Understanding Technological Evolution

Last week, a customer said something that stopped me in my tracks: “Our data is what makes us unique. If we share it with an AI model, it may play against us.” This customer recognizes the transformative power of AI. They understand that their data holds the key to unlocking that potential. But they also see risks alongside the opportunities—and those risks can’t be ignored. The truth is, technology is advancing faster than many businesses feel ready to adopt it. Bridging that gap between innovation and trust will be critical for unlocking AI’s full potential. So, how do we do that? It comes down understanding, acknowledging and addressing the barriers to AI adoption facing SMBs today: 1. Inflated expectations Companies are promised that AI will revolutionize their business. But when they adopt new AI tools, the reality falls short. Many use cases feel novel, not necessary. And that leads to low repeat usage and high skepticism. For scaling companies with limited resources and big ambitions, AI needs to deliver real value – not just hype. 2. Complex setups Many AI solutions are too complex, requiring armies of consultants to build and train custom tools. That might be ok if you’re a large enterprise. But for everyone else it’s a barrier to getting started, let alone driving adoption. SMBs need AI that works out of the box and integrates seamlessly into the flow of work – from the start. 3. Data privacy concerns Remember the quote I shared earlier? SMBs worry their proprietary data could be exposed and even used against them by competitors. Sharing data with AI tools feels too risky (especially tools that rely on third-party platforms). And that’s a barrier to usage. AI adoption starts with trust, and SMBs need absolute confidence that their data is secure – no exceptions. If 2024 was the year when SMBs saw AI’s potential from afar, 2025 will be the year when they unlock that potential for themselves. That starts by tackling barriers to AI adoption with products that provide immediate value, not inflated hype. Products that offer simplicity, not complexity (or consultants!). Products with security that’s rigorous, not risky. That’s what we’re building at HubSpot, and I’m excited to see what scaling companies do with the full potential of AI at their fingertips this year!

The conversation on my last post on AI adoption was helpful. Your comments underscore the complexity and opportunity of integrating AI into our daily work. Thank you! Here’s what I’m taking away: Changing Human Behavior is Harder Than Building A Tool Many of you underscored what I was grappling with: the real hurdle isn’t the technology, but the human response to it. To change the INTERTIA of human behavior, the “what’s in it for me” has to be clear. Without a clear “Day 1 win” that saves time, removes grunt work, or boosts productivity, even the best tools will sit on the shelf. Peer-to-Peer "Aha!" Moments Adoption accelerates when people see their peers succeeding. When colleagues demonstrate how AI genuinely transforms their work, it creates a powerful pull-through effect and a healthy "FOMO" that drives wider usage. We need to amplify these stories and create forums for peer sharing. Continuous Re-engagement AI models evolve incredibly fast. This means we need people to re-try tools they might have dismissed months ago. As one of you put it, we need to "check back in 6 weeks, not 6 months." We also recognize that early adopters often benefit from advanced techniques like curated prompt libraries, and we need to democratize this knowledge. I loved hearing about teams sharing a “prompt of the week” in meetings. We Cannot Ignore Fundamental Fears Several of you brought up the very real anxieties around trust and job security. This concern absolutely impacts willingness to engage, so it's something we must address head-on, transparently and empathetically. Our goal is to empower, not displace, and to focus on how AI can elevate our work and create new opportunities. Adoption is a cycle of curiosity, timing, and capability. It’s about building trust, fostering a safe space to experiment, and recognizing that the opportunity with AI evolves rapidly, requiring us to stay curious and re-engage. 

