Building Trust Methods

🇬🇧 Worth checking out the updated #RESIST framework designed by the UK government in order to embrace information threats more fully. 🔹A pragmatic approach focused on perceptions and a full-blown model for any institution developing its own strategic communications methodology 👉🏼RESIST Counter‑Disinformation Toolkit : a structured framework for government communicators to identify, assess and respond to disinformation. 👉🏼The toolkit frames disinformation as a risk not only to communications per se, but to policy outcomes, national security, international reputation, and democratic legitimacy. 🔹It provides checklists, matrices (ex : for prioritisation: does a message harm ability to deliver services? does it affect vulnerable audiences? etc.) and guidance on measurement. ♻️ A 6️⃣ step approach : 1️⃣ Recognise: identify possible instances of mis/dis/malinformation, check the techniques (fabrication, disguised identity, rhetoric, symbolism etc) (FIRST indicators). 2️⃣ Early Warning: Monitor the information space for signals of emerging threats, vulnerabilities, target audiences, relevant narratives. 3️⃣ Situational Insight: Turn monitoring data into actionable insights : what’s happening, who is vulnerable, what narratives are evolving, what the context is. 4️⃣ Impact Analysis: Assess the potential damage: what are the objectives of the threat actor, the reach, the likelihood, how does it affect your priorities/responsibilities. Use structured analysis rather than just “gut feeling”. 5️⃣ Strategic Communication: Decide whether and how to respond. Not all incidents merit a public response — some may self-correct. If you respond: ensure the truth is well told, choose appropriate channels/audiences, embed resilience building, engage partnerships. 6️⃣ Tracking Effectiveness: Measure output vs outcome; track metrics (reach, behaviour change, attitude change) and learn from each response. Underlying principles 🔹A government communications function must support resilience: of institutions, public trust, policy delivery. 🔹Communications is a proactive posture : pre-bunking, shaping narratives is as important as reactive posture (debunking). 🔹Partnership matters because information threats do not respect organisational boundaries : across gov departments, with civil society, academia, media, international partners 🔹Focus on audiences & vulnerabilities: recognising that some audiences are more exposed (due to digital skills, language, socio-economic factors) and that those vulnerabilities shape how to tailor prevention/response. How this could apply to other nations 🔹 a structured framework to impart discipline & consistency in detecting and responding to threats. 🔹 helps build an institutional capacity 🔹 supports the shift from reactive (respond when scandal/hit) to proactive risk management

