Principles of Human-Centered AI Development

Humanizing AI Through the Kano Model In an era where generative AI has become a ubiquitous offering, true differentiation lies not in merely adopting the technology but in integrating human values into its core. Building on my earlier discussion about applying the Kano Model to Gen AI strategy, let’s explore how this framework can refocus development metrics to prioritize ethics and human-centricity. By aligning AI systems with human needs, organizations can shift from functional tools to trusted partners that inspire lasting loyalty. Traditional metrics such as speed, scalability, and model accuracy have evolved into basic expectations the “must-haves” of AI. What truly elevates a product today is its ability to embody values like safety, helpfulness, dignity, and harmlessness. These qualities, categorized as “delighters” in the Kano Model, transform AI from a transactional tool into a meaningful collaborator. Key Human-Centric Differentiators Safety: Proactive safeguards must ensure AI systems protect users from risks, whether physical, emotional, or societal. Safety is non-negotiable in building trust. Helpfulness: Personalized, context-aware interactions demonstrate empathy. AI should anticipate needs and adapt to individual preferences, turning routine tasks into meaningful experiences. Dignity: Ethical design principles—fairness, transparency, and privacy—must underpin AI development. Respecting user autonomy fosters long-term trust and engagement. Harmlessness: AI outputs and recommendations should prioritize user well-being, avoiding unintended consequences like bias, misinformation, or psychological harm. This human-centered approach represents a paradigm shift in technology development. While traditional KPIs remain important, they are no longer sufficient to stand out in a crowded market. Organizations that embed human values into their AI systems will not only meet user expectations but exceed them, creating emotional connections that drive loyalty. By applying the Kano Model, businesses can systematically align innovation with ethics, ensuring technology serves humanity rather than the other way around. The future of AI isn’t just about efficiency it’s about elevating human potential through thoughtful, responsible design. How is your organization balancing technical excellence with human values?

The next phase of enterprise AI adoption will be building effective Humans + AI teams. This requires mapping human-AI workflows, creating clarity on decision delegation, shaping culture, and building continually improving skills and systems. These six principles are at the core of the Humans + AI Teaming course that I and my team is developing. In summary: 🟠 Humans own outcomes, even when AI does the work. Only humans can be accountable. 🔵 Hybrid teams start by aligning on outcomes and then allocating roles to best achieve them. 🟡 Human-AI teams create value by ensuring each complements the other in roles and skills, with humans doing what only they can do. 🟢 Workflow and autonomy must be deliberately designed. The level of AI agency must fit the task, the context, and the risk. 🟣 Trust in AI must be earned, not assumed. Transparency, reversibility, and guardrails make confidence possible. 🟤 The best hybrid teams learn as they work. Prompts, feedback, and results continually reshape the system and the division of labour. These principles can be very useful in the necessary shift beyond automation and individual augmentation - where most organizations are today - to start to weave AI into where work is done, building the Humans + AI organizations of the future.. I'd love to hear any thoughts on how to improve these principles. And please reach out if your organization might be interested in doing an interview on what needs you see to help shape the product, or participating in our Beta program. Thanks! 🙏

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 (𝗢𝗽𝗲𝗿𝗮𝘁��𝗻𝗴 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

