B2B Orchestration

Ian Michels
  • 5.0 (1)
  • Marketing

B2B Orchestration is a conversation-driven podcast built for the leaders who don't have time to get left behind. Every week, one focused conversation. One guest who's operating at the edge of what's working in B2B go-to-market. One set of takeaways you can bring back to your team, your stack, or your strategy before the week is out. This is not a podcast about theory. It is not a podcast about frameworks that look good on a whiteboard and fall apart in a CRM. B2B Orchestration exists because the gap between what's being published about B2B marketing and what's actually happening inside the organizations driving revenue has never been wider — and the pace at which that gap is growing has never been faster. The executives, VPs, and marketing leaders tuning in are not beginners looking for a foundation. They are practitioners and decision-makers who need signal, not noise. They are people who are already running demand gen programs, managing marketing operations teams, evaluating new technology, and trying to align with sales in an environment that keeps changing the rules. They need to hear from other people who are doing the same thing — people who have tried the new approach, broken it, fixed it, and can tell you exactly what they learned. That is what B2B Orchestration delivers.

  1. 9h ago

    How AI Is Changing the B2B Buying Journey

    Got something to say to this podcast? Send a voicemail, and you might hear yourself on the next episode! AI is changing how B2B organizations think about marketing, sales, customer data, and the buying journey. But the real opportunity goes beyond adding another AI tool to the technology stack. In this episode of B2B Orchestration, we explore the role AI can play in creating a more connected B2B strategy, bringing together customer and account data, buying groups, journey orchestration, sales engagement, and measurement. The goal is simple: turn more signals into meaningful action and help marketing and sales teams better understand who is buying, what they need, and when to engage. Topics explored include: 🔹 Using AI to better understand accounts and buying groups 🔹 Connecting customer and account signals across the buying journey 🔹 Creating more coordinated and relevant B2B experiences 🔹 Helping marketing and sales teams act on meaningful insights 🔹 Moving from disconnected campaigns toward B2B orchestration 🔹 Connecting engagement to pipeline and revenue AI alone isn't the B2B strategy. The opportunity is using AI to help organizations execute their strategy faster, with better context, and at greater scale. If you're working in B2B marketing, revenue operations, account-based marketing, marketing technology, or sales, this conversation is for you.

    How AI Is Changing the B2B Buying Journey

  2. Nov 3

    Build, Buy, or Experiment? A Practical Approach to AI in Marketing

    Got something to say to this podcast? Send a voicemail, and you might hear yourself on the next episode! AI is everywhere, but how should marketing leaders actually introduce AI to their teams? In this episode of B2B Orchestration, Ian Michels and AJ break down a practical approach to AI adoption for modern marketing organizations. Rather than chasing every new AI tool, the conversation explores how teams can begin with the technology they already own, identify a few meaningful use cases, create space for experimentation, and put the right governance around how AI is used. In this episode: How marketing leaders should introduce AI to their teamsWhy AI should enhance and supplement people's workWhere to start with existing AI capabilitiesCreating an environment for AI experimentationTraining and governance for AIProtecting customer and prospect informationCreating repeatable frameworks for AI projectsThe build-vs-buy debateHow AI can expand what marketing operations teams are capable of buildingOne of the most interesting parts of the conversation is AJ's approach to building before automatically buying. AI gives marketers and marketing operations professionals an opportunity to test ideas and create solutions that previously may have required outside development or additional technology investment. If you're responsible for B2B marketing, marketing operations, RevOps, MarTech, or AI adoption, this episode offers a grounded way to think about where AI belongs in your organization. Subscribe for more B2B Orchestration conversations on marketing technology, AI, data, revenue operations, and the future of B2B marketing.

    Build, Buy, or Experiment? A Practical Approach to AI in Marketing

  3. Oct 27

    The AI Operating Model for Modern Marketing

    Got something to say to this podcast? Send a voicemail, and you might hear yourself on the next episode! AI is everywhere, but marketing leaders face a harder question: Where should we actually use it? In this episode of B2B Orchestration, Ian and AJ unpack a practical approach to bringing AI into a marketing organization. The conversation starts with the human side of adoption, including the natural anxiety people have about AI changing their jobs, and argues for positioning AI as a way to enhance and supplement the work teams already do. From there, the discussion gets tactical: start with technology you already own, identify concrete use cases, create space for experimentation, establish appropriate governance, and focus investment on measurable outcomes instead of chasing every new AI tool. The conversation even uses Adobe Marketo as an example of looking for AI capabilities inside technology an organization is already paying for. The episode ultimately explores the question every organization will face: when should you buy an AI capability, and when does it actually make sense to build?

