AI-Ready Or Not?

About AI-Ready Or Not?

Most companies are buying AI tools. Almost none of them have the data, processes, or systems in place for those tools to deliver. The CRM is a mess. The processes aren't written down. The tribal knowledge is locked in three people's heads. Then the AI gets layered on top, and nobody understands why it doesn't work. AI-Ready or Not is the show about fixing the foundation first. Host Travis Scott talks with founders, operators, authors, and practitioners about how they're actually using AI. What's working. What's not. What they had to do to make it work. New episodes every two weeks.

Publisher

Travis Scott

Category

business · technology

Language

en

Explicit

No

First episode

5 May 2026

Latest episode

3 Nov 2026

Latest episodes

6 episodes in the feed.

1. 

   3 Nov 2026

   Why Bad Data Breaks AI with Heather Robinette

   You can build a dashboard that pulls together your forecast, call transcripts, and CRM. You can build an agent that handles one task surprisingly well. But if you don't trust the information underneath it, a confident answer doesn't get you very far. “So it's just a good reminder, too, that just 'cause it sounds confident doesn't mean it's right.” About This Episode Heather Robinette joined me to talk about what changed after she completed Overclock's 10-week AI for RevOps course. Before it, she mostly used AI through chat. By the end, she was connecting APIs, building applications, and working with agents. The useful part of that story is what she started doing with those skills. She describes pulling call information, Salesforce data, and weekly forecast discussions together to give leadership more context around a forecast. Her example of a risk-adjusted number is illustrative, not a reported business result. She also walks through a marketing dashboard that started with sourced versus influenced pipeline. Then came campaign performance, conversion rates, goals, and the questions her team actually wanted to answer. She estimates putting about 20 hours into it through repeated iteration. Now the team has somewhere to start when it wants to understand a campaign, instead of asking her to pull every number. That is a practical change in how the work gets done, even if building it took more than a clever prompt. I share some of the smaller agents I've been building for my own work: triaging email, drafting replies, and finding duplicate tasks in Asana. We also get into the context problem. Someone helping with a project needs more than a task name. They need to understand the conversations and decisions behind it. I explain how I'm connecting meeting summaries, Google Docs, and NotebookLM so the people working with me can find that context themselves. The conversation comes back to boundaries. Which systems should an agent be able to read? Which should it be able to change? Heather talks about learning to work inside a virtual machine and being deliberate about connectors. We discuss giving agents specific jobs, including checking other agents' work. She describes an instructor's team of agents as one example; she is clear that she has not built that whole setup herself. We also compare models by the actual task and cost, instead of assuming the newest option is automatically the best fit. Then we get to the data. In one client example, I describe finding that 10,000 of 14,000 company records were missing revenue, even though revenue mattered to the targeting criteria. An AI-generated account list still has to work with that gap. Heather's point is straightforward: get the data to a place where you can trust a basic report before expecting AI to produce dependable insight from it. The work can get faster. The responsibility for checking it stays with us. About Heather Robinette Heather Robinette works in revenue operations and is now Senior Revenue Operations Manager at Zafran Security. When we recorded this conversation, she was at AppOmni. Her examples come from the operational work of helping teams understand forecasts, pipeline, campaigns, and the data behind their decisions. After completing Overclock's AI for RevOps course, she began applying what she learned to dashboards, applications, and agents. In this conversation, she shares what she built, what she had to learn, and why security and data quality became a bigger part of how she thinks about AI. Key Takeaways Start with a question your team needs answered. Heather's marketing dashboard grew from a specific pipeline question into a place the team could explore campaigns and progress against goals. Give agents a clear job. Narrow responsibilities made it easier for me to see what was breaking and fix one workflow without disturbing another. Treat access as a design choice. Heather distinguishes what an agent can connect to from what it should be allowed to read or change. Test models against the work. We discuss comparing the same task across models and weighing the result against cost. Check the data and the answer. Missing fields can undermine a useful-looking output. Confidence in the wording is not evidence that the underlying answer is right. Chapter Markers 00:00 - Cold open 00:18 - AI for RevOps course 03:30 - CRO and marketing dashboards 05:59 - Travis's agents and shared context 13:01 - Security guardrails and sandboxing 15:15 - Single-task agents that check each other 18:41 - Choosing models and managing cost 22:58 - What companies want from AI 25:43 - Bad data means bad AI 31:01 - Wrap-up Resources & Links Mentioned Tools & platforms Claude, Claude Cowork, Claude Code, ChatGPT, Gong, Salesforce, Clari, Asana, AskElephant, Google Docs, NotebookLM, DigitalOcean, Perplexity, Manus, HubSpot, and HubSpot Breeze. Books & podcasts None. Other resources Overclock's AI for RevOps course. Connect with Heather Robinette LinkedIn: https://www.linkedin.com/in/heatherrobinette/ (https://www.linkedin.com/in/heatherrobinette/) Connect with Travis Rainier RevOps: https://rainierdigital.com (https://rainierdigital.com) LinkedIn: https://www.linkedin.com/in/travisscott24 (https://www.linkedin.com/in/travisscott24) Instagram: https://www.instagram.com/travis.revops (https://www.instagram.com/travis.revops) YouTube: https://www.youtube.com/@RainierRevOps (https://www.youtube.com/@RainierRevOps) If This Episode Resonated If you're ready to build with AI but aren't sure your CRM data and processes can support it, start with the AI Readiness Scorecard: https://ai-readiness-os.manus.space/ (https://ai-readiness-os.manus.space/) Recorded August 14, 2026. Platform references and examples reflect that conversation. AI-Ready or Not is a production of Rainier RevOps. Recorded in Zoom. Edited in Descript. Hosted on Transistor.

