B2B Orchestration

B2B Orchestration

por Ian Michels

Temporada 1

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

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 bullshit" 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.

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

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.

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

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.

You Don't Need a Name to Know What They Want: Rethinking B2B Data Enrichment

This episode tackles a question every B2B marketer eventually runs into: now that we've automated away the friction of asking prospects for firmographic data, what are we actually doing with everything we've collected? The conversation opens with a callback to the old days of progressive profiling — forms that asked visitors to self-report company size, job title, or function as a proxy for intent. The host and guest agree that era is largely over. Data enrichment tools now pull that firmographic information automatically, which solves the "explicit data" problem but surfaces a bigger one: teams enrich their leads and then stall out, because bypassing progressive profiling on forms didn't actually change how they go to market. The guest makes a sharp point about the behavioral signals B2B teams routinely leave on the table. Enrichment tells you a visitor works at an enterprise company, but it doesn't tell you they've read five white papers on a specific topic — a much stronger intent signal than firmographic data alone. She argues B2B has a lot to learn from B2C's approach to personalization here: using behavior as a signal doesn't require identity. You don't need a visitor's name to know they're interested in a specific product line based on what they're engaging with. From there, the discussion turns to why this so often breaks down in practice. The core issue isn't collection — it's sequencing. The guest argues you have to know how you intend to activate data before you collect it, not the other way around, especially in B2B where the data model itself is inherently more complex: one-to-many relationships between accounts and contacts, multiple buying-committee members per deal, and people who sit in the buying committee for more than one product simultaneously. The most memorable framework of the episode is the "Michelin star ingredients" analogy: you can have every ingredient a five-star kitchen would want sitting in your fridge, but if you can't assemble and activate them into an actual dish — or in this case, a targeted, scaled campaign — the data was never the point. The episode closes on a warning relevant to the current AI moment: having a mountain of enriched data and then defaulting to "just activate it with AI" (whether that's email generation or something else) isn't a strategy either. The real differentiator is a platform that actually understands the market well enough to recommend the next best action — not just execute on command. Key takeaway: Enrichment gives you facts about a company. Behavior gives you intent. The teams winning in B2B right now are the ones building a data strategy backward from activation — not collecting first and figuring out activation later.

MCP Servers Are the Starting Line, Not the Finish Line

Every B2B marketer right now is hyped about MCP servers — and Drew's first move in this conversation is to puncture that hype just enough to be useful. His core argument: an MCP server isn't the destination, it's the on-ramp. The real value only shows up once teams get past the novelty and start building something with it. He draws a sharp comparison to the early days of APIs — back then, APIs lived entirely in IT's world. MCP is different because it sits at the intersection of development and marketing, which means the average marketer is going to need to understand what it does and doesn't do, not just consume it secondhand from IT. That leads into the bigger structural question: what happens to the marketing function itself? Drew pushes back on the idea that marketing and IT simply merge into one job. Brand people, creatives, and social/media specialists aren't going anywhere — they get enhanced by technology, not replaced by it. But marketing ops and rev ops are a different story. Drew's blunt take is that the ops people who win the next few years are the ones building, troubleshooting, and maintaining AI agents themselves — which means working directly with CISOs and CTOs, touching infrastructure like Neon databases and Ingest, and yes, having a GitHub account with a real portfolio of shipped agents. His advice to anyone in ops: start that GitHub now, before you need it. On hiring, Drew's answer is deliberately role-dependent rather than a blanket rule. Brand hires still get evaluated on portfolio work, not code. But for ops roles, the bar is moving fast — within 6-12 months, hiring managers should expect to see a working portfolio of agents built, plus credentials (Anthropic, OpenAI, and the wave of university programs now teaching agentic AI workflows). The signal Drew's watching for: candidates are no longer coming in with a "marketing degree" as the qualifying credential — they're coming in with agentic AI training regardless of their original field. The most substantive part of the conversation is the build-vs-buy shift. Drew's thesis: organizations now have the ability to solve micro-problems themselves with agentic workflows built on tools like Claude or ChatGPT, instead of defaulting to "is there software for this?" That's not the same as building your own CRM or trying to replicate Marketo in-house — it's about right-sizing the problem to the solution. Counterintuitively, Drew argues this is currently slowing buying cycles, not speeding them up, because teams are over-indexing on "we can probably build this" before they've felt the real cost, which never shows up in the first two months — it shows up in maintenance, integration, and evolution six months later. He predicts a wave of public "I vibe-coded my own CRM and it wrecked my business" stories on LinkedIn and Reddit before the market recalibrates and organizations find real balance between build and buy. He closes with the Jurassic Park line — teams were so preoccupied with whether they could that they never asked whether they should — as the operating principle B2B leaders need right now.

