Human-Centric Goals for AI Development

𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 𝗱𝗼𝗲𝘀𝗻’𝘁 𝗿𝗲𝗽𝗹𝗮𝗰𝗲 𝗲𝘅𝗽𝗲𝗿𝘁𝘀; 𝗶𝘁 𝗮𝗺𝗽𝗹𝗶𝗳𝗶𝗲𝘀 𝘁𝗵𝗲𝗶𝗿 𝗲𝘅𝗽𝗲𝗿𝘁𝗶𝘀𝗲! 👉 It’s about harnessing AI to enhance our human capabilities, not replace them. 🙇♂️ Let me walk you through my realization. As a healthcare practitioner deeply involved in integrating AI into our systems, I've learned it's not about tech for tech's sake. It's about the synergy between human intelligence and artificial intelligence. Here’s how my perspective evolved after deploying Generative AI in various sectors: 𝐇𝐞𝐚𝐥𝐭𝐡𝐜𝐚𝐫𝐞: "I 𝐧𝐞𝐞𝐝 AI to analyze complex patient data for personalized care." - But first, we must understand the unique healthcare challenges and data intricacies. 𝐄𝐝𝐮𝐜𝐚𝐭𝐢𝐨𝐧: "I 𝐧𝐞𝐞𝐝 AI to tailor learning to each student's needs." - Yet, identifying those needs requires human insight and empathy that AI alone can't provide. 𝐀𝐫𝐭 & 𝐃𝐞𝐬𝐢𝐠𝐧: "I 𝐧𝐞𝐞𝐝 AI to push creative boundaries." - And yet, the creative spark starts with a human idea. 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬: "I 𝐧𝐞𝐞𝐝 AI for precise market predictions." - But truly understanding market nuances comes from human experience and intuition. The Jobs-to-be-Done are complex, and time is precious. We must focus on: ✅ Integrating AI into human-led processes. ☑ Using AI to complement, not replace, human expertise. ✅ Combining AI-generated data with human understanding for decision-making. ☑ Ensuring AI tools are user-friendly for non-tech experts. Finding the right balance is key: A. AI tools must be intuitive and supportive. B. They require human expertise to interpret and apply their output effectively. C. They must fit into the existing culture and workflows. For instance, using AI to enhance patient care requires clinicians to interpret data with a human touch. Or in education, where AI informs, but teachers inspire. 𝐌𝐚𝐭𝐜𝐡𝐢𝐧𝐠 𝐀𝐈 𝐰𝐢𝐭𝐡 𝐭𝐡𝐞 𝐫𝐢𝐠𝐡𝐭 𝐫𝐨𝐥𝐞𝐬 is critical. And that’s where I come in. 👋 I'm Umer kHan, here to help you navigate the integration of Generative AI into your world, ensuring it's done with human insight at the forefront. Let's collaborate to create solutions where technology meets humanity. 👇 Feel free to reach out for a human-AI strategy session. #GenerativeAI #HealthcareInnovation #PersonalizedEducation #CreativeSynergy #BusinessIntelligence

Orchestrating Excellence: Crafting Human-Centered Generative AI Applications In the dynamic realm of technological innovation, the imperative for organizations is not merely to deploy artificial intelligence but to strategically orchestrate human-centered Generative AI applications. This transcends conventional approaches, integrating the cutting-edge capabilities of Generative AI with an acute focus on human experiences. At the forefront of this strategic paradigm is the symbiotic relationship between the emergent intelligence of Generative AI and the nuanced understanding of human needs and emotions. Generative AI, with its capacity for creative synthesis and adaptive learning, serves as a powerful catalyst for crafting bespoke solutions. This intellectual prowess becomes truly transformative when seamlessly blended with the empathetic touch of human insight, forming the nexus of our human-centered approach. Crucially, the intelligence quotient (IQ) of Generative AI facilitates unprecedented advancements in pattern recognition, data synthesis, and problem-solving. These capabilities lay the foundation for the creation of applications that are not just efficient but intricately tailored to the unique requirements of individuals and organizations alike. However, the true artistry emerges when we integrate the emotional quotient (EQ) into the equation. Understanding the spectrum of human emotions, cultural nuances, and ethical considerations is the linchpin for applications that resonate on a profound level. Generative AI, guided by human empathy, becomes a dynamic force that not only understands but authentically connects with users, elevating the user experience to unparalleled heights. We should underscore the importance of maintaining a delicate equilibrium between the generative prowess of AI and the ethical judgment of human stewards. In developing Generative AI applications, it is paramount to ensure that human control remains central, steering the technology toward outcomes aligned with human values and aspirations. Transparency becomes a strategic asset in this journey. Users, stakeholders, and decision-makers must have clarity into the workings of Generative AI algorithms. This transparency not only fosters trust but empowers individuals to make informed choices, reinforcing the notion that Generative AI is an augmentation of human capabilities rather than a replacement. As we chart the course toward human-centered Generative AI, our focus extends beyond technological brilliance to the tangible impact on business outcomes. Crafting applications that resonate emotionally while leveraging the generative power of AI positions organizations to cultivate enduring relationships, enhance customer satisfaction, and drive transformative growth.   Embrace the symphony of generative intelligence and human insight, as we chart a course towards a future where technology is not just generative but profoundly human. #generativeai #humanintheloop

