How AI Affects Audience Trust

Harvard Business Review just found that executives using GenAI for stock forecasts made less accurate predictions. The study found that:  • Executives consulting ChatGPT raised their stock price estimates by ~$5.  • Those who discussed with peers lowered their estimates by ~$2.  • Both groups were too optimistic overall, but the AI group performed worse. Why? Because GenAI encourages overconfidence. Executives trusted its confident tone and detail-rich analysis, even though it lacked real-time context or intuition. In contrast, peer discussions injected caution and a healthy fear of being wrong. AI is a powerful resource. It can process massive amounts of data in seconds, spot patterns we’d otherwise miss, and automate manual workflows – freeing up finance teams to focus on strategic work. I don’t think the problem is AI. It’s how we use it. As finance leaders, it’s on us to ensure ourselves, and our teams, use it responsibly. When I was a finance leader, I always asked for the financial model alongside the board slides. It was important to dig in and review the work, understand key drivers and assumptions before sending the slides to the board. My advice is the same for finance leaders integrating AI into their day-to-day: lead with transparency and accountability. 𝟭/ 𝗔𝗜 𝘀𝗵𝗼𝘂𝗹𝗱 𝗯𝗲 𝗮 𝘀𝘂𝗽𝗲𝗿𝗽𝗼𝘄𝗲𝗿, 𝗻𝗼𝘁 𝗮𝗻 𝗼𝗿𝗮𝗰𝗹𝗲. AI should help you organize your thoughts and analyze data, not replace your reasoning. Ask it why it predicts what it does – and how it might be wrong. 𝟮/ 𝗖𝗼𝗺𝗯𝗶𝗻𝗲 𝗔𝗜 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝘄𝗶𝘁𝗵 𝗵𝘂𝗺𝗮𝗻 𝗱𝗶𝘀𝗰𝘂𝘀𝘀𝗶𝗼𝗻. AI is fast and thorough. Peers bring critical thinking, lived experience, and institutional knowledge. Use both to avoid blindspots. 𝟯/ 𝗧𝗿𝘂𝘀𝘁, 𝗯𝘂𝘁 𝘃𝗲𝗿𝗶𝗳𝘆. Treat AI like a member of your team. Have it create a first draft, but always check its work, add your own conclusions, and never delegate final judgment. 𝟰/ 𝗥𝗲𝘃𝗲𝗿𝘀𝗲 𝗿𝗼𝗹𝗲𝘀 - 𝘂𝘀𝗲 𝗶𝘁 𝘁𝗼 𝗰𝗵𝗲𝗰𝗸 𝘆𝗼𝘂𝗿 𝘄𝗼𝗿𝗸. Use AI for what it does best: challenging assumptions, spotting patterns, and stress-testing your own conclusions – not dictating them. We provide extensive AI within Campfire – for automations and reporting, and in our conversational interface, Ember. But we believe that AI should amplify human judgment, not override it. That’s why in everything we build, you can see the underlying data and logic behind AI outputs. Trust comes from transparency, and from knowing final judgment always rests with you. How are you integrating AI into your finance workflows? Where has it helped vs where has it fallen short? Would love to hear in the comments 👇