Trustworthy AI in production demands a fundamentally different approach to classical software. Unlike deterministic systems, AI applications – especially those built on LLMs and RAG – face constantly shifting data inputs, probabilistic outputs, and complex pipelines that span data, systems, code, and models. My colleague Shane Murray recently spoke on this same topic at the University of Arizona for IEEE International Congress on Intelligent and Service-Oriented Systems Engineering (CISOSE) alongside Vrushali C. (Dir Eng, Data & AI at Okta) Sharoon Srivastava (Principal Product Manager AI at Microsoft) Stephanie Kirmer (Senior MLE at DataGrail) Anusha Dwivedula (Director of PM at Morningstar) Vibe-coding a new AI tool might seem easy enough, but making it reliable is anything but. As Shane states in his position, to ensure reliability and trust, organizations must continuously observe every layer of their data + AI stack—not only in a secure testing environment, but live in production—by combining automated, scalable monitoring with human-in-the-loop oversight and a repeatable operational practice to rapidly root-cause and resolve issues. Only by pairing these approaches can we detect failures, mitigate risks, and sustain trust as AI systems evolve in the real world. You can see the full abstract from the session in the doc below. And if you want more from Shane, you can read his full thoughts in his latest article - or check out his feature in this week’s Alt Data Weekly (shout-out to John Farrall). Reliability isn’t a new challenge. But in the milieu of AI-everything, we need to define a different approach. The wheels are turning. Are you on board? Resources: https://lnkd.in/gZ_Nta3H https://lnkd.in/g8g2U3qs

As the physical impacts of #climatechange intensify, climate #adaptation is emerging as a vital and complementary investment theme alongside decarbonisation. New analysis suggests that global annual revenues from selected adaptation solutions could grow from US$1 trillion today to US$4 trillion by 2050, with US$2 trillion of that growth directly driven by the impacts of global warming. The associated investment opportunity across public and private markets - spanning equity and debt - is projected to expand from US$2 trillion to US$9 trillion, including US$3 trillion in incremental growth tied to #climaterisks. This shift will drive growth in both emerging technologies, such as weather intelligence and forecasting tools, and established solutions like climate-resilient infrastructure and materials. Notably, these estimates are conservative - assuming largely reactive adaptation. A shift toward anticipatory, risk-informed planning could unlock even greater opportunities, accelerating both revenue growth and capital flows well before 2050. While decarbonisation remains essential to mitigating future climate risks, climate adaptation addresses the urgent need to withstand current and projected impacts. Together, they offer distinct yet complementary pathways for long-term investors to contribute to and benefit from the transition to a more resilient, sustainable world.  #ClimateAdaptation #SustainableInvestment #Decarbonisation #Resilience #ImpactInvesting #LongTermCapital https://lnkd.in/dNC48Aeb

In our recent work with organisations, I keep seeing the same patterns emerge when it comes to adopting AI. Yes, there are technical considerations like security and privacy, but at the heart of it these are people issues. Nobody wants to use a technology if they feel it puts them or the business at risk. Trust matters, and without it, adoption stalls. Change management and training are also critical. Helping people develop an AI mindset allows them to use these tools in increasingly creative ways, producing higher-quality outcomes rather than just faster ones. Another big one is executive-level commitment. This cannot sit only with the CIO. Every leader, from the CEO to the CFO and beyond, needs to be able to explain why AI matters for the organisation. When leaders can clearly articulate that story, it signals to the whole business that this is a strategic priority, not just an IT project. Equitable access is just as important. Too often I see organisations give AI tools to a select group to control costs. While that makes sense in the short term, the result can be a cultural divide between the haves and the have-nots. People left out either disengage or start using unapproved tools, both of which create risk. Providing broad access, with the right guardrails and support, helps avoid that divide and encourages responsible experimentation across the organisation. These human, cultural, and leadership factors are what really drive successful AI adoption. The technology is only part of the equation.

Why would your users distrust flawless systems? Recent data shows 40% of leaders identify explainability as a major GenAI adoption risk, yet only 17% are actually addressing it. This gap determines whether humans accept or override AI-driven insights. As founders building AI-powered solutions, we face a counterintuitive truth: technically superior models often deliver worse business outcomes because skeptical users simply ignore them. The most successful implementations reveal that interpretability isn't about exposing mathematical gradients—it's about delivering stakeholder-specific narratives that build confidence. Three practical strategies separate winning AI products from those gathering dust: 1️⃣ Progressive disclosure layers Different stakeholders need different explanations. Your dashboard should let users drill from plain-language assessments to increasingly technical evidence. 2️⃣ Simulatability tests Can your users predict what your system will do next in familiar scenarios? When users can anticipate AI behavior with >80% accuracy, trust metrics improve dramatically. Run regular "prediction exercises" with early users to identify where your system's logic feels alien. 3️⃣ Auditable memory systems Every autonomous step should log its chain-of-thought in domain language. These records serve multiple purposes: incident investigation, training data, and regulatory compliance. They become invaluable when problems occur, providing immediate visibility into decision paths. For early-stage companies, these trust-building mechanisms are more than luxuries. They accelerate adoption. When selling to enterprises or regulated industries, they're table stakes. The fastest-growing AI companies don't just build better algorithms - they build better trust interfaces. While resources may be constrained, embedding these principles early costs far less than retrofitting them after hitting an adoption ceiling. Small teams can implement "minimum viable trust" versions of these strategies with focused effort. Building AI products is fundamentally about creating trust interfaces, not just algorithmic performance. #startups #founders #growth #ai