    The AI Operating Model for Modern Marketing

  4. Oct 20

    Multi-Touch Attribution, Lead Scoring, and the Slow Death of the Vanity Metric

    Got something to say to this podcast? Send a voicemail, and you might hear yourself on the next episode! Marketers aren’t lying to executives — they’re just explaining the end result without the how. That’s the distinction this conversation is built around, and it’s why multi-touch attribution keeps losing credibility with CFOs. The theory is elegant: distribute opportunity value across every touchpoint that contributed to a deal. But once you ask how the model actually assigns that credit, the story falls apart — it’s glorified last-touch dressed up as science, and the moment a touchpoint doesn’t get “the credit it deserves,” someone manually adjusts the model. Add in the fact that no one can closed-loop report an LLM citation, and you’ve got a measurement framework that’s been running on borrowed trust for years. The conversation traces where that trust erodes and what’s replacing it. Instead of assigning dollar values to individual milestones — a blog visit, a badge scan, a demo — the shift is toward understanding causation: what actually moved someone to engage, and can you replicate it. This is where journey analytics and Markov modeling earn their keep — not by pricing out each interaction, but by modeling the path itself as the unit of value. That reframe matters more than it sounds: it’s the difference between measuring activity and measuring intent. The second half applies the same lens to lead scoring. The 100-point threshold gets called out for what it is — a playbook that expired a decade ago, when scoring was never meant to declare someone “ready to buy.” It was designed to indicate engagement and content affinity, full stop. The episode walks through how that got corrupted — MQL, then AQL (automation qualified lead) as a stopgap after sales stopped trusting MQLs, then back to a form of MQL — and lands on where scoring is genuinely useful today: as a decay-based signal of fading intent, not a qualification gate. The takeaway for anyone running attribution or lead scoring today: stop defending the model and start defending the story it’s supposed to tell. If you can’t explain how a number got generated, the number won’t survive contact with a skeptical buying committee — whether that committee is your CFO or your sales team.

    Multi-Touch Attribution, Lead Scoring, and the Slow Death of the Vanity Metric

  5. Oct 9

    Stop Pitching 12-Month MarTech Roadmaps — Nobody’s Buying Them Anymore

    Got something to say to this podcast? Send a voicemail, and you might hear yourself on the next episode! This episode of the B2B Orchestration Podcast tackles a question most MarTech leaders are avoiding: if AI agents can already write, approve, and deploy campaign content for pennies, what happens to the traditional budget-and-roadmap model that’s run B2B marketing ops for a decade? The conversation opens with a blunt read on disruption speed — the host points to Chegg and Quizlet as early casualties of AI eating search-dependent business models, then pivots to what that pattern means inside marketing operations. The guest, who runs an AI consulting practice, walks through a real deployment: an agent built on Claude (Sonnet for writing, Opus for technical work, Fable for planning) that takes a content brief, writes the copy, and pushes finished emails directly into Marketo or Adobe Journey Optimizer via API — collapsing what used to be a full role (or an outside agency engagement) into a workflow a marketer just approves and sends. The number that anchors the whole episode: a 10-email nurture sequence, once the template and brief exist, costs roughly $0.10 to generate. That economic shift forces a second conversation — how do you justify budget for something that cheap, and how do you measure value when the unit economics don’t look anything like the old model? The guest argues this is actually an easier budget conversation, not a harder one: leaders don’t approve budget for vague transformation anymore, they approve budget for a visible, ownable outcome. “I can see this thing, I know what it’s going to do” beats a slide deck every time. The back half goes further, into why the standard 90/180-day or 12-month transformation roadmap is becoming a dead pitch. With model capability shifting quarterly — new releases, new agentic tools, new price points — multi-year MarTech commitments are a hard sell to execs who genuinely don’t know what next quarter’s tooling will make possible. The episode closes on a speculative but pointed thread: whether GitHub becomes an “Etsy for skills,” where individual builders sell narrow AI workflows directly, and why enterprise buyers — compliance, security review, and vetting requirements being what they are — are structurally unlikely to ever buy that way, even as solopreneurs and small shops already are. Takeaway for MarTech/RevOps leaders: stop selling multi-year transformation. Sell a visible, cheap, ownable outcome first, then use that proof point to open the budget conversation for what comes next.

