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Below, Nick Foster shares five key insights from his new book, Could Should Might Dont: How We Think About the Future. Nick has spent the last 25 years working within companies at the very forefront of emerging technology, from Apple and Sony to Nokia and Dyson. Most recently, he was head of design at Google X. He has established himself as a leading figure in the field of Futures Design. In 2021, he was awarded the title Royal Designer for Industry, the highest accolade for a British designer. Whats the big idea? We need to have a conversation about the future, but not the kind youd expect. Humans have already talked at length about what the future may or may not hold. What we rarely discuss, and need to start addressing, is how we think about the future. By understanding the ways in which people process what lies ahead, we can all become better-equipped critics of the futures we are shown or sold. To design a better future, we need better futurists. 1. Thinking about the future is more important than ever. There is a palpable sense of curiosity, uncertainty, and anxiety about the future. Google searches for What will the future be like? have tripled since 2020. I think thats because we have experienced more change in the last hundred years than at any other time in history. A hundred years ago, we had not yet invented penicillin, and less than half the homes in America had electricity. My father, who lived in the U.K., has experienced both a world before the creation of vinyl albums and after the creation of ChatGPT. Even if I think about myself, there were half as many people on Earth when I was born as there are today. Much of this rapid change has come with baggage, and many of the things were trying to fix today are the result of insufficient thought about the future from previous generations. This needs to change. To start, we need to have a conversation about our ability to think about the future. I see the world through a design lens, but Ive spent my entire career around entrepreneurs, scientists, engineers, strategists, investors, business leaders, and policymakers, and it seems that no one is very good at thinking about the future. This skill is significantly underprioritized, underfunded, and underdeveloped in almost all of us. Our thinking about the future tends to fall into one of four main varieties: could, should, might, dont. 2. Could Futurism. Could Futurism is a way of thinking about the future with wide-eyed optimism. This is probably the most familiar and publicly embraced type of futurism. If you type futuristic into Google images, youll see over-the-top visions of flying cars, humanoid robots, and towering glass architecture. This is the kind of stuff you see from futurists on conference stages or when futurists are invited onto TV, and this is also how we typically experience the future when we go to things like trade shows or expos. This futurism is built around the modernist idea of growth and change through strident progress, mechanization, and industrialism. This way of thinking about the future has largely grown in parallel with the growth of science fiction, which has fed ideas into the minds of powerful leaders. Its exciting, escapist, and intended to shock. This futurism is built around the modernist idea of growth and change through strident progress, mechanization, and industrialism. But Could Futurism has weaknesses that are often overlooked. Just like sci-fi storytelling, this kind of futurism is heroic. It treats the future as a world filled with extreme characters having extreme experiences in extreme placesand often treads a bit closer to advertising than truth. It encourages us to think of the future as a place of extreme transformation, but doesnt talk about transition or interstitial change. While it prides itself on imagination, Could Futurism is also incredibly repetitive, as evident in those Google search results. This futurism represents a placeholder for deep thinking, offering simplified icons that we can drop into our slideshows and conversations. It lacks genuine, rigorous consideration. 3. Should Futurism. Should Futurism is focused on finding some sort of certainty and assuredness in the future. In the olden times, this was mostly built around things like soothsaying and predictions by people like Nostradamus or cutting open the belly of a goat and interpreting the shape of the entrails that fell out. This futurism can help us narrow down the future and concentrate on one dot that lies ahead. In contemporary society, the world of Should Futurism is mostly dominated by corporate strategy. It is built on the idea that we can somehow take data from the past and convert that solid line into a dotted line that leads us to a position in the future. Though often useful, the downfall of this thinking is that we are creating pieces of well-styled, well-executed numeric fiction. Those dotted lines on charts are not real facts. Once the solid line turns into a dotted line, it ceases to be data and becomes a story. Youll often find people in the world of Should Futurism making bold predictions or statements about things that are definitely going to happen. They love that quote from Wayne Gretzky about skating to where the puck will be. But knowing where the puck will be is essentially a story. This type of thinking tends to view the world as a system that can be decoded, converted into an algorithm, and then utilized to create simulations. But anybody whos put any money into the stock market knows that the dotted line heading into the future is just a story. Our world is volatile, stochastic, and ultimately unmappable. The idea of using historical patterns to project futures is remarkably unreliable. To use an acronym from the Army, our world is VUCA: volatile, uncertain, complex, and ambiguous. 4. Might Futurism. Might Futurism emerges from the idea of plotting multiple scenarios out into the future, kind of like chess. This thinking is embedded, to an extent, in all of us whenever were planning an event or thinking through eventualities. But Might Futurism became more formalized around the Cold War with organizations like the Rand Corporation and people like physicist Herman Kahn. It was also grown upon by people like Pierre Wack, who worked for Royal Dutch Shell, trying to run scenarios for the future of their business. The problem with Might Futurism is, it can get tangled up in jargon and diagrams that provide countless possibilities but no real answers. In todays world, this is referred to mostly by the term strategic foresight, and its probably one of our most popular modern forms of futurism. It has an awful lot of methodologies, matrices, diagrams, and techniques for thinking about the future as a series of decision trees. The problem with Might Futurism is, it can get tangled up in jargon and diagrams that provide countless possibilities but no real answers. Its also not very good at imagining things. We often think that certain things are ridiculous or unlikely, but just look at companies like Blockbuster, Nokia, and Kodak, which didnt anticipate what was coming for them in the future. Or, if they did, put them in the realm of near impossibility. That how our brains work. Thats why when we watch things like magic and illusions, they fill us with wonder. Imagining what might happen in the future and building a sufficiently broad number of scenarios is extremely difficult. 