Implementing trust at scale in tech delivery

Why would your users distrust flawless systems? Recent data shows 40% of leaders identify explainability as a major GenAI adoption risk, yet only 17% are actually addressing it. This gap determines whether humans accept or override AI-driven insights. As founders building AI-powered solutions, we face a counterintuitive truth: technically superior models often deliver worse business outcomes because skeptical users simply ignore them. The most successful implementations reveal that interpretability isn't about exposing mathematical gradients—it's about delivering stakeholder-specific narratives that build confidence. Three practical strategies separate winning AI products from those gathering dust: 1️⃣ Progressive disclosure layers Different stakeholders need different explanations. Your dashboard should let users drill from plain-language assessments to increasingly technical evidence. 2️⃣ Simulatability tests Can your users predict what your system will do next in familiar scenarios? When users can anticipate AI behavior with >80% accuracy, trust metrics improve dramatically. Run regular "prediction exercises" with early users to identify where your system's logic feels alien. 3️⃣ Auditable memory systems Every autonomous step should log its chain-of-thought in domain language. These records serve multiple purposes: incident investigation, training data, and regulatory compliance. They become invaluable when problems occur, providing immediate visibility into decision paths. For early-stage companies, these trust-building mechanisms are more than luxuries. They accelerate adoption. When selling to enterprises or regulated industries, they're table stakes. The fastest-growing AI companies don't just build better algorithms - they build better trust interfaces. While resources may be constrained, embedding these principles early costs far less than retrofitting them after hitting an adoption ceiling. Small teams can implement "minimum viable trust" versions of these strategies with focused effort. Building AI products is fundamentally about creating trust interfaces, not just algorithmic performance. #startups #founders #growth #ai

4 AI Governance Frameworks To build trust and confidence in AI. In this post, I’m sharing takeaways from leading firms' research on how organisations can unlock value from AI while managing its risks. As leaders, it’s no longer about whether we implement AI, but how we do it responsibly, strategically, and at scale. ➜ Deloitte’s Roadmap for Strategic AI Governance From Harvard Law School’s Forum on Corporate Governance, Deloitte outlines a structured, board-level approach to AI oversight: 🔹 Clarify roles between the board, management, and committees for AI oversight. 🔹 Embed AI into enterprise risk management processes—not just tech governance. 🔹 Balance innovation with accountability by focusing on cross-functional governance. 🔹 Build a dynamic AI policy framework that adapts with evolving risks and regulations. ➜ Gartner’s AI Ethics Priorities Gartner outlines what organisations must do to build trust in AI systems and avoid reputational harm: 🔹 Create an AI-specific ethics policy—don’t rely solely on general codes of conduct. 🔹 Establish internal AI ethics boards to guide development and deployment. 🔹 Measure and monitor AI outcomes to ensure fairness, explainability, and accountability. 🔹 Embed AI ethics into product lifecycle—from design to deployment. ➜ McKinsey’s Safe and Fast GenAI Deployment Model McKinsey emphasises building robust governance structures that enable speed and safety: 🔹 Establish cross-functional steering groups to coordinate AI efforts. 🔹 Implement tiered controls for risk, especially in regulated sectors. 🔹 Develop AI Guidelines and policies to guide enterprise-wide responsible use. 🔹 Train all stakeholders—not just developers—to manage risks. ➜ PwC’s AI Lifecycle Governance Framework PwC highlights how leaders can unlock AI’s potential while minimising risk and ensuring alignment with business goals: 🔹 Define your organisation’s position on the use of AI and establish methods for innovating safely 🔹 Take AI out of the shadows: establish ‘line of sight’ over the AI and advanced analytics solutions  🔹 Embed ‘compliance by design’ across the AI lifecycle. Achieving success with AI goes beyond just adopting it. It requires strong leadership, effective governance, and trust. I hope these insights give you enough starting points to lead meaningful discussions and foster responsible innovation within your organisation. 💬 What are the biggest hurdles you face with AI governance? I’d be interested to hear your thoughts.