Here’s the easiest way to make your products 10x more robust: Start treating your AI evals like user stories. Why? Because your evaluation strategy is your product strategy. Every evaluation metric maps to a user experience decision. Every failure mode triggers a designed response. Every edge case activates a specific product behavior. Great AI products aren’t just accurate; they’re resilient and graceful in failure. I recently interviewed a candidate who shared this powerful approach. He said, "𝘐 𝘴𝘱𝘦𝘯𝘥 𝘮𝘰𝘳𝘦 𝘵𝘪𝘮𝘦 𝘥𝘦𝘴𝘪𝘨𝘯𝘪𝘯𝘨 𝘧𝘰𝘳 𝘸𝘩𝘦𝘯 𝘈𝘐 𝘧𝘢𝘪𝘭𝘴 𝘵𝘩𝘢𝘯 𝘸𝘩𝘦𝘯 𝘪𝘵 𝘴𝘶𝘤𝘤𝘦𝘦𝘥𝘴." Why? Because 95% accuracy means your AI confidently gives wrong answers 1 in 20 times. So he builds: • Fallback flows • Confidence indicators • Easy ways for users to correct mistakes. In other words, he doesn’t try to hide AI’s limitations; he designs around them, transparently. He uses AI evaluations as his actual Product Requirements Document. Instead of vague goals like “the system should be accurate,” he creates evaluation frameworks that become product specs. For example: Evaluation as Requirements - • When confidence score < 0.7, show “I’m not sure” indicator • When user corrects AI 3x in a session, offer human handoff • For financial advice, require 2-source verification before display Failure Modes as Features - • Low confidence → Collaborative mode (AI suggests, human decides) • High confidence + wrong → Learning opportunity (capture correction) • Edge case detected → Graceful degradation (simpler but reliable response) • Bias flag triggered → Alternative perspectives offered Success Metrics Redefined - It’s not just accuracy anymore: • User trust retention after AI mistakes • Time-to-correction when AI is wrong • Percentage of users who keep using the product after errors • Rate of escalation to human support Plan for failure, and your users will forgive the occasional mistake. Treat your AI evaluations like user stories, and watch your product’s robustness soar. ♻️ Share this to help product teams build better AI products. Follow me for more practical insights on AI product leadership.

Need to build trust as an AI-powered company? There is a lot of hype - and FUD. But just as managing your own supply chain to ensure it is secure and compliant is vital, companies using LLMs as a core part of their business proposition will need to reassure their own customers about their governance program. Taking a proactive approach is important not just from a security perspective, but projecting an image of confidence can help you to close deals more effectively. Some key steps you can take involve: 1/ Documenting an internal AI security policy. 2/ Launching a coordinated vulnerability disclosure or even bug bounty program to incentivize security researchers to inspect your LLMs for flaws. 3/ Building and populating a Trust Vault to allow for customer self-service of security-related inquiries. 4/ Proactively sharing methods through which you implement the best practices like NIST’s AI Risk Management Framework specifically for your company and its products. Customers are going to be asking a lot of hard questions about AI security considerations, so preparation is key. Having an effective trust and security program - tailored to incorporate AI considerations - can strengthen both these relationships and your underlying security posture.

Do you tell people when you use AI? Or do you quietly cover it up? In some teams, using AI makes you look cutting-edge. In others, it makes you look like you’ve cut corners. But here’s the problem: when people hide how they’re using AI—especially leaders—it becomes harder to evaluate their decisions. That silence can inflate risk, suppress innovation, and erode trust. 🔍 In a recent experiment, nearly 300 executives were asked to forecast Nvidia’s stock price. Half were allowed to consult ChatGPT. Half were allowed to consult their peers. The AI group became more confident—and more wrong. Why? Because the authoritative tone and polished language of the AI made them feel certain, without the benefit of human challenge, doubt, or discussion. 🤖 AI isn’t just a tool—it’s a voice that shapes our confidence, sometimes dangerously so. At work, I notice a spectrum: Some people proudly announce they use AI for everything from emails to strategic plans. Others whisper it or say nothing at all. This reminds me of covering—a term from sociologist Erving Goffman and legal scholar Kenji Yoshino. It’s when people downplay part of their identity to fit in with the dominant group. In this case, it’s not identity—but practice. Covering AI use isn’t trivial. It: Makes collaboration harder Hides important context behind decisions Discourages open experimentation So here’s my invitation to leaders: ✅ Normalize talking about when and how you use AI—and when you don’t. ✅ Model transparency instead of perfection. ✅ Invite conversations that balance human judgment with machine input. Because the best decisions won’t come from pretending we’re not using AI—or blindly trusting it. They’ll come from working with it—out in the open. #AI #Leadership #FutureOfWork #DecisionMaking #OrganizationalCulture #Transparency Thank you to Karl Schmedders, Patrick Reinmoeller, and José Parra Moyano for their excellent research. More at https://lnkd.in/gw9KX9K2 (I used AI to shorten this post and create this cartoon)

