Balancing AI and Human Expertise

AI will always find you the fastest path. The dangerous part is assuming fastest and right are the same thing. 🧠 After 13 years and 200+ enterprise AI deployments, this is the distinction I keep coming back to in every system I build and every leadership team I work with. AI optimises for efficiency. It does not have access to the relationship history, the political context, the ethical weight, or the lived experience that determine whether the efficient path is actually the right one for this specific situation, with these specific people, right now. That is not a flaw to be engineered away. It is the permanent and irreplaceable role of human judgment. Here is a framework I use when working with AI outputs on high-stakes decisions. ➡️ What context does this decision require that AI does not have access to? ➡️ What would I decide if I had not seen the AI recommendation first? ➡️ Am I using this output to inform my thinking or replace it? The third question is the most important. Using AI to inform your thinking is amplification. Using it to replace your thinking is atrophy. And the line between the two is easier to cross than most people realise. What is one decision in your work where you would never let AI have the final say? #ai #leadership #futureofwork #artificialintelligence #aistrategy #teamhuman #criticalthinking #intellectualatrophy

🤖 How To Design Better AI Experiences. With practical guidelines on how to add AI when it can help users, and avoid it when it doesn’t ↓ Many articles discuss AI capabilities, yet most of the time the issue is that these capabilities either feel like a patch for a broken experience, or they don't meet user needs at all. Good AI experiences start like every good digital product by understanding user needs first. 🚫 AI isn’t helpful if it doesn’t match existing user needs. 🤔 AI chatbots are slow, often expose underlying UX debt. ✅ First, we revisit key user journeys for key user segments. ✅ We examine slowdowns, pain points, repetition, errors. ✅ We track accuracy, failure rates, frustrations, drop-offs. ✅ We also study critical success moments that users rely on. ✅ Next, we ideate how AI features can support these needs. ↳ e.g. Estimate, Compare, Discover, Identify, Generate, Act. ✅ Bring data scientists, engineers, PMs to review/prioritize. 🤔 High accuracy > 90% is hard to achieve and rarely viable. ✅ Design input UX, output UX, refinement UX, failure UX. ✅ Add prompt presets/templates to speed up interaction. ✅ Embed new AI features into existing workflows/journeys. ✅ Pre-test if customers understand and use new features. ✅ Test accuracy + success rates for users (before/after). As designers, we often set unrealistic expectations of what AI can deliver. AI can’t magically resolve accumulated UX debt or fix broken information architecture. If anything, it visibly amplifies existing inconsistencies, fragile user flows and poor metadata. Many AI features that we envision simply can’t be built as they require near-perfect AI performance to be useful in real-world scenarios. AI can’t be as reliable as software usually should be, so most AI products don’t make it to the market. They solve the wrong problem, and do so unreliably. As a result, AI features often feel like a crutch for an utterly broken product. AI chatbots impose the burden of properly articulating intent and refining queries to end customers. And we often focus so much on AI that we almost intentionally avoid much-needed human review out of the loop. Good AI-products start by understanding user needs, and sparkling a bit of AI where it helps people — recover from errors, reduce repetition, avoid mistakes, auto-correct imported files, auto-fill data, find insights. AI features shouldn’t feel disconnected from the actual user flow. Perhaps the best AI in 2025 is “quiet” — without any sparkles or chatbots. It just sits behind a humble button or runs in the background, doing the tedious job that users had to slowly do in the past. It shines when it fixes actual problems that it has, not when it screams for attention that it doesn’t deserve. Useful resources: AI Design Patterns, by Emily Campbell https://www.shapeof.ai AI Product-Market-Fit Gap, by Arvind Narayanan, Sayash Kapoor https://lnkd.in/duEja695 [continues in comments ↓]

