How to Use AI to Support Human Expertise

After 1.5 years of using AI in my user research, here’s what I’ve learned: 1. AI doesn’t replace researchers—it amplifies them. AI handles the repetitive parts of research. This frees you up to focus on: - Asking why instead of what next - Deeply synthesizing insights that change designs and strategy Your expertise combined with AI efficiency delivers deeper impact. 2. The “blank page problem” wastes hours—and it’s avoidable. Staring at a blank document kills productivity and momentum. AI tools give you a starting point instantly through being a thought partner. Some of my favorite prompts: - What pushback will I get on this? - What critical points/questions am I missing? - What risks am I taking with this and how do I mitigate them? Instead of struggling to begin, you can focus on refining, analyzing, and delivering results. 3. Efficiency isn’t about cutting corners—it’s about focus. Think of AI as a research assistant: - It helps you frame the questions - It structures your approach - It organizes your thoughts You have more time for strategic thinking, aligning stakeholders, and delivering clear impact. 4. Busywork is creativity’s biggest enemy. Every minute spent rewriting frameworks or brainstorming prompts is a minute you’re not: - Talking to users - Synthesizing insights - Influencing decisions AI can help eliminate busywork so you can focus on uncovering what users really need—and how to act on it. 5. The future of research is AI-powered—but human-driven. AI can structure the work, but it’s your judgment, expertise, and experience that turn data into decisions. - AI drafts the a response - You add the nuance - Together, you deliver better, faster insights AI enhances the process—you’re still the expert in the room. That’s why I’m building the AI Prompt Library for User Researchers. Over 50 expertly crafted, ready-to-use prompts, reflection questions, and follow-ups. Support for researchers who want to work smarter, not harder. Early bird access is coming In January 2025, with at least 50 UXR prompts I use in my research work weekly -- these are prompts I have experimented on, developed, and actually use. If you’re ready to streamline your research and focus on what matters, drop a ‘I’m in’ in the comments and I’ll send you the link to the waitlist! 💬

The inner driver: using AI to elevate your judgment, not replace it. Too many platforms treat AI as a decision engine or a toy. The smarter play is to build AI as an advisor that amplifies human judgment. That shift matters now because data volume and regulatory scrutiny are increasing, and accountability still lives with people. Common challenge - teams either automate away expertise or ignore AI outputs. Both outcomes erode value. Practical approach: - Map decisions and tag which need human context versus repeatable scoring. - Surface AI rationale and confidence in the interface so people can judge outputs fast. - Create human-in-the-loop thresholds and capture overrides as training labels. - Track three metrics time to decision, override rate, and outcome delta to prove impact. This method reduces routine review work while preserving critical judgment and creates a feedback loop that improves models and trust. How are you using AI to elevate your judgement? Share in the comments!!

AI in healthcare offers transformative potential—better diagnoses, streamlined operations, and personalized care. But navigating this safely and ethically hinges on our most vital investment: our PEOPLE. AI is a powerful tool; human expertise must guide its application. I propose we strategically cultivate and deepen four key skills within our clinical teams to ensure they can partner effectively with AI: 1️⃣ The 𝗖𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝗔𝗣𝗣𝗥𝗔𝗜𝗦𝗘𝗥: Equipping staff to 𝗙𝗥𝗔𝗠𝗘 the AI output through a critical lens: 𝗙it for Purpose? (Is the tool validated for this specific scenario, patient, and context?) 𝗥eliable & Relevant Data? (Is input data accurate, complete, current, and truly representative? How might missing data skew AI outputs?) 𝗔ligned with healthcare Knowledge? (Does AI output align with your vital expertise for this patient or scenario?) 𝗠echanism Clear & Fair? (Is AI reasoning sufficiently transparent?) 𝗘ffect on Decision (Safe & Beneficial)? (What's the holistic impact, balancing potential benefits with any risks in this individual’s or condition’s specific circumstances?) 𝗦tewardship of Clinical Responsibility? (Does the clinician team maintain ultimate accountability, using AI as an assistive tool?)   2️⃣ The 𝗗𝗶𝗹𝗶𝗴𝗲𝗻𝘁 𝗘𝗗𝗜𝗧𝗢𝗥: Training teams to refine AI-generated content, ensuring it's 𝗦𝗘𝗧 for responsible clinical use: 𝗦crutinize for Accuracy: (Meticulously verify AI text for factual correctness, clinical appropriateness, and the absence of errors, omissions, or 'hallucinations'.) 𝗘valuate AI's Uncertainties: (Critically assess any AI's low confidence areas; independently verify or correct these findings before any clinical application.) 𝗧ransparency with Patients: (Ensure appropriate AI disclosure in patient communications, fostering transparency, trust as necessary.) 3️⃣ The 𝗘𝗻𝗴𝗮𝗴𝗲𝗱 𝗥𝗘𝗩𝗜𝗘𝗪𝗘𝗥: Fostering robust, workflow-integrated feedback loops for continuous AI tool improvement and usability. Encourage sharing specific, contextual insights (e.g., issues, successes, workarounds) via the designated channels. This real-world feedback is invaluable for iterative refinement and ensuring tools are genuinely supportive. 4️⃣ The 𝗖𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 𝗟𝗘𝗔𝗥𝗡𝗘𝗥: Committing dedicated time to ongoing education and professional development in the rapidly evolving AI landscape. This includes building AI literacy, staying current on ethical considerations, new functionalities, and evolving regulations. Pairing advanced AI with highly skilled, critical-thinking, and empowered clinicians is vital for translating AI insights into genuinely improved patient outcomes. How is your staff being upskilled to use AI tools? #HealthcareAI #AISkills #CMIO #Informatics #HealthcareLeadership #Upskilling, #EHR