𝐀 𝐇𝐮𝐦𝐚𝐧-𝐂𝐞𝐧𝐭𝐫𝐢𝐜 𝐫𝐞𝐯𝐨𝐥𝐮𝐭𝐢𝐨𝐧 Often I get the question: Rob, what is Human Centric AI? Let's give it a try! 😀 Human-Centric AI shifts from traditional tech to systems that prioritize humans. It's not just about functionality and efficiency; it enhances user experience by considering human needs, ethics, context, and emotions. Here's how it stands out: 𝐅𝐨𝐜𝐮𝐬 𝐨𝐧 𝐭𝐡𝐞 𝐡𝐮𝐦𝐚𝐧 𝐮𝐬𝐞𝐫: Centers on human needs, making technology user-friendly and intuitive. 𝐄𝐭𝐡𝐢𝐜𝐬 𝐚𝐧𝐝 𝐫𝐞𝐬𝐩𝐨𝐧𝐬𝐢𝐛𝐢𝐥𝐢𝐭𝐲: Emphasizes transparency, fairness, and unbiased systems, building trust and preventing discrimination. 𝐂𝐨𝐧𝐭𝐞𝐱𝐭 𝐚𝐰𝐚𝐫𝐞𝐧𝐞𝐬𝐬: Understands the broader context and intent of human interactions, leading to relevant and personalized responses. 𝐂𝐨𝐥𝐥𝐚𝐛𝐨𝐫𝐚𝐭𝐢𝐨𝐧 𝐛𝐞𝐭𝐰𝐞𝐞𝐧 𝐡𝐮𝐦𝐚𝐧𝐬 𝐚𝐧𝐝 𝐀𝐈: Supports human-machine collaboration, enhancing decision-making while keeping humans in control. 𝐄𝐦𝐩𝐚𝐭𝐡𝐲 𝐚𝐧𝐝 𝐡𝐮𝐦𝐚𝐧 𝐢𝐧𝐭𝐞𝐫𝐚𝐜𝐭𝐢𝐨𝐧: Integrates empathy and emotional intelligence, recognizing and responding to human emotions. 𝐒𝐮𝐬𝐭𝐚𝐢𝐧𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐚𝐧𝐝 𝐥𝐨𝐧𝐠-𝐭𝐞𝐫𝐦 𝐯𝐢𝐬𝐢𝐨𝐧: Aims for sustainable solutions that benefit society and the environment. 𝐄𝐱𝐚𝐦𝐩𝐥𝐞: 𝐓𝐫𝐚𝐝𝐢𝐭𝐢𝐨𝐧𝐚𝐥 𝐂𝐡𝐚𝐭𝐛𝐨𝐭 𝐯𝐬. 𝐇𝐮𝐦𝐚𝐧-𝐂𝐞𝐧𝐭𝐫𝐢𝐜 𝐀𝐈 Traditional Chatbot: Answers simple questions. Human-Centric AI Chatbot: 👉 Personalized Interaction: Considers user history and preferences. 👉 Empathy: Adjusts responses based on user tone. 👉 Ethics: Ensures fairness and unbiased responses. 👉 User-Friendliness: Intuitive and easy to use. 𝐂𝐨𝐧𝐜𝐥𝐮𝐬𝐢𝐨𝐧 Human-Centric AI prioritizes humans, ethics, context, collaboration, empathy, and sustainability. It offers a holistic approach, leading to better results for businesses and customers compared to traditional AI solutions. Share your thought is comments 👇 Read more about Human Centric AI: https://lnkd.in/e4EixN9G ——————— 👋🏻 Hi, I'm Rob B., Building the HCAI HumanSwitch.ai platform for you! 🔔 Follow me for more strategic insights in AI 📧 Sign up for my free HCAI LinkedIn newsletter (4300 readers) https://lnkd.in/eTjwSis5 💡 LinkedIn Top Voice AI, ML, Digital Transformation, Data, SaaS & Innovation

🧠 What is human-centric design, and why does it matter? In too many organizations, humans have become variables to optimize rather than the source of innovation and growth. That's why human-centered design isn't a "soft" discipline — it's a strategic necessity. Real human-centered design begins with empathy: understanding people deeply and designing with them, not just forthem. It connects customer experience to employee experience and creates lasting value. Here's what changes with AI: When deployed intentionally, AI doesn't diminish what makes us human — it amplifies it. Rather than automating empathy away, AI can scale it across cultural divides, knowledge silos, and geographic boundaries. What becomes possible: Empathy at scale. AI helps humans respond with context and care at every interaction point. Knowledge without barriers. AI connects teams across traditional boundaries and disciplines. Human reach extended. AI enables connection across cultures and languages previously impossible at scale. This isn't AI or humans. It's AI plus humans, designed deliberately around human values. Practical Steps: 1. Map your human touchpoints. Document every person who will interact with or be affected by the system. If you can't name them, you're not ready to build. 2. Observe before you build. Watch what users do, not just what they say. The gap between the two is where design insight lives. 3. Design personas deliberately. Specify how your AI should interact differently with different stakeholders. Document and revisit these choices. 4. Build in human audit points. Identify where human judgment must remain and design those roles explicitly. 5. Don't stop — cycle. Build feedback mechanisms for continuous refinement as needs evolve. Leaders who embed human-centered design with AI as an enabler aren't just preparing for the future — they're shaping it. 📍 Find out more in our Fast Company article here: https://lnkd.in/eMgyz5jN. 📍 And in our IMD article here: https://lnkd.in/eAuVbHM5