Yesterday I spoke with hospital AI advisor Dr. Adenike 'Omo’ D. about what matters to clinicians regarding AI. She highlighted five core elements: Data - Bias - Explainability - Transparency - Human-Centred Design 1. Where's the data from? Diversity in data drives diversity in performance. 2. Bias checks - what's done to actively minimise it? No system is neutral. 3. Explainability - opening the "black box". Trust requires understanding how conclusions are drawn. 4. Transparency - clearly communicating limitations. Progress derives from an honest appraisal of strengths and weaknesses. 5. Human-centred design - integrating simply into workflows. Has it been built with clinicians' onboard? The last point really stood out. Technology has often not considered real clinical context, meaning that systems - like EHRs - have often created more work rather than less. Dr. Omo shared an example of a clinician-centric design project she worked on. She had 4 teams who shadowed workflows across different settings. Then, they collaboratively mapped user journeys, pain points and custom protocols. Constantly thinking: 'How can we simplify? Reduce the steps? The product proved not just usable but really valuable: "Clinicians who typically didn't like technology would come and tell me how much they love it." There is a massive potential for ethical AI in healthcare. Still, the magic only happens when diverse groups work together towards one shared goal: helping all people live happier and fuller lives.   My TDLR: Adoption of AI in healthcare will be governed by trust. #aihealthcare #aihealth #digitalmentalhealth

I brainstormed a list of things I ask myself about when designing for Human-AI interaction and GenAI experiences. What's on your list? • Does this person know they are interacting with AI? • Do they need to know? • What happens to the user’s data? • Is that obvious? • How would someone do this if a human was providing the service? • What parts of this experience are improved through human interaction? • What parts of this experience are improved through AI interaction? • What context does someone have going into this interaction? • What expectations? • Do they have a specific goal in mind? • If they do, how hard is it for them to convey that goal to the AI? • If they don't have a goal, what support do they need to get started? • How do I avoid the blank canvas effect? • How do I ensure that any hints I provide on the canvas are useful? • Relevant? • Do those mean the same thing in this context? • What is the role of the AI in this moment? • What is its tone and personality? • How do I think someone will receive that tone and personality? • What does the user expect to do next? • Can the AI proactively anticipate this? • What happens if the AI returns bad information? • How can we reduce the number of steps/actions the person must take? • How can we help the person trace their footprints through an interaction? • If the interaction starts to go down a weird path, how does the person reset? • How can someone understand where the AI's responses are coming from? • What if the user wants to have it reference other things instead? • Is AI necessary in this moment? • If not, why am I including it? • If yes, how will I be sure? • What business incentive or goal does this relate to? • What human need does this relate to? • Are we putting the human need before the business need? • What would this experience look like if AI wasn't in the mix? • What model are we using? • What biases might the model introduce? • How can the experience counteract that? • What additional data and training does the AI have access to? • How does that change for a new user? • How does that change for an established user? • How does that change by the user's location? Industry? Role? • What content modalities make sense here? • Should this be multi-modal? • Am I being ambitious enough against the model's capabilities? • Am I expecting too much of the users? • How can I make this more accessible? • How can I make this more transparent? • How can I make this simpler? • How can I make this easier? • How can I make this more obvious? • How can I make this more discoverable? • How can I make this more adaptive? • How can I make this more personalized? • How can I make this more transparent? • What if I'm wrong? ------------ ♻️ Repost if this is helpful 💬 Comment with your thoughts 💖 Follow if you find it useful Visit shapeofai.substack.com and subscribe! #artificialintelligence #ai #productdesign #aiux #uxdesign