𝐎𝐧𝐞 𝐥𝐞𝐬𝐬𝐨𝐧 𝐦𝐲 𝐰𝐨𝐫𝐤 𝐰𝐢𝐭𝐡 𝐚 𝐬𝐨𝐟𝐭𝐰𝐚𝐫𝐞 𝐝𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭 𝐭𝐞𝐚𝐦 𝐭𝐚𝐮𝐠𝐡𝐭 𝐦𝐞 𝐚𝐛𝐨𝐮𝐭 𝐔𝐒 𝐜𝐨𝐧𝐬𝐮𝐦𝐞𝐫𝐬: Convenience sounds like a win… But in reality—control builds the trust that scales. We were working to improve product adoption for a US-based platform. Most founders instinctively look at cutting clicks, shortening steps, making the onboarding as fast as possible. We did too — until real user patterns told a different story. 𝐈𝐧𝐬𝐭𝐞𝐚𝐝 𝐨𝐟 𝐫𝐞𝐝𝐮𝐜𝐢𝐧𝐠 𝐭𝐡𝐞 𝐣𝐨𝐮𝐫𝐧𝐞𝐲, 𝐰𝐞 𝐭𝐫𝐢𝐞𝐝 𝐬𝐨𝐦𝐞𝐭𝐡𝐢𝐧𝐠 𝐜𝐨𝐮𝐧𝐭𝐞𝐫𝐢𝐧𝐭𝐮𝐢𝐭𝐢𝐯𝐞: -Added more decision points -Let users customize their flow -Gave options to manually pick settings -instead of forcing defaults -Conversions went up. -Engagement improved. Most importantly, user trust deepened. You can design a sleek two-click journey. But if the user doesn’t feel in control, they hesitate. Especially in the US, where data privacy and digital autonomy are non-negotiable — transparency and control win. Some moments that made this obvious: People disable auto-fill just to type things in manually. They skip quick recommendations to compare on their own. Features that auto-execute without explicit consent? Often uninstalled. It’s not inefficiency. It’s digital self-preservation. A mindset of: “Don’t decide for me. Let me drive.” I’ve seen this mistake cost real money. One client rolled out an automation that quietly activated in the background. Instead of delighting users, it alienated 20% of them. Because the perception was: “You took control without asking.” Meanwhile, platforms that use clear prompts — “Are you sure?” “Review before submitting” Easy toggles and edits — those build long-term trust. That’s the real game. What I now recommend to every tech founder building for the US market: Don’t just optimize for frictionless onboarding. Optimize for visible control. Add micro-trust signals like “No hidden fees,” “You can edit this later,” and toggles that show choice. Make the user feel in charge at every key step. Trust isn’t built by speed. It’s built by respecting the user’s right to decide. If you’re a tech founder or product owner, stop assuming speed is everything. Start building systems that say: “You’re in control.” 𝐓𝐡𝐚𝐭’𝐬 𝐰𝐡𝐚𝐭 𝐜𝐫𝐞𝐚𝐭𝐞𝐬 𝐚𝐝𝐨𝐩𝐭𝐢𝐨𝐧 𝐭𝐡𝐚𝐭 𝐬𝐭𝐢𝐜𝐤𝐬. 𝐖𝐡𝐚𝐭’𝐬 𝐲𝐨𝐮𝐫 𝐞𝐱𝐩𝐞𝐫𝐢𝐞𝐧𝐜𝐞 𝐰𝐢𝐭𝐡 𝐭𝐡𝐢𝐬? 𝐋𝐞𝐭’𝐬 𝐝𝐢𝐬𝐜𝐮𝐬𝐬. #UserExperience #ProductDesign #TrustByDesign #TechForUSMarket #businesscoach #coachishleenkaur LinkedIn News LinkedIn News India LinkedIn for Small Business

Superior technology usually loses to trust deficits. The NewCos who win understand this deeply. LegacyCos provide coverage - decades of embedded relationships, compliance infrastructure, institutional safety. NewCos provide velocity - direct paths to outcomes without legacy constraints. Enterprise buyers increasingly ask: "Will this deliver outcomes fast?" and "Can I justify this choice?" LegacyCos excel at the second. NewCos must excel at both. The solution isn't copying LegacyCo's relationship playbook. It's building trust infrastructure optimized for a high clockspeed world. The Trust Ceiling Trust deficits create velocity ceilings regardless of technology quality. This is structural, not situational. Your product solves problems 10x faster, but if buyers don't trust delivery, speed becomes irrelevant. NewCos must engineer trust through domain expertise and proven outcomes - without institutional baggage. The NewCo Playbook: Wedge and Expand Today's mega-deals are fragmenting into smaller, milestone-driven projects. This creates opportunities LegacyCos are too expensive and slow to pursue effectively. Your strategy isn't competing on massive transformations. It's winning wedges and expanding from strength. Domain Expert Credibility Hire thought leaders who understand enterprise needs but aren't tied to legacy delivery models. Import domain knowledge, not institutional constraints. Champion-Led Wedge Entry Embed with operational teams before procurement involvement. Find specific pain points where speed matters more than coverage. Example: You deliver working inventory optimization in 30 days, reducing costs 15%. LegacyCo proposes 12-month workflow optimization before any results. The Speed Advantage LegacyCos take 12 months because they're carrying decades of institutional process. You take 3 months because you're purpose-built for outcomes. This speed differential compounds: - Faster implementation → faster results → stronger references - Wedge wins → adjacent problems → organic expansion - Proven outcomes → higher trust → shortened sales cycles The Readiness Test: Before entering any market → Do you have domain experts who'll publicly endorse your approach? → Can you name three specific wedges where your speed beats their coverage? → Do you have proof points of 3x faster outcomes than traditional approaches? If yes, you have trust infrastructure built for velocity. If no, you're just another vendor. The Choice Enterprise buyers increasingly prefer fast wins over comprehensive coverage. You can build trust infrastructure optimized for velocity, or watch superior technology stall in trust deficit. In enterprise markets: speed without trust stalls; trust without speed stagnates. The network rewards companies that understand both realities. (Full version sent to newsletter subscribers)