Yuval Noah Harari warns that we’re on the brink of creating a new form of intelligence—and he’s not sure humanity is ready for it. On this week’s episode of Possible, we explore AI’s potential to outpace humanity and create a “useless class,” the meaning of digital consciousness, and why rebuilding trust (both in ourselves and our institutions) is the only way to guide AI toward a benevolent future. YouTube: https://lnkd.in/gvd2cpN5 Spotify, Apple, et al: https://lnkd.in/gS874PD2 Transcript: https://lnkd.in/gcC-t6gY

AI products like Cursor, Bolt and Replit are shattering growth records not because they're "AI agents". Or because they've got impossibly small teams (although that's cool to see 👀). It's because they've mastered the user experience around AI, somehow balancing pro-like capabilities with B2C-like UI. This is product-led growth on steroids. Yaakov Carno tried the most viral AI products he could get his hands on. Here are the surprising patterns he found: (Don't miss the full breakdown in today's bonus Growth Unhinged: https://lnkd.in/ehk3rUTa) 1. Their AI doesn't feel like a black box. Pro-tips from the best: - Show step-by-step visibility into AI processes - Let users ask, “Why did AI do that?” - Use visual explanations to build trust. 2. Users don’t need better AI—they need better ways to talk to it. Pro-tips from the best: - Offer pre-built prompt templates to guide users. - Provide multiple interaction modes (guided, manual, hybrid). - Let AI suggest better inputs ("enhance prompt") before executing an action. 3. The AI works with you, not just for you. Pro-tips from the best: - Design AI tools to be interactive, not just output-driven. - Provide different modes for different types of collaboration. - Let users refine and iterate on AI results easily. 4. Let users see (& edit) the outcome before it's irreversible. Pro-tips from the best: - Allow users to test AI features before full commitment (many let you use it without even creating an account). - Provide preview or undo options before executing AI changes. - Offer exploratory onboarding experiences to build trust. 5. The AI weaves into your workflow, it doesn't interrupt it. Pro-tips from the best: - Provide simple accept/reject mechanisms for AI suggestions. - Design seamless transitions between AI interactions. - Prioritize the user’s context to avoid workflow disruptions. -- The TL;DR: Having "AI" isn’t the differentiator anymore—great UX is. Pardon the Sunday interruption & hope you enjoyed this post as much as I did 🙏 #ai #genai #ux #plg

There is a lot of talk about AI, data, analytics, and algorithms. And they all matter. But for the next several years, human judgment is what will matter most. AI can process 1.7 billion variables. The human mind can deal with four to six. That gives us better data, better options, and more consistency. All of that helps. But judgment is still not quantifiable. It’s processed by the brain in ways we don’t fully understand. And we never will. Judgment shows up in places AI cannot reach: 🔹 How you define the problem 🔹 Which assumptions you challenge 🔹 Which risks you take 🔹 Which goals you choose 🔹 How much ego damage you can stand Good judgment comes from bad experiences. That’s been true for centuries. When something goes wrong, reflect: 🔹 Were the facts wrong? 🔹 Was the reframing wrong? 🔹 Was the weighting of facts wrong? 🔹 Or was it a judgment shaped by bias or risk preference? Use AI to expand your thinking. But don’t outsource judgment. Strengthen it. That’s what leaders are admired for.