5. Dont Futurism. Dont Futurism is focused on what might go wrong or things that we dont want to do. We may refer to these things as dystopian. Fear is a potent storytelling technique, which is why fairy tales and rhymes often focus on what might go wrong if we choose the wrong path. We also see this in oppositional democratic politics, where you have a position of power and a party thats in opposition, who are there to point out the mistakes that might happen if we follow the rules of whats been put in place by the leading power. Its also found at the center of things like protests. There is a more nuanced form of Dont Futurism emerging today, which focuses on the externalities and implications of the things were bringing about in the world, such as how new services and technologies we embed in society will age. The problems with Dont Futurism are numerous. They force us into oppositional, divided factions. Theyre often polemic and call for immediate and often impossible action. And they dont integrate well with the people or industries they want to change. Dont Futurism is difficult because it often wants to be. Finding a balance between dont ideas and actionable change can be tricky. This article originally appeared in Next Big Idea Club magazine and is reprinted with permission.
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AI fluency is quickly becoming the new leadership divide: Some executives are already embedding it into strategy, while others are still asking what it means. The gap is wideningand its shaping who gets hired to lead. Thats why AI fluency is becoming a top priority in leadership searches. Not deep technical mastery, but a practical understanding of how these tools work and where they apply. Companies want leaders who arent just talking about transformation but are actively engaged in it. People whove run pilots, evaluated risks, collaborated with product and tech, or led adoption efforts in their function. They dont need to be engineers. But they do need to know what these tools can (and cant) doand how to help others use them responsibly. How executives are actually using AI Executives at the forefront are already putting AI to work in meaningful, strategic ways. According to Salesforce, top-tier leaders are leveraging AI for critical tasks: running high-stakes market analysis, stress testing new business ideas before launch, and anticipating market shifts before they happen. A recent TechRadar piece reports that 74% of executives now trust AIs input more than that of colleagues, with 44% willing to let it override their own decisions. AI has become more than a dashboardits a boardroom copilot. Behind the scenes, back-office leaders are increasing AI spending: 92% of executives surveyed plan to ramp up investments in AI over the next three years, and 55% expect a boost of at least 10%. Yet execution is uneven. A recent IBM study found that while CEOs expect AI investment growth to more than double in the next two years, only 25% of AI initiatives have delivered expected ROIand just 16% have scaled enterprise-wide. Similarly, PwC found that while 79% of senior executives are adopting AI agents, many see success only when implementations are tied directly to measurable productivity gains in targeted areas. But high adoption doesnt always mean high impact. MIT researchers recently found that 95% of generative AI pilots fail to deliver measurable ROI, often because theyre launched without clear objectives or integration into core workflows. Meanwhile, another study warns of workslopa proliferation of low-quality output from poorly managed AI usage. These findings underscore a growing reality: AI fluency among leaders isnt just a nice-to-haveits the difference between pilots that fizzle and initiatives that scale. Leaders who understand both the capabilities and constraints of these tools are far better equipped to unlock value while avoiding the hidden costs of misuse. What leaders who use AI well do differently Heres what separates AI-fluent executives from the rest: Hands-on experimentationThese leaders gain firsthand experience with generative AIunderstanding not just the techs capabilities, but its limitations. Visible, scalable fluencyHarvard Business Publishings new study shows that employees with fluency arent just dabblingthey integrate AI into daily workflows. In “best-in-class” organizations, 98% of AI-fluent users are confident in using tools and report significant team performance gains. Strategic, not siloed, useAI isn’t just owned by the CTO. Leaders from across the organizationfrom chief human resources officers (CHROs) to CFOsare embedding AI literacy into their domains, turning it from a technical specialty into leadership capability. Intentional oversightEven when AI is applied, responsible use is rare: Infosys found that 95% of executives experienced AI mishaps, and only 2% of firms meet responsible-use standards. Dont just hire fasterhire toward the future Most companies today arent ignoring AItheyre trying to figure out how to keep up. They know they cant afford to fall behind, especially when competitors are investing aggressively in AI across operations. The challenge is finding people who can lead that shiftnot just within their function, but across the business. Thats the conversation Im having with clients right now. Not how do we hire someone fast? but how do we hire someone who can take us where we want to go? Takeaways for talent teams and leaders Screen for real fluency. Ask candidates to share where theyve deployed tools, navigated roadblocks, coled adoption, and managed both opportunity and risk. Favor handson experience, not academic abstraction. AI fluency is demonstrated, not talked aboutfrom pilot artifacts to team processes. Insist on governance and oversight. Pair fluency with accountability. Use AI, yesbut responsibly. Prioritize curiosity and adaptability. Leaders dont need to master every tool, but they do need to stay agile, ask questions, and foster a culture of experimentation. AI will keep evolving, and so must the people leading its adoption. Leaders arent expected to be coders. But they must know how to marshal AI, translate insight, and guide adoptionbalanced with judgment. The future of leadership is not running from change. Its defining it.