Consulting success is built on ‘trust at scale’. And too many consultancies don't get that. Yet most consulting firms try to build trust with vanity claims: - “we transform your organisation” - “we drive substantial growth” - “we deliver impact and results” They believe bold outcome language signals confidence. In reality, it erodes trust. What consulting firms see as their transformational expertise, buyers often read as a lack of substance. Real trust is built when a consulting firm proves 1) it deeply understands the problem, 2) has done the required transformation before, and 3) helps the buyer make a confident decision. That’s how consulting firms prevent stalled or ghosted proposals: by replacing persuasion with predictive trust that converts interest into commitment. In our work with consulting firms, we’ve learned that trust has three layers: 1️⃣ Value articulation specificity – the foundation of trust When the value proposition is sharply defined — issue-led, outcome-driven, audience-specific — the buyer instantly feels, “this consultancy gets it.” Specificity creates familiarity. It tells the client: we’ve solved this before. 2️⃣ Proof of value – the reinforcement of trust Repeated patterns of similar projects turn claims into evidence. An ideal client success journey, outcome stories from clients facing similar challenges, and thought leadership that inspires action, all grow trust in the buyer’s mind: they’ve done this repeatedly and reliably. 3️⃣ Decision confidence – the enabler of trust Too many consulting proposals are lost not to competitors but due to client hesitation. In one of my favourite books, The JOLT Effect, Matt Dixon and Ted McKenna explain that buyers rarely doubt the service firm. They doubt themselves. They’re asking: - “Have we explored all the alternatives?” - “Can we really implement this successfully?” - “Are we making the best decision for our company?” Trust at this level involves assisting buyers to answer those questions confidently. By reducing the risks associated with their choices, guiding their exploration, and building shared confidence, consulting firms shift from persuasion to collaboration. It’s not about persuading more forcefully, it’s about helping clients feel secure in making their decision. 👉 THE TAKEAWAY When consulting firms get all three layers right — value articulation specificity, proof of value, and decision confidence — they can create trust at scale. And in a consulting market drowning in sameness and AI-generated noise, trust becomes the only real differentiator left.

Tech moves at lightning speed. Trust doesn’t. That’s the challenge every founder faces: - Technology evolves overnight - People take time to adapt - And trust is what holds the middle together If you want your team to thrive when change feels constant, Here are 5 ways to build trust while tech races ahead: 1. Communicate early, not perfectly Don’t wait for the “perfect plan.” Share what you know. Admit what you don’t. Keep updating as you learn. 2. Involve people in the change Don’t just roll out a new tool. Ask for feedback. Run small pilots. Make your team co-owners, not just users. 3. Invest in learning, not just tools AI and automation won’t work if your team isn’t confident. Training, workshops, mentorship, show them you care about growth, not just speed. 4. Balance transparency with reassurance Be upfront about challenges. But also remind people of the bigger vision. Trust grows when people feel both informed and secure. 5. Recognize effort, not just results Adapting to change is tough. Celebrate progress, not just outcomes. It signals you value resilience, not just output. Technology will always move faster than people. But leaders who build trust don’t just help their teams catch up They help them lead the change.

AI at scale is a trust challenge, and that’s the biggest opportunity for every enterprise leader right now. As we partner with our customers, we see a clear shift: the conversation has moved beyond “AI for efficiency” and “responsible AI” to one of tangible, measurable value. The key to unlocking that value is trust. For our most regulated customers in healthcare, finance, and government, trust is not a “nice-to-have,” it’s a gatekeeper. These industries need to prove AI agents operate exactly as intended, protecting sensitive data and respecting jurisdictional rules from Day One. The reality is, a pilot program is only a pilot until it is built to scale. And what separates a successful pilot from a transformative enterprise solution is the foundation of trust. The governance layer has to be designed within the solution from the start, weaving together these three critical enablers: ☑️ Verification: You must be able to prove your AI agent is operating within approved business policies. This is not a technical feature; it is a way to ensure accountability and speed up time-to-market in a regulated world. ☑️ Sovereignty: Your sensitive data must be protected and observable in its rightful jurisdiction. This gives customers the peace of mind they need to truly innovate with their most valuable asset – their data. ☑️ Auditability: You must confidently demonstrate how a decision was made through transparent records that empower human oversight. This allows enterprises to stand behind their AI investments with certainty. When these three pillars converge, they remove the barriers that slow down progress and allow customers to move faster, smarter, and with greater confidence. This is where real business value emerges. Performance plateaus when trust is an afterthought. But when trust, structure, and impact advance together, we lay the foundation for true business transformation. #AIGovernance #GoogleCloud #DataSovereignty #AgentVerification