Buying Groups Are B2B's Most Underused Asset

Most B2B teams treat buying groups as a checkbox — something marketing builds for account targeting or sales throws together in a spreadsheet. In this episode, Drew makes the case that buying groups (or buying committees) are one of the most undervalued levers in B2B go-to-market, precisely because marketing and sales tend to view them in isolation instead of as a shared operating asset. The conversation breaks down why this matters most at the enterprise level, where a single account might have hundreds or thousands of contacts in the CRM, but only a handful actually touch any given deal. Drew argues that without a clearly defined buying group, marketing ends up reporting attribution on people who have no idea an opportunity even exists, and sales stays dangerously single-threaded. He frames buying groups as a de-risking mechanism on the sales side specifically — if a rep goes on leave or a book of business needs to be reassigned, a documented buying group means a sales leader can pick up 40 open opportunities without weeks of discovery to figure out who's actually involved and what role they play. From there, the discussion moves into how the two functions should actually hand off to each other. When sales identifies the real players in a deal and shares that with marketing, it should trigger targeted one-to-one outreach — pushing those specific contacts into LinkedIn audiences and tailoring messaging to their role and the specific problem being solved, without shutting off broader account-level programs like brand air cover or webinar invites that still add value even to people outside the immediate buying group. Marketing also plays a validation role: when sales chases a title without real market context, marketing (armed with personas and product marketing insight) can flag whether that's actually the right person — and can warm up unfamiliar contacts before a sales meeting even happens, so the rep isn't starting from zero. The throughline: buying groups aren't a marketing artifact or a sales artifact — they're the connective tissue that makes attribution accurate, sales multi-threaded, and the handoff between the two functions actually collaborative instead of parallel.

Sales and Marketing Are Both Wrong About Buying Groups

Most B2B teams have a buying group problem they don't know they have: they've split buying committees into two silos — marketing owns targeting, sales owns relationships — and neither side is getting the full value of the concept. In this episode of the B2B Orchestration Podcast, Drew breaks down why buying groups are one of the most undervalued levers in B2B go-to-market, especially at the enterprise level where a single account might have hundreds or thousands of contacts in the CRM, but only 5–12 of them actually touch a given opportunity. The buying group is what narrows that funnel down to the people who matter — and almost every org is only using half of what it can do. On the marketing side, buying groups enable laser-focused targeting: messaging tailored to a contact's specific role in the committee, plus cleaner attribution reporting, since marketing stops crediting influence on people who have no idea an opportunity even exists. On the sales side, the case is different but just as strong. Buying groups force multi-threading, which de-risks every deal against the classic single-threaded failure mode — including the scenario Drew walks through where a rep suddenly goes on leave and 40 opportunities need to be picked up cold. If the buying group is already mapped, that handoff is trivial instead of a multi-week fire drill. It also gives sales leaders a fast way to audit an opportunity's real health just by looking at who's attached, and makes it far easier to loop in an executive sponsor without a lengthy briefing first. The conversation then turns to orchestration: what happens once sales identifies the buying group and hands it to marketing. Drew's answer is that this should trigger targeted one-to-one outreach — pushing the buying group into LinkedIn audiences, tailoring messaging to "person A at account X with problem Z" instead of generic account-based messaging — without turning off the broader air-cover marketing (brand, display, webinars) still running underneath. The two operate in parallel, not in place of each other. The most practical section covers how marketing supports sales before a cold outreach even happens: validating that sales is chasing the right title using personas and product marketing data (since sales sometimes locks onto a title without confirming it's actually the right persona), and pre-warming buying group members with brand and value-prop exposure so that by the time sales gets a meeting, the prospect already has context. As Drew puts it, it's infinitely easier to get a meeting with someone who already knows your brand than someone who's never heard of you — and that head start changes the quality of the first sales conversation, not just whether it happens. If you're in RevOps, demand gen, or a sales leadership role and your buying group strategy currently lives in either a CRM field sales fills out or a marketing segment nobody on the sales side has seen, this episode makes the case for why that split is costing you pipeline efficiency on both sides.

