Improving Project Outcomes with AI Analytics

Let’s say your support center is getting hammered with repeat calls about a new product feature. Historically, the team would escalate, create a task force, and maybe update a knowledge base weeks later. With the tech available today, you should be able to unify signals from tickets, chat logs, and social mentions instead. This helps you quickly interpret the root cause. Perhaps in this case it's a confusing update screen that’s triggering the same questions. Instead of just sharing the feedback with the task force that'll take weeks to deliver something, galvanize leaders and use your tech stack to orchestrate a fix in real time. Don't have orchestration in that stack? Start looking into this asap. An orchestration engine canauto-suggest a targeted in-app message for affected users, trigger a proactive email campaign with step-by-step guidance, and update your chatbot’s responses that same day. Reps get nudges on how to resolve the issue faster, and managers can watch repeat contacts drop by a measurable percentage in real time. But the impact isn’t limited to operations. You energize the business by sharing these results in a company-wide standup and spotlighting how different teams contributed to the OUTCOME. Marketing sees reduced churn, operations sees lower cost-to-serve, and leadership sees a team aligned around outcomes instead of activities. If you want your AI investments to move the needle, focus on unified signals, real-time orchestration, and getting the whole business excited about customer outcomes....not just actions. Remember: Outcomes > Actions #customerexperience #ai #cxleaders #outcomesoveraction

Most of us are using AI wrong. Short prompts. Zero context. Quick requests. Then we're disappointed with mediocre results. In a recent Supra Insider podcast, Tal Raviv shared a counterintuitive insight that changed how I think about AI: Stop treating AI like a quick-fix tool. Start treating it like a new team member you're onboarding. Think about it: Would you ask a new PM to write a PRD without any context about your: • Company strategy • Customer segments • Team dynamics • Past decisions • Current challenges Of course not. So why do we expect AI to do it? Here's how Tal built his AI teammate: 1/ First, he created a foundation: ↳ Uploaded the company strategy deck ↳ Added customer research findings ↳ Shared org structure & team dynamics ↳ Documented past project outcomes ↳ Included key stakeholder relationships 2/ For each project, he treated it like a teammate: ↳ Shared all meeting notes ↳ Added summaries of customer conversations ↳ Included hallway discussions and insights ↳ Updated it on new data & developments 3/ The magic happened when he got stuck: One day when feeling overwhelmed, instead of getting generic "make a to-do list" advice, the AI responded: "I notice you haven't looped in Design yet - that's been a blocker in your past projects. And remember, Sarah from Marketing always needs extra context. Let's schedule those discussions first." This wasn't generic AI advice. This was a teammate who understood his work patterns, team dynamics, and project history. The power is in the context. What are you doing to give your AI tools more context? --- Our full episode with Tal Raviv and Ben Erez covers:  ↳ Common myths about AI for PMs ↳ Why most PMs underutilize AI tools ↳ The difference between AI Copilots vs Agents ↳ How to build your own PM productivity system  ↳ And much more! Full episode in the comments 👇

Most teams are underperforming. Not because of laziness. Not because of bad culture. But because the people are stuck doing work AI should be doing. Want better meetings? Want stronger collaboration? Want better performance? Then free your team from doing things they shouldn’t be doing. Here are 5 simple, powerful ways AI is already helping teams build better ⬇️ ↳ 1. Summarizing conversations and meetings Let AI generate transcripts, write summaries, and pull action items. Tools like Fireflies(dot)ai, Sembly, or Cluely let humans listen deeply instead of multitasking. 👉 Deep listening creates better decisions. ↳ 2. AI-powered dashboards and alerts Use AI to surface KPIs, anomalies, and early warning signals. When AI tells the team what’s changing, the team can focus on what to do about it. 👉 Conversations shift from data-gathering to decision-making. ↳ 3. Detecting tone and team sentiment AI can analyze Slack, Zoom, or email data to flag mood swings, overload, or risk of burnout. This gives managers a chance to step in before trust or morale break down. 👉 Tech reads the room, so humans can step up. ↳ 4. Automating low-EQ, repetitive tasks Inbox triage, calendar management, ticket routing, and basic reporting don’t need a human. AI frees up emotional bandwidth so people can do higher-order, higher-empathy work. 👉 Free your team from the robotic parts of their jobs. ↳ 5. Drafting the first version of everything Project plans, presentations, emails, proposals—let AI write the messy first version. Humans can then refine, adapt, and build something great together. 👉 Collaboration thrives when people start from something, not nothing. This isn’t about replacing people. It’s about amplifying them. AI isn’t just a productivity tool—it’s a team design tool. ✅ Start mapping tasks that are low EQ, high repetition, and ripe for automation ✅ Pick 1 AI tool to test across a core workflow next week ✅ Train your team to co-create with AI, not just delegate to it ♻️Repost & follow John Brewton for content that helps. ✅ Do. Fail. Learn. Grow. Win. ✅ Repeat. Forever. ⸻ 📬Subscribe to Operating by John Brewton for deep dives on the history and future of operating companies (🔗in profile).