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Yes, there are the New Years traditions of setting ambitious goals and ditching bad habits, but one evergreen resolution that ought to top lists is to banish bad design. Why endure something that simply doesnt work (or is an affront to aesthetics) any longer than we have to? In the spirit of fresh starts, we polled experts in architecture, tech, industrial design, and urbanism on the everyday annoyances and the big-picture issues that they think are in desperate need of a refresh in 2026. (Top on my personal list? Eye-searing headlights.) Design is inherently an optimistic act, and by fixing these issues, were a step closer to a more beautiful and better world. [Photo: Snehit/Adobe Stock] Data Centers Data centers are the significant buildings of the moment, and we have a responsibility to make them part of our cities as theyre really powering the future. The buildings have to perform at the highest technical level, but they also need to connect and respond to a sense of place and to the community around it. For example, we designed a data center with a facade that has an intricate pattern language that feels more like a theater or civic building and other centers with mass timber, which lends warmth and beauty to the structure while also bringing a sustainability story to the structure. Every data center project of ours now starts with thinking through resilient strategies, including reducing or eliminating evaporative cooling and integrating next-generation thinking on energy usage. At the same time, there are people still working in these buildings and there needs to be consideration for the workplace as well. It’s about technology plus people, and we can’t ignore the human side of this because recruitment and retention are still key considerations. Its also interesting to think about what to do in a really dense urban environment. Were involved in conversion projects that take aging, underutilized office buildings and explore vertical mixed use. It’s not just about converting office to residential, which were doing in many locations. Can you take an aging office building and part of its reuse becomes a data center? In 2026, well see more of a global dialogue from a real estate standpoint on urban opportunities that includes thinking about data centers vertically. Jordan Goldstein, Co-CEO of Gensler [Source Image: whitecityrecords/Adobe Stock] Crossover and Compact SUVs Living in Los Angeles, Im surrounded by automobiles all day. Im always disappointed by how homogenous so many archetypes are. Crossover and compact SUVs are all so similar that you could swap the badges on any of them, and no one would know the difference between the brands. Unfortunately, over the last decade the same can be said of most sports cars. All the major brands have adopted the wide rear body of the [Porsche] 911, and for no reason; their engines are in the front of the car and dont demand the stability and width to balance the weight that sits on the rear wheels of the 911. Every brand has an origin story, and many of their older iconic cars were based on original ideas. As recently as the 90s, car brands held a unique design language. In the past, the only market that had homogeneous design was the Soviet Union. Our culture is based on differentiation in the market, where customers have choices. Today we lack real choice. This all points to a lack of vision and conservative leadership at the major automakers. There is no risk-taking, and the customer is given a design thats the result of market research rather than innovation and design. This should be a priority because it instills poor valueslack of originality, fear-driven business strategy, zero risk-takingon the built environment and our culture. Jonathan Olivares, Creative Director of Knoll [Illustration: FC] Data Ownership Every time we swipe our MetroCard, visit a doctor, buy groceries, or scroll through our phones, we are creating data. But we almost never get to see it to understand ourselves better. The data flows in one direction only, from us into systems that are used to optimize operations and algorithms and train models. What if instead our data could come back to us in a form that can help us see the patterns in our lives and understand our own stories? I want to redesign this fundamental relationship.The issue is that data has become the language that we need to navigate life but we haven’t been taught to speak it; and the interfaces that could help us learn are designed for administrators and quarterly reports only, rarely for actual people trying to understand their own lives. Imagine getting home from a doctor’s appointment and receiving a beautiful understandable visualization of your healthover time, where you can see patterns you didnt know existed. This is the type of context that can help us ask better questions about our health. Or imagine your transit system revealing the mundane rhythms of your own life back to you (the coffee shop you always stop at on Tuesdays, the routes you take when you’re stressed