What Makes a CDP a CDP? Separating Capabilities from Marketing Hype

In this episode of B2B Orchestration, we dive into one of the most debated topics in customer data: what actually qualifies as a Customer Data Platform (CDP)? Rather than focusing on vendor labels, we explore the three foundational capabilities every CDP should provide: data ingestion, identity resolution, and audience activation. The conversation highlights why unifying customer identities across channels is often the hardest and most valuable part of the equation, and why simply collecting data is not enough. We also discuss the importance of data strategy, governance, privacy, and security, along with the role data modeling plays in making customer insights actionable for marketers and business users. Finally, we examine why organizations should evaluate CDPs based on business outcomes, not product categories. Key topics include: The three core functions of a CDP Why identity resolution matters more than data collection Different approaches to customer identity stitching Data strategy and governance best practices Turning data into actionable audiences and insights How to evaluate CDPs based on capabilities and outcomes Tune in to learn how leading organizations transform fragmented customer data into unified profiles that drive better experiences, smarter decisions, and measurable business results.

Why B2B Marketers Can't Win Without a CDP

Every B2B marketer has heard an executive say the same thing about a CDP: "Isn't this just another tool?" In this episode of B2B Orchestration, host Ian Michels sits down with Jessica Kao — a marketing operations and MarTech veteran who built her career owning the automation and data analytics stack at multiple companies — to unpack why that framing is exactly backwards, and how to change the conversation at the executive level. Jessica opens by tracing her own path into CDP ownership: marketing operations and marketing technology naturally evolve toward owning more of the data stack, because B2B marketers increasingly need to unify product data, call center data, and every other signal source into one place. She makes the case that data analytics fluency is one of the two clearest career-growth paths inside marketing operations today — and that AI has fundamentally lowered the barrier to entry. Understanding schemas and data mapping used to require real coding chops; now, AI agents handle much of that translation work, meaning the strategic skill — knowing how to turn data into actionable outcomes — matters more than the technical skill of building the pipes. The heart of the episode is Jessica's framework for selling a CDP internally. Her advice: don't lead with the technology. She compares a CDP to the foundation of a house — nobody admires cement, but nothing stands without it. Instead of opening with "we'll have unified profiles," she built her business case backward from revenue: identify the highest-value use cases, attach real pipeline dollars to each one, and only then explain the CDP capabilities powering them underneath. Technical architecture is the "double click," not the headline. Ian connects this to a broader shift in B2B buying behavior: the research window before a buyer ever raises their hand is shrinking, and the widely cited stat that 60–70% of the B2B buying journey happens before a rep is contacted means most of the signal is invisible unless you have a system built to catch it. He extends the point to expansion motions too — the same blind spot that costs new logos also costs upsells, since a prospect that's already a customer is still showing intent signals about what they're evaluating next. The most actionable takeaway: build the business case top-down, not bottom-up. Anchor the CDP pitch in dollars and use cases before architecture, and treat AI not as a replacement for data strategy but as an accelerant that frees marketers to focus on the connections and activation layer — because as Jessica notes, the real value now sits less in the audience list you send and more in the context and freshness behind it.