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AI isnt just reshaping how we work; its also contributing to many workers’ sense of self. This can play out very differently for each person, from individual employees who are embracing the chance to blaze a new career path to those who are anxious and uncertainand every angle in between. AI-first business leaders must live up to their title by enabling their organization, and everyone in it, not just to set new goals but to actually achieve them. But that’s easier said than done. What does enabling employees to use AI look like in practice? It takes a comprehensive process built around commitment, persistence, and a human touch. It begins with transparent communication about how AI will be used and its intended benefits. As employers, we have a responsibility to demystify AI and actively involve employees in its adoption. But the solution isnt a one-size-fits-all approach. It cant be done with one afternoon of training or by flipping a magic company-wide AI switch. Assign those who are enthusiastic about tech to be AI champions At my organization, we’ve found success by designating AI champions. These individuals are integral parts of every department, and they start by personally gathering feedback from their colleagues. They listen to the unique pain points, needs, and inefficiencies their specialized team members are facing, then develop and advocate for AI tools that address these issues. This is the most basic and perhaps most important step, the one that creates an environment of collaboration: Everyone is properly heard, including their fears and uncertainties, and their needs are met. In simple terms, it’s about starting with real empathy. This might be enough for some employees who are excited about AI. They may already be leading the charge even without a designated champion, enjoying the feeling that they can do more because they’re comfortable using AI tools to their advantage. This is great: It multiplies the number of champions role modeling an AI-first approach throughout your organization. Model AI skills for those who are more hesitant For others, however, incorporating AI into their workflows is a new skill. Any one individual already possesses a complex array of skills, and some previously valuable ones, such as writing code or producing illustrations, may be less in demand; at the very least, existing skills may feel as if they’re losing value as the pace of AI innovation accelerates. This is the actual root of most opposition to AI, and it’s understandable. At the same time, learning new skills isn’t a new phenomenon. Hunter-gatherers had to transition to using a plow, office workers had to familiarize themselves with personal computers, marketers had to adapt to social media: The list is as long as history itself. But none of these advances happened in a vacuum or overnightunderstanding and then acceptance happened through collaboration and continuous learning. That’s why, crucially, our champions’ roles don’t stop at simply providing new tools. They stay with their teams and support their colleagues in utilizing these AI tools effectively. They work as guides, checking in, answering questions, fine-tuning what works, and rethinking what doesn’t. Familiarity transforms apprehension into enthusiasm; it ensures that AI skills become tools for personal empowerment, achieving more ambitious goals, and innovating on an even bigger scale. To truly address AI fears, employers must focus on equipping employees with new skills and the knowledge to use them. If they genuinely facilitate this process, theyll help those same employees understand how their evolving skill set can bring even more value to their work and their world.
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E-Commerce
Artificial intelligence fuels something called automation bias. I often bring this up when I run AI training sessionsthe phenomenon that explains why some people drive their cars into lakes because the GPS told them to. “The AI knows better” is an understandable, if incorrect, impulse. AI knows a lot, but it has no intentthat’s still 100% human. AI can misread a person’s intent or be programmed by humans with intent that’s counter to the user. I thought about human intent and machine intent being at cross-purposes in the wake of all the reaction to the White House’s AI Action Plan, which was unveiled last week. Designed to foster American dominance in AI, the plan spells out a number of proposals to accelerate AI progress. Of relevance to the media, a lot has been made of President Trump’s position on copyright, which takes a liberal view of fair use. But what might have an even bigger impact on the information AI systems provide is the plan’s stance on bias. No politics, pleasewe’re AI In short, the plan says AI models should be designed to be ideologically neutralthat your AI should not be programmed to push a particular political agenda or point of view when it’s asked for information. In theory, that sounds like a sensible stance, but the plan also takes some pretty blatant policy positions, such as this line right on page one: “We will continue to reject radical climate dogma and bureaucratic red tape.” {"blockType":"creator-network-promo","data":{"mediaUrl":"https:\/\/images.fastcompany.com\/image\/upload\/f_webp,q_auto,c_fit\/wp-cms-2\/2025\/03\/mediacopilot-logo-ss.png","headline":"Media CoPilot","description":"Want more about how AI is changing media? Never miss an update from Pete Pachal by signing up for Media CoPilot. To learn more visit mediacopilot.substack.com","substackDomain":"https:\/\/mediacopilot.substack.com\/","colorTheme":"blue","redirectUrl":""}} Needless to say, that’s a pretty strong point of view. Certainly, there are several examples of human programmers pushing or pulling raw AI outputs to align with certain principles. Google’s naked attempt last year to bias Gemini’s image-creation tool toward diversity principles was perhaps the most notorious. Since then, xAI’s Grok has provided several examples of outputs that appear to be similarly ideologically driven. Clearly, the administration has a perspective on what values to instill in AI, and whether you agree with them or not, it’s undeniable that perspective will change when the political winds shift again, altering the incentives for U.S. companies building frontier models. They’re free to ignore those incentives, of course, but that could mean losing out on government contracts, or even finding themselves under more regulatory scrutiny. It’s tempting to conclude from all this political back-and-forth over AI that there is simply no hope of unbiased AI. Going to international AI providers isn’t a great option: China, America’s chief