Prompting Revenue

Brett Jansen

Prompting Revenue is the podcast for anyone using AI to grow something: a business, a team, a career, or a body of work. Hosted by Brett Jansen, CEO and Chief Growth Officer of Cresera AI Partners, the show brings you honest takes on where AI is taking the work. Brett has closed more than $175M in enterprise deals and helped founders raise over $310M. Her perspective comes from the field, where she ships AI into real revenue teams every single week. Every week you get three things: The AI news that matters. Filtered for operators, executives, and founders who need to know what changed before Monday morning. No list of every model release that dropped. Company spotlights. Close reads on the operators, incumbents, and AI-native challengers being reshaped by AI. What they are doing, what is working, what is breaking, and what you should steal or avoid. Workflows that work. Reusable prompts built with Brett's CRTO framework, taught live, and downloadable so you can run them for your role this week. If you are accountable for a number, running a team, or building something real with AI, you are in the right place.

  1. 4d ago

    EP20: AI Pipeline Generation: The Sales Workflows That Actually Move Revenue in 2026

    A healthcare technology company was losing deals it should have won not on product, not on price, but on response time. A competitor was following up with qualified prospects within four minutes of initial contact. This company's average was eleven hours. By the time their follow-up landed, the prospect had already taken a demo with someone else. Then they made one AI workflow change. Response time dropped from eleven hours to under six minutes, and pipeline conversion improved 31% the following quarter. In this episode, Brett Jansen breaks down why response speed has become a structural competitive advantage in sales, and which AI-enabled changes actually move revenue versus which ones just produce the appearance of activity. Key Takeaways AI isn't replacing salespeople, it's eliminating the administrative drag. The revenue teams seeing real results right now are the ones who understood that distinction early. CRM updates, call logging, meeting summaries, and proposal drafting are being automated, freeing reps to focus on the human judgment that actually decides whether a deal closes. Speed is now a structural advantage, not a nice-to-have. The probability of qualifying a lead drops more than 80% if follow-up takes longer than five minutes after initial contact. Most sales teams aren't within a mile of that threshold without AI assistance. Time saved isn't automatically time reinvested. Gartner found AI is already saving sellers nearly five hours a week, but 72% of sales organizations are failing to reinvest that time into high-value selling activity. What the rep does with the time saved is what actually determines whether AI moves the number. Automation volume isn't the goal, pipeline acceleration is. A sales team generating twice the email volume at the same conversion rate hasn't improved its commercial position. A sales team that's cut its average sales cycle by three weeks has. The sales divide is already here, and it's widening. Top-performing reps are 1.7 times more likely to use AI prospecting tools than underperformers, and that gap grows every quarter, compressing the window slower competitors have to catch up. Who This Matters To Sales leaders and VPs of Sales: This is a board-ready framework for talking about AI as a revenue strategy rather than an IT initiative, along with the specific metrics, response time, sales cycle length, revenue per selling hour that actually prove ROI. RevOps and sales enablement: The distinction between AI workflows that compress pipeline and ones that just generate more activity without moving conversion. Founders and revenue-focused executives: A clear look at why response speed is becoming a durable competitive moat, and why the gap between AI-adopting and non-adopting sales orgs is widening rather than closing. Individual reps and account executives: A practical picture of how the job is changing, less time managing systems, more time managing relationships and reading the room. Connect with Brett LinkedIn: linkedin.com/in/brettjansen YouTube Channel: youtube.com/@buildwithbrettjansen Website: creseraai.com