We keep talking about model accuracy. But the real currency in AI systems is trust. Not just “do I trust the model output?” But: • Do I trust the data pipeline that fed it? • Do I trust the agent’s behavior across edge cases? • Do I trust the humans who labeled the training data? • Do I trust the update cycle not to break downstream dependencies? • Do I trust the org to intervene when things go wrong? In the enterprise, trust isn’t a feeling. It’s a systems property. It lives in audit logs, versioning protocols, human-in-the-loop workflows, escalation playbooks, and update governance. But here’s the challenge: Most AI systems today don’t earn trust. They borrow it. They inherit it from the badge of a brand, the gloss of a UI, the silence of users who don’t know how to question a prediction. Until trust fails. • When the AI outputs toxic content. • When an autonomous agent nukes an inbox or ignores a critical SLA. • When a board discovers that explainability was just a PowerPoint slide. Then you realize: Trust wasn’t designed into the system. It was implied. Assumed. Deferred. Good AI engineering isn’t just about “shipping the model.” It’s about engineering trust boundaries that don’t collapse under pressure. And that means: → Failover, not just fine-tuning. → Safeguards, not just sandboxing. → Explainability that holds up in court, not just demos. → Escalation paths designed like critical infrastructure, not Jira tickets. We don’t need to fear AI. We need to design for trust like we’re designing for failure. Because we are. Where are you seeing trust gaps in your AI stack today? Let’s move the conversation beyond prompts and toward architecture.

One of the things that's important about implementing AI is to ensure people know when they are interacting with AI, whether that be in a live interaction or via AI-produced content. Brands that fail to be transparent can risk doing damage to customer relationships and reputation. By offering AI transparency and options, people can decide if they wish to engage with the AI or prefer an alternative. But if you offer AI interactions or content without transparency, it can leave people feeling deceived and manipulated. Arena Group, which owns Sports Illustrated, fired its CEO. The announcement only mentions "operational efficiency and revenue," but it comes weeks after an AI scandal hit the sports magazine. A tech publication discovered articles on SI that appeared to be from real humans were, in fact, created by AI. Even the headshots and biographies of the "authors" were AI-created. At the time, Arena Group blamed a third-party ad and content provider and severed its relationship with the firm. #GenAI can provide some remarkable benefits, but leaders must recognize the variety of risks that AI can bring. Being transparent about when customers are interacting with AI is one of the ways to mitigate the risks. Make it clear and conspicuous when you provide a #CustomerExperience facilitated by AI so that customers have the information and control they desire. https://lnkd.in/gnC2fE57

In the realm of artificial intelligence, discerning truth from falsehood is more than a philosophical question—it’s a practical challenge that impacts business decisions and consumer trust daily. We are designing our new systems inspired by the classic dilemma of the Village of Truth and Lies, that can reliably manage the accuracy of their outputs. Here are some practical approaches that we are finding useful. 1. Multiple Agents: Use different AI models to answer the same question to cross-verify responses. 2. Consistency Checks: Follow-up with related questions to check the consistency of AI responses. 3. Confidence Estimation: Measure how confident an AI is in its answers, using this as a heuristic for reliability. 4. External Validation: Integrate verified databases to confirm AI responses wherever possible. 5. Feedback Loops: Incorporate user feedback to refine AI accuracy over time. 6. Adversarial Testing: Regularly challenge the system with tough scenarios to strengthen its discernment. 7. Ethical Responses: Design AIs to admit uncertainty and avoid making up answers. 8. Audit Trails: Keep logs for accountability and continuous improvement. I am also looking at game theoretic approach to estimating AI confidence. If you are interested in learning more, please feel free to connect for a discussion. Managing accuracy and trust is critical factor. By crafting smarter, self-aware AI systems, we pave the way for more reliable, transparent interactions—essential in today’s data-driven landscape. Please share your thoughts in the comments. #ArtificialIntelligence #MachineLearning #DataIntegrity #BusinessEthics #Innovation

