Right now, half of all consumers question the authenticity of almost everything they see online. And that segment is growing fast. And it's not just some of the things they see online, it's almost everything. And this is the world that your company is deploying AI into. And most companies have no system in place to prove their AI outputs are real, audited, or accountable. Actually, studies say that only 30% of organizations have even reached a mature level of AI governance. So, what's all that mean? Well, the majority of companies are shoving AI down their employees and their consumers' throats, but they have no way to verify almost anything that they create. All that AI text, the AI graphics, the AI apps. Is it real? And for consumers, that's why it's turned into a trust problem. But for companies, it's a responsible AI nightmare. And this isn't a future issue. This is a right now issue. And today we're going to give you the responsible AI playbook to actually deal with it on everyday AI. Welcome. What's going on? Here is the big picture for today's show. And as we tackle responsible AI and give you the playbook, tell you what it means and the five uh moves to ensure your strategy survives. So here's the big picture. Well, responsible AI now determines whether companies can scale AI or just if they're gonna stay stuck. And we're talking, it's uh kind of a point now where trust lawsuits and regulation and consumer confidence are just shaping AI's real business value. And most companies know the risks that exist, but they don't have systems in place to deal with them. It's almost like everyone's just kind of ignoring them. And well, that's why many companies are treating responsible AI as, well, just a checkbox that they may or may not check. But those that do and that are treating it properly with the uh respect and time that it deserves, well, they're pulling ahead right now. So on today's show, stick around for the next 20 minutes, and here's what you're gonna learn. So you're gonna learn the real reason most companies can't get past the AI pilot stage. You're gonna see why 2026 became the year that AI trust, billion-dollar lawsuits, and regulation collided. And I'm gonna give you at the end a five-move responsible AI playbook that separates the leaders from everyone else. Let it let's get into it. This is the Start Here series on Everyday AI. So if you're new here, after 700 plus episodes, everyone's asking, Jordan, where do I start on this journey? You have so many podcasts. Well, you start with the Start Here series, preferably if you can do it in order. Uh, this is now what are we on? Volume 17. But uh Start Here Series, it is the essential podcast series to both learn the AI basics and to double down on your AI knowledge. So if you haven't already, please go to startheseries.com. That's going to give you free access to our exclusive inner circle community. You literally can't get access right now any other way. Uh, and then in the Start Here Series space, there inside our community, you can go listen and watch to uh watch every single uh Start Here series. They're fairly quick, it's all in there, and then you can connect with other business leaders who are trying to do the same. So if you missed last episode, that was volume 16 of our series. We went from chatbots to super agents and talked about the 11 AI categories explained. So make sure you go check that one out. But today we're talking about the responsible AI playbook, what it means, and five moves to ensure your AI strategy survives. Uh, and if you are really new here, uh, this isn't an AI version of me, or if you listen all the time, I just allergies now, right? I was sick for uh months and now I sound like this. So yeah. Uh, anyways, I before we get into responsible AI, I want to talk a little bit on some definitions because it can actually get confusing because uh a lot of times people uh mistake uh or kind of uh cross over between ethical AI and responsible AI. So today we're gonna be tackling responsible AI, which is different than ethical AI, because uh I don't think there's really a blanket um kind of statement that you can apply ethical AI because well, ethical AI defines the moral principles of what is right, fairness, safety, and privacy. And depending on well, what country you're in, that's a big one. We have listeners from all over the world. Uh, but also a lot of well, if C-suites are being honest, they look at AI as a headcount thing, right? They're like, okay, well, if we deploy AI everywhere, we can reduce headcount by 30%. And for them, maybe that's an ethical decision, even though I would argue it's not. So we're not gonna really tackle ethical AI today, but we're gonna be talking about responsible AI. And that's the operational framework underneath ethical AI that puts those ethical principles into practice. So, whatever, whatever those ethical principles are, responsible AI is kind of the framework that you put into place to make sure that those principles work. So, ethics essentially is the question of should we do this? And responsible AI asks, well, how do we do it right? And then if you're like, wait, did you already cover this? No, we covered uh AI governance. So make sure to go check out volume 14 of the series. That was episode 737. But essentially, ethics gives the values, tells you what's fair when it comes to AI safe and aligned. Uh, and then responsible AI, which we're going to be tackling today, that's how you turn those values into real decisions about use cases, oversight, and data. And then governance, which we already covered, that provides the roles, rules, and review systems that enforce responsible AI over time. So make sure after you listen to this uh episode, if you haven't already, make sure to go listen to volume 14, that's episode 737 