Human-AI Collaboration

Are you ready for this future?! A future where a quiet, rubber wheeled humanoid slides next to an elder loved one (or even you) extends two telescopic arms, and lifts its passenger into a wheelchair with the grace of a seasoned nurse, minus the back strain and risk of mishaps. Engineers at Hebei University of Technology have spent years perfecting this caregiver bot. It now hoists up to 90 kg, pivots every joint with two degrees of freedom, and carries its load in one smooth, AI-balanced arc. Sensors guide each grip, lithium batteries recharge while the ward sleeps, and human staff are freed for conversation, reassurance, and real-time medical decisions instead of heavy lifting. The metallic chill we see today may fade as designers wrap these helpers in soft skins and friendly faces. Yet beneath the silicone smiles they will remain machines, lines of code driving steel and servos...only harder to notice as AI grows subtler...but maybe the extra minutes they hand back to nurses and families could make care more human, not less? How does that make you feel?

In my role as Chair of the International AI Safety Report, an effort backed by over 30 countries and international organisations including the European Union, OECD - OCDE and United Nations, I work with 100 researchers to help policymakers understand the capabilities and risks of general-purpose AI. The field is clearly changing far too quickly for a single annual report to suffice. That’s why today we’re introducing Key Updates: shorter, focused reports on critical developments in AI that will be published between editions of the full report. Our first Key Update focuses on advancements in AI capabilities, and what they mean for AI safety. You can read it here: https://lnkd.in/eKVGF7dy Some of the key findings it covers include: ➡️ Impressive performance improvements. Several AI systems can now solve International Mathematical Olympiad problems at gold medal level and complete a majority of problems in several databases of real-world software engineering tasks. ➡️ The rise of “reasoning” models. Recent gains have come mainly from training and deployment techniques that allow AI models to generate interim steps before producing final answers. This demonstrates that AI capabilities can advance significantly through post-training techniques and additional computing power at inference time, not just through scaling model size. ➡️ Some signals of real-world adoption. In a recent StackOverflow survey, a majority of software developers report using AI tools daily to help design experiments, process data, and write reports. Yet we still don’t know much about AI use in many other domains, nor crucially about how AI use affects productivity overall. ➡️ Stronger safeguards from developers. Leading AI developers recently activated enhanced protections on their most capable models as a precautionary measure, given possibilities like misuse to build weapons. ➡️ Emerging oversight challenges. AI models increasingly demonstrate an ability to distinguish evaluation tasks from real-world tasks, possibly complicating our ability to reliably test their capabilities before deployment. These developments raise further questions about control, monitoring, and governance as AI systems become more capable.

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

AI can make big mistakes. And when it does, people pay the price.   Last week in the Netherlands, a driver was fined €439 for using a phone while driving. Except she wasn't using a phone. She held an ice pack to her cheek after a wisdom tooth surgery.   This incident shows a flaw not only in the AI system, but also in the review process. The AI in the MONOcam is designed to spot phones in hand and it flagged her. But two human reviewers checked the image, and they also confirmed the ice pack was a phone - this despite the fact that the phone is actually visible in the bottom of the photo, being pinned to the dashboard. The fine was issued. This is what scaled enforcement powered by AI looks like when the system isn’t built for edge cases - and the human fallback doesn’t catch them either. When these systems are rolled out at scale, even rare misfires can erode public trust. It’s not enough to say “a human looked at it” - you need workflows that are designed to challenge the AI, not rubber-stamp it. If this is how the system handles an ice pack, what else is it getting wrong and who doesn’t have the time, the evidence photo, or the energy to fight it? Trust in enforcement isn’t built on efficiency. It’s built on the certainty that when the system fails, someone will notice and stop it. This time, it was an ice pack and a driver who spoke up (and will likely get the fine quashed). But the next mistake might not be so easy to catch - or so easy to contest. #AIinLawEnforcement #HumanInTheLoop #TrustworthyAI #MachineLearning #PublicPolicy #EthicalAI Original photo by CJIB

