alone would generate. (Assembly theory similarly predicts complexity-based biosignatures.) There might be other ways of putting the ideas to the test. Wong said there is more work still to be done on mineral evolution, and they hope to look at nucleosynthesis and computational 'artificial life.' Hazen also sees possible applications in oncology, soil science and language evolution. For example, the evolutionary biologist Frédéric Thomas of the University of Montpellier in France and colleagues have argued that the selective principles governing the way cancer cells change over time in tumors are not like those of Darwinian evolution, in which the selection criterion is fitness, but more closely resemble the idea of selection for function from Hazen and colleagues. Hazen's team has been fielding queries from researchers ranging from economists to neuroscientists, who are keen to see if the approach can help. 'People are approaching us because they are desperate to find a model to explain their system,' Hazen said. But whether or not functional information turns out to be the right tool for thinking about these questions, many researchers seem to be converging on similar questions about complexity, information, evolution (both biological and cosmic), function and purpose, and the directionality of time. It's hard not to suspect that something big is afoot. There are echoes of the early days of thermodynamics, which began with humble questions about how machines work and ended up speaking to the arrow of time, the peculiarities of living matter, and the fate of the universe. Original story reprinted with permission from Quanta Magazine, an editorially independent publication of the Simons Foundation whose mission is to enhance public understanding of science by covering research developments and trends in mathematics and the physical and life sciences.
The Independent
04-06-2025
- Business
- The Independent
The hidden cost of tech complexity – and what you can do about it
Freshworks is a Business Reporter client Most tech solutions promise simplicity but deliver chaos, costing time, decisions and connection – it's time for change. As companies grow, they often move fast. New markets, new customers, new demands. But growth tends to bring a flood of quick tech purchases – each solving a specific problem, each adding another layer. Before long, the very tools meant to enable speed begin to slow everything down. It's a familiar trap: complexity creeps in quietly. A duplicate process here, a siloed system there, and suddenly teams are misaligned, data is fragmented and performance suffers. Complexity is the enemy of scale As a tech leader with experience across the sector, I've seen this pattern repeat across industries and continents. Businesses of all sizes end up fighting the same invisible force: fragmentation. Teams operate from conflicting versions of the truth. Manual handoffs and makeshift integrations clog up workflows. And tech investments stall before delivering value. And it's not just operational. Fragmented systems slow down operations and obscure visibility. When your support desk, product analytics, customer database and financial systems can't communicate effectively, you're essentially making decisions without real insight. Take customer retention. If your support platform can't surface relevant in-app behaviour or billing anomalies, your team can't intervene at critical moments. That's not just a missed support ticket – it's a lost customer. Worse, it may signal dozens more if warning signs aren't shared across departments. Good intentions, bad outcomes Ironically, fragmentation often stems from good intentions. Departments adopt specialised tools to solve local challenges. But without a coherent architecture or integration strategy, organisations end up with tech stacks that resemble patchwork quilts and intelligent automation falls flat. It's what Stanford researchers Bob Sutton and Huggy Rao, authors of The Friction Project, call 'addition bias' – the instinct to add features, tools or steps instead of removing them. In their study of global brands, this tendency increased friction and slowed performance. Simplifiers, they found, often faced resistance, while adders, those who added complexity, were rewarded – even when performance suffered. Too often, organisations are sold bloated platforms packed with unused features, marketed as 'added value' but delivering the opposite. Implementations drag on for months, results take years, and the very tools meant to empower teams end up complicating their work. Meanwhile, the real cost is paid by employees, who now spend their time navigating systems rather than solving problems. AI only works if it's connected Artificial intelligence has enormous potential to accelerate business. But that promise breaks down fast without integration. Disconnected systems can't fuel automation and half-built workflows create more work – not less. But when applied strategically, AI delivers real results. Finance teams can analyse costs and optimise spending in real time. Support teams can use AI-powered agents to handle routine support tasks. Engineering can automate troubleshooting. HR can screen candidates more efficiently. And the payoff is clear: 98 per cent of employees are already getting time back in their workday thanks to AI – reinvesting it in higher-value efforts such as boosting productivity (71 per cent), coaching others (67 per cent) and tackling more creative or complex challenges (66 per cent). When AI is properly integrated across functions, it doesn't just streamline operations. It empowers people. Escape the cycle: a strategic path to uncomplicating systems The good news? It's possible to break the cycle. Here's how forward-thinking organisations are simplifying by design: Inventory everything. Map every tool across departments. You can't fix what you can't see. Use workflow automation to identify data gaps, redundancies and ownership. Prioritise integration. Evaluate platforms for open APIs and native integrations. Tools that don't integrate easily should raise red flags. Unify your data. Create a single source of truth for customer information – whether via a centralised platform or a modern data unification layer. Ensure every team works from shared insights. Designate integration leaders. Empower individuals or teams to connect departments, break silos and ensure systems integrate strategically, not reactively. Collaboration tools help align efforts. Think in platforms, not point solutions. Consolidate where it makes sense. Choose platforms that support multiple workflows – not only for current needs but also for future direction. Simplicity as a competitive edge Customer experiences are powered by the systems employees use every day. That's why tech leaders must focus on alignment, not just implementation. Sustainable speed doesn't come from scattered bursts of progress. It comes from unified momentum. In any context – business, productivity or daily operations – complexity breeds inefficiency, higher costs and slower decisions. Simplicity unlocks focus, clarity and results. For teams, unnecessary complexity causes stress and burnout. Simplicity fuels effectiveness. So the question leaders should be asking isn't whether they can afford to simplify. It's whether they can afford not to. At Freshworks, we believe simplicity isn't a sacrifice. It's a competitive edge. It's time to uncomplicate and get maximum value from your tech stack.