2. 

   6 Oct 2026

   How AI Gave a Vacation Rental Business Confidence to Scale

   TrustFirst Management founder Scott Dinan explains how AI guest messaging, a custom work order system, and a Slack triage agent changed the way he runs a Gulf Coast vacation rental business, and why the biggest payoff was the confidence to grow.

3. 

   22 Sept 2026

   The Retention Leak: How AI Can Catch Churn Before It Happens

   Latch AI founder Alisha Esmail explains why client retention fails like a slow leak instead of a fire, how she built a churn-prediction system by hand before adding AI, and why messy CRM data has to be cleaned up first.

4. 

   6 Jul 2026

   Her Business Was Going Under, Then She Saved It With AI

   Andrea Palacio and her husband bought a landscaping company and nearly lost it within a month — staff dropped from 12 to 2 and revenue was cut in half. She rebuilt it with AI: phones answered and leads booked automatically, Google Ads optimized by an AI agent. Now she helps other service-business owners do the same at RunCrewless.

5. 

   18 Jun 2026

   Divine Intervention: How One Founder Cracked a Two-Company Monopoly with AI

   Aaron VanStone built ScriptSafe, an AI-powered pharmacy-accreditation platform challenging an industry with only two gatekeepers, coding the entire stack himself with zero prior experience using Claude Code. He breaks down the workflow, the cheap starter stack, and the hard-won lessons behind it.

6. 