"𝐀𝐈 𝐢𝐧 𝐭𝐡𝐞 𝐖𝐨𝐫𝐤𝐩𝐥𝐚𝐜𝐞: 𝐓𝐡𝐞 𝐁𝐮𝐳𝐳𝐰𝐨𝐫𝐝 𝐄𝐯𝐞𝐫𝐲𝐨𝐧𝐞’𝐬 𝐓𝐚𝐥𝐤𝐢𝐧𝐠 𝐀𝐛𝐨𝐮𝐭 (𝐁𝐮𝐭 𝐍𝐨𝐛𝐨𝐝𝐲’𝐬 𝐑𝐞𝐚𝐝𝐲 𝐅𝐨𝐫)" AI is the shiny new toy in every workplace. Leaders rave about it, teams scramble to roll out tools, and everyone’s racing to be “AI-ready.” But let’s be honest, behind the buzz, most people are thinking: ⇢ “Will this replace my job?” ⇢ “How much will this cost (and how hard will it be to learn)?” ⇢ “Why are we even doing this?” Here’s the problem: 𝐀𝐈 𝐚𝐝𝐨𝐩𝐭𝐢𝐨𝐧 𝐢𝐬𝐧’𝐭 𝐟𝐚𝐢𝐥𝐢𝐧𝐠 𝐛𝐞𝐜𝐚𝐮𝐬𝐞 𝐭𝐡𝐞 𝐭𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐲 𝐢𝐬𝐧’𝐭 𝐫𝐞𝐚𝐝𝐲, 𝐢𝐭’𝐬 𝐟𝐚𝐢𝐥𝐢𝐧𝐠 𝐛𝐞𝐜𝐚𝐮𝐬𝐞 𝐭𝐡𝐞 𝐩𝐞𝐨𝐩𝐥𝐞 𝐚𝐫𝐞𝐧’𝐭. It’s like rolling out a self-driving car and expecting people to trust it without explaining how it works—or what’s in it for them. At its core, this isn’t a tech problem. 𝐈𝐭’𝐬 𝐚 𝐜𝐡𝐚𝐧𝐠𝐞 𝐦𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭 𝐩𝐫𝐨𝐛𝐥𝐞𝐦. Organizations focus too much on the tool and too little on the people it’s meant to help. ⇢ AI feels like that treadmill you bought during lockdown, amazing in theory, intimidating in practice. ⇢ No one wants to admit they’re unsure how to use it—or worse, that they’re scared of it. 𝐓𝐡𝐞 𝐫𝐞𝐬𝐮𝐥𝐭? Resistance, skepticism, and a lot of wasted potential. So what should we do? The secret to successful AI adoption isn’t tech, It’s trust. If you are the leader, here's what you can do: ➡️ Start with the “Why” (for Them) ⇢ Don’t say: “This tool will transform operations.” ⇢ Say: “This tool will save you hours on admin work so you can focus on strategy—or finish early.” ➡️ Acknowledge the Fear ⇢ “AI isn’t here to replace you—it’s here to handle the boring, repetitive stuff so you can do what humans do best: think, create, and lead.” ➡️ It's always about people, not tech ⇢ Instead of just explaining what AI can do, show how it fits into their day-to-day. ⇢ Example: “Here’s how you can use this tool to draft a report in minutes, saving you time for more meaningful work.” ➡️Celebrate the Small Wins ⇢ “The support team resolved 20% more tickets last week using AI, freeing up time for complex customer issues.” Small victories build momentum—and trust. AI isn’t just a new tool—it’s a cultural shift. If your team doesn’t see what’s in it for them, they won’t adopt it. Always, ask yourself: ⇢ “Does this solve their real frustrations?” ⇢ “Have I shown them why it matters to them?” ⇢ “Are we building their confidence to succeed?” Remember: AI doesn’t fail because it’s too advanced. It fails because change isn’t managed well. BTW: That’s me—snapped at an AI camera booth, channelling my future avatar. Don’t worry, I’m still human (for now). But seriously, if this version of me could help manage change better, I’d take it. #changemanagement #artificialintelligence #ai #adoption

