Implementing AI While Maintaining Human Touch

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?

 AI is doomed to fail if you don’t put your employees first. Here’s how you can do that.  When it comes to AI transformation, most organizations fall into the trap of focusing solely on technology but the truth is, without considering people, even the best AI solutions struggle to deliver real impact. Research shows that 70 percent of AI projects fail to meet their objectives, largely due to poor adoption by employees. That’s where the FriendlyCHRO Method comes in. It’s a 3-step framework I developed that puts human connection at the core of AI adoption, ensuring sustainable and effective change. Here’s how it works: 📌Involve everyone:  Engage all levels of your organization early on. Invite leaders, team members, and frontline employees to AI strategy meetings. Let them participate in defining the transformation’s vision and roadmap. This way, they feel ownership in the process and have a stake in its success. 📌Create emotional buy-in:  Address fears and provide clear answers. Hold regular Q&A sessions where leadership can engage directly with employees about AI’s benefits and challenges. Share success stories of AI adoption in similar companies or teams to demonstrate its positive impact on people’s roles. 📌Train and upskill: Implement a comprehensive AI training program that goes beyond just using the technology. Focus on how to integrate AI into daily tasks, with special emphasis on making employees feel confident in using these tools. Offer ongoing support through AI mentoring sessions or dedicated helpdesks. It’s time to shift the focus from just tech to people. When you lead with empathy, AI adoption isn’t just successful, it’s transformational. What’s your approach to human-centered AI adoption?

In the rush to integrate AI, it's easy to focus on what it can automate. But in healthcare, AI's most profound impact might be its ability to support human connection, not replace it. Imagine AI as a tool that: • Creates smoother transitions between care teams, so patients feel consistently supported • Preserves time for face-to-face interactions, even in tech-driven workflows • Amplifies trust-building moments that truly impact patient outcomes Five ways AI can strengthen human connection: 1. Protect Conversation Time • Automate documentation in the background • Handle routine coordination invisibly • Free mental space for active listening • Enable eye contact instead of screen focus 2. Support Team Relationships • Share insights across care teams naturally • Enable smoother handoffs • Facilitate timely collaboration • Build trust through better information flow 3. Create Space for Empathy • Handle routine tasks quietly • Allow for longer patient interactions • Support emotional awareness • Enable presence over process 4. Enable Better Transitions • Keep everyone informed appropriately • Reduce communication gaps • Support continuous care relationships • Maintain connection through changes 5. Amplify Human Insight • Surface patterns that need human attention • Support clinical judgment, don't replace it • Enable deeper patient understanding • Strengthen team collaboration By approaching AI with a relationship-centered lens, we can design technology that strengthens the interactions and collaborations that make healthcare effective—and deeply human.

AI adoption is slowly changing the human side of work. Not in dramatic ways. In small ones. The weekly check-in that slowly faded away. The hallway debrief that doesn’t happen anymore. The mentoring conversation replaced by a dashboard update. Most people don’t notice the shift. We celebrate automation wins. We report efficiency gains. We hit rollout milestones. But something is changing underneath. 📈 The World Economic Forum found that early AI adopters report weaker connections with colleagues and a lower sense of productivity. Not higher. Lower. 📊 The American Psychological Association found that 38% of workers fear AI will make their job obsolete. Of those, 51% say work is harming their mental health, compared to 29% among those without that fear. The issue is not just job loss. It is relational loss. The small conversations. The mentorship moments. The messy, collaborative problem solving. The things that make work feel human. When we automate a workflow, we often automate away the human touchpoints attached to it. Nobody plans that outcome. It just happens. This is the blind spot nobody talks about. We optimise systems. We forget to redesign connection. ⚡️Four ways to keep work human:⚡️ ➊ Design AI for collaboration, not isolation. Before automating a task, map the human interactions attached to it and intentionally replace them. ➋ Rebuild reciprocity. Make mentoring, helping, and knowledge sharing visible and rewarded. Automation should not erase apprenticeship. ➌ Protect the small moments. Keep space for live problem solving, shadowing, and peer review. Trust forms in the margins, not in dashboards. ➍ Use AI as a bridge, not a replacement. Deploy tools to enhance human judgment, not bypass it. ⸻ Before your next automation rollout, ask: What human touchpoints will disappear and what will you design in their place? _____ ➕ Follow me Ana Petras for insights on AI adoption, leadership, and human-centered growth.

