Revalize

Metrics are great. What’s greater is the team who manages this on a daily basis and our partner Sitation who helped us build the infrastructure to make it possible. Proud of what we’ve accomplished together.

You keep asking "which AI platform?" That's the wrong question and you know it. I've watched this pattern repeat across every technology wave. Leaders spend months evaluating vendors, running proof-of-concepts, and debating architecture. Then the winning platform gets deployed and...nothing changes. Adoption flatlines; the ROI deck collects dust. The question that actually matters: "Is my organization ready to absorb this?" At my last company we achieved 96% weekly Copilot adoption and roughly 30 daily AI interactions per employee. The technology was table stakes. What made it work was months of groundwork: stakeholder alignment, manager buy-in, workflow redesign, and honestly just sitting with people while they figured out how to make it useful for THEIR job. The uncomfortable reality is that most AI projects don't fail because the tech was wrong; they fail because nobody invested in the messy human stuff. Communication plans, training that goes beyond a lunch-and-learn, org structure changes that actually give people permission to work differently. This is especially true in real estate and homebuilding. We're not dealing with a workforce that grew up in Silicon Valley. We're asking field teams, sales consultants, and trade partners to fundamentally change how they operate, and that requires trust before technology. Something I've learned the hard way: the ratio is roughly 30% technology decisions and 70% change management. That 70% is where most leaders under-invest because it doesn't feel like "real work." It doesn't show up on a Gantt chart, but it's the difference between a successful rollout and an expensive shelf decoration. Before your next AI initiative kicks off, ask yourself...have you spent as much time on your adoption strategy as your architecture diagram? Link in comments. #AIAdoption #ChangeManagement #RealEstateTech #Leadership #DigitalTransformation

Most support leaders cannot answer the one question their CFO actually cares about. Not "what's your CSAT?" Not "how's ticket volume trending?" The question is: what did this function return on the dollars it consumed? I learned this the hard way early in my career. I walked into a budget review with a deck full of operational metrics. Response times. Resolution rates. Headcount utilization. All of it accurate, but none of it financially relatable. The CFO looked at me and asked how much revenue the customer success and support orgs had protected or influenced that year. I had no answer. That moment changed how I built every Customer Experience operation after that. From that point, I tracked customer success and support-influenced retention, upsell leads sourced through case interactions, and cost-per-case movement relative to volume absorbed. Not because finance demanded it. Because the business needed its Customer Experience leader to speak the same language as the people controlling the budget. In one organization, that discipline produced $1M+ in qualified upsell leads generated directly through support and customer success interactions. That number did not appear automatically. Someone had to build the motion that created it and the tracking system that proved it. If you lead a Customer Experience function and your budget conversation starts with headcount justification rather than revenue logic, you are already behind. What metric do you lead with when you walk into a CFO conversation, and how did you land on it?

Support isn't a cost center. It's where customer and revenue retention either holds or breaks. The cost center frame feels logical until you watch a support org generate $1M+ in qualified upsell leads in a single year. That didn't happen because the team got lucky. It happened because we built structured handoffs between support interactions and customer success, defined what a qualified signal looked like, and held both teams accountable to a revenue outcome. That pipeline didn't come from a sales motion. It came from a support and success infrastructure designed to surface what customers needed next. The frame you operate with determines the infrastructure you build. Cost center thinking produces ticket queues, handle time targets, and headcount ratios. Revenue protection thinking produces tiered account segmentation, escalation logic, and proactive engagement triggers. At one of my PE-backed organizations, we maintained 98% annual customer retention and greater than 100% net revenue retention across a nine-figure ARR portfolio. Support was not a passenger in that number. It was one of the primary mechanisms producing it. Across my organizations, that same model produced efficiency as a byproduct. We reduced cost-per-case 6.6% with flat headcount and flat budget. But that result followed from the right question, not the wrong one. The question was never "how do we reduce cost-per-case?" The question was "what does this interaction tell us about the health of this account?" If your support org is measured only on cost and speed, you've already decided what it's allowed to be. For operators who've reframed support and success as revenue functions: what was the first metrics you changed, and what resistance did you hit internally when you made the shift?

Let’s be honest, Food & Beverage professionals: 🥛 How many products do we still launch without being 100% sure which recipe is finally approved? 🧠 How often do we make pricing, packaging or launch decisions while critical product data is spread across emails, spreadsheets and people’s heads? 🔥 And why are we still surprised by recalls, delayed launches or margin pressure — when the root causes are well known? The reality in many F&B organizations: Too many manual processes Too little transparency around product data Critical decisions made far too late The good news: These challenges are not inevitable. They are solvable — if we are willing to address them honestly. ❓Where does it really hurt in your organization? ❓Recipes, specifications, variants, compliance? ❓Or simply a lack of end‑to‑end visibility? I’m looking forward to an open, practical exchange. 👇 Let’s discuss. #FoodAndBeverage #FoodIndustry #ProductDevelopment #PLM #DigitalTransformation #DataTransparency #Innovation

Unsurprising statistics out! The disparity is actually getting worse, not better, in my experience in tech.

Almost everyday I am hitting my Claude token usage limit. Not because I'm running anything exotic. Just daily sales workflows: Prospecting BDR reporting AE handover briefs. Routine work. And the meter is running fast. That moment hit differently when I just read about Salesforce's Headless 360 announcement. Salesforce just made the biggest architectural bet in their 27-year history: exposing their entire platform, CRM data, business logic, workflows, as APIs, MCP tools, and CLI commands. No browser required. The explicit goal is to let AI agents operate Salesforce directly, without a human in the loop. This is not a feature launch. This is Salesforce saying: the human clicking through the UI is no longer the primary user. The agent is. And they're not alone. The entire industry is making this shift. From UI-first to agent-first. The infrastructure is moving fast. But here's the question I keep coming back to: if my token consumption already feels significant running a handful of workflows manually, what happens when agents are autonomously executing hundreds of multi-step processes per day across an enterprise stack? We're designing agent-first architectures. We're not yet having honest conversations about what the cost curve looks like at scale, and who bears it. This weekend I' ve read about a VC-backed startup that pays Anthropic $10M monthly... The productivity gains are real. I'm living them. But the economics of agentic AI will need a rethink, and sooner than most are planning for. #AgentFirst #Salesforce #GenerativeAI #SalesOps #Headless360