Over the last year, I’ve seen many people fall into the same trap: They launch an AI-powered agent (chatbot, assistant, support tool, etc.)… But only track surface-level KPIs — like response time or number of users. That’s not enough. To create AI systems that actually deliver value, we need 𝗵𝗼𝗹𝗶𝘀𝘁𝗶𝗰, 𝗵𝘂𝗺𝗮𝗻-𝗰𝗲𝗻𝘁𝗿𝗶𝗰 𝗺𝗲𝘁𝗿𝗶𝗰𝘀 that reflect: • User trust • Task success • Business impact • Experience quality    This infographic highlights 15 𝘦𝘴𝘴𝘦𝘯𝘵𝘪𝘢𝘭 dimensions to consider: ↳ 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗲 𝗔𝗰𝗰𝘂𝗿𝗮𝗰𝘆 — Are your AI answers actually useful and correct? ↳ 𝗧𝗮𝘀𝗸 𝗖𝗼𝗺𝗽𝗹𝗲𝘁𝗶𝗼𝗻 𝗥𝗮𝘁𝗲 — Can the agent complete full workflows, not just answer trivia? ↳ 𝗟𝗮𝘁𝗲𝗻𝗰𝘆 — Response speed still matters, especially in production. ↳ 𝗨𝘀𝗲𝗿 𝗘𝗻𝗴𝗮𝗴𝗲𝗺𝗲𝗻𝘁 — How often are users returning or interacting meaningfully? ↳ 𝗦𝘂𝗰𝗰𝗲𝘀𝘀 𝗥𝗮𝘁𝗲 — Did the user achieve their goal? This is your north star. ↳ 𝗘𝗿𝗿𝗼𝗿 𝗥𝗮𝘁𝗲 — Irrelevant or wrong responses? That’s friction. ↳ 𝗦𝗲𝘀𝘀𝗶𝗼𝗻 𝗗𝘂𝗿𝗮𝘁𝗶𝗼𝗻 — Longer isn’t always better — it depends on the goal. ↳ 𝗨𝘀𝗲𝗿 𝗥𝗲𝘁𝗲𝗻𝘁𝗶𝗼𝗻 — Are users coming back 𝘢𝘧𝘵𝘦𝘳 the first experience? ↳ 𝗖𝗼𝘀𝘁 𝗽𝗲𝗿 𝗜𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝗼𝗻 — Especially critical at scale. Budget-wise agents win. ↳ 𝗖𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻 𝗗𝗲𝗽𝘁𝗵 — Can the agent handle follow-ups and multi-turn dialogue? ↳ 𝗨𝘀𝗲𝗿 𝗦𝗮𝘁𝗶𝘀𝗳𝗮𝗰𝘁𝗶𝗼𝗻 𝗦𝗰𝗼𝗿𝗲 — Feedback from actual users is gold. ↳ 𝗖𝗼𝗻𝘁𝗲𝘅𝘁𝘂𝗮𝗹 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 — Can your AI 𝘳𝘦𝘮𝘦𝘮𝘣𝘦𝘳 𝘢𝘯𝘥 𝘳𝘦𝘧𝘦𝘳 to earlier inputs? ↳ 𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆 — Can it handle volume 𝘸𝘪𝘵𝘩𝘰𝘶𝘵 degrading performance? ↳ 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆 — This is key for RAG-based agents. ↳ 𝗔𝗱𝗮𝗽𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗦𝗰𝗼𝗿𝗲 — Is your AI learning and improving over time? If you're building or managing AI agents — bookmark this. Whether it's a support bot, GenAI assistant, or a multi-agent system — these are the metrics that will shape real-world success. 𝗗𝗶𝗱 𝗜 𝗺𝗶𝘀𝘀 𝗮𝗻𝘆 𝗰𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝗼𝗻𝗲𝘀 𝘆𝗼𝘂 𝘂𝘀𝗲 𝗶𝗻 𝘆𝗼𝘂𝗿 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀? Let’s make this list even stronger — drop your thoughts 👇

🤝 How Do We Build Trust Between Humans and Agents? Everyone is talking about AI agents. Autonomous systems that can decide, act, and deliver value at scale. Analysts estimate they could unlock $450B in economic impact by 2028. And yet… Most organizations are still struggling to scale them. Why? Because the challenge isn’t technical. It’s trust. 📉 Trust in AI has plummeted from 43% to just 27%. The paradox: AI’s potential is skyrocketing, while our confidence in it is collapsing. 🔑 So how do we fix it? My research and practice point to clear strategies: Transparency → Agents can’t be black boxes. Users must understand why a decision was made. Human Oversight → Think co-pilot, not unsupervised driver. Strategic oversight keeps AI aligned with values and goals. Gradual Adoption → Earn trust step by step: first verify everything, then verify selectively, and only at maturity allow full autonomy—with checkpoints and audits. Control → Configurable guardrails, real-time intervention, and human handoffs ensure accountability. Monitoring → Dashboards, anomaly detection, and continuous audits keep systems predictable. Culture & Skills → Upskilled teams who see agents as partners, not threats, drive adoption. Done right, this creates what I call Human-Agent Chemistry — the engine of innovation and growth. According to research, the results are measurable: 📈 65% more engagement in high-value tasks 🎨 53% increase in creativity 💡 49% boost in employee satisfaction 👉 The future of agents isn’t about full autonomy. It’s about calibrated trust — a new model where humans provide judgment, empathy, and context, and agents bring speed, precision, and scale. The question is: will leaders treat trust as an afterthought, or as the foundation for the next wave of growth? What do you think — are we moving too fast on autonomy, or too slow on trust? #AI #AIagents #HumanAICollaboration #FutureOfWork #AIethics #ResponsibleAI