People are simultaneously overestimating what AI will be able to do and underestimating the impact of what can be done with the current wave of breakthroughs. With the current wave and near-term improvements the focus should be on using AI to assist people in the flow of their work! Put the human at the center of your design and identify ways to help them gather context faster, iterate quickly on ideas, check blindspots, and preserve mental energy. This has already happened with developers, but I expect it to happen for portfolio managers, research analysts, wealth advisors, and for most forms of high-skill knowledge work. By contrast, I think it's a mistake to focus on automation. Advanced multi-step reasoning and especially automated decision-making are high-risk and will be much harder to get a safe, production-grade solution. Is it possible there's some breakthrough that changes this? Sure. But I'd focus on where the value is, not where it might be. You can always pivot if there's truly a breakthrough. Think big, start small and put people at the center! #GenAI #copilots #hypecycle

Insightful and timely post,ChandraKumar R Pillai. Wimbledon 2025 should have been a showcase of AI precision — instead, it exposed the high cost of over reliance without human oversight. ✅ AI may deliver speed, consistency, and scale — but context, empathy, and situational awareness still belong to us. 🚫 When we remove people entirely from the loop, we don’t just lose backup — we lose the ability to interpret nuance, communicate with inclusion, and recover when systems fail. And let’s be clear: AI won’t make the unskilled talented. It won’t give the lazy ambition. It won’t replace courage, curiosity, or critical thinking. It amplifies what we bring to the table — but it doesn’t replace the table. Full replacement often backfires. What’s needed is augmentation, not abdication. This is more than a sports story. It’s a mirror for every leader implementing automation: → Do you have a human safety net? → Are you designing for edge cases and accessibility? → Is your AI enhancing trust — or eroding it? ⚠️ Tech doesn’t fail because it’s “bad.” It fails when it’s implemented without empathy, tested without reality, and scaled without accountability. Thanks for sparking this conversation. We need it — across sectors. Linda Restrepo - Inner Sanctum Vector N360™ #AIethics #HumanCenteredAI #AugmentedIntelligence #AIfailures #TechLeadership   #ResponsibleAI #DigitalTrust #AutomationStrategy #Wimbledon2025 #InclusionMatters   #AIandHumanity #AIimplementation #EmpathyInTech #FutureOfWork #AItransparency

Generative AI in HR: A Reality Check The buzz around generative AI, like ChatGPT, has been unmissable. But when HR pros put it to the test, the results were eye-opening. Real-World HR Tests: AI vs Human Insight In one corner, Mineral's HR experts. In the other, ChatGPT's AI. The mission? Tackle complex HR and compliance queries. The outcome? A revealing look into AI's strengths and its limitations. Experiment 1: ChatGPT on Trial ChatGPT, across its versions, faced off against tricky HR questions. The verdict? Later versions showed promise, but when it came to nuanced, complex queries, human expertise still ruled supreme. The message? AI's got potential, but HR's nuanced world needs the human touch. Experiment 2: Knowledge Work and AI Harvard Business School and BCG took it further, exploring AI's impact on knowledge work. Surprise finding? While AI boosted some creative tasks, it sometimes hampered performance on complex analytical challenges. The Takeaway: AI's Not a Solo Act What's clear is this: AI, especially in HR and knowledge-intensive roles, isn't a standalone solution. It shines brightest when paired with human expertise, enhancing efficiency and insight rather than replacing it. For those navigating the future of work, it's a blend of AI's rapid processing with the irreplaceable depth of human understanding that'll pave the way forward. Embrace AI, but remember, the human element is your ace card. Stay tuned for more insights on blending AI with human expertise in the workplace. Follow our newsletter for updates. Check out the full article here: https://lnkd.in/gznn43vp #AIinHR #FutureOfWork #HumanAIcollaboration