versus calm). This would close the literacy gap by making data comprehensible in the moments when it matters most without dumbing down complexity and nuances. I’ve spent my career proving we can do this. Better design here means more agency. It means people who can advocate for themselves. It means closing the gap between those who can speak data and those who can’t. Giorgia Lupi, Partner at Pentagram [Illustration: FC] AI Interfaces Im excited to see how teams rethink and redesign user interfaces for an AI-native world. Today, were still in the MS-DOS era of AI where every prompt, every agent, and every emerging modality is, for the most part, a long text response in a conversational interface. My prediction is that in 2026, well see a shift toward richer, more dynamic interfaces where both inputs and outputs evolve far beyond text. Its not surprising that AI user interfaces began as chatbots. Large language models operate on tokens, and text is the fastest, cheapest medium to build, debug, and evaluate. But decades of software and interface design have made something clear: humans dont think in language alone. We think spatially. We understand through motion, contrast, hierarchy, and causality, and our instinct is to act through direct manipulation, not just typed commands. As AI capabilities evolve, design is more important than ever. Visual interfaces arent going away, and neither is the need to see, shape, and refine ideas as we work. Designers have a rare chance to define the rules and patterns of this new interface era, shaping what work, play, and productivity will look like for decades to come. Loredana Crisan, Chief Design Officer of Figma [Source Photo: serdarerenlere/Adobe Stock] Material Labeling When anyone (architects, clients, contractors) walks into a big-box store, it would be transformative to see a Nutri-score or Local Law 33 energy grade for materials, but for wood in particular since its so widely used. A better system would treat wood like food, with clear, standardized material labeling. You should be able to see where the wood comes from, almost like buying eggs when youre faced with this wall of different levels of chicken torture. Material supply chains struggle with standardization and transparency for many reasons, but in my opinion, it is because consumers didn’t know they should be demanding it. For example, once it became clear that Quartz countertops were causing silicosis by those cutting the material, consumers were horrified. So much so that the Australian government made the material illegal. The problem is big-box retailers, where most wood is purchased, rarely surface this information, despite occasionally stocking high-quality or responsibly sourced material hidden in plain sight. Greater transparency at the point of purchase would empower people to make more precise decisions about a whole host of values that are important to them. When I walk into a box retailer, I want to know which 2xs are Code A (regeneratively cultivated through methods of land conservation and repair by a local within 100 miles who has been historically disenfranchised) or Code B (selectively harvested and replanted by a fifth generation land and sawmill owner using Indigenous cool burning to prevent forest fires) or Code C (small batch monocultures grown at high efficiency to prevent the replacement of biodiverse unproductive forests), etc. Lindsey Wickstrom, Architect and Founding Principal of Mattaforma [Source Photo: Emagnetic/Adobe Stock] Outdoor Lighting How about we all start taking a neighborly approach to outdoor lighting? When colleagues and friends talk to me about lighting, they used to mention wonderful festival lights they had just seen or lamps they appreciated or hated. But these days they mostly complain about light streaming into their windows from someone elses outdoor lighting. In the city, a new commercial tower in midtown streams constantly changing light into bedroom windows literally miles away. Entertaining for some, apparently, and intensely disruptive for others. Not to mention the damage to fish and bird habitat.
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E-Commerce
The majority of us see change as a blind scary leap into the unknowna scary evolution that demands we give up on everything we know. But what if we reframed change, not as something that happens to us, but as something we actively choose? Traditionally people perceived change in black-and-white terms: either you can change, or you cant. That kind of thinking sets us up for failure by assuming that change requires some grand, perfect plan or major shift in direction. However, we also have the power to make small changes, no matter how minor they seem. And it’s these small changes that, over time, lead to profound transformation. Fear Takes the Wheel The most common reason people resist change is fear. And fear takes many forms: fear of failure, fear of the unknown, and fear of making the wrong move hold us back from making choices that could improve our lives. The fear of taking that first step is