The Pentagon, 2017. The U.S. defense industrial base was stagnant, built to win the Cold War. Legacy prime contractors operated on cost-plus contracts, incentivizing 10-year timelines and billion-dollar overruns. Lockheed's F-35: 17 years, $1.7 trillion lifecycle cost. Boeing's KC-46: 8 years late, $5 billion over. The bottleneck wasn't technology. It was the trust architecture. The DoD trusted process over performance. Anduril's founders saw an arbitrage opportunity: the Pentagon's procurement model was designed for a low clockspeed world. Palmer Luckey: the wild card genius who'd built the future outside the system, solving VR at Oculus before selling to Facebook for $2 billion. Trae Stephens: the inside man. Founders Fund partner who'd served on Trump's Defense transition team, learned how to sell complex software to government buyers at Palantir. Brian Schimpf: the execution engine. Nearly a decade at Palantir, rising from Forward Deployed Engineer to Director. Together, they assembled the credibility to get into decision rooms. While competitors optimized government relations teams, Anduril removed the need for them. They didn't promise a future system; they showed up with a working one funded by their own capital. Legacy primes move in 5-10 years.  Anduril ships in 5-10 months. Everyone credits the tech.  The real disruption was the trust model, built for velocity. The Moat Isn't Tech. It's the Trust Model. Anduril's advantage isn't what they built. It's what they removed: cost-plus contracts that reward delays, requirements cycles that kill urgency, integration nightmares that fragment systems. Competitors can't copy this without gutting their operating assumptions. The switching costs aren't technical. They're existential. They didn't target a massive DoD program first. They found a customer with an urgent problem: U.S. Customs and Border Protection. They deployed their Sentry Tower on a private Texas ranch and proved it worked: 55 arrests, 982 lbs of contraband in 10 weeks. They didn't sell a proposal; they delivered an outcome. Proof had beaten pedigree. When Microsoft stumbled on the Army's $22 billion IVAS program, the Pentagon handed it to Anduril. The lesson extends beyond defense: every industry has trust models waiting to be arbitraged. All industries hide behind process when performance fails. Every NewCo has a choice: inherit that process, or replace it with outcomes. The companies that win won’t just move faster. They’ll re-architect trust itself - not as a promise, but as a product. Take the beach. Earn the fleet. Redraw the map. (Full case study sent to subscribers)

Here's 7 key metrics every AI PM should track — not just to measure engagement, but to ensure your AI is useful, safe, and trusted. Too often, we focus on DAU or churn… but, especially if you're building conversational products, you need new metrics — ones that capture meaning, depth, and trust. Here’s the framework I use 👇 I used a pyramid because each layer supports the next: without factual, safe foundations, you can’t earn trust or scale responsibly. -The foundation is Model Quality — your AI must be accurate, safe, and fast before anything else matters. -Above that is Interaction Quality — can users have meaningful, multi-turn conversations that feel natural and helpful? -Then comes Trust & Delight — do users enjoy the experience and come back because they trust it? -Higher still is User Value — are people actually achieving their goals faster, easier, and better? - And at the top sits Sustainability — are you doing all of this responsibly and efficiently (revenue / compute $, LTV / CAC)? Success in conversational AI = Useful × Safe × Trusted <><><><><><><><><><> Follow Marily Nika, Ph.D for AI PM education, certifications and insights.