As artificial intelligence systems advance, a significant challenge has emerged: ensuring these systems align with human values and intentions. The AI alignment problem occurs when AI follows commands too literally, missing the broader context and resulting in outcomes that may not reflect our complex values. This issue underscores the need to ensure AI not only performs tasks as instructed but also understands and respects human norms and subtleties. The principles of AI alignment, encapsulated in the RICE framework—Robustness, Interpretability, Controllability, and Ethicality—are crucial for developing AI systems that behave as intended. Robustness ensures AI can handle unexpected situations, Interpretability allows us to understand AI's decision-making processes, Controllability provides the ability to direct and correct AI behavior, and Ethicality ensures AI actions align with societal values. These principles guide the creation of AI that is reliable and aligned with human ethics. Recent advancements like inverse reinforcement learning and debate systems highlight efforts to improve AI alignment. Inverse reinforcement learning enables AI to learn human preferences through observation, while debate systems involve AI agents discussing various perspectives to reveal potential issues. Additionally, constitutional AI aims to embed ethical guidelines directly into AI models, further ensuring they adhere to moral standards. These innovations are steps toward creating AI that works harmoniously with human intentions and values. #AIAlignment #EthicalAI #MachineLearning #AIResearch #TechInnovation

I’ve had the chance to work across several #EnterpriseAI initiatives esp. those with human computer interfaces. Common failures can be attributed broadly to bad design/experience, disjointed workflows, not getting to quality answers quickly, and slow response time. All exacerbated by high compute costs because of an under-engineered backend. Here are 10 principles that I’ve come to appreciate in designing #AI applications. What are your core principles? 1. DON’T UNDERESTIMATE THE VALUE OF GOOD #UX AND INTUITIVE WORKFLOWS Design AI to fit how people already work. Don’t make users learn new patterns — embed AI in current business processes and gradually evolve the patterns as the workforce matures. This also builds institutional trust and lowers resistance to adoption. 2. START WITH EMBEDDING AI FEATURES IN EXISTING SYSTEMS/TOOLS Integrate directly into existing operational systems (CRM, EMR, ERP, etc.) and applications. This minimizes friction, speeds up time-to-value, and reduces training overhead. Avoid standalone apps that add context-switching or friction. Using AI should feel seamless and habit-forming. For example, surface AI-suggested next steps directly in Salesforce or Epic. Where possible push AI results into existing collaboration tools like Teams. 3. CONVERGE TO ACCEPTABLE RESPONSES FAST Most users have gotten used to publicly available AI like #ChatGPT where they can get to an acceptable answer quickly. Enterprise users expect parity or better — anything slower feels broken. Obsess over model quality, fine-tune system prompts for the specific use case, function, and organization. 4. THINK ENTIRE WORK INSTEAD OF USE CASES Don’t solve just a task - solve the entire function. For example, instead of resume screening, redesign the full talent acquisition journey with AI. 5. ENRICH CONTEXT AND DATA Use external signals in addition to enterprise data to create better context for the response. For example: append LinkedIn information for a candidate when presenting insights to the recruiter. 6. CREATE SECURITY CONFIDENCE Design for enterprise-grade data governance and security from the start. This means avoiding rogue AI applications and collaborating with IT. For example, offer centrally governed access to #LLMs through approved enterprise tools instead of letting teams go rogue with public endpoints. 7. IGNORE COSTS AT YOUR OWN PERIL Design for compute costs esp. if app has to scale. Start small but defend for future-cost. 8. INCLUDE EVALS Define what “good” looks like and run evals continuously so you can compare against different models and course-correct quickly. 9. DEFINE AND TRACK SUCCESS METRICS RIGOROUSLY Set and measure quantifiable indicators: hours saved, people not hired, process cycles reduced, adoption levels. 10. MARKET INTERNALLY Keep promoting the success and adoption of the application internally. Sometimes driving enterprise adoption requires FOMO. #DigitalTransformation #GenerativeAI #AIatScale #AIUX

Back when I was launching my startup eight years ago, I believed this wholeheartedly, and it remains true now as I develop #AI solutions: a deep understanding of the user journey underpins every successful AI roadmap. Forget about first playing around with AI or dreaming up revenue models in a vacuum. You start here: ✅ 𝐈𝐝𝐞𝐧𝐭𝐢𝐟𝐲 𝐔𝐬𝐞𝐫 𝐏𝐚𝐢𝐧 𝐏𝐨𝐢𝐧𝐭𝐬: What tasks do they wish were easier, faster, or more intuitive? Where are they losing time, money, or energy? ✅ 𝐅𝐢𝐧𝐝 𝐔𝐧𝐦𝐞𝐭 𝐔𝐬𝐞𝐫 𝐀𝐬𝐩𝐢𝐫𝐚𝐭𝐢𝐨𝐧𝐬: How do they define success? What future are they hoping to build? What would make them say, "Finally, someone gets it"? Then, you work your way backwards from "the 𝒘𝒉𝒚" (user) to "the 𝒘𝒉𝒂𝒕" (product) to "the 𝒉𝒐𝒘" (tech). The solution might not be AI-driven at all! Let needs alone drive the solution. For product people, this may all seem obvious but greed reverses the flow from tech to user every time there's a hype cycle. Human greed is the most predictable force in the universe! Real impact starts with empathy, not excess compute. When you anchor your AI strategy in real human needs, everything else — model selection, infrastructure, UX — becomes clearer and more purposeful. It’s not about what’s possible with AI. It’s about what’s meaningful! If you’re not solving a real problem, you’re just shipping complexity disguised as innovation. And in a world flooded with AI hype, clarity is a competitive advantage. Start with the user. Stay with the user. Let that be your edge. #AIProductDesign #HumanCenteredAI