   5 May 2026

   Jenny Blake, Author of Free Time — The Things Most Founders Skip Before Using AI

   The Foundation Before the Agents: Jenny Blake on What Has to Be True Before AI Actually Works. After two years of watching the AI hype cycle from the sidelines, author and keynote speaker Jenny Blake (Free Time, Pivot) finally dove in — and now she's hitting inbox zero for the first time in fifteen years of self-employment. But the leap wasn't about the tools. It was about what she had already built underneath them. In this opening episode, Travis and Jenny unpack the infrastructure - context files, documentation, clean processes - that separates people who get leverage from AI from people who get noise. "The AI is only as good as what it knows about you." About This Episode Jenny Blake has spent the last few years publicly underwhelmed by generative AI. It impressed her (everyone can write a poem in ten seconds) but it didn’t transform her business. Then, at the start of 2026, something shifted. Claude Cowork launched for non-developers. Notion released Custom Agents. Her brain, in her words, "caught fire." This episode is not about which tool to buy. It’s about what has to be done in your business before any of these tools can give you leverage. Jenny walks through the practical prerequisites - context files, markdown documentation, the "interview me until you're 95% clear" prompting pattern - and Travis connects those principles back to what founders and revenue leaders are dealing with at larger scale: messy CRM data, tribal knowledge in people's heads, and processes that were never written down. If you’ve been sitting on the sidelines wondering whether AI is finally worth your time, or you've tried it and found it more annoying than useful, this conversation is the context you've been missing. About Jenny Blake Jenny Blake is the author of three award-winning books: Free Time: Lose the Busywork, Love Your Business; Pivot: The Only Move That Matters Is Your Next One; and Life After College. She runs a media and growth strategy company, licensing her IP to clients such as Google, CHANEL, and the Stanford Graduate School of Business. Before launching her own business in 2011, Jenny co-created Google's global drop-in coaching program, Career Guru, which is still in operation today. She hosts two podcasts with over two million downloads combined: the Webby-nominated Free Time podcast for heart-based business owners, and Pivot with Jenny Blake for navigating change. She lives in New York City with her husband and their German shepherd, Ryder. Key Takeaways Context files are the foundation, not the AI. Jenny's agents only became useful once she'd fed them markdown files describing her business, her clients, her voice, and her goals. The AI is only as good as what it knows about you. Before every prompt, she tells the model to read her context files first. Reverse prompting: make the AI interview you. Instead of trying to front-load every piece of context in a prompt, tell the model: "AskUserQuestion until you're 95% clear on what I'm trying to do." The questions it asks will surface blind spots you would have missed. The agentic shift was specific and recent. Jenny was a skeptic for years. The turning point was agents (Claude Cowork, Notion Custom Agents, Perplexity Computer) launching for non-developers in early 2026. The difference from older automation: you direct them in natural language, and they improve themselves with every run. AI levels the playing field for neurodiverse operators. Travis, diagnosed with ADHD in 2023, describes the shift from seeing AI as a way to replace assistants to seeing it as a way to work around personal weaknesses, so he can spend more time in his strengths. The frame is not "replace a person," it's "build a better version of yourself." A good idea is no longer enough. Anyone can vibe-code a copy of your product in an hour. The moat is now distribution, marketing, and taste, the things AI can't replicate on command. A "one-person billion-dollar startup" usually means the founder already had marketing leverage, not just a working prototype. If it's not documented, it can't be delegated. This applies to humans AND agents. Travis realized the SOP he was writing to hire someone was also the exact prompt an agent needed. Documentation isn't overhead, it's the interface between you and everything you could offload. Most companies have skipped this step, and it's about to bite them. You have many doorways in and you only need one. Jenny's closing advice: you don't have to be a software engineer anymore. If you're a good people manager, problem-solver, builder, opinionated about taste, or simply curious - that's your doorway in. Pick one and stop waiting. Chapter Markers 00:00  —  Cold open & welcome 00:30  —  "Impressed but underwhelmed": what changed in early 2026 05:30  —  Reverse prompting: make the AI interview you 07:30  —  Context files and why Claude Desktop is the unlock 10:30  —  The Claude Council prompt and open-source prompt sharing 12:30  —  Travis's origin story: HubSpot, INBOUND, and the ADHD unlock 17:00  —  Starting small: solve a real pain point, not a novelty 19:30  —  Why learning time has to live on your calendar 22:30  —  The vibe-coding trap: when a good idea isn't a moat 27:30  —  Small businesses have the advantage — for now 30:00  —  Notion vs. Claude Desktop: when to use which 33:30  —  The prerequisite nobody talks about: clean data, documented processes 35:30  —  SOP-to-agent: 30 minutes of cleanup becomes 2 minutes 37:30  —  Every question lives three lives: Jenny's documentation principle 39:30  —  Why Jenny doesn't use AI to write 41:00  —  "Words I Hate" and tropes.fyi: controlling AI voice 44:00  —  Closing: the many doorways in — pick one, stop waiting Resources & Links Mentioned Jenny's Books Free Time: Lose the Busywork, Love Your Business (https://www.amazon.com/Free-Time-Lose-Busywork-Business/dp/1646870662/ref=sr_1_2) Pivot: The Only Move That Matters Is Your Next One  (https://www.amazon.com/Pivot-Only-Move-That-Matters/dp/1591848202/ref=tmm_hrd_swatch_0) Life After College (https://www.amazon.com/Life-After-College-Complete-Getting/dp/0762441275/ref=sr_1_3) Jenny's Podcasts, Newsletter, & Community Free Time Podcast (https://podcasts.apple.com/us/podcast/free-time-with-jenny-blake/id1557702330) (https://podcasts.apple.com/us/podcast/pivot-with-jenny-blake/id10548...)

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