How to build a first-party trust layer

The AI-agent conversation is stuck. It is not only about efficiency. It is about reclaiming the opportunities we walked away from. 🚀 After years leading enterprise-scale digital programs and launching an AI Center of Excellence, I have learned that the noise around orchestration layers distracts us from the real prize. The goal is not simply to speed up today’s workflows. It is to revive strategic work we once labeled impossible. I watched a dormant lake of rail telemetry become a platform that now predicts failures, optimizes entire networks, and transforms daily operations. That is the frontier: turning forgotten data into predictive, revenue-generating engines that pay for their own growth. Beyond efficiency ➡️ recover abandoned value Think about the projects that never cleared pilot: • Indexing ten years of customer feedback. • Personalizing service for millions in real time. • Stress-testing every node in a global supply chain. Agents finally give us the cognitive muscle to tackle work at that scope—provided we pair them with rigorous retrieval pipelines and fine-tuned models rather than just “dropping an agent on the problem.” Why pilots stall ❌ weak data foundations Most stalled agent pilots I review break at the same point: the data model is blurry. No algorithm can reason with half-truths. Winning teams invest their energy up front, building precise domain-specific data structures before writing a single prompt. An agent’s power equals its data quality. My 4-step playbook ✅ 1. Model first – Design a semantic layer your agents trust. Capture the real language of your business. 2. Govern early – Create rules that let units share context without risking security or compliance. A strong data mesh is an accelerator. 3. Grow AI architects – Develop leaders who see abandoned opportunities and connect strategy, data, and delivery. 4. Iterate in the open – Run tight design–build–test loops. Visible progress builds trust each cycle. Five signs you are ready for agents 🔍 1. Architecture is model-first; data outranks UI polish. 2. Secure, context-aware agent calls (MCP, A2A—promising but still emerging) are planned from day one. 3. Observability—logs, replays, guardrails—is wired in up front. 4. A library of reusable agents stands on a common, trusted data layer. 5. Business and tech teams share a studio to co-create, monitor, and refine solutions. The race to agentic AI will not be won with marketplaces or shiny interfaces. Durable advantage belongs to leaders who transform lost ambitions and dormant data into measurable outcomes. 💡 #AIStrategy #DigitalTransformation #DataCentricAI #ValueCreation #AgenticAI #Innovation

In a world of deep fakes, trust is more valuable than ever. Here's how to build unshakeable trust in the digital age: 🔒 Radical Transparency: Share your process, not just your results. • Open-source parts of your code • Live-stream product development • Publish raw data alongside analysis This builds credibility and invites collaboration. 🤝 The Art of the Public Apology: • Acknowledge mistakes quickly • Explain what happened (no excuses) • Outline concrete steps to prevent recurrence Swift, honest responses turn crises into trust-building opportunities. 🔬 Trust by Design: • Build privacy safeguards into products from day one • Conduct regular third-party security audits • Create an ethics board with external members Proactive trust-building beats reactive damage control. 📊 Blockchain for Verification: • Use smart contracts for transparent transactions • Create immutable audit trails for sensitive data • Implement decentralized identity solutions Blockchain isn't just for crypto – it's a trust engine. 🗣️ Trust Cascade: • Train employees as trust ambassadors • Reward those who flag issues early • Share customer trust stories widely Trust spreads exponentially when everyone's involved. 🧠 Harness AI Responsibly: • Develop explainable AI models • Implement bias detection algorithms • Offer users control over their AI interactions Show you're using AI to empower, not replace human judgment. 🌐 Trust Ecosystem: • Partner with trusted third-party verifiers • Join industry-wide trust initiatives • Create a customer trust council Your network becomes your net worth in the trust economy. Remember: In a world of infinite information, trust is the ultimate differentiator. Build it deliberately, protect it fiercely, and watch your business soar. Thanks for reading! If you found this valuable: • Repost for your network ♻️ • Follow me for more deep dives • Join our 300K+ community https://lnkd.in/eDYX4v_9 for more on the future of API, AI, and tech The future is connected. Become a part of it.

Building trust with B2B buyers is crucial for SaaS companies. Why? Because 43% of B2B buyers make defensive purchase decisions more than 70% of the time, according to Forrester. These buyers aren't cowardly; they're rational. They're accountable to their company and colleagues for spending decisions. Often, their purchase directly impacts their daily work. So how can SaaS companies bridge the gap between risk-averse buyers and purchase decisions? The answer is trust. One powerful lever for building trust is website optimization. Let's explore how to identify trust gaps and implement specific tactics to build trust through UX design and content. The Trust & Authority Heuristic, part of The Good's Heuristics for Digital Experience Optimization™, focuses on establishing perceived trust throughout the digital experience. Violations of this heuristic can lead to user disengagement. To identify trust gaps, look for signs in user research like bugs, attentive reading, or halted scrolling. Speak with customer support teams and conduct data analysis to gather both quantitative and qualitative data. Once you've identified trust issues, implement tactics like: 1. Adding social media handles to customer reviews 2. Including "customer since" dates with testimonials 3. Integrating social proof into user journeys, like registration forms 4. Featuring logos of integration partners 5. Displaying privacy certifications and data policy badges 6. Offering and highlighting guarantees 7. Adding "how it works" models for complex products After implementing these tactics, measure their effectiveness using a theme-based roadmap. This will help to plan, communicate, and track initiatives and associated metrics. By aligning your website with the Trust & Authority Heuristic, you'll build confidence and position your SaaS business for sustained growth, transforming both registrations and retention. Go get 'em!

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.

Innovation can’t come at the expense of trust. Organizations need to manage the privacy, security, and AI-related risks that come with it. Companies like Grammarly get it. That’s why they’re leading the way with a customer centric approach - embedding trust, transparency, and user control into the core of their privacy, security, and AI programs. They have one of THE best TRUST centers out there!! Curious how they do it? Discover Grammarly’s approach by tuning into week’s special edition of the She Said Privacy/He Said Security episode. Justin Daniels and I chat with Jennifer T. Miller, General Counsel, AND Suha Can, CISO, at Grammarly about how the company has built a privacy and security program centered on trust and transparency. I've been SO excited to release this episode - it's packed with how to put the customer first, how privacy and security work together, and why it matters. We covered: ✅ How Grammarly prioritizes privacy and security for its 30 million global users ✅ The evolving partnership between Grammarly’s General Counsel and CISO ✅ Why Grammarly created a transparent privacy, security, and AI web page ✅ Grammarly’s review process for AI-integrated products ✅ Tips for infusing trust into privacy and security programs Key takeaways from our chat: 1️⃣ Build trust by creating transparent, user-focused privacy and security practices 2️⃣ Regularly audit products for privacy, security, and AI-related risks 3️⃣ Foster collaboration between legal and technical teams to mitigate risks and comply with regulations Listen to the full episode here: https://lnkd.in/e3jqhMhZ *** ♻ Share so more companies learn how to put the customer first. 🔔 Subscribe to the podcast to never miss an episode!