  2. Oct 7

    EP19: Why 90% of RevOps Teams Aren't Seeing ROI From AI

    Your CRO walks into the Monday pipeline review. The forecast says $4.2 million for the quarter. But three "likely to close" deals haven't had activity in 23 days. Two "healthy" accounts have declining product usage. And the rep who owns your largest opportunity just gave notice. Every one of those signals was sitting in the data the whole time, none of them surfaced in time to act. In this episode, Brett Jansen breaks down why RevOps is becoming the strategic engine connecting sales, marketing, customer success, and finance and why so few organizations are actually seeing ROI from the AI they're deploying on top of it. Key Takeaways RevOps has changed jobs. It's no longer CRM admin or pipeline reporting, it's the connective tissue linking sales, marketing, customer success, and finance around one revenue goal. AI has shifted RevOps from steward of process and data to governor of intelligent systems. Adoption is high, impact isn't. 75% of high-growth companies will run a RevOps model in 2026, and 96% of revenue leaders expect their teams to use AI tools. But fewer than 10% of RevOps organizations are seeing measurable ROI from it. Five AI applications are driving real results. Conversation intelligence (Gong, Chorus) for deal risk and coaching, AI forecasting (Clari, Forecastio) for pipeline accuracy, CRM data enrichment (Clay, People.ai) for clean records, agentic pipeline management for autonomous monitoring, and intent data platforms (6sense, Demandbase) for prioritizing outreach. Clean data is the unlock. Organizations with clean data foundations report 40% higher sales efficiency and 22% less deal slippage. At one company in the research, 80% of renewal opportunities had to be manually cleaned before any analytics were even possible. Who This Matters To RevOps leaders: The data says fix your foundation before adding another tool, the ROI is in the boring work, not the next platform. Sales and GTM executives: Ownership and accountability, not more tools, is usually the reason AI hasn't moved the needle yet. CROs and revenue leaders: The signals you need are already in your CRM, the question is whether your systems surface them in time to act. Teams building RevOps from scratch: Nearly 40% of RevOps teams were established in just the past two years, so the sequence of data, process, ownership, then AI matters more than speed. Connect with Brett LinkedIn: linkedin.com/in/brettjansen BUILD Community: build.brettjansen.ai Website: brettjansen.ai Podcast: brettjansen.ai/prompting-revenue

    EP19: Why 90% of RevOps Teams Aren't Seeing ROI From AI

  3. Sep 23

    EP18: Agentic AI And The Future Of Companies

    Most of us think of AI as an assistant, you ask, it answers, and then it waits for the next prompt. Agentic AI is a different category entirely: instead of waiting to be asked, it takes a goal and figures out the steps to get there on its own, planning, deciding, using tools, and coordinating across systems until the job is done. In this episode, Brett Jansen breaks down what actually changes when AI stops assisting work and starts operating workflows and why the technology is moving faster than the governance structures built to manage it. Key Takeaways Agentic AI is an operator, not just an assistant. It plans, acts, and completes multi-step tasks independently coordinating across tools and systems, adapting based on outputs (not just inputs), and carrying persistent memory across sessions. Adoption is accelerating fast. In Q1 2026, 80% of enterprise applications shipped or updated already embedded at least one AI agent up from 33% in 2024. Gartner and Digital Applied project 40% of enterprise applications will include task-specific AI agents by the end of 2026, up from under 5% in 2025. Governance is the real bottleneck, not the technology. 72% of enterprises now have agentic AI in production, but 60% have no formal governance framework to manage it. Only 33% of organizations have reached McKinsey's level-three AI Trust Maturity or higher. The risk is concrete. Gartner estimates over 40% of agentic AI projects are at risk of cancellation by 2027 if organizations don't establish governance, observability, and ROI clarity. The ROI is already showing up. FifthRow and McKinsey found a median ROI of 171% globally (192% for US enterprises) among organizations deploying agentic AI at production scale, with payback in 7–9 months and top-quartile deployments exceeding 540% ROI within 18 months. Who This Matters To Executives and strategic planners: 72% of enterprises already have agentic AI in production, but 60% have no formal governance framework to manage it, meaning oversight, not capability, is the current competitive risk. Finance leaders: Global enterprise AI agent spend is projected to reach $1.4 trillion by 2027, and median ROI on production deployments is already 171% globally. This is a budgeting conversation now, not a future one. Operations and technology leaders: Agentic systems coordinate across tools and adapt based on outputs, not just inputs, changing what needs to be built, monitored, and escalated when something goes wrong. Founders and leaders at small or mid-market companies: Turnkey agentic solutions are letting smaller organizations build AI-native operations from scratch, while larger enterprises are stuck retrofitting agents into legacy systems, a starting-position advantage worth acting on early. Connect with Brett LinkedIn: linkedin.com/in/brettjansen BUILD Community: build.brettjansen.ai Website: brettjansen.ai Podcast: brettjansen.ai/prompting-revenue