For decades, we've heard that "people are our greatest asset," but that phrase alone doesn't create competitive advantage. The real question is: how do we turn this platitude into a talent advantage that delivers stakeholder value? The answer lies in a simple but powerful formula: Talent Advantage = AI (artificial intelligence) * HI (human ingenuity). Notice the multiplier, not addition. Without both elements working together, talent potential remains severely limited. A score of 2/10 in AI multiplied by 8/10 in human ingenuity gives you only 16/100, not the 10/10 you might expect from adding them together. I've been tracking how AI is transforming talent processes through automation, sourcing, screening, and content creation. But here's what many miss: AI without human ingenuity creates parity at best. The real advantage comes when we combine AI's ability to amplify information with human creativity, judgment, empathy, and imagination. One asks "what happened?" while the other explores "why did this happen?" One optimizes the past while the other creates the future. In my latest article, I share seven practical ways business and HR leaders can integrate AI and HI to build this talent advantage, from making AI an enabler rather than an end goal, to relying on intuition alongside metrics, to ensuring HR experts have a seat at the table in AI initiatives. The stakes are high. When we get this right, employees become more productive and find meaningful work, organizations achieve strategic goals, customers deepen relationships, investors gain confidence, and communities benefit from stronger reputations. I'd love to hear your thoughts: How are you balancing AI efficiency with human ingenuity in your talent strategy? What tensions or opportunities have you discovered?

The conversation around AI is shifting. It's no longer about if the technology works, but if we can operationalize it for genuine, enterprise-wide impact. Too many organizations are stuck in "pilot purgatory"- impressive demos that never translate into production value. The gap isn't in the technology; it's in the operating model, and the leadership behind it. At ServiceNow, we built a foundational pact between the offices of CIO and COO. Kellie and I agreed, we need to treat AI not as a standalone tool, but as an integrated business system with shared ownership and clear, measurable outcomes. This disciplined approach is how we generate significant value from our AI investments. Moving from potential to performance requires a clear blueprint. Here’s the framework we use: 1️⃣ Start with the Work, Not the Model: Begin by identifying high-impact business problems, not by experimenting with the latest model. Focus on use cases that directly move the needle for your employees and customers. 2️⃣ Fix Data Chaos with Platform Power: A resilient, integrated platform is essential. It’s the only way to turn siloed data into actionable workflows and drive adoption across the entire enterprise. 3️⃣ Govern AI Like a Business System: Effective governance isn't a one-time check. It's an ongoing discipline - a central function that ensures every AI agent is secure, observable, and aligned with business goals. 4️⃣ Redesign Work for Human + Agent Teams: Our goal is to amplify human potential, not replace it. By using AI to handle routine tasks, we free our teams to focus on strategic priorities, innovation, and relationship-building. 5️⃣ Make the CIO-COO Pact Real: This is the cornerstone. It means co-owning a unified backlog, tracking outcomes on a shared dashboard, and creating a culture where responsible innovation can thrive. The future belongs to organizations that can make AI a seamless part of their operational fabric. It’s about building the discipline to scale and the partnerships to lead. The time for experiments is over. The time for execution is now. https://lnkd.in/g7Ycw29u #OperationalExcellence #FutureOfWork

If you're using AI agents just to speed things up, you're missing their real value. Working with agents isn’t about shortcuts. It’s about designing collaborative systems that think with you. And this is how it should work: → Start with context Before you ask for outputs, define your goals, your audience, and the “why” behind your initiative. Agents perform best when they understand the bigger picture. → Design the workflow together Map out how agents and humans will interact. Who leads what? What tools are involved? What feedback loops do you need? → Only then, begin prompting This is where most teams start. But if you haven’t aligned on strategy, you’ll get fragmented results. At Mchange, we learned this the hands-on way. We had no background in marketing or content creation. But our AI agent team helped us build a content workflow from the ground up. It looks like this: → We set the mission: who we want to reach and why → We share that with our agents, often including docs, data, and vision → Together, we design the content flow and assign agent roles →Only then do we prompt for drafts, visuals, and distribution plans And the best part, The more we share up front, the more strategic and creative our outputs become. AI doesn’t just support our process, it teaches us how to improve it. Because when agents understand why something matters, they help you figure out how to make it matter more. That’s the real shift. AI inot as a tool, but as a thinking partner in your system. If you want deeper insights into how agent–human collaboration should look like DM me or book a call on our website. And remember, create value, not hype.