Forbes
19-05-2025
- Business
- Forbes
How AI Can Help Leaders Deal With Ever-Increasing Volatility
For some time now, leaders of organizations have been warned that they live in uncertain times and so need to be able to cope with volatility and complexity. Indeed, they are repeatedly told that in order to thrive they need to embrace such challenges. But it is one thing to deal with events and situations that — although they may be unpredictable and unique — are within the bounds of what might be if not exactly expected then not completely unforeseeable and quite another to handle things that can change from day to day and back again. Since the recent flurry of announcements from the Trump White House on tariffs and trade deals falls squarely into this category, leaders need to adopt fundamentally different mindsets than the ones that have served them until now. Sure, the pandemic accelerated a profound change in the way that many organizations work, while the invasion of Ukraine by Russia has forced a shift in European attitudes towards defence and security. But a world in which tariffs can be imposed on a company's goods or services one day only for them to be reduced, raised or even lifted entirely a few days later is something completely different. This is not something that that old corporate standby, scenario planning, is much use for. As a banker quoted in a recent Financial Times article on the issue said: 'The one scenario everyone seems to have forgotten to plan for is the one where all the scenarios are wrong.' Although all leaders — whether in politics, business or elsewhere — still seem to think that those they lead require something akin to certainty from them, it seems self-evident that the opposite is true. With so many unpredictable things happening at once, how can leaders expect, and be expected, to have the answers? Instead, suggests Sharmla Chetty, CEO of Duke Corporate Education, which provides customised leadership programs around the world, leaders should be curious and asking questions. Pointing out that 'agility is no longer just about speed,' she adds that it is also about leaders listening and learning. They need to demonstrate authenticity and vulnerability while also encouraging trust. A more traditional response comes — inevitably perhaps — from the management consultancy McKinsey & Co. At its heart is one of the words of the moment — 'resilence.' (The concept is also in the vocabulary of Chetty, albeit more in the context of her overcoming serious challenges growing up and developing her career in South Africa.) In an article just published, the firm argues that this resilience is vital for success in a time of 'uncertainty and permacrisis' and that CEOs — as the only executives with the 'holistic perspective' required — must take the lead in investing in and building it. Among the five actions that it says CEOs must take to make their organizations stronger and less susceptible to shocks are embedding resilience in the company's vision so that there is an inextricable link between the organization's strategy and its levels of resilience and building 'full-body resilience' by paying attention to all the resilience dimensions in an organization and assessing how and where they compensate for and reinforce one another. McKinsey also advocates hiring 'gritty' individuals who show adaptable traits and behaviours. This all makes sense — to a degree. But it also seems like more of the same. In the view of Emiko Caerlewy-Smith, partner with Elixirr, which bills itself as the challenger management consultancy, the much faster pace and the requirement for leaders to be constantly connected is creating a situation that is not sustainable without moments of recovery and support from infrastructure. 'Human beings have a limited capacity to give energy,' she says. While she shares the McKinsey view that building strong partnerships with a diverse set of external stakeholders can help build resilience, she also sees a role for technology, specifically AI. Using it as a sort of business hub that keeps executives one step ahead almost in a predictive way could make a huge difference to how organisations are able to respond to a hyper-volatile economic climate. Caerlewy-Smith points to a recent project the consultancy recently carried out with a telecommunications company in the U.S. that wanted to use AI to modernise its sales and unlock new revenue. In just a few months, they built two custom AI models that cut sales representatives' research time from eight minutes to 45 seconds, effectively giving them instant access to critical insights, and streamlined prospecting to generate higher-quality leads and accelerate deal closures. 'Imagine multiplying that across the business,' she says of the 93% increase in productivity. But AI is not the only tool. Like the McKinsey authors, she sees strength in numbers at times like these and recommends organizations develop collaborative eco-systems. 'A platform-based business with a network of suppliers or partners is likely to be strong,' she adds, pointing out that younger leaders are more open-minded than their predecessors about collaboration. Businesses also need to look at how they organize themselves internally. With a clear need to be able to make decisions more quickly, they should move away from the production line approach to one more based on systems thinking. At the same time, they need to be dealing with risk more systematically. The recent cyber attacks on U.K. retailers such as Marks & Spencer and the Co-op should serve as a reminder that executives need to look at the issue in the round. In the end, though, it is — as nearly always — about the intersection of humans and technology. 'Leaders that are successful cannot rely on data,' says Caerlewy-Smith. 'They need to have experience.' That said, there is no denying the ability of AI to create data much more quickly than before and to use it to challenge the gut feelings on which executives used to rely.