often so overwhelming that we decide to stay stuck, because inaction feels safer than risking the potential for discomfort or failure. We keep telling ourselves, Im not ready yet, or Ill probably fail. But these stories we tell ourselves only deepen our sense of powerlessness. They might make us feel comfortable by letting us off the hook, but these excuses dont help us become more capable, either. The issue is that fear doesnt just make us inactive; it keeps us stuck. As humans, we’re always making choicesconsciously or unconsciously. The hamster running on its wheel is a perfect metaphor here: it runs tirelessly, not because it doesnt have the ability to stop, but because it doesnt choose to stop. At any moment, that hamster can step off the wheel. And in many instances, so can you. The Cost of Inaction You have more control than you think. Staying stuck is a decision in itself, one that often carries a higher price than taking a leap. Consider this: Even if you stand in the middle of the road, you risk getting run over. This is the paradox of fear: Were afraid of making a “bad” choice, yet the failure to choose can often be the most costly decision we make. Research on organizational change shows how employees who resist change are more likely to experience disruption, anxiety, and negative emotions the longer they resist, which can make changing in the future even harder. Unchecked resistance can decrease productivity, lower morale, cause project delays, and increase turnover. Leaders and organizations that proactively manage resistance by building trust, clarity, and support can transform these challenges into opportunities for growth and adaptation. In contrast, those who embrace even small, incremental changes are more likely to experience increased confidence, a sense of accomplishment, and a willingness to face bigger challenges. The learning? Its the small wins that build momentum. In his 20 years as manager of the All Blacks, the New Zealand Rugby team, Darren Shand has seen how embracing even small change can catalyze teams to perform in remarkable ways. For over a decade, the All Blacks were the top ranked rugby team in the world, driven largely not just by talent but by embracing trust, positivity, and growth: During my time with the All Blacks, I learned that transformation rarely comes from radical changeit comes from consistent small choices made with purpose. At the highest level, we found that growth was less about doing more, and more about doing the little things better, every single day. The Power of Minor Shifts So, whats holding you back from better embracing change? Instead of seeing change as a monumental task, think of it as a series of small choices that add up over time. Start small: maybe it’s trying a new hobby, having a conversation with someone that you’ve been avoiding, or taking a short walk every day. These tiny decisions may seem insignificant in the moment, but theyre the building blocks of personal transformation. Each time you make a choice to step out of your comfort zone, no matter how small, youre signaling to yourself that change is possible. Ready for Change? Consider This If youre ready to embrace change, start by asking yourself a few simple but powerful questions: Whats the cost of staying where I am? Reflect on what youre risking by not making a change. Sometimes, the discomfort of the present moment is less painful than the long-term consequences of staying stuck. What ONE small step can I take today? Change doesnt have to be grandiose. Whats one tiny action you can take today that will start to shift your course? What am I afraid of? Often, fear is exaggerated in our minds. What is the worst thing that could happen if you tried something new? Could the benefits outweigh the risks? Who can support me in this change? Change doesnt have to be a solo endeavor. Who can be your accountability partner, or who can offer guidance along the way? By asking these questions, youll gain clarity on why change matters to you and how you can begin to make it happen, step by step. Change is Always a Choice Change is not as hard or as out of reach as we often make it out to be. The key is recognizing that, just like a hamster on its wheel, you have the power to stop running in circlesand step off. You have the power to make a change, however small, and with each choice, your world transforms. In the end, so much of the change we face isnt something that happens to us. Its something we choose.