    EP18: Agentic AI And The Future Of Companies

  4. Sep 9

    EP17: Company Spotlight: Anthropic

    A four-year-old company just closed the largest private funding round in history, $30 billion at a $380 billion valuation then filed confidentially for an IPO near $1 trillion four months later. That company is Anthropic. The product is Claude. And 80% of its revenue now comes from enterprise customers who treat it as mission-critical infrastructure, not an experiment. This episode breaks down what Anthropic actually is, why its safety-first founding premise became a competitive advantage instead of a constraint, and why eight of the Fortune 10 are already customers. Key Takeaways Anthropic's valuation grew sixfold in a single year, from $61.5 billion (March 2025) to $380 billion (February 2026), with a confidential IPO filing now approaching $1 trillion. Annualized revenue hit $47 billion in May 2026, up from just $1 billion eighteen months earlier, and up from $9 billion at the end of 2025 alone. Eight of the Fortune 10 are Claude customers, and over 1,000 customers now spend more than $1 million annually on Claude, up from just a dozen two years ago. Claude Code hit $1 billion in annualized revenue faster than any enterprise software product in history, then more than doubled to $2.5 billion by February 2026. Claude is now the only frontier AI model available simultaneously on AWS, Google Cloud, and Microsoft Azure, a major procurement advantage for enterprise buyers. Who This Matters To Executives and strategic planners: Anthropic's safety-first positioning, once seen as a constraint, is now the reason enterprise customers choose Claude over competitors. Vendor selection is increasingly a trust decision, not just a capability decision. Finance leaders: 80% of Anthropic's revenue comes from enterprise, not consumers, and the customers spending over $1 million annually are expanding usage quarter over quarter. This is a signal about where AI budget commitments are heading, not a one-time pilot spend. Technology and operations leaders: With Claude now available across all three major cloud platforms, deployment no longer requires switching cloud providers, removing a common infrastructure blocker to enterprise adoption. Anyone setting AI strategy: Dario Amodei has said a twelve-month delay in AI progress would bankrupt Anthropic. That pace of change means the gap between where AI is today and where it will be in a year is larger than most corporate planning cycles are built to handle. Connect with Brett LinkedIn: linkedin.com/in/brettjansen Build Community: https://build.brettjansen.ai/ Work with Brett: brettjansen.ai

    EP17: Company Spotlight: Anthropic

  5. Aug 26

    EP16: The AI ROI Problem

    85% of Enterprises Can't Prove Their AI ROI. Here's the Fix. Your CFO doesn't want another AI pilot. She wants one number: what is it actually returning? Most leaders can't produce it. Worldwide AI spending is on track to pass $2.5 trillion in 2026, but only 28% of enterprise AI use cases fully meet their ROI expectations, and 85% of large enterprises cannot properly track their AI ROI at all. This episode breaks down why traditional ROI math breaks for AI, the four types of value AI actually creates, and the six-step framework any organization can run starting today, beginning with the one step 80% of companies skip. Key Takeaways 85% of large enterprises cannot properly track their AI ROI, and 72% of AI investments are actively destroying value through waste. Only 28% of enterprise AI use cases fully meet ROI expectations, despite worldwide AI spending on track to pass $2.5 trillion in 2026. Most AI programs get killed not because they failed, but because nobody could prove they succeeded, 49% of CIOs cite proving AI's value as their single biggest blocker. There's a real gap between belief and proof: 64% of executives say AI enables state-of-the-art solutions, but only 39% can link those solutions to EBIT impact. Enterprises with a documented AI ROI framework are 3x more likely to scale AI initiatives beyond the pilot stage. Who This Matters To Executives and strategic planners: The programs that survive the next budget cycle will be the ones with before-and-after evidence, not the ones with the most enthusiasm. Boards fund evidence. Finance leaders: Traditional ROI math (net profit over investment cost) breaks down for AI because benefits are indirect and costs are distributed. A credible number requires full total cost of ownership, not just the license fee. Operations and deployment leads: The step most organizations skip is setting a baseline before deployment. Without it, there's no way to prove anything worked, no matter how well the tool performs. Anyone running or scaling a pilot: A 90-day measurement cycle is the checkpoint. If a use case can't show measurable improvement in that window, it needs to be redesigned, not scaled. Connect with Brett: LinkedIn: linkedin.com/in/brettjansen Build Community: https://build.brettjansen.ai/ Work with Brett: brettjansen.ai