    Stop Pitching 12-Month MarTech Roadmaps — Nobody’s Buying Them Anymore

  6. Oct 6

    Marketing Leaders Don't Get the Benefit of the Doubt Anymore

    Got something to say to this podcast? Send a voicemail, and you might hear yourself on the next episode! This episode of the B2B Orchestration Podcast is a reality check for anyone still pitching marketing plans on vibes. The host sits down with Raja of GNW Consulting to unpack why the old playbook — "trust me, we'll estimate this much and hit it" — no longer survives contact with today's buying committees. The core tension: a generational shift in how leadership evaluates marketing spend. Raja argues that newer executives, having grown up inside the "marketing b******t" of inflated PowerPoint wins and vague Q-over-Q narratives, simply won't accept forecasts without hard justification. A 5-10% miss against forecast is tolerable; a 30-40% miss demands a real explanation. The days of throwing out a number and moving on are over — every dollar now needs a defensible line back to outcomes. That shift reshapes how Raja's team engages clients. Rather than accepting a stated goal at face value, they reverse-engineer it: pulling apart what's actually driving current results, what's working, what isn't, and building a realistic target from that evidence — instead of inheriting an inflated number a leader is already boxed into defending. The conversation's sharpest turn is on AI. Raja is blunt that most of what passes for "using AI" in marketing — email copywriting, subject lines, LinkedIn content — is low-value noise. The real unlock is AI applied to data analytics and reverse engineering: querying account and revenue data conversationally (he describes running ad hoc analysis in Slack instead of building reports in Salesforce) to get faster, cleaner answers about what's actually driving pipeline. That's the "left brain" use case he trusts. The "right brain" example is more novel: Leo, a GEO (generative engine optimization) agent his team built for clients. It doesn't write content — Raja is firm that AI-written content is still weak — but it audits and edits existing content from a technical/schema standpoint so LLM crawlers can actually parse and cite it correctly. He describes clients going from zero AI-driven citations to several within a fairly short window, crediting the win to how the content is structured for machine readability, not to what was actually written. The practical takeaway: stop treating AI as a blanket solution and start piecemealing it into the specific parts of the workflow where it measurably compounds — data interrogation and technical content structuring — rather than the parts (strategy, writing, judgment) that still need a human running the reverse-engineering.

    Marketing Leaders Don't Get the Benefit of the Doubt Anymore

  7. Oct 1

    Why AI Is Making the Exec-to-VP Expectation Gap Worse, Not Better

    Got something to say to this podcast? Send a voicemail, and you might hear yourself on the next episode! Most leaders assume AI is closing the gap between what executives expect and what teams can actually deliver. This episode argues the opposite: that gap is widening — and there's data behind it. The core tension: executives increasingly hear that a peer "did this with AI," ask an AI system if it can do the same thing, get a confident yes, and greenlight it — without understanding what actually sits between a working prototype and something safe to run in production. The conversation traces why that's happening now in a way it didn't with traditional build-vs-buy decisions, and why the gap is structural, not just a communication problem. Key frameworks and arguments raised: The "chasm" between piloting and scaling. Everyone is experimenting with AI right now — the divide is between orgs stuck in pilot mode and the (few) actually capturing ROI. The guest argues the difference comes down to consistently underestimated work: governance, UAT for probabilistic systems, and defining ownership.Probabilistic vs. deterministic thinking is a genuinely new skill. Traditional QA and UAT were built for deterministic software. Testing an AI agent for edge cases requires a different mental model that most orgs — and most VPs — haven't built yet.Build vs. buy has quietly changed shape. It used to be: an exec hears what a peer's team did, tells their VP to replicate it, and the VP scopes real engineering work. Now an exec can validate the idea directly with an AI tool in minutes, which compresses the perceived distance between "idea" and "shipped" — while the actual distance (governance, tuning, edge cases) hasn't moved. That mismatch lands squarely on VPs, who are increasingly expected to operate like software leaders even when that isn't their background."Launch" is being redefined. For traditional software, launch means done — shipped, GA, stable. For AI agents, the argument is that launch is closer to day zero: the agent will produce wrong outputs at first, and the real work — tuning, refining outcomes, building feedback loops — starts after launch, not before.Ownership is the unresolved question. If refining an agent's output is ongoing work, who owns it? Is it the go-to-market team using the tool, or a separate function tuning the model? The episode frames this as one of the most urgent org-design questions leaders aren't yet asking clearly enough — and one worth putting directly in front of an executive team: who owns this, what does "done" actually mean here, and how do we catch drift before it compounds.If you're trying to figure out why your AI pilots aren't translating into scaled ROI, or how to talk to your exec team about what "launched" should actually mean, this one's worth the full listen.