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As 2026 begins, many organizations are launching AI transformation initiatives. The new year brings with it fresh budgets, renewed strategic focus, and mounting pressure to capture value from artificial intelligence. Yet studies consistently show that most AI projects fail to generate meaningful returns. Companies pour resources into promising experiments that never scale, accumulate tools that are never integrated, and watch initial enthusiasm curdle into skepticism. What separates organizations that create lasting value from those that dont is rarely the technology to which they have access. Instead, the critical secret sauce lies in having a systematic, rigorous, and repeatable approach that allows the leadership team to move from the identification of opportunities to operational deployment. This article offers a practical playbook for that journey, using the illustrative example of a midsize manufacturing firm (Aurora Windows). While the playbook itself distills learnings gained from large, technically sophisticated businesses in sectors such as defense and finance, our example shows how these lessons can be applied even in late-adopting companies with limited resources. At present, there are few examples of systematic end-to-end AI innovation pipelines that have been deployed successfully in the real world, so our example can only be illustrative. Nevertheless, forward-looking companies are already beginning their journeys along this path and evidence from decades of organizational and digital transformation efforts allow us to model what success will ultimately look like. {"blockType":"mv-promo-block","data":{"imageDesktopUrl":"https:\/\/images.fastcompany.com\/image\/upload\/f_webp,q_auto,c_fit\/wp-cms-2\/2025\/10\/creator-faisalhoque.png","imageMobileUrl":"https:\/\/images.fastcompany.com\/image\/upload\/f_webp,q_auto,c_fit\/wp-cms-2\/2025\/10\/faisal-hoque.png","eyebrow":"","headline":"Ready to thrive at the intersection of business, technology, and humanity? ","dek":"Faisal Hoques books, podcast, and his companies give leaders the frameworks and platforms to align purpose, people, process, and techturning disruption into meaningful, lasting progress.","subhed":"","description":"","ctaText":"Learn More","ctaUrl":"https:\/\/faisalhoque.com","theme":{"bg":"#02263c","text":"#ffffff","eyebrow":"#9aa2aa","subhed":"#ffffff","buttonBg":"#ffffff","buttonHoverBg":"#3b3f46","buttonText":"#000000"},"imageDesktopId":91420512,"imageMobileId":91420514,"shareable":false,"slug":""}} I will be using this playbook in my upcoming guest lecture for IMD Business School’s AI strategy and implementation executive program, delivered in collaboration with Misiek Piskorski, dean of executive education at IMD, and Amit Joshi, codirector of the program. IMD is a world-leading business school, ranked No. 1 globally in custom executive education by the Financial Times (2025), renowned for transforming rigorous research into actionable leadership results. An Illustrative Example: Aurora Windows Aurora Windows is a 35-year-old, second-generation manufacturing company that designs and produces doors, windows, and architectural glass for commercial and residential building projects. With roughly 220 employees across one main plant and two regional distribution hubs, it sits in the classic too big to be small, too small to be big SME band: large enough to feel pressure from global competitors and construction giants, but without a dedicated transformation department or a large consulting budget. Over the next five years, the leadership team aims to position Aurora as the go-to innovation partner for sustainable, smart building projects by becoming a fully AI-driven business. The Innovation Pipeline To succeed in its goals, Aurora needs to take a disciplined approach to AI enterprise transformation that treats the innovation process as a continuous structured pipeline with clear stages. Projects flow from initial ideation into a rigorous assessment phase and on to operational deploymenta narrowing funnel that sees many ideas entering but only the strongest and most strategically aligned reaching production. Firms in some sectorssuch as tech and pharmaceutical companieshave long relied on continuous product development pipelines that systematically advance projects from abstract ideas to market-ready products. In the AI age, every organization needs to adopt this kind of systematic approach to innovation. But this is more than just a new product development pipeline: Innovation projects must be aligned with the broader organizational culture and processes within which they will be embedded. Step 1: Current-State AssessmentEstablishing Your Baseline Before Aurora can begin managing an innovation pipeline, the leadership team needs to understand where the company currently stands. They conduct a baseline assessment across three dimensions: Organizational purpose and strategic clarity Auroras executive team revisits its core mission: creating high-performing, sustainable door, window, and glass solutions that make buildings safer, more comfortable, and more energy efficient. The team articulates three specific five-year goals: 40% revenue growth without proportional headcount increases Margin protection despite volatile input costs Positioning as the go-to AI-driven innovation partner. This clarity becomes the North Star for evaluating every AI initiative. Knowledge baseline The team then assesses the companys current AI literacy. At present, there is a scattering of expertise across departments, with individual enthusiasts driving the current pilot programs. AI knowledge in the