Tired of AI projects that don't deliver? Try this human-centred approach. From my research over the past couple of years, I’ve noticed a recurring pattern. We often treat AI as a technology experiment rather than an upgrade to how people actually work. That mindset can quietly limit a project’s success. To support better decisions, I’ve developed a human-centred AI readiness checklist based on that research. I hope it’s useful for your next initiative. 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆 𝗮𝗻𝗱 𝗢𝘂𝘁𝗰𝗼𝗺𝗲 𝗖𝗵𝗲𝗰𝗸 (𝗖𝗥𝗜𝗦𝗣-𝗗𝗠 𝗺𝗶𝗻𝗱𝘀𝗲𝘁) →Are we clear on the operational outcome and metric we are improving? ↳If we cannot say “this reduces X by Y%”, we are chasing tools, not performance. 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗠𝗮𝗽𝗽𝗶𝗻𝗴 𝗖𝗵𝗲𝗰𝗸 (𝗟𝗲𝗮𝗻 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝘁𝗵𝗶𝗻𝗸𝗶𝗻𝗴) →Which real human decisions are we supporting? ↳AI should strengthen judgment points like prioritisation or scheduling, not automate activity without purpose. 𝗣𝗿𝗼𝗰𝗲𝘀𝘀 𝗦𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗖𝗵𝗲𝗰𝗸 (𝗟𝗲𝗮𝗻 𝗽𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲) → Is the workflow stable enough to augment? ↳Automating instability scales, defects and frustrates the people doing the work. 𝗩𝗮𝗹𝘂𝗲 𝘃𝘀 𝗗𝗶𝘀𝗿𝘂𝗽𝘁𝗶𝗼𝗻 𝗖𝗵𝗲𝗰𝗸 (𝗣𝗼𝗿𝘁𝗳𝗼𝗹𝗶𝗼 𝘁𝗵𝗶𝗻𝗸𝗶𝗻𝗴) →Does the benefit outweigh frontline disruption? ↳Operational AI should improve flow, not create friction for teams. 𝗗𝗮𝘁𝗮 𝗥𝗲𝗮𝗹𝗶𝘁𝘆 𝗖𝗵𝗲𝗰𝗸 (𝗖𝗥𝗜𝗦𝗣-𝗗𝗠 𝗱𝗮𝘁𝗮 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴) →Does our data reflect lived operational reality? ↳Human trust collapses when AI runs on distorted inputs. 𝗛𝘂𝗺𝗮𝗻 𝗖𝗼𝗻𝘁𝗿𝗼𝗹 𝗖𝗵𝗲𝗰𝗸 (𝗛𝘂𝗺𝗮𝗻-𝗰𝗲𝗻𝘁𝗲𝗿𝗲𝗱 𝗔𝗜 𝗱𝗲𝘀𝗶𝗴𝗻) →Where does AI advise, where do humans review, and where does automation act? ↳Clear boundaries protect autonomy and accountability. 𝗥𝗶𝘀𝗸 𝗮𝗻𝗱 𝗥𝗲𝘀𝗶𝗹𝗶𝗲𝗻𝗰𝗲 𝗖𝗵𝗲𝗰𝗸 (𝗡𝗜𝗦𝗧 𝗔𝗜 𝗿𝗶𝘀𝗸 𝗺𝗼𝗱𝗲𝗹) →Have we planned for failure, overrides, and fallback workflows? ↳Operations must remain safe and continuous when systems misfire. 𝗢𝘄𝗻𝗲𝗿𝘀𝗵𝗶𝗽 𝗖𝗵𝗲𝗰𝗸 (𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗻𝗴 𝗺𝗼𝗱𝗲𝗹 𝗰𝗹𝗮𝗿𝗶𝘁𝘆) →Who owns outcomes, model behaviour, and data quality? ↳Human accountability must remain visible after launch. 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 𝗥𝗲𝗮𝗹𝗶𝘁𝘆 𝗖𝗵𝗲𝗰𝗸 (𝗦𝘆𝘀𝘁𝗲𝗺𝘀 𝘁𝗵𝗶𝗻𝗸𝗶𝗻𝗴) →Will this support how people actually work? ↳Tools that slow teams are quietly abandoned. 𝗔𝗱𝗼𝗽𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗧𝗿𝘂𝘀𝘁 𝗖𝗵𝗲𝗰𝗸 (𝗖𝗵𝗮𝗻𝗴𝗲 𝗱𝗶𝘀𝗰𝗶𝗽𝗹𝗶𝗻𝗲) →Are we designing for understanding, transparency, and behavioural adoption? ↳Trust grows when teams see AI improving their work, not replacing it. AI is an amplifier. It scales what we already have: good or bad ↳𝐆𝐚𝐫𝐛𝐚𝐠𝐞 𝐢𝐧. 𝐀𝐦𝐩𝐥𝐢𝐟𝐢𝐞𝐝 𝐠𝐚𝐫𝐛𝐚𝐠𝐞 𝐨𝐮𝐭.⁣ ⁣⁣⁣⁣⁣⁣⁣⁣⁣⁣⁣⁣⁣ ⁣⁣⁣⁣⁣⁣⁣⁣The strongest AI initiatives aren’t just technology deployments. They are human-centred operating upgrades that happen to use AI. ♻️ Share if you found this useful. #AIinBusiness #HumanCenteredAI #Operations #Leadership #AIStrategy