    EP16: The AI ROI Problem

  6. Aug 19

    EP15: AI Has a Land Problem And It's About to Become Everyone's Problem

    AI's next constraint isn't a better model. It's a permit, a power line, and a community vote. In the first four months of 2026 alone, there were 79 data center rejections or restrictions across the United States, more than all of 2025 combined (49). Data centers now consume 6% of all US electricity, past the threshold where research says political pushback begins. Virginia alone has seen over 60 data center-related bills introduced this year, with data centers already consuming 26% of the state's electricity. This episode breaks down why communities are pushing back, what it means for enterprise AI adoption, and why the organizations still treating AI as a software purchase are about to be caught flat-footed. Key Takeaways Data center rejections are accelerating fast: 79 in the first four months of 2026 versus 49 for all of 2025. Research shows community and political pushback intensifies once data centers reach 5% of national grid consumption. The US (6%), UK (5.8%), and Germany (9.5%) have all crossed it. This isn't partisan: over 70% of Americans believe AI is advancing too quickly (68% of Republicans, 77% of Democrats). Morgan Stanley has flagged public pushback as a binding constraint on data center buildout, not a soft concern. In a single quarter (Q2 2025), $98 billion in data center projects across 11 states were blocked or delayed, two-thirds of everything being tracked. Who This Matters To Executives and strategic planners: Infrastructure constraints won't stop AI adoption, but they will shape pricing, access, and competitive positioning. Organizations that locked in cloud agreements early may already have a structural edge. Finance and compliance leaders: AI emissions reporting is moving from voluntary disclosure to compliance requirement for large organizations. Operations and infrastructure leaders: Energy access is becoming a competitive advantage. Companies near infrastructure or with long-term energy agreements will outpace those still treating compute as a commodity. Global and geopolitical strategists: Sovereign AI infrastructure is emerging as countries build compute capacity independent of US cloud providers, a shift that will reshape AI access over the next decade. Connect with Brett: LinkedIn: linkedin.com/in/brettjansen Build Community: https://build.brettjansen.ai/ Work with Brett: brettjansen.ai

