How AI adoption boosts team performance and leadership

I laughed when I saw this, funny but uncomfortably true. This is exactly why so many AI rollouts fail. Leaders rush to buy tools before they’ve even defined the problem. Then 6 months later, the tool sits unused and the team is frustrated. That’s the problem. Here’s how to avoid that trap: 1) Start with your pain points. What tasks drain your team’s energy every week? Scheduling? Reports? Customer emails? Write them down. 2) Define success in numbers. AI works best when success is measurable. “Cut reporting time from 10 hours to 2” NOT “save time.” 3) Check your data. AI needs clean, structured information. If your data is messy or scattered, fix that first. 4) Assign ownership. Someone has to drive adoption. If AI belongs to everyone, it belongs to no one. 5) Pilot before you scale. Pick one use case. Test it and measure results. Then expand. The CEOs winning with AI are not the ones who rushed in. They’re the ones who asked better questions before starting. P.S. What’s the first question you’d ask before rolling out AI?

Most cross-functional AI projects collapse before the technology even launches. Leading alignment across 20+ stakeholders on multiple AI initiatives taught me that the real challenge isn't getting people to understand the technology. It's getting them to reimagine their role in the new workflow. When teams see AI as something happening to them rather than something they help shape, resistance becomes inevitable. I discovered the shift needed was from presenting AI capabilities to co-creating new ways of working: ✅ START WITH WORKKFLOW PAIN POINTS, NOT AI SOLUTIONS: Before discussing machine learning or automation, map out where each stakeholder group loses time, encounters frustration, or feels bottlenecked. Whether it's data analysts spending hours on manual categorization or content reviewers drowning in queue backlogs, lead with the problem they already want solved. ✅ GIVE EACH FUNCTION OWNERSHIP OF AI SUCCESS METRICS: Rather than universal KPIs, let engineering define what "successful automation" looks like, let operations define "meaningful time savings," and let executives define "measurable business impact." When stakeholders help set the benchmarks, they become invested in achieving them. ✅ MAKE AI DECISION-MAKING TRANSPARENT ACROSS ALL FUNCTIONS: Build interfaces that show why the system flagged specific items, categorized content in certain ways, or prioritized particular actions. Teams need to see the logic, not just the results, to develop confidence in collaborating with automated systems. ✅ ADDRESS SKEPTICISM WITH EXPERIMENTATION, NOT EVANGELISM: Create safe spaces for stakeholders to test, challenge, and refine AI outputs. When people can see both the strengths and limitations firsthand, trust builds organically rather than through persuasion. The insight: successful cross-functional AI alignment requires designing for stakeholder ownership, not just stakeholder agreement. When teams help shape how AI integrates into their work, adoption follows naturally. What changes when we treat AI transparency as a collaboration tool rather than a technical requirement? #ProductLeadership #CrossFunctionalTeams #AIAdoption

Forget the whole “using AI” thing we’re stepping into a world where AI is running underneath everything you touch. Sana just dropped agents that can tap into 100+ tools Gmail, Outlook, Slack, Salesforce, Teams, Google Drive, BigQuery… basically your whole digital workspace. Here’s why that’s huge: No more siloed apps actions get orchestrated across your entire stack. Search, summarize, automate… without hopping between tabs. Operational drag? Cut down by 10–20x. This isn’t about another shiny chatbot. It’s AI becoming the invisible layer of your workflow. So the real question for leaders isn’t “should we test AI?” anymore. It’s: Are you ready for agents to rewire the way your org works, communicates, and executes? Because the ones who say yes early… they’re about to lap everyone else.

Most teams I’ve seen start their AI journey from the wrong end, the tools! They experiment, try a few things, maybe build a small use case, and then it stops. If you actually want to build capability, not just test ideas, the order matters. Start with intent, then look at your data, build capability, and only then think about applications. It sounds basic, but most teams run it the other way around. #ArtificialIntelligence #AI #DigitalTransformation #Leadership #Innovation #DataStrategy #Automation #FutureOfWork #BusinessStrategy #TeamDevelopment #AIAdoption

Insights from a recent Highspot report reveal that while AI adoption in sales is on the rise, just 28% of leaders see a boost in revenue performance. Organizations often struggle to translate AI insights into action, leading to execution breakdowns. As companies face burnout and stalled deals, establishing a strategic AI framework and supporting team training is crucial. Dive into the full report to understand how to bridge the gap between strategy and execution.