Too much AI conversation today sounds like an engineering stand-up. No CEO wakes up looking to deploy MCP or fine-tune transformer architectures. They wake up worrying about problems: Why does my sales team spend half their week hunting for information instead of meeting customers? Why do my product launches take eighteen months when they should take three? The disconnect happens when business leaders step back and leave AI to the technologists. That’s when conversations drift into technical complexity instead of staying grounded in business outcomes. AI is designed to work in human language, and the technical barriers to entry are falling every day. The first time you deploy an agent, you onboard it like a new employee: you explain what you want, give feedback, coach it through tasks. That’s Saturday-morning plain English, not engineering jargon. Companies making real progress with AI build bilingual teams—fluent in both business and technology. They frame AI in terms of outcomes, not specs. The magic happens when technology conversations start—and stay—with business problems. Competitive advantage comes from applying AI to the workflows that make your business unique. That only happens when business leaders and technologists work side by side — but always through the lens of solving real business problems. No more technobabble.

In just a few minutes, here’s one thing you can do to make AI outputs 10x sharper. One of the most common reasons that prompts fail is not because they are too long, but because they lack personal context. And the fastest fix is to dictate your context. Speak for five to ten minutes about the problem, your audience, and the outcome you want, then paste the transcript into your prompt. Next, add your intent and your boundaries in plain language. For example: “I want to advocate for personal healthcare. Keep the tone empowering, not invasive. Do not encourage oversharing. Help people feel supported in the doctor’s office without implying that all responsibility sits on them.” Lastly, tell the model exactly what to produce. You might say: “Draft the first 400 words, include a clear call to action, and give me three title options.” Here’s a mini template: → State who you are and who this is for → Describe your stance and what to emphasize → Add guardrails for tone, privacy, and any “don’ts” → Set constraints like length, format, and voice → Specify the deliverable you want next Until AI memory reliably holds your details, you are responsible for supplying them. Feed the model your story - no need to include PII - to turn generic responses into work that sounds like you.

AI will not kill coaching. Coaches will. Most just won’t realise it until their clients quietly stop coming back. The coaches heading for extinction aren’t bad. They’re just safe. The model pushers. The progress trackers.   The wisdom sellers masquerading as transformers. Here’s the uncomfortable truth: AI already does 80% of what most coaches offer. Better. Faster. Without ego. Templates. Logic trees. Consistent advice. Done. So what’s left? Not strategy. Not frameworks. Not another assessment. What AI can’t touch is this: Humanity. Identity disruption. Emotional exposure. The moment someone realises they can’t go back to who they were. Most coaches run from this. Because it means watching someone become someone else. Framework addiction is emotional avoidance. Because it requires something harder than skill. Presence. Audit your last session. Not what you said. What you avoided. Track A: "How did those strategies work out?" "Let's map this challenge using the wheel." "What's your biggest takeaway from our time?" "Which action item resonates most?" Safe. Predictable. Replaceable. Track B: "You're doing that thing again." "I can feel you pulling back right now." "What's happening in your body as you say that?" "The story you just told contradicts what you said earlier." Uncomfortable. Human. Irreplaceable. One path gets automated. The other requires being fully human. Three days ago, a coach with 12 years experience asked me for more tools. She didn’t need tools. She needed to stop hiding. She'd built an arsenal of tools to avoid the moment a client breaks down. The frameworks weren’t helping her clients. They were protecting her from feeling anything. That realisation doesn’t come with a certification. It's the part no certification teaches. AI will wipe out guidance disguised as transformation. Those clients running to ChatGPT or Claude? They wanted instructions, not identity shifts. The coaches who survive will ditch "supportive." They'll deliver relentless presence. The willingness to witness someone's unravelling. Most coaches won’t admit which track they’re actually on. The coaches who survive won’t be the smartest. They’ll be the most honest. If it converts to code, it’s replaceable. If it rewrites someone’s story, it isn’t. Most people reading this will disagree. That’s exactly why they’ll struggle. ➕ If this hits home, follow Graham Nicholls. I write for coaches, just like you, every day.