Focus Your AI Journey on Hybrid Intelligence As AI moves deeper into the enterprise, many companies aren’t diving into full automation—they’re starting with Hybrid Intelligence (HI). They build systems where humans and AI work together, each doing what they do best. HI blends Natural Human Intelligence (empathy, ethics, judgment, and creativity) with Artificial Intelligence (speed, scale, pattern recognition, and data processing). The goal isn’t to replace people. It’s to augment them—giving employees AI tools that make them faster, more informed, and more capable. Why Companies Start with Hybrid Models - Trust: AI systems can’t always explain themselves. Keeping humans in the loop builds transparency and accountability. - Adoption: People are more likely to use tools that help them—not replace them. HI creates space for upskilling, not fear. Humans can spend more time on complex tasks and decisions.  - Complexity: In areas like finance, healthcare, and supply chain, there’s no substitute for experience, ethics, or emotion. - Control: Organizations can start small, test and learn, and scale as confidence grows. (While the benefits are clear, implementing HI still presents challenges such as ensuring data quality and integration, or addressing potential cultural resistance to new ways of working. The frameworks discussed below offer strategies to navigate these complexities effectively.) Examples: Walmart uses AI in supply chain control towers to forecast disruptions—like weather delays—and alerts analysts who make final decisions on action. It combines machine foresight and human judgment. Morgan Stanley equips wealth advisors with AI-powered insights—portfolio trends, market alerts, client preferences—while keeping advisors fully in charge of client decisions. Airbus uses predictive AI to catch maintenance issues early. Engineers still decide what action to take, how urgent it is, and when to intervene. KLM runs an AI-assisted customer service model where bots handle common questions, but anything emotional or complex gets escalated to a human—supported by AI-surfaced info to help resolve the issue quickly and personally. In all these examples, AI behaves like a trusted confidant and doesn’t deliver ultimatums. Making Hybrid Work: Frameworks That Help - Walther’s A-Frame: Awareness, Appreciation, Acceptance, Accountability - Shneiderman’s Human-Centered AI: Pair high automation with high human control - PAI Guidelines: Ask the right questions about transparency, oversight, and task division Bottom Line: HI gives companies a smart, low-risk way to build AI into the business—without losing the human edge that still drives real value. The question is, how will the Human-AI workload and focus evolve over time? Sources: Dellermann (2019): Hybrid Intelligence Walther (2025): Why Hybrid Intelligence Is the Future of Human-AI Collaboration HBR (2025): Agentic AI Is Already Changing the Workforce Shneiderman (2020): Human-Centered AI

Most AI implementations can be technically flawless—but fundamentally broken. Here's why: Consider this scenario: A company implemented a fully automated AI customer service system, and reduced ticket solution time by 40%. What happens to the satisfaction scores? If they drop by 35%, is the reduction in response times worth celebrating? This exemplifies the trap many leaders fall into - optimizing for efficiency while forgetting that business, at its core, is fundamentally human. Customers don't always just want fast answers; they want to feel heard and understood. The jar metaphor I often use with leadership teams: Ever tried opening a jar with the lid screwed on too tight? No matter how hard you twist, it won't budge. That's exactly what happens when businesses pour resources into technology but forget about the people who need to use it. The real key to progress isn't choosing between technology OR humanity. It's creating systems where both work together, responsibly. So, here are 3 practical steps for leaders and businesses: 1. Keep customer interactions personal: Automation is great, but ensure people can reach humans when it matters. 2. Let technology do the heavy lifting: AI should handle repetitive tasks so your team can focus on strategy, complex problems, and relationships. 3. Lead with heart, not just data (and I’m a data person saying this 🤣) Technology streamlines processes, but can't build trust or inspire people. So, your action step this week: Identify one process where technology and human judgment intersect. Ask yourself: - Is it clear where AI assistance ends and human decision-making begins? - Do your knowledge workers feel empowered or threatened by technology? - Is there clear human accountability for final decisions? The magic happens at the intersection. Because a strong culture and genuine human connection will always be the foundation of a great organization. What's your experience balancing tech and humanity in your organization?

In violent agreement: Human-AI interaction should apply "reciprocal algorithmic output" that increases human agency and learning, even when the AI is removed. Some key concepts in this marvellous paper by Anuschka Schmitt: 🔄 Reciprocal Algorithmic Output: Algorithmic output should be reflective and open-ended rather than prescriptive, encouraging users to expend cognitive effort and maintain control over decisions, fostering human agency and learning. 🚀 Augmentation Outcomes Beyond Productivity: The traditional productivity-focused view of augmentation is deficient; AI systems should be designed to unlock the broader potential of human-AI collaboration beyond mere efficiency gains. 💪 Building Human Competencies: The paper introduces "reversibility," suggesting that effective human-AI interactions should empower users to perform tasks effectively even without AI, building lasting human competencies rather than fostering over-reliance. In short, system design should lead to three critical outcomes: 🔍 Task Performance: Gains in accuracy, quality, and speed to improve productivity and effectiveness. 🌐 Human Agency: Individual autonomy in decision-making, thought, and action while interacting with AI. 📚 Human Learning: Preserving and enhancing skills, allowing individuals to improve performance independently of AI. These are crucial messages that need to be more widely acknowledged, understood, and incorporated into all AI system design.