    EP15: AI Has a Land Problem And It's About to Become Everyone's Problem

  7. Aug 12

    EP14: COMPANY SPOTLIGHT: Microsoft Copilot

    Company Spotlight: Microsoft Copilot The AI Already Inside Your Organization And the Question of Whether Anyone Is Actually Using It Microsoft Copilot is already inside your organization. If your company runs Microsoft 365, Word, Excel, Outlook, Teams, PowerPoint, Copilot is embedded in every one of those tools. The question isn't whether to adopt it. The question is whether anyone on your team is actually using it. Microsoft has 420 million monthly active Copilot users and over 20 million paid Microsoft 365 Copilot seats, with an AI business growing 123% year over year past $37 billion in annualized revenue. But when employees have simultaneous access to Copilot, ChatGPT, and Gemini, Copilot's active usage share falls to just 8%. This episode breaks down what Copilot actually does, why the adoption gap exists, and what it's costing organizations that haven't closed it. Key Takeaways Distribution and adoption are very different things, 420 million monthly users doesn't mean 420 million people are getting value. When Copilot is the only AI tool available, adoption reaches 68%. When employees also have ChatGPT or Gemini, that share falls to 8%. The most common adoption barrier isn't cost or access, it's lack of training and unclear use cases at the team level. Microsoft's real advantage isn't the model, it's distribution: Copilot sits inside the productivity software 400 million people already use every day. The organizations reporting the highest Copilot ROI defined use cases and trained their teams before rolling out licenses. The ones reporting low ROI did neither. Who This Matters To Executives: A 500-person org paying for Copilot at 8% adoption is leaving roughly $165,000 a year on the table, a number worth putting in front of the board. Operations and process owners: Report generation, email summarization, and meeting follow-through are the proven starting points, with 50% faster completion on repetitive tasks already documented. IT and technical leaders: Azure's AI infrastructure buildout and the OpenAI partnership matter for platform decisions well beyond Copilot licensing. People managers: Low adoption is rarely a tooling problem, it's a training and change-management problem, and closing the gap requires defining use cases, not just issuing seats. Connect with Brett: LinkedIn: linkedin.com/in/brettjansen Build Community: https://build.brettjansen.ai/ Work with Brett: brettjansen.ai

    EP14: COMPANY SPOTLIGHT: Microsoft Copilot

  8. Aug 5

    EP13: Why Companies Are Spending on AI but Struggling to Prove the Return

    Are We Doing Enough With AI? Why Most Companies Can't Prove It's Working Companies are spending on AI. Employees are experimenting with it. Vendors are adding it to every platform. But most leadership teams still can't name one important workflow where AI has produced a measurable before-and-after business result. This episode breaks down why corporate AI programs fail at the moment they're defined, which use cases actually create enterprise value, and what the organizations getting real results are doing differently. Brett walks through the five questions every leader should ask before approving the next AI pilot, and why one measured workflow is worth more than twenty disconnected demos. Key Takeaways "We need an AI strategy" is a goal that creates motion, not an outcome. Individual productivity and enterprise value are not the same thing. Without a baseline, every AI success story is just a feeling. The strongest use cases are bounded, repeatable, and measurable, not open-ended chat access. AI initiatives need shared ownership across business, technical, legal, and frontline teams. Who This Matters To Executives: A workflow-first approach replaces vague adoption metrics with evidence leadership can actually defend to the board. Operations and process owners: Contract review, meeting follow-through, document processing, and knowledge retrieval are proven starting points with clear baselines to measure against. IT and technical leaders: Shared ownership means architecture and security decisions stay connected to the business outcome instead of running in parallel to it. People managers: AI adoption surfaces real tension around role security and inconsistent execution, and that has to be led, not automated around. Connect with Brett: LinkedIn: linkedin.com/in/brettjansen Build Community:Brettjansenbuild.brettjansen.ai Work with Brett: brettjansen.ai

    EP13: Why Companies Are Spending on AI but Struggling to Prove the Return
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Ratings & Reviews

5
out of 5
3 Ratings

About

Prompting Revenue is the podcast for anyone using AI to grow something: a business, a team, a career, or a body of work. Hosted by Brett Jansen, CEO and Chief Growth Officer of Cresera AI Partners, the show brings you honest takes on where AI is taking the work. Brett has closed more than $175M in enterprise deals and helped founders raise over $310M. Her perspective comes from the field, where she ships AI into real revenue teams every single week. Every week you get three things: The AI news that matters. Filtered for operators, executives, and founders who need to know what changed before Monday morning. No list of every model release that dropped. Company spotlights. Close reads on the operators, incumbents, and AI-native challengers being reshaped by AI. What they are doing, what is working, what is breaking, and what you should steal or avoid. Workflows that work. Reusable prompts built with Brett's CRTO framework, taught live, and downloadable so you can run them for your role this week. If you are accountable for a number, running a team, or building something real with AI, you are in the right place.

Information

  • Creator
    Brett Jansen
  • Years Active
    2026
  • Episodes
    20
  • Rating
    Clean
  • Copyright
    © Copyright J.Bridges, LLC 2026 All rights reserved.
  • Show Website
    Prompting Revenue