on governance. So we're technically zooming out here uh because we already gave you the uh the governance playbook and the five governance rules. And this uh episode kind of explains why those rules exist. All right, we're gonna start with five and then we're gonna end with five. So, first, uh just to get this framework really in place, I want to first go over the five core pillars that hold up every responsible AI decision. So that's fairness, transparency or explainability, uh, accountability, privacy or security, and then safety or reliability. So, number one, fairness. That's how you actively, how your company actively identifies and mitigates any algorithm bias for equitable outcomes, right? We have to understand that most large language models that we use are biased. And we have to start tackling that issue. What systems do you have in place to make sure that your outcomes, whatever those are, those are text uh-based outcomes that you put on your website, maybe they're uh they play into your hiring practices, whatever it is, you need to understand that by default, AI is extremely biased. So, uh, how can you make sure that you are putting those systems in place to make sure that AI is equitable and fair? Number two, transparency or explainability. You need to understand how and why AI reaches its decisions. So if you've already listened to the Start Here series, you already know that because we've tackled everything from here's what a large language model is, how it works, you know, cutting through the buzzword and jargon. So go back and listen to that one, it'll teach you. Then three, accountability. That's how you establish clear human responsibility for AI actions and outcomes. And we did tackle that one a little bit more in governance, but essentially it's this if the 10-second test, if something goes terribly wrong, if you have an agent crash in your organization and an agent goes and you know does something absolutely terrible within 10 seconds, if someone says, Who's like who's responsible for this? Can you with 100% certainty identify the one human being that is ultimately responsible for that? Yes or no? You need to understand the accountability chain, just as it's as important as it is uh you know to have agentic observability and traceability, you also have to have that on the accountability side with your humans. Number four, privacy or security. And that's protecting personal data from misuse and adversarial attacks. And this becomes even more important as we get into agentic AI, right? So as we give AI agents the keys to the castle to make proactive decisions for us, right? Um, large language models are no longer reactive read-only. They are now proactive read write. They are making decisions on our behalf, oftentimes without expert guardrails, right? You might have a catch-all safety human in the loop that may or may not have any understanding of how to properly protect personal data. Um and then last but not least, number five, safety and reliability. You need to ensure in your company that AI performs as intended without causing harm. All right. And you might say, okay, well, how can AI cause harm? Well, if you're only thinking of, you know, using AI, like, oh, I just go to copilot and you know make my uh, you know, make my email sound better, that's that's not what I'm talking about here, right? We are in the year 2026. And hopefully, if you're an avid listener of this show, you are already deploying right agentic societies uh throughout your organization. So I think at that point you will understand uh how AI, how it's important to make sure that your AI is making safe decisions that don't cause humans harm ultimately, right? Uh, because as we give more and more agency away, right? And that's ultimately uh kind of this this uh I think rough uh 18-month transition that we're gonna be going through mid-2026 to the end of 2027, is human experts giving away their agency, uh, right? So you're like, okay, well, what do we do now? Well, you have to make sure that the systems that you deploy are safe and reliable. So, why is responsible AI even important? Why, well, we talked about it. It is the trust crisis. So, right now, about half of consumers, according to an iProve study, about half of consumers question the authenticity of almost everything they encounter online. And I agree, right? We actually had a start here series. Let me go ahead and and bring it up and tell you uh what episode it was. So this was episode 740, it's volume 15 of the Start Here series, called Everything Is Fake, right? And how your company can leverage human expertise to fight AI work slop. So, consumers, I don't think most people see or understand this yet. And as the technology gets better, right, as AI video gets better, uh, as AI images get better, as AI audio in deepfakes, unfortunately, get better. That percentage of people questioning the authenticity is only going to rise, right? So synthetic media, uh, deepfake fraud, all of those things just erode the baseline trust that people rely on, right? It used to be something like, you know, oh, you go on the internet as an example, you go to Google page one, you probably have a lot of trust in that, right? If you went to page 20 and it's some random blog from some dude in the basement, you might look at that and be like, not sure about that, right? That page 20, take that times 10, right? And that's gonna be the commonplace level of distrust that we are gonna see every single day. And that's what your right, whatever sector you're in, if you haven't started to experience yet, it is gonna slap you, right? It's gonna slap you. I think it's starting with social media, right? You're seeing more and more things, and you know, things that happen in real life, people are like, oh, that's fake, right? And