Forbes
19-05-2025
- Forbes
5 Mental Models For Resilient Leadership In Times Of Change
Sometimes the road is unclear unless we're able to zoom out. We live in a world that won't sit still. Tech leaps weekly. Politics go Orwellian. The climate is boiling. The economy rolls like a slot machine. And still we're expected to lead, build, create in this context. That's why, especially in times of great change, many look to frameworks and methodologies to regain a sense of clarity. Recently, Nicolas Francisco Arroyo of the strategy and design firm Manyone shared a curated set of five mental models that, together, offer a powerful lens through which leaders can approach complexity, build resilience, and drive meaningful progress. 'I spent the last few months looking for concepts that help me not just cope, but think clearer, move smarter, and lead braver in this chaos,' said Arroyo. 'What I found isn't a framework. It's not a method. It's a mindset. A mental infrastructure. A collection of sharp lenses to see the mess and find ways through it.' So here are 5 ideas that won't fix the future, but they might just help you shape it. These are not rigid tools. They are conceptual anchors, drawn from systems theory, philosophy, and biology, that can help individuals and organizations evolve rather than simply endure. Coined by Nassim Nicholas Taleb, the idea of antifragility goes beyond resilience. Whereas resilient systems withstand shocks, antifragile ones actually grow stronger because of them. The classic metaphor is the immune system and how it must encounter stressors to adapt and build strength. As Arroyo puts it, 'In volatile times, fragility breaks, robustness resists but only antifragility learns and grows.' In a family office context, this means not shielding structures or successors from every potential threat, but rather designing governance and operations to adapt under pressure. Exposure to well-calibrated risks can be a form of long-term investment in capability. Chaos and instability are not always enemies. They are often the catalysts of the next evolution. From Chilean biologists Humberto Maturana and Francisco Varela comes the concept of autopoiesis. This is the capacity of living systems to self-maintain, adapt, and evolve from within. Cut your skin, and it heals. No command needed. Applied to leadership, this mental model suggests the most resilient organizations are not top-down, but rather cellular. Think decentralized decision-making, empowered teams, and cultures that regenerate in response to change. For family offices transitioning between generations, building internal capacity and adaptability may matter more than legacy processes. Borrowed from complexity theory and championed by author Steven Johnson, the adjacent possible suggests that transformative innovation often comes from small, near-term combinations of existing elements. Those who are driving radical change don't try to predict the distant future with total certainty, but they continuously push the boundaries of what's possible by experimenting, iterating, and making connections between already existing ideas, in order to build momentum, learn from mistakes and incrementally steer change towards a desired direction. The same applies to individuals: the best way to future-proof yourself isn't by trying to guess what's the next big thing in a distant future , but by constantly expanding your own 'adjacent possible' through learning, new experiences, and interdisciplinary thinking. By exponentially growing your own curiosity towards the world. Consider the iPhone. It was not a leap into the unknown, but a brilliant recombination of known technologies. In business, especially during chaotic periods, the path forward may not require a moonshot but rather a door already ajar. In a world obsessed with the next big thing, the Lindy Effect tells us: don't just ask what's new, ask what's stood the test of time. According to Arroyo, 'The future isn't only built from what's next. It's also built on what refuses to die.' The longer an idea, practice, or institution has survived, the longer it likely will. Not everything new is better and not everything old is obsolete. Meditations by Marcus Aurelius remains a bestseller not because it's ancient, but because it's timeless. In wealth strategy, too, enduring principles such as stewardship, discipline, intergenerational perspective, often outlast fast-moving trends. Finally, from the world of sports psychology comes playing hurt. This is the act of showing up, performing, and leading despite setbacks or discomfort. Waiting for perfect timing or conditions is often fear in disguise. We keep telling ourselves we'll act when we feel ready. But readiness is a myth. The people who shape the future aren't the ones who wait, they're the ones who step in, imperfect and in motion. Play hurt. Or sit out. Those are the options. Think of Olympic gymnast Kerri Strug landing a gold-winning vault on an injured ankle. Or of countless entrepreneurs and principals making tough calls without all the data. True leadership isn't about pristine conditions. It's about stepping forward when it matters most. Mental resilience is doing the hard thing when it matters, not when it's easy. What unites these five mental models is their refusal to be passive. Each insists that strength, clarity, and progress are possible even in uncertainty. In the fragmented, opaque world of wealth and leadership, where long-term thinking meets constant change, these ideas offer more than comfort. They offer direction. As Arroyo puts it, 'We treat uncertainty like a glitch in the system. But it's the system. Nature evolves through randomness. Innovation comes from noise. Even your brain relies on prediction errors to learn.' We don't need a fixed map. We need a new way of seeing the terrain.
