Perry Loud

One of the biggest misconceptions about Revenue Enablement is that it’s simple. "Train the team." "Build a deck." "Send out a playbook." If that were the job, every company would have it figured out. Real Revenue Enablement sits at the intersection of strategy, behavior change, and adult learning. The work isn’t just about delivering information — it’s about changing how experienced professionals think, speak, and execute in live customer conversations. That means translating company strategy into something sellers can actually use in a meeting. Designing learning experiences that stick for adults who already have habits, instincts, and opinions. Embedding process, messaging, and tools into a way of working — not just another slide deck that gets shared and forgotten. And then comes the hardest part: adoption. Because enablement doesn’t succeed when a training is delivered. It succeeds when the field actually uses it. When discovery gets sharper. When messaging shows up consistently on calls. When deals move through the pipeline with more clarity and confidence. That kind of change takes reinforcement, iteration, and alignment across the entire revenue organization. Which is why the conversation around enablement needs to shift. Enablement isn’t just a support function that runs training. It’s the discipline responsible for turning strategy into execution. The teams doing it well are aligning messaging, process, tools, and learning so the revenue organization can actually operate at a higher level. When organizations start thinking about enablement that way — and invest accordingly, that’s when you see the real impact. Because great enablement doesn’t just teach teams what to do, It helps the entire revenue organization perform better.

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CRO firing season: boards, CEOs, and PE firms are demanding predictable growth — and replacing revenue leaders to get there. But that approach often fails to address underlying commercial engine issues. Read my latest 4-minute piece on what boards are missing and how to create durable revenue performance: https://wix.to/ryOYlO4 #CRO #Boardroom #RevenueOperations #PrivateEquity #LeadershipDevelopment

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If you think all of your company’s AI spend is showing up in the cloud bill, there’s a good chance it isn’t. Teams often open direct accounts with OpenAI, Anthropic, Cohere, and other providers, put the charges on a corporate card, and start spending outside the budget structures finance already has in place. This is shadow AI — the new version of shadow IT spend. Finding the spend is step one. The harder question is whether finance can actually trace those dollars back to the teams, products, customers, and workloads that created them. You need to be able to answer three questions: • Can I see all of it? Direct API spend, cloud AI services, shared accounts, and other AI costs need to roll into one view so shadow spend doesn’t sit outside finance. • Can I assign it? Every AI dollar should map to a team, product, customer, or environment.  • Can I use it in the P&L? Allocation needs to be granular enough to get reliable numbers for your margin and ROI calculations. A couple practical tips: • Consider virtual tagging and allocation solutions that use existing metadata rather than time-consuming manual processes. • Have clear rules for shared and unallocated costs — direct assignment, percentage-based splits, equal splits, or whatever reflects the business. The bottom line for CFOs: “AI spend” should never be one blended line item. If you can’t map the dollars back to ownership, you can’t tell what’s actually profitable — or whether the AI investment is paying off.

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My hiring philosophy has always centered around over indexing on builders vs operators. Some may ask what's the difference? Operators rely on playbooks. Builders write new ones. Operators scale what worked. Builders ask if it should still exist. In most orgs, especially legacy SaaS, operators dominate. That works in stable markets. Spoiler alert, that's not the market we're currently in. What's exciting is AI just reset the cost of building. Projects that used to be impossible to even imagine taking on, can now take quarters or weeks. Things that required teams now may be accomplished by one sharp person and the right tools. There's a lot of chatter around "Is SaaS Dead?". My take is many of the current incumbents will fail as they're stuffed with operators, they’ll optimize the current motion. Improve the funnel. Tighten the process. Defend the roadmap. A builder will ask: What would we do if we started today? Can we ship a version of this by EOW? What's also interesting is that even STRONG builders are still thinking inside pre-AI constraints. “That would take too long/cost too much.” Part of the challenge with the AI shift isn’t skill. It’s the mental models. Many orgs are trying to screen for "AI competency" and TBH I'm not sure what that means. Can they write prompts, use ChatGPT, etc? A better question is tell me something you built over the last 90 days? We're hiring builders at CockroachDB, come join us.

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