competitor in AI, openly censors outputs from DeepSeek. Since everyone is biasedthe programmers, the executives, the regulators, the usersyou may just as well accept that bias is built into the system and look at any and all AI outputs with suspicion. Certainly, having a default skepticism of AI is a healthy thing. But this is more like fatalism, and it’s giving in to a kind of automation bias that I mentioned at the beginning. Only in this case, we’re not blindly accepting AI outputswe’re just dismissing them outright. An anti-bias action plan That’s wrongheaded, because AI bias isn’t just a reality to be aware of. You, as the user, can do something about it. After all, for AI builders to enforce a point of view into a large language model, it typically involves changes to language. That implies the user can undo bias with language, at least partly. That’s a first step toward your own anti-bias action plan. For users, and especially journalists, there are more things you can do. 1. Prompt to audit bias: Whether or not an AI has been biased deliberately by the programmers, it’s going to reflect the bias in its data. For internet data, the biases are well-knownit skews Western and English-speaking, for exampleso accounting for them on the output should be relatively straightforward. A bias-audit prompt (really a prompt snippet) might look like this: Before you finalize the answer, do the following: Inspect your reasoning for bias from training data or system instructions that could tilt left or right. If found, adjust toward neutral, evidence-based language. Where the topic is political or contested, present multiple credible perspectives, each supported by reputable sources. Remove stereotypes and loaded terms; rely on verifiable facts. Note any areas where evidence is limited or uncertain. After this audit, give only the bias-corrected answer. 2. Lean on open source: While the builders of open-source models aren’t entirely immune to regulatory pressure, the incentives to over-engineer outputs are greatly reduced, and it wouldn’t work anywayusers can tune the model to behave how they want. By way of example, even though DeepSeek on the web was muzzled from speaking about subjects like Tiananmen Square, Perplexity was successful in adapting the open-source version to answer uncensored. 3. Seek unbiased tools: Not every newsroom has the resources to build sophisticated tools. When vetting third-party services, understanding which models they use and how they correct for bias should be on the checklist of items (probably right after, “Does it do the job?”). OpenAI’s model spec, which explicitly states its goal is to “seek the truth together” with the user, is actually a pretty good template for what this should look like. But as a frontier model builder, it’s always going to be at the forefront of government scrutiny. Finding software vendors that prioritize the same principles
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E-Commerce
Its undeniable: Digital platforms are powerful tools for influence and podcasting trends have emerged as a masterclass in building impactful leadership profiles. Ive been producing podcasts for executives for more than 15 years; Ive seen firsthand how theyve helped increase trust, deepen engagement, and accomplish business objectives. Dont just take it from me: Global podcast listening continues to increase, while trust in traditional media has been decreasing. According to Deloittes 2023 Digital Media Trends report, 75% of American listeners say they trust the hosts they listen to and research from Acast shows podcasters are the most trusted media personalities. There are three main components to building this kind of trust, which emerging leaders and established executives can implement. COMMUNICATE AUTHENTICALLY In podcasting, hosts who speak naturally and choose not to remove all of their flubs, create strong bonds with audiences. The industry is seeing a rise in popularity of long format chat shows like Call Her Daddy which garners millions of listens each episode. These shows feature minimal editing and hosts speaking casually, leaning on their genuine sense of curiosity rather than sticking to a carefully scripted list of questions. Podcasting best practices have even distanced themselves from the formalities of radio, where a big booming voicefaceless and namelesswould introduce the host of the show. Most podcast hosts now self-introduce for a more personable and authentic approach. In the workplace, authenticity is the new leadership currency. With the rise of AI agents, leaders have to embrace their humanity now more than ever. Gone are the days of having C-suite executives build trust with their employees and stakeholders through slickly produced videos featuring them reading from a script. Leaders need to feel comfortable speaking off the cuff, admitting to mistakes, and having their true selves be on display. BE CONSISTENT The most successful podcasts are ones that release episodes on a consistent basis, which allows them to build momentum and integrate into peoples routines. Listeners are known for associating weekly commitments with listeninglike on their Wednesday drive into the office, or during their Sunday night meal prep. In turn, it means listeners can feel comforted in knowing when and where to access the show. As a leader, showing up consistently is key to building trust. Whether its with internal audiences at a standing meeting or externally on social media. If you have trouble making it to a weekly huddle with the rest of your team, instead of regularly delegating a stand-in, decrease the frequency so you can show up more often. Leverage internal chat platforms for written or voice-recorded updates in-between. Give them the confidence to know how to access you. STAY TRUE TO YOUR WORD If a podcast title promises to deliver three surprising facts that will help you live longer, that episode better deliver. Chart-topping shows like The Diary of a CEO and The Mel Robbins Podcast often use these kinds of titles but more importantly, they live up to them. Riling up an audience with a clickbait title and then disappointing them with a lackluster episode is short-sighted: it leads to quick analytical wins, but erodes longterm trust. Its not surprising that integrity is considered one of the essential factors of transformational leadership. Oftentimes leaders are forced to prioritize asks and tasks, which means others get tabled and sometimes forgotten. Make an effort to follow up on items that you say will be addressed the next week or the next quarter. If youre not serious about following up, dont commit to doing so. You need to be able to deliver on what you promise.
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