𝐎𝐧𝐞 𝐥𝐞𝐬𝐬𝐨𝐧 𝐦𝐲 𝐰𝐨𝐫𝐤 𝐰𝐢𝐭𝐡 𝐚 𝐬𝐨𝐟𝐭𝐰𝐚𝐫𝐞 𝐝𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭 𝐭𝐞𝐚𝐦 𝐭𝐚𝐮𝐠𝐡𝐭 𝐦𝐞 𝐚𝐛𝐨𝐮𝐭 𝐔𝐒 𝐜𝐨𝐧𝐬𝐮𝐦𝐞𝐫𝐬: Convenience sounds like a win… But in reality—control builds the trust that scales. We were working to improve product adoption for a US-based platform. Most founders instinctively look at cutting clicks, shortening steps, making the onboarding as fast as possible. We did too — until real user patterns told a different story. 𝐈𝐧𝐬𝐭𝐞𝐚𝐝 𝐨𝐟 𝐫𝐞𝐝𝐮𝐜𝐢𝐧𝐠 𝐭𝐡𝐞 𝐣𝐨𝐮𝐫𝐧𝐞𝐲, 𝐰𝐞 𝐭𝐫𝐢𝐞𝐝 𝐬𝐨𝐦𝐞𝐭𝐡𝐢𝐧𝐠 𝐜𝐨𝐮𝐧𝐭𝐞𝐫𝐢𝐧𝐭𝐮𝐢𝐭𝐢𝐯𝐞: -Added more decision points -Let users customize their flow -Gave options to manually pick settings -instead of forcing defaults -Conversions went up. -Engagement improved. Most importantly, user trust deepened. You can design a sleek two-click journey. But if the user doesn’t feel in control, they hesitate. Especially in the US, where data privacy and digital autonomy are non-negotiable — transparency and control win. Some moments that made this obvious: People disable auto-fill just to type things in manually. They skip quick recommendations to compare on their own. Features that auto-execute without explicit consent? Often uninstalled. It’s not inefficiency. It’s digital self-preservation. A mindset of: “Don’t decide for me. Let me drive.” I’ve seen this mistake cost real money. One client rolled out an automation that quietly activated in the background. Instead of delighting users, it alienated 20% of them. Because the perception was: “You took control without asking.” Meanwhile, platforms that use clear prompts — “Are you sure?” “Review before submitting” Easy toggles and edits — those build long-term trust. That’s the real game. What I now recommend to every tech founder building for the US market: Don’t just optimize for frictionless onboarding. Optimize for visible control. Add micro-trust signals like “No hidden fees,” “You can edit this later,” and toggles that show choice. Make the user feel in charge at every key step. Trust isn’t built by speed. It’s built by respecting the user’s right to decide. If you’re a tech founder or product owner, stop assuming speed is everything. Start building systems that say: “You’re in control.” 𝐓𝐡𝐚𝐭’𝐬 𝐰𝐡𝐚𝐭 𝐜𝐫𝐞𝐚𝐭𝐞𝐬 𝐚𝐝𝐨𝐩𝐭𝐢𝐨𝐧 𝐭𝐡𝐚𝐭 𝐬𝐭𝐢𝐜𝐤𝐬. 𝐖𝐡𝐚𝐭’𝐬 𝐲𝐨𝐮𝐫 𝐞𝐱𝐩𝐞𝐫𝐢𝐞𝐧𝐜𝐞 𝐰𝐢𝐭𝐡 𝐭𝐡𝐢𝐬? 𝐋𝐞𝐭’𝐬 𝐝𝐢𝐬𝐜𝐮𝐬𝐬. #UserExperience #ProductDesign #TrustByDesign #TechForUSMarket #businesscoach #coachishleenkaur LinkedIn News LinkedIn News India LinkedIn for Small Business