AI and automation offer us an incredible opportunity: the chance to free up time, energy, and attention for the human connections that matter most in healthcare. When we're intentional about implementation, we can create systems that are both more efficient and more deeply human - where technology handles the transactional so people can focus on the relational. Here are ten principles for using AI and automation to strengthen human connection: 1. Start with Human Needs, Not Technical Capabilities Before asking what you can automate, ask what people actually need. Observe where friction exists. Listen to where patients and staff struggle. Let those insights guide your technology decisions. 2. Automate the Transactional to Protect the Relational Routine scheduling, wayfinding, and basic information transfer are ideal for automation. This frees up your team for moments that truly need human attention - difficult conversations, emotional support, and relationship building. 3. Test with Real People in Real Conditions What works in an outpatient setting might not work in an inpatient procedural space. Prototype different approaches and observe how people respond in the specific contexts where they'll use these tools. 4. Design for Everyone, Especially the Most Vulnerable When your automation works for people with varying comfort with technology, different language needs, and different digital access levels, you've created something that expands access rather than creating new barriers. 5. Make Human Interaction Always Available Give people easy, judgment-free ways to connect with a human whenever they need to. When automation is truly helpful, most people will use it. When they need a person, that option should be readily available. 6. Measure Whether You're Creating Capacity for Connection The best automation frees staff from routine tasks so they can spend more time on complex care conversations, emotional support, and personalized attention. If your team isn't gaining that capacity, refine your approach. 7. Be Clear About What's Automated and What's Human People appreciate knowing when they're interacting with AI versus a person. Transparency builds trust and sets appropriate expectations. 8. Design Seamless Handoffs Between Technology and Humans When someone moves from an automated system to human interaction, the transition should feel smooth. Information should carry forward, staff should have context, and patients shouldn't repeat themselves. 9. Learn and Adapt Continuously Pay attention to what's actually happening as people use your systems. Where does automation help? Where does it frustrate? Use these insights to keep improving. 10. Let Your Values Guide What Stays Human Your organizational values should illuminate where human presence is essential. If you value dignity and compassion, those values can guide which moments need human interaction and which can be effectively supported by technology.

A 2022 paper by Sharon K. Parker and Gudela Grote makes the deceptively simple argument that technology doesn’t shape work; design choices do. Even though this paper is three years old, its message feels more urgent than ever. We’ve raced ahead with AI tools that can automate, analyze, and “assist,” but few organizations have paused to ask what kind of work are we designing for humans to do. Parker and Grote argue that too many organizations still treat AI as something people must adapt to, rather than as systems that can and should be designed around people. The result is expensive technology that underdelivers and employees who quietly disengage. They call for a reorientation: ⚡Stop obsessing over “upskilling” alone and start building work-design literacy, so leaders, technologists, and employees understand how technology alters autonomy, feedback, and connection. ⚡Recognize that “technocentric” change implemented without attention to social systems is far more likely to fail. ⚡Treat every AI deployment as a joint design problem, not just an IT project. The research outlines four intervention strategies that still serve as a playbook for today’s leaders: 1️⃣ Redesign roles proactively. Don’t automate first and retrofit humans later. Apply joint optimization by designing technology and work processes together. 2️⃣ Insist on human-centered technology. Evaluate tools by how they enhance judgment, learning, and agency. In other words, think beyond efficiency. 3️⃣ Shape the environment around the tech. Align incentives, feedback systems, and job structures so humans and algorithms actually complement one another. 4️⃣ Train for design thinking, not just digital skills. Every employee, especially managers, should understand how autonomy, skill use, and social connection drive performance in tech-enabled work. For leaders guiding AI transformations, the takeaway is that work design is not a side issue; it’s the operating system that determines whether your AI transformation drives tangible business outcomes. Machines may learn on their own, but organizations don’t. Leaders must design that learning in through conscious choices about autonomy, feedback, and the flow of human judgment.The best leaders I've worked with understand that technology outcomes are not predetermined; we need to be deliberate and thoughtful about how we drive these outcomes. #futureofwork #aitransformation #genai #organizationaldesign #chro #privateequity #executivecoach #artificialintelligence #ethicalai #responsibleai