As artificial intelligence becomes more prevalent, there is a growing need to assess how much we can trust AI systems. The article argues that every AI model should have something akin to a FICO credit score that rates its trustworthiness. Some key reasons why AI trust scores are important: - AI systems make mistakes and have biases, so understanding the limitations of a system is critical before deploying it. A trust score would help identify risky AI models. - Different users have different trust requirements - a model safe for low-risk applications may not be appropriate for high-risk ones. Trust scores would enable better matching of AI to use cases. - Trust decays over time as data changes. Regular evaluation and updated trust ratings will help identify when a model is no longer fit for purpose. - Trust scores allow easier comparison of AI systems to select the most appropriate one. - Transparency over how scores are calculated allows users to make informed choices about AI adoption. In summary, AI trust scores empower users to make smarter decisions on how and where to use AI models safely and effectively. Just as FICO scores help assess credit risk, AI trust scores are needed to assess risks of unfairness, inaccuracy and harm. #AI #TRUST #DATA #FICO

I think l am loosing my capacity for trust… Generative artificial intelligence is now capable of creating fake pictures, clones of our voices, and even videos depicting and distorting world events. The result: From our personal circles to the political circuses, everyone must now question whether what they see and hear is true. We’ve long been warned about the potential of social media to distort our view of the world, and now there is the potential for more false and misleading information to spread on social media than ever before. Just as importantly, exposure to AI-generated fakes can make us question the authenticity of everything we see. Real images and real recordings can be dismissed as fake. “When you show people deepfakes and generative AI, a lot of times they come out of the experiment saying, ‘I just don’t trust anything anymore,’” says David Rand, a professor at MIT Sloan who studies the creation, spread and impact of misinformation. This problem, which has grown more acute in the age of generative AI, is known as the “liar’s dividend,” says Renee DiResta, a researcher at the Stanford Internet Observatory. The combination of easily-generated fake content and the suspicion that anything might be fake allows people to choose what they want to believe, adds DiResta, leading to what she calls “bespoke realities.” Examples of misleading content created by generative AI are not hard to come by, especially on social media. One widely circulated and fake image of Israelis lining the streets in support of their country has many of the hallmarks of being AI-generated—including telltale oddities that are apparent if you look closely, such as distorted bodies and limbs. For the same reasons, a widely shared image that purports to show fans at a soccer match in Spain displaying a Palestinian flag doesn’t stand up to scrutiny. The signs that an image is AI-generated are easy to miss for a user simply scrolling past, who has an instant to decide whether to like or boost a post on social media. And as generative AI continues to improve, it’s likely that such signs will be harder to spot in the future. Is Anything Still True? On the Internet, No One Knows Anymore https://lnkd.in/dACHjeUM

Harsh truth: AI has opened up a Pandora's box of threats. The most concerning one? The ease with which AI can be used to create and spread misinformation. Deepfakes (AI-generated content that portrays something false as reality) are becoming increasingly sophisticated & challenging to detect. Take the attached video - a fake video of Morgan Freeman, which looks all too real. AI poses a huge risk to brands & individuals, as malicious actors could use deepfakes to: • Create false narratives about a company or its products • Impersonate executives or employees to damage credibility • Manipulate public perception through fake social media posts The implications for PR professionals are enormous. How can we maintain trust and credibility in a world where seeing is no longer believing? The answer lies in proactive preparation and swift response. Here are some key strategies for navigating the AI misinformation minefield: 🔹 1. Educate your team: Ensure everyone understands the threat of deepfakes and how to spot potential fakes. Regular training is essential. 🔹 2. Monitor vigilantly: Keep a close eye on your brand's online presence. Use AI-powered tools to detect anomalies and potential threats. 🔹 3. Have a crisis plan: Develop a clear protocol for responding to AI-generated misinformation. Speed is critical to contain the spread. 🔹 4. Emphasize transparency: Build trust with your audience by being open and honest. Admit mistakes and correct misinformation promptly. 🔹 5. Invest in verification: Partner with experts who can help authenticate content and separate fact from fiction. By staying informed, prepared, and proactive, PR professionals can navigate this new landscape and protect their brands' reputations. The key is to embrace AI as a tool while remaining vigilant against its potential misuse. With the right strategies in place, we can harness the power of AI to build stronger, more resilient brands in the face of the misinformation minefield.