Last week at an AI healthcare summit, a Fortune 500 CTO admitted something disturbing: "We spent $7M on an enterprise AI system that sits unused. Nobody trusts it." And this is not the first time I have come across such cases. Having built an AI healthcare company in 2018 (before most people had even heard of transformers), I've witnessed this pattern from both sides: as a builder and as an advisor. The reality is that trust is the real bottleneck to AI adoption (not capability). I learned this firsthand when deploying AI in highly regulated healthcare environments. I have watched brilliant technical teams optimize models to 99% accuracy while ignoring the fundamental human question: "Why should I believe what this system tells me?" This creates a fascinating paradox that affects both enterprises, as well as people like you and me, so we can effectively use AI today: Users want AI that works autonomously (requiring less human input) yet remains interpretable (providing more human understanding). This tension is precisely where UI design becomes the determining factor in market success. Take Anthropic's Claude, for example. Its computer use feature reveals reasoning steps anyone can follow. It changes the experience from "AI did something" to "AI did something, and here's why" – making YOU more powerful without requiring technical expertise. The business impact speaks for itself: their enterprise adoption reportedly doubled after adding this feature. The pattern repeats across every successful AI product I have analyzed. Adept's command-bar overlay shows actions in real-time as it navigates your screen. This "show your work" approach cut rework by 75%, according to their case studies. These are not random enterprise solutions. They demonstrate how AI can 10x YOUR productivity today when designed with human understanding in mind. They prove a fundamental truth about human psychology: Users tolerate occasional AI mistakes if they can see WHY the mistake happened. What they won't tolerate is blind faith. Here's what nobody tells you about designing UI for AI that people actually adopt: • Make reasoning visible without overwhelming. Surface the logic, not just the answer • Signal confidence levels honestly. Users trust systems more when they admit uncertainty • Build correction loops that let people fix AI mistakes in seconds, not minutes • Include preview modes so users can verify before committing This is the sweet spot. — The market is flooded with capable AI. The shortage is in trusted AI that ordinary people can leverage effectively. The real moat is designing interfaces that earn user trust by clearly explaining AI's reasoning without needing technical expertise. The companies that solve for trust through thoughtful UI design will define the next wave of AI. Follow me Nicola for more insights on AI and how you can use it to make your life 10x better without requiring technical expertise.

Ever had a user ask, “𝘞𝘢𝘪𝘵… 𝘸𝘩𝘺 𝘥𝘪𝘥 𝘵𝘩𝘦 𝘈𝘐 𝘥𝘰 𝘵𝘩𝘢𝘵?” That’s not just a question. It’s a warning sign. Designing for AI means designing for trust. And trust requires explainability. 𝗕𝘂𝘁 𝗵𝗲𝗿𝗲'𝘀 𝘁𝗵𝗲 𝗰𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲: ⚠️ Too much explanation = overload ⚠️ Too little explanation = confusion or skepticism 𝗛𝗲𝗿𝗲 𝗮𝗿𝗲 𝟯 𝘄𝗮𝘆𝘀 𝗜 𝗯𝗮𝗹𝗮𝗻𝗰𝗲 𝗶𝘁: 🔍 𝗖𝗼𝗻𝗳𝗶𝗱𝗲𝗻𝗰𝗲 𝗶𝗻𝗱𝗶𝗰𝗮𝘁𝗼𝗿𝘀 (“We’re 80% sure this is your best match”) 🧩 𝗣𝗿𝗼𝗴𝗿𝗲𝘀𝘀𝗶𝘃𝗲 𝗱𝗶𝘀𝗰𝗹𝗼𝘀𝘂𝗿𝗲 (let users dig deeper if they want to) 🎛 𝗔𝗱𝗷𝘂𝘀𝘁𝗮𝗯𝗹𝗲 𝗶𝗻𝗽𝘂𝘁𝘀 (so users can tweak and re-generate) 👇🏼 Share below what strategies have worked for you? #ExplainableAI #UXDesign #TrustInAI #HumanCenteredDesign