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! 🙏

🧠 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

AI doesn’t fail because of intelligence - it fails because of misalignment. Designing human-centric AI means understanding that systems learn from patterns, not meaning, and that people interpret those patterns through trust, context, and purpose. An AI system is essentially an agent interacting with an environment: it senses (data), decides (policy), and acts (output). The challenge for designers is to shape these loops so that what the system optimizes aligns with what the user values. Every interaction is part of a probabilistic chain of inference. AI doesn’t say, “this is true,” it says, “this is 87% likely to be true.” That means interfaces must expose uncertainty and design around error tolerance, not perfection. The goal isn’t to make AI seem flawless, but to make it understandable when it fails - and recover gracefully. Feedback loops are critical here. Whether explicit (a correction) or implicit (a click, a pause), every behavior reshapes the model. Designers must plan how this feedback is collected, weighted, and surfaced so that learning feels visible and reciprocal. Trust isn’t achieved through good visuals; it’s achieved through transparency of reasoning. Users need to see why a recommendation, prediction, or decision occurred. Tools like confidence indicators, natural-language rationales, or example-based explanations can reveal the system’s thinking process. Trust calibration becomes a design problem: too little information and users overtrust; too much and they disengage. Ethics in AI design is not a checklist - it’s an architectural constraint. Fairness, privacy, and accountability must be embedded in how data is handled, how models are trained, and how decisions are logged. Human-in-the-loop design is not about control; it’s about responsibility. Each feedback point or override is a governance node in a socio-technical system. Prototyping intelligent behavior means simulating cognition, not just interaction. Before the model even works, designers can model system reasoning: what inputs it listens to, how it weighs them, and how it communicates uncertainty. That’s how you prototype explainability early-before accuracy takes over the agenda. In practice, the best AI teams combine technical literacy with behavioral empathy. Data scientists understand distributions; designers understand interpretation. Together, they build systems that not only learn from data but learn from people. Human-centric AI doesn’t just optimize performance - it aligns cognition, decision, and design around human meaning. That’s what makes intelligence truly useful.

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

💡 Human-centered AI isn't just a feel-good idea. 💡 Human-centered AI (HCAI) is a growing discipline committed to creating #AI systems that retain humans as a critical component. The premise is that AI should be human-controlled and augment human ability rather than replace humans in context. I've spoken about #HybridIntelligence and the idea that human + AI is better than either on its own. HCAI takes that a step further, recognizing that human control is necessary to ensure that AI operates ethically and transparently. HCAI core principles include: ⭐️ a focus on human needs ⭐️ human-AI collaboration ⭐️ user-centered design ⭐️ transparency and accountability ⭐️ positive social impact ⭐️ iterative improvement The idea is to ensure that AI benefits not only our bottom line but our society at large. 💡 And it's important to recognize that HCAI has clear business benefits. 💥 Informed decision-making: While the profound data analysis capabilities of AI are useful, combining that with human values and understanding provides more comprehensive strategies and solutions. 💥 Ethical efficiency and productivity: The computational strength of AI can scale the ideas and human insight of workers while retaining nuanced understanding and moral reasoning. 💥 Improved user experience: By focusing on user needs and preferences, HCAI can create more personalized products and engaging experiences for customers. 💥 Enhanced creativity and innovation: The collaboration of humans and AI can result in new ideas and solutions that would not be possible for either alone. 💥 Ethical considerations and trust: With increased transparency and explainability, and prioritization of human needs and values, HCAI helps to build trust with customers and partners. 💥 Continuous improvement: HCAI enables continuous refinement through iterative feedback loops, providing user feedback to make AI systems smarter and more effective over time. Can you think of other things humans bring to the equation that can benefit business?