Last week, a senior manager presented me with a strategic roadmap during an advisory session. It was polished, grammatically perfect, and filled with current buzzwords. It looked like a fantastic job but my gut feeling gave me a strange feeling I asked one simple question: "𝘞𝘩𝘺 𝘥𝘪𝘥 𝘺𝘰𝘶 𝘱𝘳𝘪𝘰𝘳𝘪𝘵𝘪𝘻𝘦 𝘤𝘩𝘢𝘯𝘯𝘦𝘭 𝘟 𝘰𝘷𝘦𝘳 𝘤𝘩𝘢𝘯𝘯𝘦𝘭 𝘠 𝘪𝘯 𝘘3?" I was not surprised by the reaction. He froze. He couldn't give a proper answer. Why? Because he hadn't made that decision. The algorithm did. He had fallen for the "𝗢𝗿𝗮𝗰𝗹𝗲 𝗠𝘆𝘁𝗵". He treated the AI as a "know-it-all" guru rather than what it actually is: a high-power probabilistic engine. This passive approach is dangerous. When we view AI as an oracle, we stop analyzing and start obeying. We confuse “𝘨𝘰𝘰𝘥 𝘸𝘳𝘪𝘵𝘪𝘯𝘨” with “𝘨𝘰𝘰𝘥 𝘪𝘥𝘦𝘢𝘴 𝘵𝘩𝘢𝘵 𝘐 𝘳𝘦𝘢𝘭𝘭𝘺 𝘶𝘯𝘥𝘦𝘳𝘴𝘵𝘢𝘯𝘥 𝘢𝘯𝘥 𝘐 𝘤𝘢𝘯 𝘸𝘰𝘳𝘬 𝘸𝘪𝘵𝘩”. Here is the uncomfortable reality: LLMs do not "reason" in the human sense; they predict the next most likely word based on patterns. They are designed to sound convincing, not to be factually accurate. If you want to survive the Algorithm Era, you must shift from a passive user to an active driver. Here is how to break the AI toxic dependency:   • 𝗗𝗲𝗺𝗼𝘁𝗲 𝘁𝗵𝗲 𝗔𝗜: Stop treating ChatGPT as a Vice President of Strategy. Treat it as a brilliant but sometimes intoxicated summer intern. It generates volume but YOU provide the judgment.   • 𝗧𝗵𝗲 "𝗝𝗮𝗴𝗴𝗲𝗱 𝗙𝗿𝗼𝗻𝘁𝗶𝗲𝗿" 𝗥𝘂𝗹𝗲: AI excels at creative brainstorming but often fails at simple logical tasks. Never delegate the final decision on high-stakes logic to a black box.   • 𝗜𝗻𝘁𝗲𝗿𝗿𝗼𝗴𝗮𝘁𝗲, 𝗗𝗼𝗻'𝘁 𝗝𝘂𝘀𝘁 𝗔𝘀𝗸: Don't just ask for an answer. Ask the AI to show its work. Force it to reveal its "Chain of Thought" so you can verify the logic, not just the result.   • 𝗢𝘄𝗻 𝘁𝗵𝗲 𝗪𝗵𝘆: If you cannot explain the rationale behind an AI-generated strategy without looking at your notes, you do not have a strategy. You have a hallucination. Let’s be honest: What is the most plausible lie an AI has told you recently that almost slipped into a final report?. I’ll start: AI confidently claimed a competitor had discontinued a specific product line because it seemed "logical." It hadn't. Let me know in the comments. #AIAugmentedProfessional #HybridIntelligence #AiforExecutives #OracleMyth

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!

Over the past few weeks, I’ve had time to reflect — between AI briefing sessions, leading workshops, and teaching at LinkedIn offices in London. One question keeps coming up: “Will I still have a job?” It’s a fair question. The reports, social posts, and videos can be overwhelming — even frightening. But here’s what I’ve come to believe: -- AI isn’t replacing jobs outright. It’s replacing tasks. And that changes everything. In my role as Course Director at the CIM | The Chartered Institute of Marketing — and through my work with marketers, consultants, and business owners — I’ve seen the same shift again and again: Those who are moving forward are reframing their role. They’re not competing with AI — they’re learning how to work alongside it. Here’s how I break down the Human + AI relationship in practical terms: 👉 20% – Spark Ideas Use AI to generate ideas, explore angles, and overcome creative blocks. 👉 40% – Co-Pilot Mode Let AI support your process — drafting, outlining, and iterating while you steer. 👉 60% – Efficiency Booster AI automates and structures routine work so you can focus on higher-value thinking. 👉 80% – Heavy Lifter AI takes on the complexity. You bring the insight, oversight, and strategic direction. 👉 100% – Full AI Execution In some cases, AI completes the task. But the human still sets the brief, guides the tone, and makes the decisions. For Marketers, Consultants, and Business Owners, this is not the time to resist. It’s time to rethink your value, redistribute your time, and reimagine your workflows. Humans won’t be replaced.... But we will be expected to become smarter — by collaborating with AI. Of course, there’s a much bigger discussion to be had. This post is just one thought in that wider conversation. But it’s a good place to start. With Positivity, Imran. #AIinMarketing #MarketingLeadership #CharteredInstituteOfMarketing #FutureOfWork #AIMarketer