    Why AI Is Making the Exec-to-VP Expectation Gap Worse, Not Better

  8. Sep 29

    Your Buyer Already Built Their Own Battle Card — And They Didn't Need You

    Got something to say to this podcast? Send a voicemail, and you might hear yourself on the next episode! B2B buying signals are compressing — and it's not because buyers care less, it's because they're doing more of the work somewhere you can't see. In this episode, the host and Jessica dig into how LLMs have changed the shape of the buying cycle: prospects are showing up to calls having already asked ChatGPT, Copilot, or Claude to compare your product against a competitor's. They're building their own battle cards before your sales team ever gets a chance to hand one over. The conversation traces what that does to the signals B2B marketers have always relied on — website visits, content downloads, form fills — and why those signals are getting both scarcer and less reliable as more research moves off-property. It also touches on why GEO and AEO (optimizing for how generative engines represent your brand) are becoming as important as traditional SEO. The second half of the episode tackles a debate coming up constantly in B2B orgs right now: build vs. buy, specifically for AI-powered tools. Jessica makes the case that the "just build it ourselves" instinct badly underestimates what off-the-shelf software was actually doing under the hood. A traditional UI wasn't just a UI — it was a deterministic guardrail system: who can click what, who has access to which data, what actions are and aren't allowed. Replace that UI with a prompt-driven AI layer and you've traded a bounded, testable system for a probabilistic one that will always produce an output, even a wrong one, and will keep trying rather than fail cleanly like traditional software does (no clean "404" for AI). That shift changes what QA and UAT even mean. Traditional software testing covers discrete, known use cases. Prompt-based AI has to hold up against unlimited phrasings of the same intent — "improve my campaign" said a hundred different ways — which means the real cost of building your own AI tooling isn't the initial build, it's the ongoing tuning, refining, and governance required to keep the model inside the boundaries your business actually needs. Jessica's central point: whether you build or buy, you still have to define your own governance — who approves what, what "QA'd" means at your company, what data access looks like — and skipping that step is where AI implementations go sideways. She closes with a sharp analogy comparing model drift mid-conversation to cutting a kid's hair during COVID lockdown: once it drifts a little off, there's no correcting your way back — you start over. If you're evaluating build-vs-buy for any AI-powered MarTech capability, or trying to figure out why your funnel signals feel thinner than they used to, this one's worth the full watch.

    Your Buyer Already Built Their Own Battle Card — And They Didn't Need You
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About

B2B Orchestration is a conversation-driven podcast built for the leaders who don't have time to get left behind. Every week, one focused conversation. One guest who's operating at the edge of what's working in B2B go-to-market. One set of takeaways you can bring back to your team, your stack, or your strategy before the week is out. This is not a podcast about theory. It is not a podcast about frameworks that look good on a whiteboard and fall apart in a CRM. B2B Orchestration exists because the gap between what's being published about B2B marketing and what's actually happening inside the organizations driving revenue has never been wider — and the pace at which that gap is growing has never been faster. The executives, VPs, and marketing leaders tuning in are not beginners looking for a foundation. They are practitioners and decision-makers who need signal, not noise. They are people who are already running demand gen programs, managing marketing operations teams, evaluating new technology, and trying to align with sales in an environment that keeps changing the rules. They need to hear from other people who are doing the same thing — people who have tried the new approach, broken it, fixed it, and can tell you exactly what they learned. That is what B2B Orchestration delivers.

Information

  • Creator
    Ian Michels
  • Years Active
    2026
  • Episodes
    14
  • Rating
    Explicit
  • Copyright
    © Ian Michels 2026
  • Show Website
    B2B Orchestration