Can you trust a system that can’t explain itself? AI is moving fast — but trust doesn’t scale with speed. It scales with clarity, accountability, and shared decision-making. At Datagrid, we’re not just building agents that act. We’re building agents that 𝗮𝗰𝘁 𝘄𝗶𝘁𝗵 𝗶𝗻𝘁𝗲𝗻𝘁 — and can be held to it. That's where 𝗔𝗰𝘁𝗶𝗼𝗻 𝗥𝗲𝘃𝗶𝗲𝘄 comes in. Here’s what that means in practice: ✅ 𝗘𝘃𝗲𝗿𝘆 𝗮𝗰𝘁𝗶𝗼𝗻 𝗶𝘀 𝗿𝗲𝘃𝗶𝗲𝘄𝗮𝗯𝗹𝗲: No black boxes. Every decision has a trail. 🧠 𝗔𝗴𝗲𝗻𝘁𝘀 𝗮𝗿𝗲 𝗮𝗰𝗰𝗼𝘂𝗻𝘁𝗮𝗯𝗹𝗲: They explain their reasoning, not just their output. 🧍♂️ 𝗛𝘂𝗺𝗮𝗻𝘀 𝗺𝗮𝗸𝗲 𝘁𝗵𝗲 𝗰𝗮𝗹𝗹: “Ship it” or “Stop” — someone owns the outcome. 🔁 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸 𝗶𝘀 𝗳𝗲𝗲𝗱𝗳𝗼𝗿𝘄𝗮𝗿𝗱: Every review improves the system. This isn't slowing down AI. It’s how we build 𝗿𝗲𝗮𝗹 𝘁𝗿𝘂𝘀𝘁 — inside the team and with the tech. What would change in your org if every AI action came with a “why”?

Health tech leaders: Is your AI strategy missing the trust factor? Just reviewed the new WEF/BCG report on AI in healthcare.   What you need to know: - 50% of healthcare professionals suffer from burnout. - Healthcare AI market hit $200B in 2023 (3x growth since 2020). - Yet most AI health solutions fail to scale due to trust barriers.   3 Actionable Steps for Your AI Strategy:   1️⃣ Build Technical Literacy   Don't delegate AI understanding to your CTO alone. Every executive needs baseline AI literacy. Start with 30-min weekly sessions on AI capabilities and limitations.   2️⃣ Embrace Regulatory Sandboxes   Stop viewing compliance as a barrier. Make use of regulatory sandboxes to test innovations safely. This accelerates time-to-market while building regulator trust.   3️⃣ Create Your Trust Framework Implement continuous post-market monitoring (not just pre-launch testing). Document your AI's decision-making process transparently. Establish clear accountability for AI outcomes. (DM if you don't know how)   4️⃣ Form Strategic Public-Private Partnerships The report emphasizes PPPs are essential. Partner with regulators early in development, not just for approval. Join initiatives like CHAI or TRAIN to co-develop standards. 5️⃣ Prepare for Global Regulatory Divergence EU has the AI Act, US prioritizes innovation, Global South faces resource gaps. Build flexibility into your AI architecture to adapt to different regulatory environments from day one. The winners will be those who embed trust-building into their development process from the start.   💡 Quick Win: Start by mapping your current AI evaluation process against the CHAI (Coalition for Health AI) standards. Identify one gap to fix this quarter.

Network automation builds trust gradually in an organization. I've recently been thinking about this as three successive layers of trust. The first is personal trust. An engineer decides to build skills, create scripts and personal tools that enhance their accuracy and efficiency for various tasks. They grow and progress and become expert. The second is team trust. That engineer becomes a champion and technical leader to help a team grow its network automation capabilities, which requires tooling and practice to deliver trustworthy outcomes for the team. This means that collaboration, peer review, and some sort of version control become essential. As the team practice, skill, software tooling, and tangible benefits strengthen, this provides the foundation for what comes next. The third layer is organizational trust, where the larger organization is willing to make investments in the team and software tooling. The organization needs trust in the team's leadership, technical skills, sound practices, track record of localized success, and likelihood of positive top and/or bottom-line impact, in order to make real investment. It also means that the software stack itself must be robust, resilient, and maintainable enough to ensure that investments won't get wasted due to technical debt, but will deliver solid benefits to the organization over time. What do you think of this simple, three-layer trust idea? I'd love to hear your comments.