then things that didn't happen in real life, people are like, oh, that's real, right? You're starting to see it play out on social media. It is going to play out in our everyday lives. And I think even things that people start to see with their own eyes, they're gonna start to second guess those things as well, because it is gonna be a come a default to assume that everything you see is fake or AI generated, right? Whether it's on a screen or whether you hear uh you know something on the on the radio, uh, right? Apparently I'm 90 talking about the radio. I still love AM radio, by the way. Um, but customers are already shifting toward brands that can prove their outputs are authentic, and that's what you need to understand, and that's why responsible AI is so important. And well, aside from just consumer trust, well, you got to pay attention to the regulatory side as well. Because uh current there's been a lot of lawsuits, but the Mobly versus Workday certified that as a collective action for AI hiring discrimination. So uh a federal court just said the algorithm did it is not a defense anymore. So your company, that's why you need to start paying attention to what you produce, right? So in this case, right, there's been a lot of different lawsuits, and I think the first ones that uh, you know, kind of rose to prominence are ones that where algorithms made decisions that discriminated against people for whatever reason, uh, right? Whether it's hiring, getting a loan, uh, you know, healthcare coverage, et cetera. And that's not new, that's been around for technically decades, but it's becoming more and more prevalent as more and more companies that are not necessarily machine learning companies, right? But they're just, you know, wrapping their data lake house around uh, you know, a large language model company, and then they're putting that into production. That doing that without having the proper safeguards, that is bypassing responsible AI. Because now courts are saying that, well, no, you're on the hook for it. It's not the AI's fault. Uh, you know, so courts are rejecting any distinction between software decision makers and humans make. So, right, there's no difference between if I go and make a decision versus if the AI we're using makes a decision. And there's now state laws, as an example, in California, Illinois, New York, and Colorado that regulate how you use AI in hiring. So we'll see because we obviously know uh here in the US at least, there's uh some federal momentum to try to uh not allow individual states to mandate AI, but there's already state laws. They are in place, they are real, and we're gonna talk here in a minute about how it's actually getting bigger than that. And yeah, it can be costly. It's not just about maybe getting it wrong and maybe not winning back uh, you know, trust or winning trust uh from a potential customer. It is real money, right? Anthropic, obviously, one of the uh now one of the largest companies in the world. Uh Anthropic paid a $1.5 billion uh suit to settle a copyright case with authors, and courts treat AI outputs as relevant to whether they compete with copyrighted works. So right now, enterprise users are facing real IP exposure if their vendors trade on unlicensed data. So yeah, you need to understand, well, who are you using? OpenAI, Google Gemini, Microsoft Copilot, and are you on the hook? Right? Are you on the hook if something that you create, right? Uh let's say a uh, I don't know, a new, a new piece of software, right? And uh it's it's something related to health, and you're using uh one of these enterprise tools, but you're using AI to help you build this, right? If you get in trouble, do you have protection? Do you know? These are questions that you have to have. It starts, right? Starts in the boardroom, but then it it has to trickle down to every single major decision maker in your organization. You have to be aware of those things. And I think we may start to see some of this hit the fan in August. Now, the rollout of the EU AI Act could get pushed back again. It's already got pushed back a couple of times, but bookmark it August 2026, that is when the EU AI Act enforcement begins for high-risk AI uh in hiring credits and biometrics. So essentially, if your company and you're like, okay, why does this matter? Well, if your company does business in the EU, if they're offering business in the EU and you're using AI in that process, which is probably a good majority of our listeners, this applies to you. And non-compliance with these high-risk AI rules in the EUI and the EU AI Act carries fines of up to 35 million euros. Or here's here's a wild one, right? If you're a large company, you're like, okay, 35 million euros, right? That's our catering costs, or 7% of global revenue. Oh, now it's got your attention, right? So if you have completely bypassed responsible AI and governance, right? But that's later down the line, right? If you've bypassed responsible AI, the clock is quite literally ticking. Right? We'll see if anything like this ever catches on in the US. I think maybe at the state level it will, but then, like I've talked about on the show, if a federal judge throws it out, well, that will be the deciding factor, right? It's probably gonna get uh litigated at some point. But if you do business in Europe, which is many, many companies, and you're using AI, you could be on the hook. If you go, if you are found non-compliant with any of those high-risk AI rules. It's not just about enforcement, it's not just about doing uh, well, we better be responsible uh with AI so we don't get in trouble. No, being responsible, yes, it creates trust, but responsible AI leads to higher ROI on AI. So, according to McKinsey, companies that are investing heavily in responsible AI report over