leadership team is limited and most of the businesss staff are unfamiliar with basic machine learning concepts. Risk appetite Aurora is a family business that has survived by not taking reckless bets. But the market is shifting. Competitors are beginning to offer AI-enhanced design services and predictive maintenance. The leadership team articulates a balanced stance toward risk: Aurora needs to advance more rapidly than they would normally be inclined to move, but with guardrails in place to protect the brands hard-won reputation. This assessment reveals uncomfortable truths. Aurora has enthusiasm for AI transformation but no shared knowledge base or language for discussing AI, and no accepted criteria for assessing the value of pilot projects. The leadership team has ambition but there is currently no defined path to move projects from the pilot phase to company-wide operation. Most importantly, there is no mechanism for deciding what to do next. Step 2: OpportunitiesPopulating the Innovation Pipeline Auroras leadership now launches a structured ideation process to identify projects that are explicitly aligned with the companys strategic goals. Rather than asking What can we do with AI? cross-functional teams ask What poblems prevent us from achieving our strategic goals, and can AI help us solve them? The teams quickly generate two dozen initial ideas spanning multiple AI types: analytical AI for process optimization, workflow automation to reduce manual tasks, generative AI for design acceleration, and even agentic AI systems operating semiautonomously within defined parameters. Each idea receives a rapid initial assessment using five criteria scored 1 to 10: Priority: How urgently does this support our core goals? Risk: Whats the potential downside if this fails after deployment? Value: Whats the likely financial or strategic return? Cost: What investment is required to reach production? Difficulty: How challenging will implementation and adoption be? When scored and ranked, clear patterns emerge. Several high-scoring opportunities cluster around production efficiencyusing computer vision for defect detection, AI-driven equipment maintenance prediction, and automated quality documentation. A number of initiatives focusing on design acceleration and customer experience receive medium scores. Several moon shot projects that were initially very popular with senior leaders receive low scores because they are technically difficult, expensive, and come with significant risks, despite their high potential payoff. This process also surfaces important dependencies. A design acceleration project that has many supporters would require clean CAD libraries and standardized templateswork that hasnt started yet. Similarly, a maintenance prediction system needs sensor data that is not yet available but that would be generated if one of the quality inspection projects goes ahead. The ideation exercise produces more than a ranked list of ideas. It creates a common vocabulary for discussing AI opportunities at the same time as revealing capability gaps and building consensus around which directions make strategic sense. Of Auroras 24 ideas, 6 scored highly enough to warrant further detailed assessment. The rest remain in the backlognot definitively rejected, but requiring either new capabilities or a shift in strategic priority to make them viable. Step 3: AssessmentEnterprise Architecture Analysis and Fit The six projects that ranked highest in the initial screening now enter detailed assessment. Auroras leadership team first maps the organizations Strategic Enterprise Architecture (SEA) and then assesses each projects degree of fit across four dimensions: Purpose and Strategic Intent Does this project directly advance Auroras three strategic goals with clear, measurable outcomes? People and Culture Are leadership and staff ready for the changes the project involves? Processes and Governance Can the initiative integrate with current processes and operating models? Technology Architecture and Data Is the initiative feasible using existing or available systems? The results are sobering. Of the six projects under assessment, only three demonstrate clear alignment across all four SEA dimensions. Two of the others could become viable with specific capability-building work. The SEA analysis also reveals positive insights. The quality inspection camera project will generate structured defect data that several other proposed projects can use. By recognizing this dependency, Aurora can sequence projects to build on this foundation. Step 4: OperationalizationFrom Experimentation to Production The three projects that passed detailed assessment now undergo active experimentation. Aurora structures these experiments as learning journeys, not just technical validations. The visual quality inspection project runs bounded pilots on specific production lines. The AI-assisted design tools are tested with a small R&D team before broader rollout. The data infrastructure project proceeds in phases, upgrading one integration at a time while minimizing