We have had enough AI pilots. The next decade will be decided by who gets AI into production. In my first two months in this role, through dozens of conversations with customers, partners, board members, and operating leaders, a few patterns stand out. First, AI is already delivering returns when it is tied to real work. Enterprises that embed AI into specific workflows in marketing, engineering, customer operations, and finance are reporting positive ROI. The pattern is consistent. Clear use case, clean data, strong guardrails, and disciplined change. Second, the edge is shifting from the model to the system. Closed and open models are improving quickly. By system I mean the combination of data, integration, security, governance, and workflows that surround the models. The durable advantage is in how all of that fits together. The winners will be the organizations that orchestrate many models against their own data and processes, not the ones that chase every new benchmark. Third, AI infrastructure has become a strategic priority. In most client and boardroom discussions this now shows up as 1 to 3 year planning around where mission critical workloads land, how they are orchestrated across private, public, edge, and sovereign environments, how flexible contracts are, and how easily workloads can move as needs and regulations evolve. The goal is to secure capacity, resilience, and jurisdictional control without creating new technical debt or concentration risk. To move from pilots to production at scale, enterprises now need to solve three linked problems. 1) Data sovereignty and governance Sovereign cloud is no longer a niche topic. It is about who has legal, operational, and digital control over sensitive data, and under which jurisdiction. That is a strategic choice, not only a compliance obligation. 2) Data gravity and private cloud As AI workloads grow, models will increasingly move to where the data resides. That is pulling more organizations toward private and sovereign cloud environments close to core systems, people, and regulators, while still connecting to public cloud innovation. The physics of data gravity are becoming as important as the economics of compute. 3) Unified control planes Without a unified way to observe, govern, and secure workloads across private, public, edge, and sovereign environments, enterprises end up with islands of automation and risk. With a unified control plane, they can apply consistent policy, security, and observability while giving teams the flexibility to choose the right environment for each workload. AI is no longer a science experiment or a marketing slogan. It is an operating system change for how companies run. Leaders and teams who treat it that way, with rigor and ambition, will set the pace for the next decade.

It used to take me 8 hours toiling over a hot laptop to write my fortnightly newsletter. Now I do it in half that time. Generative AI, right? Nay-nay, friends... pretty much the opposite. It's about Process. "Process" sounds as much fun as scrubbing dirt from beets. But stick with me. Here's the non-robot, non-A.I., counter-intuitive first step in mine: 1. START WITH PEN + PAPER. Write a list of your key points on paper with a pen. Flesh out those key points with a few bullet points. Don't worry about "writing." You're welding the scaffolding for ideas that will become writing. Why this works: 1️⃣ You write slower than you type. Working with analog tools slows you down. Your high-speed locomotive brain isn't screaming ahead to get to Next Sentence Depot. It has to wait patiently for your hands to catch up, like a car driver at a railroad crossing waiting for the train's caboose. That slower pace ultimately delivers better insights. 2️⃣ You can't backspace or start over. You can only keep going. 3️⃣ It's the ultimate in distraction-free writing. Checking email. Scrolling LinkedIn. Clicking to another tab. You cannot. Because... well, paper. *** Gen AI as a tool can help us write more efficiently (I share an idea or two about that in my Process, too). But the point of our writing is to deliver insights and craft that could come only from us. No amount of AI-fueled efficiency or optimization is going to replace that. Pens and paper and other analog tools can help exponentially. Sometimes, the slowest way is the fastest way. Love, Ann p.s. If you're not on my list, I would love to write to you every two weeks, too: https://lnkd.in/gsiMkmzZ