The future of sales isn't just about automating as much as we can—it's about augmentation and balance between tech and human capabilities. 🤖🤝 In the latest post on the Clarify‎ blog, my co-founder Austin Hay‎ dives into the why and how behind this need for balance. ⚖️  As our tools have become more advanced, they've also become less effective. Why? Because everyone's using the same AI-powered playbook.  Our customers are more aware of–and more burnt out by–over-used AI strategies than ever. This means more outreach ends up in spam every day. 😬 So, what's the solution? It's not about abandoning technology, but about using it more intelligently.  Here are some of the key insights Austin shares: 🧠 Context is king: We need AI that understands deep patterns in customer behavior, not just surface-level personalization. 🔄 Rethink CRMs: Imagine systems that actively provide insights without manual input, freeing us to focus on relationship-building. 💡 Quality over quantity: Use AI to identify the most promising leads and craft genuinely valuable interactions. 🤝 Enhance, don't replace: The goal is to use tech to amplify our human skills, not substitute them. 📊 Redefine success metrics: It's not just about volume anymore—focus on engagement quality and long-term relationship building. This shift presents both challenges and opportunities for founders and product builders. How do we create solutions that leverage AI while preserving that crucial human element? How do we build tools that enhance, rather than replace, genuine human connection? 🤔 The answer: Use AI not as a replacement for human interaction, but as a powerful tool to augment our capabilities. It should handle routine tasks and provide deeper insights, freeing up our teams to do what humans do best: Understand nuanced needs, provide strategic value, and build lasting relationships. ❤️ I’ll link Austin’s blog in the comments for folks who want to learn more. 🔗 I’d also love to hear your thoughts: How are you navigating the balance between automation and human touch in your business?

Ten years ago, AI in customer service was cutting-edge and costly. Today, almost any business can implement AI-driven experiences using tools like ChatGPT. But just because you can use AI doesn’t mean the experience always meets customer expectations. That’s why my latest annual research is so important. This year’s Customer Service and CX Study surveyed more than 1,000 U.S. consumers and uncovered powerful insights: ·      AI is getting better: 50% of customers resolved issues using AI without human help—up from just 32% in 2024. ·      But it’s not perfect: 51% said they’ve received incorrect information from AI bots, which erodes trust. ·      Human support still matters: 68% prefer the phone, especially Boomers (82%). ·      Generational divide: Gen-Z is far more comfortable and successful with AI than older generations. ·      Trust is critical: 70% worry about privacy when dealing with AI. Here’s the takeaway: The future of AI in customer service isn’t about replacing humans—it’s about blending technology with the human touch. Customers want convenience, but they also want connection. Remember: “The greatest technology in the world can’t replace the ultimate relationship-building tool between a customer and a business: the human touch.” #CX #CustomerExperience #AI #ShepHyken

🚀 Here's a human-centered take on AI implementation that often gets overlooked: Legacy industries can tap into AI not only as a tool for automation but as a way to bridge critical talent gaps. I've seen this pattern emerge across industrial and mining companies. Veteran workers with decades of expertise are retiring faster than knowledge can be transferred, while pressure mounts to modernize operations through AI. What stands out is that some of the most innovative companies are turning to AI implementation specifically to accelerate knowledge transfer, rather than treating these as isolated issues. Let me break down what that looks like: First, get systematic about capturing expertise. Maintenance logs and operator insights are full of hard-won wisdom that, if structured well, can form the backbone of robust AI systems. Next, set up hands-on collaboration between outgoing experts and digitally-native team members. By having senior folks help shape AI knowledge bases while newer staff drive implementation, you encourage a kind of creative abrasion that nudges the whole org forward. Finally, keep augmentation front and center. AI that crunches sensor data overnight means experienced staff can focus on trickier troubleshooting and mentoring instead of chasing routine metrics. I’ve watched companies pull off this blend—preserving crucial operational knowledge while staying agile for the future. It’s less about choosing between honoring experience or pursuing change, and more about weaving those threads together. Curious how others are handling this: How do you hold onto essential know-how while keeping your innovation engine running?