🛠️Your Organization Isn't Designed to Work with GenAI. ❎Many companies are struggling to get the most out of generative AI (GenAI) because they're using the wrong approach. 🤝They treat it like a standard automation tool instead of a collaborative partner that can learn and improve alongside humans. 📢This Harvard Business Review article highlights a new framework called "Design for Dialogue" ️ to help organizations unlock the full potential of GenAI. Here are the key takeaways: 🪷Traditional methods for process redesign don't work with GenAI because it's dynamic and interactive, unlike previous technologies. ✍Design for Dialogue emphasizes collaboration between humans and AI, with each taking the lead at different points based on expertise and context. This approach involves  📋Task analysis ensures that each task is assigned to the right leader — AI or human 🧑💻Interaction protocols that outline how AI and humans communicate and collaborate rather than establish a fixed process 🔁Feedback loops to continuously assess and fine-tune AI–human collaboration based on feedback. 5-step guide to implement Design for Dialogue in your organization 🔍Identify high-value processes. Begin with a thorough assessment of existing workflows, identifying areas where AI could have the most significant impact. Processes that involve a high degree of work with words, images, numbers, and sounds — what we call WINS work are ripe for providing humans with GenAI leverage. 🎢Perform task analysis. Understand the sequence of actions, decisions, and interactions that define a business process. For each identified task, develop a profile that outlines the decision points, required expertise, potential risks, and contextual factors that will influence the AI’s or humans’ ability to lead. 🎨Design protocols. Define how AI systems should engage with human operators and vice versa, including establishing clear guidelines for how and when AI should seek human input and vice versa. Develop feedback mechanisms, both automated and human led. 🏋🏼♂️Train teams. Conduct comprehensive training sessions to familiarize employees with the new AI tools and protocols. Focus on building comfort and trust in AI’s capabilities and teach how to provide constructive feedback to and collaborate with AI systems. ⚖Evaluate and Scale. Roll out the AI integration with continuous monitoring to capture performance data and user feedback and refine the process. Continuously update the task profiles and interaction protocols to improve collaboration between AI and human employees while also looking for process steps that can be completely automated based on the interaction data captured.  By embracing Design for Dialogue, organizations can: 🚀Boost innovation and efficiency, 📈Improve employee satisfaction 💪Gain a competitive advantage 🗣️What are your thoughts on the future of AI and human collaboration? Please share your insights in the comments! #GenAI #AI #FutureOfWork #Collaboration

Most organizations treat the implementation of AI as a technical challenge. What’s often overlooked is the opportunity to treat AI as a valued member of the team. University of Phoenix research conducted among 604 #HR leaders and workers found that workers want to partner with AI as a new team member, not just learn the technical skills to use AI in their job. Nearly 4 out of 10 workers want to learn how to collaborate with AI in their job, and this ranked slightly behind learning how to use AI to get their job done faster and with greater efficiency. So what can leaders do about this? I suggest the following: #1. Develop a culture of shared AI knowledge Leaders need to role model how they are using #AI rather than just mandate #AI usage. Alex Laurs is profiled in the article shares how he built a strategy and innovation #AI agent and then challenged his team to use it, break it and create the next iteration. #2. Use AI to develop human skills In a workplace where there is an expectation to use AI daily, training and development must be re-imagined leveraging AI. Matt Walter CHRO of Medtronic has done this, using AI/VR role playing to train sales teams on how to navigate ambiguity, exercise judgment in complex sales situations, and resolve conflicts with customers. #3. Balance your investment in AI literacy with an investment in human literacy Being a student of #AI is now a workplace competency. Udacity's new program, called Agentic AI Fluency course, trains learners in how to work with #AI to enhance both their productivity and creativity. My message for leaders: Go beyond setting mandates for being an AI first organization to role modeling an AI mindset for their teams. The link to the article is here: https://lnkd.in/eZNE6nHk University of Phoenix EY Medtronic Matt Walter Alex Laurs Udacity Victoria Papalian

Our latest research published in Harvard Business Review reveals the importance of specific human capabilities for successful use of AI. We observe two categories of human skills as critical. First are effective interpersonal skills - basic conflict resolution, communication, skills of disconnecting from emotions, and even mindfulness practices. Second is domain expertise, deep knowledge of one’s environment. Rushing to replace talent with AI is a huge mistake. Competitive advantage cannot be achieved without humans in the loop. Companies should focus on #reskilling #upskilling, and preserving domain knowledge among experienced talent while developing it among young inexperienced workers. #futureofwork #artificialintelligenceforbusiness #artificialintelligence #hr #humanresourcedevelopment #talentmanagement #ai D'Amore-McKim School of Business at Northeastern University Heather Hill Polly Mitchell-Guthrie Anne Robinson John Sicard Maria Villablanca Ted English John Wood https://lnkd.in/embyjKWW https://lnkd.in/eEh2ubRn