Trust in American institutions has been declining for years. Today less than half of Americans trust health care leaders, and health care journalism is rated last in terms of trust from America’s public—all according to the 2024 Edelman Trust Barometer. While some researchers say the phenomenon of mistrust isn't new and has come in waves across a century of American history, the recent Edelman findings feel especially troubling now as we look ahead to the future of US health care.    No one has all the answers on where to go from here, but as I consider the road ahead, I’m grounded in part by the strategies shared by David Rousseau and Noam Levey on separate past episodes of the podcast I host with Don Berwick, Turn on the Lights. The strategies they each offered for building public trust in journalism can be applied by health care leaders in the work we do every day --   1. Be transparent about your methods. Show people the data, sources, best practices that inform your thinking.   2. Have the humility to know you don’t always know the answers.    3. Bring in local expert voices that your community/audience connects with and trusts, and make sure those voices are diverse.   4. Use plain language, never jargon. Connect with people in their terms and on their terms.    5. Make people/patients the focus, always. Put their experiences, needs, assumptions, point of view at the center of EVERY cause, case, and communication you make.    Trust is crucial for optimal functioning of the health care system. Whether you’re a health care journalist, leader, or provider you can put these strategies to work and contribute to our collective rebuilding of trust in health care.    For more, listen to past episodes of Turn on the Lights here: https://bit.ly/3YWXL5f and explore IHI’s theory of how to repair, build, and strengthen organizational trustworthiness in health care: https://bit.ly/40MNQkh. 

Just got off a call with a founder who's sent 1,000+ cold emails with ZERO responses... Let me ask you something... Have you ever crafted what you thought was the perfect outreach message, only to be met with complete silence? One of my clients (a SaaS founder) just shared their frustrating experience that might sound familiar... They spent weeks perfecting their message, researching prospects, and personalizing every email. The result? Radio silence. Zero responses. Zero meetings. Zero opportunities. And here's what really hurts... Their competitor, with an inferior product, was landing meetings left and right with the same prospects. After analyzing thousands of outreach campaigns, I’ve discovered that trust isn't built through volume - it's built through three specific elements that buyers actually care about. Here are the 3 trust drivers that actually get decision-makers to reply: 1) Social Proof That Matters Stop leading with generic logos. I've found buyers instantly engage when you share specific results from companies in their exact industry. They need to see themselves in your success stories. ✅ POWER MOVE:  Reference a similar company's specific metrics improvement (e.g., "We helped Company X increase their conversion rate by 47% in 60 days") 2) Thought Leadership Signals Your prospects are drowning in "experts." I've tested this extensively - buyers respond when you demonstrate deep industry knowledge through specific insights about their business challenges. ✅POWER MOVE: Share a unique observation about their market position or recent company changes that others missed. 3) Micro-Deliverables This is the game-changer most miss. I've seen response rates triple when founders offer immediate value before asking for anything in return. ✅POWER MOVE: Provide a quick competitive analysis or specific growth opportunity they can implement today, regardless of whether they reply. The data is clear: 89% of cold outreach fails because it focuses on what YOU want instead of what THEY need. These aren't just theories - I've watched these exact strategies transform response rates from 2% to 20%+ across hundreds of campaigns. Here's the real question: How many of these trust drivers are you actually incorporating in your outreach right now? #ColdOutreach #B2BSales #TrustBasedSelling #OutboundMarketing #SalesStrategy

Most teams pick metrics that sound smart… But under the hood, they’re just noisy, slow, misleading, or biased. But today, I'm giving you a framework to avoid that trap. It’s called STEDII and it’s how to choose metrics you can actually trust: — ONE: S — Sensitivity Your metric should be able to detect small but meaningful changes Most good features don’t move numbers by 50%. They move them by 2–5%. If your metric can’t pick up those subtle shifts , you’ll miss real wins. Rule of thumb: - Basic metrics detect 10% changes - Good ones detect 5% - Great ones? 2% The better your metric, the smaller the lift it can detect. But that also means needing more users and better experimental design. — TWO: T — Trustworthiness Ever launch a clearly better feature… but the metric goes down? Happens all the time. Users find what they need faster → Time on site drops Checkout becomes smoother → Session length declines A good metric should reflect actual product value, not just surface-level activity. If metrics move in the opposite direction of user experience, they’re not trustworthy. — THREE: E — Efficiency In experimentation, speed of learning = speed of shipping. Some metrics take months to show signal (LTV, retention curves). Others like Day 2 retention or funnel completion give you insight within days. If your team is waiting weeks to know whether something worked, you're already behind. Use CUPED or proxy metrics to speed up testing windows without sacrificing signal. — FOUR: D — Debuggability A number that moves is nice. A number you can explain why something worked? That’s gold. Break down conversion into funnel steps. Segment by user type, device, geography. A 5% drop means nothing if you don’t know whether it’s: → A mobile bug → A pricing issue → Or just one country behaving differently Debuggability turns your metrics into actual insight. — FIVE: I — Interpretability Your whole team should know what your metric means... And what to do when it changes. If your metric looks like this: Engagement Score = (0.3×PageViews + 0.2×Clicks - 0.1×Bounces + 0.25×ReturnRate)^0.5 You’re not driving action. You’re driving confusion. Keep it simple: Conversion drops → Check checkout flow Bounce rate spikes → Review messaging or speed Retention dips → Fix the week-one experience — SIX: I — Inclusivity Averages lie. Segments tell the truth. A metric that’s “up 5%” could still be hiding this: → Power users: +30% → New users (60% of base): -5% → Mobile users: -10% Look for Simpson’s Paradox. Make sure your “win” isn’t actually a loss for the majority. — To learn all the details, check out my deep dive with Ronny Kohavi, the legend himself: https://lnkd.in/eDWT5bDN

