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C (1:33)

So you've integrated the latest LLMs and automated your workflows to future proof. Your company but your team, they aren't actually using it. In fact, they might be terrified of it. Most AI projects fail because they are function focused, built for what the machine is capable of doing while completely ignoring the human. Why? In 2026, AI is no longer a technical challenge, it is a behavioral one. AI implementations today often feel like black hat design. There's a lot of how am I missing out? What happens if I don't use this? A lot of urgency. But if an employee feels that AI is just tracking their output to eventually replace them, they won't engage. They will start protecting their territory. The research and harmonizing human AI synergy confirms that AI fails without user autonomy. When the AI feels like a cop rather than a coach, it triggers immediate resistance. By applying the octalsis framework, we can shift AI from being a C suite efficiency tool to an individual contributor's superpower. We must balance short term data, which usually relies on black hat, with longer term engagement Also known as White Hat. We want the user to feel smart, not replaced. By the way, I'm Rob, I'm the founder of the Professor King Podcast, the number one podcast in gamification. And I'm also the head of engagement strategy at the Optalysis Group, the leading gamification and behavioral design consultancy in the world. And it is where we've been looking closely at how people do these AI implementations. There's a massive difference. There's a gap between the projects that stay focused, function focused, and the ones that consider the human behind the keyboard. When AI starts to automate a task that an employee is proud of, like a salesperson, you know, the gut feeling, or that complex spreadsheet that they spent weeks perfecting, or that unique voice of a writer, it stops feeling like a win. It feels like an identity threat. People resist AI not because we are lazy, which we might be a little bit, but because of the fear of losing out on things like core drive two development and accomplishment. If the AI does all the work, the human no longer feels a win. State of overcoming those challenges. You have to consider how you're integrating behavioral insights into your implementation. Again, the challenge is no longer technical, it's about how we get people on board. Does your AI implementation make your team feel like masters of their craft with new superpowers? Or are they feeling like data entry clerks for an algorithm? And if this is already sounding like some of the implementations you've been working with, or what you are looking to do in your own project, let's have a quick chat and figure out how to get you and your project out of that AI cemetery and bring it into all those promises. That AI is giving into productivity and making work a lot more better. Many companies are just stitching together different features based on short term metrics. You see a quick metric bump from an A B test and assume success, you shout we did it. The problem is you're actually building what we call a Frankenstein. This is what users eventually abandon because it feels manipulative. When you over rely on strategies that only cater for short term data, like we discussed in a previous video, you prioritize black hat motivations, urgency, scarcity, fear without including white hat strategies as well. Things that look at the longer term value, the empowerment, making the user feel good, they are going to burn out and quit once that novelty wears off. So are you using AI to trick your employees into short term compliance or to convince them of the long term value? And the third point I wanted to bring is that AI is often just dropped into a workflow like a black box. There's no guidance, which creates a massive unnecessary friction. Now don't get me wrong, games and gamification create intentional friction, but there's a massive difference between unnecessary friction and useful fun friction. Resistance to change is often just a lack of win states. To drive adoption, the AI needs to provide immediate individual benefit. Most metrics about AI productivity only cater to upper management. What about the person using it? Does it make my job different? Does it let me be more creative? You want to scaffold the user? Think of the concept of flow by Mihaly Csikszentmihalyi It's a balance between your skill level and the level of a challenge. As your skill increases, the challenge must increase as well. To keep you in the sweet spot, AI should be used to maintain that flow. Can your user achieve a meaningful win within the first three minutes of your new AI tool? Or are they left wondering, is this tool just going to replace me? Am I training my own replacement? You see, the magic of AI does not start with the model you're using or the technology itself. It starts when you respect the human behind the keyboard. Don't build a time bomb. Build trust, not traps. There is plenty of further reading and research in the show notes, including the research on human AI synergy and our work at the Actualysis group. If your AI implementation is technically flawless, but your user world usually looks like a cemetery because nobody is using it, you're missing out on that massive roi. That AI is promising. So let's have a quick chat. Just click on the link below so we can align your AI strategy with actual human motivation. And as we like to say at the end of our episodes, as you know, at least for now and for today, it is time to say that it's game over.