a 5% profit impact versus those companies that aren't. And senior leadership involvement in AI governance drives significantly greater business value. So if your business leaders are heavily involved in the process and they understand the basics of responsible AI and they're making those decisions from top to bottom, it's not just about not getting in trouble, right? This is foundational for growing your organization because it unlocks real scale and real returns. So let's start to wrap with this. We started with the five pillars that kind of helped define what responsible AI is. Now, here is the playbook, the five moves to ensure your responsible AI strategy survives. And again, make sure you go uh check out the governance episode because a lot of these things are aligned. But here we go. Uh, rule number one, know what you have. You need to audit every AI system and classify each one by risk level. The cool thing is the EU AI Act actually put those risk levels out. Number two, assign real ownership. You need to be able to name the individual with the authority, budget, and accountability. That 10-second rule when someone goes wrong, who is the human responsible? You have to know with the utmost certainty. Number three, you need to audit for bias before the courts do. You need to test your AI tools against your actual data and to see, well, is there any leaks that could cause legal troubles, whether it's against the uh the EU high risk or uh any of the state laws here in the US? Number four, you need to build expert-driven oversight. If you listen to the show, you know I hate human in the loop. You need to have expert-driven oversight, you need to have proactively your domain experts driving your AI, uh responsible AI policy. So that is domain professionals reviewing outputs, not just well, bill an IT rubber stamping your new agentic workforce. And then number five, you need to trade treat transparency as a competitive advantage. You should be disclosing when and how AI is involved, uh, right? Not just to your consumers, but to your employees, to your stakeholders, both internal and external, right? When we saw that uh consumers are starting to make decisions on those companies that are transparent in AI, right? Like you kind of think back. I think that there's been this kind of boom. Maybe it's a generational thing, I don't know. Uh, right. But when you think of like organic and you know, food with clean ingredients, right? You can see over the last 10 years how these types of brands have exploded because they're being transparent about what you are consuming and they're saying what's real, what's fake. Think of the same thing, right? With what you are putting out there to the world, with what your company is putting out there to the world. Trust me, the trust, the trust crisis has barely started, and it is a much more significant issue than much than most people realize. And it's something that if you are like, uh, well, we'll we'll worry about this when we know it's a problem, when we hear rumblings of it, it's gonna be too late at that point, right? Because your competitors are already gonna be doing this. So I think the fifth one here uh is huge. You have to become overly transparent now as a competitive advantage. And again, it's not just uh how you communicate these things externally or to you know external stakeholders, to uh current clients, potential customers, etc. It's also how you communicate these things in your own organization, right? How do you deploy AI agents? Do all of your staff know? Do all of your leaders know? Do frontline workers know how these decisions are ultimately being made, right? When they go click that, you know, agent run? Probably not. Transparency starts at home and then it is going to expand uh right to your neighborhood or your potential clients. So as we wrap here, here's what I want to leave you with. The same thing with governance, right? The governance are the rules in which you carry this out, but responsible AI, right? When you think about ethics, what's right, what's wrong. And then you think about how we are actually uh, you know, the foundational rules that help set this. Remember that responsible AI turns your company's AI values into real decisions. It is the foundation on how you look at use cases, data, and oversight. And it also sets the pace for how you will ultimately govern AI. But responsible AI helps you technically go faster. In the same way people think governance slows your AI efforts down. Well, responsible AI doesn't slow your efforts down either because it builds trust. And trust uh builds the roads, it builds the highway for speed, right? Without those lanes uh of responsible AI, you're just gonna have your cars going all over the place, right? So trust is that infrastructure. The ethics provides the values, responsible AIs provide the decisions, and governance enforces both. And the companies that are earning trust now, while distrust is on the rise, will own the competitive advantage in 2027. All right, that's a wrap, y'all. Thanks for sticking with me as I can barely uh you know talk. It feels like my nose is plugged. Hopefully, at one point here, I'm not gonna be sick or have crazy allergies. So you can all actually hear me. But thank you for sticking around. And I hope this was helpful as we tackled the responsible AI playbook. Uh, and if this was helpful, make sure you go to starthearseries.com. That is gonna give you free access to our exclusive inner circle community. You're not gonna find it anywhere else. Uh, and if you haven't already, please do me a favor go subscribe to the podcast on Spotify or Apple. Leave us a rating if you could. I'd appreciate that. Thank you for tuning in. Hope to see you back tomorrow and every day for more. Every day, a lot. Thanks, y'all.