🔥 If your Data Catalog isn’t measured, it’s probably failing. Most data catalogs don’t fail because of technology. They fail because success is never clearly defined. So let’s be blunt. Here’s how you actually know whether your data catalog works. ❌ Vanity metric to forget: “Number of datasets cataloged” ✔️ Metrics that matter: 🔴 1. Do people come back? (Adoption) One login ≠ success. Are users still active after onboarding? Are they searching… or asking Slack instead? If usage drops, your catalog is just expensive documentation. 🔴 2. Is the metadata good enough to trust? Auto-ingested metadata ≠ usable metadata. Do datasets have owners? Are descriptions written for humans? No context = no trust = no usage. 🔴 3. Does it actually save time? If analysts still spend hours “data hunting”, the catalog failed. Can users find the right dataset in minutes? Are the same questions still asked every week? If nothing changes, value is zero. 🔴 4. Who is accountable for the data? “Shared responsibility” usually means “no responsibility”. Is every critical dataset owned? Do stewards respond? Governance starts with naming names. 🔴 5. Can users tell which data is safe to use? Without trust signals, catalogs create confusion — not clarity. Certified datasets Data quality visibility Clear warnings for risky data No signals = no confidence = shadow data. 🔴 6. Is the platform reducing manual effort — or creating more? If stewardship feels like extra work, it won’t scale. How much is automated? Is steward workload increasing or decreasing? If governance doesn’t scale, it dies. 🔴 7. Does the business feel the impact? This is the uncomfortable question. Faster decisions? More reuse? Fewer duplicated datasets? If leadership can’t feel the difference, they won’t fund it. ⚠️ Hard truth: A data catalog is not a compliance tool. It’s not a metadata repository. It’s not a checkbox. It’s a product, and products live or die by adoption, trust, and impact. 💬 Be honest: Which of these KPIs are you actually tracking today?

A programme is six months old. The donor wants impact stories. The field team is still figuring out logistics, hiring, community trust, baseline data. This is where credibility is decided. Most organisations choose visibility over accuracy. They package two anecdotes. - Add photos. - Call it “early impact.” Here is the problem. When you overstate results at six months, you are training your donor to expect speed that systems cannot sustain. Next year, when outcomes take their natural time, you look like you have slowed down. But you have not. You were just premature. Serious institutions handle this differently. They say: Here is what we have stabilised. Here is what we have built. Here is what is still too early to measure. They report process indicators. Hiring completed. Partnerships signed. Baseline done. Training cycles finished. Not glamorous. But credible. Early-stage reporting is not a storytelling test. It is a governance test. If your communication is ahead of your operations, trust will eventually catch up and correct it. The real question is not “How do we show impact quickly?” It is “Are we disciplined enough to show progress honestly?” That is what compounds over time. . . . . #VisualStorytelling #Communications #Nonprofits #SocialSector #CreativeAgency #SimitBhagatStudios