Human in the Loop: the fifth ingredient of GenAI that works in practice   The more we work with GenAI, the clearer one principle becomes. The technology can scale effort, but judgment still rests with people. A system performs best when humans guide, review, and refine the output.   GPT models are ultimately pattern recognizers. They are not domain experts and they do not understand the deeper context, the stakes, or the nuance behind a decision. This matters because every model can hallucinate. When teams rely on models without human oversight, quality tends to deteriorate, errors compound and trust disappears. Ultimately, dissatisfaction grows and user adoption slows.   Human in the loop does not mean slowing everything down. It means placing expertise where it matters most. Define what good looks like, review outputs at the critical points, and make the final call on anything that carries risk or requires domain knowledge. Use human expertise to iterate and improve the AI-supported process.   Strong results come from combining human expertise, context, and judgment with the right model and a well designed workflow. This pairing lifts productivity while keeping standards high. GenAI becomes most valuable when it amplifies people rather than replaces them.

Everyone’s scared of AI taking jobs. But AI isn’t the threat. We're not in a technology crisis. We're in a leadership development crisis. Every team member watching AI demos is asking themselves the same question: "What happens to me?" And most leaders are answering with tools and training sessions instead of growth strategies. Here's what I've learned after leading teams for 25+ years: Leaders who help their people evolve with technology don't just retain talent. They build the teams everyone else is trying to recruit from. Here's 8 ways to help your team grow with AI, not despite it: 1/ Give them guilt-free AI exploration time → 2 hours weekly, zero deliverables expected → Pure experimentation beats forced adoption 💡 Reality: Teams discover 3x more valuable AI applications when given unstructured exploration time. 2/ Redefine success around human superpowers → Strategy, creativity, emotional connection → AI makes these skills more valuable, not less 💡 Reality: Start measuring insight quality and stakeholder trust instead of speed and volume. 3/ Make your AI failures public → Share what didn't work and why → Vulnerability creates permission to learn 💡 Reality: The fastest-learning teams have leaders who openly share their AI mistakes. 4/ Teach AI literacy, not just AI buttons → Help them understand how AI thinks → Critical evaluation beats blind trust 💡 Reality: Employees who understand AI limitations catch expensive errors. Those who just know which buttons to push become the liability. 5/ Double down on uniquely human development → Complex reasoning, relationship building → The skills AI can't replicate become premium 💡 Reality: The market is already paying 30-40% premiums for roles requiring high-context judgment and cross-functional influence. 6/ Connect them beyond your company walls → AI communities, mentorship programs → External perspective accelerates internal growth 💡 Reality: Employees with external AI networks bring back insights worth 10x. 7/ Train pattern recognition, not task completion → Teach them to spot what AI misses → Human judgment becomes their competitive advantage 💡 Reality: The most valuable employees aren't the fastest at using AI. They're the ones who know when NOT to use it. 8/ Measure adaptability over output → How fast do they learn new capabilities? → Resilience beats productivity in uncertain times 💡 Reality: In 12 months, today's AI tools will be outdated. Learning velocity is the only sustainable competitive advantage. Every single person on your team is making a quiet decision right now: evolve here or leave to evolve somewhere else. Your development strategy in the next 90 days will determine which path they choose. The best teams aren't AI-powered. They're human-led and AI-amplified.