Strategies to shift from automatic to informed trust

I once worked with a team that was, quite frankly, toxic. The same two team members routinely derailed meeting agendas. Eye-rolling was a primary form of communication. Side conversations overtook the official discussion. Most members had disengaged, emotionally checking out while physically present. Trust was nonexistent. This wasn't just unpleasant—it was preventing meaningful work from happening. The transformation began with a deceptively simple intervention: establishing clear community agreements. Not generic "respect each other" platitudes, but specific behavioral norms with concrete descriptions of what they looked like in practice. The team agreed to norms like "Listen to understand," "Speak your truth without blame or judgment," and "Be unattached to outcome." For each norm, we articulated exactly what it looked like in action, providing language and behaviors everyone could recognize. More importantly, we implemented structures to uphold these agreements. A "process observer" role was established, rotating among team members, with the explicit responsibility to name when norms were being upheld or broken during meetings. Initially, this felt awkward. When the process observer first said, "I notice we're interrupting each other, which doesn't align with our agreement to listen fully," the room went silent. But within weeks, team members began to self-regulate, sometimes even catching themselves mid-sentence. Trust didn't build overnight. It grew through consistent small actions that demonstrated reliability and integrity—keeping commitments, following through on tasks, acknowledging mistakes. Meeting time was protected and focused on meaningful work rather than administrative tasks that could be handled via email. The team began to practice active listening techniques, learning to paraphrase each other's ideas before responding. This simple practice dramatically shifted the quality of conversation. One team member later told me, "For the first time, I felt like people were actually trying to understand my perspective rather than waiting for their turn to speak." Six months later, the transformation was remarkable. The same team that once couldn't agree on a meeting agenda was collaboratively designing innovative approaches to their work. Conflicts still emerged, but they were about ideas rather than personalities, and they led to better solutions rather than deeper divisions. The lesson was clear: trust doesn't simply happen through team-building exercises or shared experiences. It must be intentionally cultivated through concrete practices, consistently upheld, and regularly reflected upon. Share one trust-building practice that's worked well in your team experience. P.S. If you’re a leader, I recommend checking out my free challenge: The Resilient Leader: 28 Days to Thrive in Uncertainty  https://lnkd.in/gxBnKQ8n

What’s it going to take for you to trust network automation? Or AI? This is how Damien Garros opened his talk “Building Trustworthy Network Automation, From Principles to Practice” at #AutoCon3 from the Network Automation Forum. Damien points out that engineers don’t trust a black box. They need to know what’s going on inside. Therefore, when we build automation systems, we have to do more than make it work. We have to add functionality that fosters trust. Move from a system that *I* would use to a system that *we* would use. Damian’s Six Principles To Build Trust 1️⃣ Predictable 2️⃣ Manageable 3️⃣ Transparent 4️⃣ Simple 5️⃣ Reliable 6️⃣ Human Friendly From there, Damien defined 3 design principles that make a virtuous cycle of trust building. 1️⃣ Idempotency. Running the job gets the same result every time. Without idempotency, we have to build additional logic. 2️⃣ Dry runs. Gives operators the chance to test, review, and approve before executing. 3️⃣ Transactional. Make changes all or nothing. If there's a failure , roll back to where you started. Building on that virtuous cycle, Damien moved on to more key ideas. 1️⃣ Declarative (WHAT - focused on outcomes) versus imperative (HOW - focused on specific actions) making the point that you really want declarative functionality in your system. Declarative systems tend to be simpler to implement and easier to roll back, fostering trust. 2️⃣ Version Control. With version control, changes can be prepared off to the side, validated to have no risk, and reviewed. Once past those steps, the change can be integrated into the main automation environment. Version control fosters trust. 3️⃣ Testing. To test a system, it’s got to be broken down into modules small enough to test without too much complexity. Testing is a tradeoff, though. Not enough tests are insufficient. Too many tests weigh you down. But you must test to foster trust. Unit tests. Integration tests. End to end tests. And then automate that testing. Damien brought his talk together asking, "What are some practical patterns to build trust?" 1️⃣ Don’t reinvent the wheel. Integrate with existing tools when you can. Build something new only if you have to. But...pick your tools carefully. Will the tools work with the system you’re designing? For example, do they work idempotently if idempotency is a core design principle you’re committed to? 2️⃣ Damien also advocated for classifying your data. Know what it is and does, as that will help you process that data in your workflows correctly. 3️⃣ Finally, Damien suggested providing safe default options for your tools. For instance when using Ansible, call out safe playbooks explicitly, ensure default values are safe, and activate diff mode by default. A great talk from Damien, one of folks behind the FOSS project InfraHub by OpsMill. Follow Network Automation Forum to be notified when all of the talks from #AutoCon3 are posted to YouTube.