We’re learning that AI is more than a new set of technology products — it’s a business transformation that unites technology, process, and people expertise. The hardest part? Connecting new AI capabilities to the knowledge work that actually drives business performance. That’s why I’m launching The Frontier Playbook — a series where I’ll share what we’re learning at Microsoft about how leaders can guide their organizations to become what we call Frontier Firms: AI-powered, human-led. Here’s where the Frontier Playbook begins: Step 1: Set CEO-level AI priorities 💡 Start at the very top. AI transformation succeeds when CEOs define what “winning with AI” means for the enterprise — whether that’s faster product cycles, sharper customer experiences, or fundamentally higher productivity. When AI priorities are set at the CEO level, they anchor the organization and ensure every function is moving in the same direction. 💡 Avoid pilot purgatory. Without CEO-led priorities and a well-defined playbook, teams default to isolated experiments that don’t scale, waste resources or erode confidence in transformation. Clear direction from the top creates the mandate to move beyond “tech demos” into business-wide adoption. 💡 Measure what matters. Priorities must be tied to business outcomes — the metrics that matter most to shareholders, employees, and customers. If AI doesn’t change how you measure success, it won’t change how the business performs. The exciting part? Each organization will chart its own priorities — from transforming employee experience to reimagining customer engagement, reshaping core processes, and accelerating innovation to drive competitive advantage. This is step one of the Frontier Playbook: setting the destination. Next up: how business leaders and employees take ownership to bring those priorities to life. 👉 What priorities are you seeing CEOs set as they take the first steps in AI transformation?

⏳ The cost of inaction isn’t zero — it’s compounding. In my latest article, I dive into “The Hidden Cost of Doing Nothing: What Businesses Lose by Delaying AI Adoption.” It’s not just about missed tech opportunities — it’s about losing time, momentum, market edge, and internal morale. 👉 Read it here: https://lnkd.in/dmQGbNPY Some questions I’d love your thoughts on: • What’s holding businesses back from adopting AI today? • Have you seen first-hand what delay costs your team? #AI #DigitalTransformation #Innovation #Leadership #BusinessGrowth #VoiceAI

I spent the week in DC with Pavilion digging into how AI is actually being applied in GTM. Lots of smart operators, investors, and enablement leaders shared what’s working and what’s not. Here are my Top 10 AI Takeaways for GTM Teams: 1. AI should enhance top performers and lift low performers, not replace them. It will not replace the need for sales and CS training on core skills to land, expand, and renew 2. AI adoption takes time. Map where you are on a maturity curve (piloting, scaling, or high-value) and set next-step goals. 3. Start with proven use cases: signal capture, account research, high intent outbound, and account prioritization. 4. Push for clear ROI metrics. Track often and iterate from learnings. 5. Get your playbooks right first with messaging, objections, and handoffs, then use AI to accelerate execution. 6. Data quality and ownership matter more than the tools. Weak foundations stall adoption. 7. Remove blockers like process ambiguity, governance gaps, and lack of time. They drag everything else down. 8. Boosters such as training and template libraries only work once blockers are gone. 9. Achieving high value from AI takes time. Only 1 in 5 companies are there today. 10. The risk of AI backfiring is low. Only 6 percent of companies reported issues. 🤖Bottom line: Let AI handle the busy work so your reps can focus on selling. #GTM #AI

3 Business Workflows You Can Automate with AI Today For many business leaders, AI still feels like a distant concept. Here are three workflows you can start automating right now: 1️⃣ Customer Support – Deploy AI agents to handle FAQs and routine queries instantly. 2️⃣ Reporting & Analytics – Generate real-time dashboards and financial summaries automatically. 3️⃣ Lead Qualification – Use AI to identify, score, and route high-potential leads efficiently. Each of these automations saves valuable time and eliminates repetitive tasks but more importantly, they empower your team to focus on strategy, creativity, and growth. AI isn’t about replacing people. It’s about enabling them to work smarter and make a greater impact. #AI #Automation #DigitalTransformation #BusinessGrowth

With great power comes great responsibility and AI gives us a LOT of power. Give someone access to AI and suddenly they're writing copy, designing interfaces, building roadmaps, maybe even running finance while they're at it. The tool makes it feel possible to do everything. But just because we can doesn't mean we should. AI is extraordinary at accelerating what we already do well: → A designer iterating faster? That's leverage. → An engineer debugging in half the time? That's efficiency. But when we use it to bypass collaboration—to replace the judgment and context that comes from people who actually own those disciplines—we're not being innovative. We're avoiding the hard conversations. The best outcomes happen at the intersections. They require tension, debate, and the humility to know what we don't know. When we use AI to smooth over those intersections, we end up with work that looks complete but lacks depth. It's productivity theater. Here's the real question: Is AI making you better at your job, or is it just making you feel like you can do everyone else's? ⚡ Use AI to amplify what you do best. Use people for everything else. What's your take? Drop a comment below. 👇 #ProductLeadership #Collaboration #AI #Leadership #ProductManagement #CrossFunctionalTeams #TeamWork #Innovation