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In early 2023, a couple of months after ChatGPT launched and became the fastest-growing consumer application in history, I remember feeling both excited but also a bit overwhelmed by the rapid pace of AI. The barrage of news, product launches, and innovative use cases was relentless. We held an executive meeting at that time and decided to immediately reassign additional teams from other long-planned initiatives to double down on AI. We saw an opportunity to deliver even more value to our customers. My experience is not unique. Across the board, leaders have been aggressively implementing AI to improve productivity, lower costs, and improve communicationbut the results have been disappointing to date for many organizations. Only 34% of organizations say their AI projects have returned a positive ROI for most or all initiatives, according to Lucids AI readiness survey. Unlocking the tremendous value AI offers isnt a technology problem. Its an operational one. Leaders need to be more intentional about their workflows and practices to realize AIs vast potential. OPERATIONS ARE DRAGGING AI INITIATIVES DOWN In the race to keep pace with AI, businesses are moving quickly. But their emphasis on speed comes at a cost. About 61% of knowledge workers said in the survey that their firms AI strategy is only somewhat to not at all well aligned with operational capabilities. Most are glossing over foundational steps today that jeopardize their chances for success tomorrow. One notable example is documenting company processes and knowledge, a critical input for AI initiatives. The survey found that most organizations lack process documentation for their AI initiatives. Only 16% of survey respondents replied that their workflows are extremely well documented. The top obstacle to documenting knowledge at scale is a lack of time, according to 41% of respondents. Before implementing AI, leaders should ensure their teams understand the importance of documenting processes so that they always make time for it. Teams cant harness AI to its fullest without well-documented, clearly structured processes. If an organization is already well into its implementation but didnt prioritize this upfront, its never too late to course-correct. Its actually critical to do so. The next top barrier to knowledge documentation is the lack of tools (30%). Recently, I met with a Fortune 500 executive whose company is mandating AI to drive significant efficiency and productivity gains, yet relying on a legacy tool to collaborate that was never built for teams and centered on the individual user. If companies want AI to be adopted across the enterprise, they need a common space for brainstorming, decision making, planning, and collaborative documentation. Even with all of AIs transformative capabilities, the fundamentals of successfully integrating technology into a workplace still apply. Companies need the right tools that enable better collaboration and help them document current processes and best practices easily. FRICTION AROUND COLLABORATION LIMITS AIS IMPACT A while back, our executive team tackled a strategic challenge together. A product leader used AI to generate an impressive preparatory memo in a short timeframe, summarizing the challenge, benchmarking solutions, and offering recommendations. But the AI-generated memo was the starting point, not the end. We still needed to debate nuances specific to Lucid’s context, prioritize actions and assign ownership, and document takeaways and define next steps. Even with the amount of work that can be accelerated and automated with AI, collaboration is still critical. The survey found 23% of respondents say collaboration is often or always the bottleneck in complex work. Implementing AI is a major undertaking. Only by consistently engaging key stakeholders for in-depth discussions, clarifying decisions, and ensuring shared understanding can these bold initiatives succeed. THE NEW COMPETITIVE EDGE IN AI The success of a company’s AI strategy is only as strong as its execution, and a large perception gap proves this. The survey found that 61% of C-suite executives feel their AI approach is well considered, but a much smaller percentage of managers (49%) and entry-level employees (36%) agree. Closing this gap requires more than just a good plan; it requires operational readiness. Organizations must build stronger processes, improve documentation, and foster better collaboration to successfully implement AI. Harnessing the power of this revolutionary technology requires a level of rigor most organizations have yet to demonstrate. The new competitive advantage for AI adoption lies in the operational systems behind it. Dave Grow is CEO of Lucid Software.
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