disruption. After six months of experimentation, the newly developed quality inspection tool passes all tests and moves to production. The data infrastructure project shows promise but needs another quarter of refinementit remains in experimentation. After a promising start, the AI-assisted design tools run into a technical wall. With no clear path forward, the project is paused until a technical solution is identified. Systems that reach production require ongoing monitoring, cost tracking, and impact measurement. Aurora establishes guardrails to prevent misuse and implements continuous monitoring to catch issues before they become problems. Sustaining the Pipeline Auroras innovation pipeline is a long-term, repeatable system that provides the engine for continuous AI transformation. But to deliver its value, it must be carefully tended. The leadership team establishes a quarterly review process with three goals: Project health checks Are experimental projects meeting milestones? Are production systems delivering expected value? Do any initiatives need intervention, resources, or retirement? Pipeline rebalancing As projects advance, move into production, or are killed, the pipeline needs replenishment. The leadership team takes a view across the entire pipeline to ensure that the right mix of projects is moving through, balanced across time horizons, risk levels, and strategic targets. Strategic recalibration Markets, technologies, and organizational priorities shift. Quarterly reviews explicitly ask: Do our scoring criteria still reflect strategy? Are new capabilities or partnerships available? Have competitors made moves that change our priorities? This operating rhythm transforms Auroras relationship with AI. Instead of episodic enthusiasm followed by disappointment when pilots dont scale, the leadership team has a sustainable engine for continuous improvement. Each quarter brings visible progresssome quick wins, some foundation building, some ambitious bets advancing. Within 18 months, Auroras transformation becomes tangible. The company now has three AI systems in production (quality inspection across all lines, automated quality documentation, and a new LLM-powered customer portal). The projects in experimentation and assessment build on these initial experiences and include initiatives that have become viable thanks to the technical capacity, skills, and processes developed while working on the initial round of projects. By avoiding wasteful efforts to develop a series of unconnected pilots with no clear strategic value, Aurora has built a foundation of success that is propelling it past its competitors. Conclusion: The Management System Behind the Pipeline Auroras story highlights a fundamental truth about AI transformation: Technology is rarely the constraint. Most companies can access impressive AI tools. What they lack are the management systems needed to deploy those tools strategically, build repeatable capabilities, and create sustained value. An innovation pipeline like the one in our example does not run itself. It requires systems and structures that creat both horizontal and vertical collaborationlinking the C-suite to project teams and linking project teams to the rest of the organization. Without these connections, even the best-designed pipelines will stall. Cultural change is often framed as a precondition for AI transformation. But culture doesnt shift as a result of exhortation alone. It is shaped and steered by the processes, review rhythms, and governance structures that determine how decisions get made and how work flows through the organization. Quarterly reviews, cross-functional assessment teams, and clear advancement criteria arent bureaucratic overhead. They are the mechanisms through which a culture of disciplined innovation takes root. The companies that succeed with AI wont be those with the most ambitious pilots or the earliest adoption of new tools. They will be those that build the management systems that are needed to move systematically from opportunity to assessment to operationand to sustain that movement over time. {"blockType":"mv-promo-block","data":{"imageDesktopUrl":"https:\/\/images.fastcompany.com\/image\/upload\/f_webp,q_auto,c_fit\/wp-cms-2\/2025\/10\/creator-faisalhoque.png","imageMobileUrl":"https:\/\/images.fastcompany.com\/image\/upload\/f_webp,q_auto,c_fit\/wp-cms-2\/2025\/10\/faisal-hoque.png","eyebrow":"","headline":"Ready to thrive at the intersection of business, technology, and humanity? ","dek":"Faisal Hoques books, podcast, and his companies give leaders the frameworks and platforms to align purpose, people, process, and techturning disruption into meaningful, lasting progress.","subhed":"","description":"","ctaText":"Learn More","ctaUrl":"https:\/\/faisalhoque.com","theme":{"bg":"#02263c","text":"#ffffff","eyebrow":"#9aa2aa","subhed":"#ffffff","buttonBg":"#ffffff","buttonHoverBg":"#3b3f46","buttonText":"#000000"},"imageDesktopId":91420512,"imageMobileId":91420514,"shareable":false,"slug":""}}
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