I walked into a room full of frustration. The project was off track, the budget was bleeding, and trust had worn thin. As the new project manager, I had 30 days to rebuild what was broken not just the plan, but the relationships. 💡 Here’s the exact trust-building strategy I used to shift the momentum - one conversation, one quick win, and one honest update at a time. ▶ Day 1–5: I started with ears, not answers. 🎧 Active Listening & Empathy Sessions I sat down with stakeholders - one by one, department by department. No slides. No status updates. Just questions, empathy, and silence when needed. 💬 I didn’t try to fix anything. I just listened - and documented everything they shared. Why it worked: They finally felt heard. That alone opened more doors than any roadmap ever could. ▶ Day 6–10: I called out the elephant in the room. 🔍 Honest Assessment & Transparent Communication I reviewed everything - timelines, budgets, blockers, and team dynamics. By day 10, I sent out a clear, no-spin summary of the real issues we were facing. Why it worked: I didn’t sugarcoat it - but I didn’t dwell in blame either. Clarity brought calm. Transparency brought trust. ▶ Day 11–15: I delivered results - fast. ⚡ Quick Wins & Early Action We fixed a minor automation glitch that had frustrated a key stakeholder for months. It wasn’t massive, but it mattered. Why it worked: One small win → renewed hope → stakeholders leaning in again. ▶ Day 16–20: I gave them a rhythm. 📢 Clear Communication Channels & Cadence We set up weekly pulse updates, real-time dashboards, and clear points of contact. No more guessing who’s doing what, or when. Why it worked: Consistency replaced confusion. The team knew what to expect and when. ▶ Day 21–25: I invited them to the table. 🤝 Collaborative Problem-Solving Instead of pushing fixes, I hosted solution workshops. We mapped risks, brainstormed priorities, and made decisions together. Why it worked: Involvement turned critics into co-owners. People support what they help build. ▶ Day 26–30: I grounded us in reality. 📅 Realistic Expectations & Clear Next Steps No overpromising. I laid out a realistic path forward  timelines, budgets, trade-offs, and all. I closed the month by outlining what we’d tackle next together. Why it worked: Honesty created stability. A shared plan gave them control. 💬 In 30 days, we hadn’t fixed everything but we had built something more valuable: trust. And from trust, everything else became possible. Follow Shraddha Sahu for more insights

When the deal of the year turned into the mess of the year... I once walked into an acquisition integration that looked brilliant on paper, and was a disaster in practice. The systems were colliding, leaders were pointing fingers, and employees were caught in the middle. Every update from the top was polished, precise, and…completely hollow. The truth? People didn’t need another update. They needed to feel understood. Where it all went wrong? I sat in meetings where leaders rolled out flawless powerpoints, crisp emails, and “efficient” town halls. From the outside, it looked like best-in-class communication. But behind the scenes? Disengagement. Frustration. Turnover. There was a glaring gap between foundational communication (telling people what they “need” to know) and human communication (making people feel seen, heard, and connected). The Reset? That’s when my team and I stepped in. We pressed pause on the corporate theater and focused on being human first: -We coached leaders to share stories, not just strategies. When people could see themselves in the narrative, they cared again. -We taught leaders to acknowledge what was messy, confusing, and hard - because vulnerability builds trust faster than any polished script. -We rebuilt dialogue. Not quarterly updates, but ongoing conversations where employees could question, push back, and be part of shaping the new culture. Slowly, things shifted. Employees stopped bracing for the next “update” and started leaning in. Leaders discovered that trust doesn’t come from having all the answers - it comes from being willing to show up as human. The lesson for leaders? In an age of automation and endless change, the real competitive edge isn’t efficiency. It’s empathy. If you’re leading through change - an acquisition, a reorg, even a tough quarter, ask yourself: Am I just communicating information? Or am I creating connection? Because when everything else is in flux, relationships are the one thing that will hold your culture together. Your people don’t need another update. They need to feel understood.

As I have long said, you can deliberately develop trust. In GenAI workforce transformation, that is critical. McKinsey propose 5 steps for effective change management transition in a new article, including on how to build trust. Below are the five suggested steps. These are solid and strongly align with my work, notably on clarity of vision for the future of work, setting trust development programs, designing Humans + AI workflows and team structures, and driving change through energizing champions. Their MVO concept is definitely interesting. 1️⃣ Define a clear North Star CEOs should set a simple but bold vision that shows how gen AI will create value and competitive advantage, not just add tools. A clear North Star aligns the organization while preparing for fast-moving technologies. Companies that define outcome-driven AI strategies can capture more enduring value than those chasing features. 2️⃣ Build trust through data and governance Without trust, adoption stalls. High-performing companies, those attributing 10%+ of EBITDA to gen AI, are nearly twice as likely to invest in trust-building activities. Accessible data, robust governance, and enterprise-specific knowledge bases ensure employees believe and rely on AI outputs, boosting both adoption and performance. 3️⃣ Reimagine workflows around AI teams Gen AI isn’t just another software tool, it transforms how work gets done. Instead of bolting AI onto old processes, companies should redesign workflows in stages: from discrete AI helpers, to agent groups, to autonomous “agent swarms.” Firms that integrate AI into daily work, like McKinsey’s Lilli now used by 92% of staff, see massive efficiency gains. 4️⃣ Reshape organizations with MVOs and augmented teams Some functions can evolve into highly automated Minimum Viable Organizations (MVOs), while others thrive by augmenting humans with AI superpowers. For example, back-office processes may become MVOs, but customer-facing roles like sales and service work best with human-AI collaboration. CEOs must redesign structures and talent strategies to balance cost savings, speed, and customer experience. 5️⃣ Empower employees as change agents Widespread employee involvement is key. Companies involving 7%+ of staff in transformations double their odds of strong shareholder returns. Encouraging “superusers” (often millennial managers, 62% of whom already show high AI expertise) to lead adoption accelerates culture change. Programs like Singtel’s AI Academy, which is training 10,000+ employees, show how large-scale reskilling builds momentum.