That is the least interesting application.

The real opportunity is in complex, multi-stakeholder projects where the primary challenge is not writing but coordination, consistency, and context management. Analyst relations is a textbook example.

At my last company, I led the submission for a major industry analyst evaluation. It required a tiger team of 25-30 people working long hours for two weeks. I flew to Seattle to sit with the core team in person. Product, engineering, finance, CS, legal, sales ops. Everyone had their sections. A project manager kept it all on track. It was a massive, coordinated effort.

This year, I did the same type of submission. 146 questions across 13 sections. Financial data, product capabilities, competitive positioning, customer metrics, pricing strategy, innovation roadmap. The full scope.

My team: Claude Code and me.

Total time: about 24 working hours.

A typical analyst RFI involves:

• Hundreds of questions spanning product, finance, legal, customer success, and competitive positioning

• Input from many stakeholders across the company

• Strict formatting and character limit requirements

• Cross-referencing across sections to ensure consistency

• Strategic judgment calls on positioning, data disclosure, and framing

• A hard deadline with no extensions

This is exactly the kind of work where agents excel. Not because they are creative. Because they are systematic.

The Framework

After running a full analyst RFI with AI agents, here is the framework I would use again. This is Agentic PMM in practice.

Step 1: Build the Context Layer

Before an agent writes a single word, feed it everything:

• Prior-year submissions (baseline, not blank page)

• Analyst briefing and inquiry notes

• Product documentation and roadmap

• Financial metrics and customer data

• Competitive intelligence

• Internal messaging and positioning frameworks

But loading documents into folders is not enough. The game changer was creating markdown files that explained what each document is, why it matters, and how the agent should use it. A source priority hierarchy. Instructions on which documents to trust when sources conflict.

I built custom agent skills. Context that taught Claude Code how to interpret analyst evaluation criteria, how to weight different source types, and how to distinguish between what is GA today versus what is on the roadmap. I wrote a detailed spec that enforced specific terminology (the exact product names to use, phrases to avoid), set character limits per question, and defined tagging conventions for missing data.

That upfront investment in the spec and context architecture was the most important work of the entire project. It turned a general-purpose LLM into a purpose-built analyst relations machine.

And here is the thing: I will not have to do most of that work again. The spec, the skills, the source hierarchy. It carries forward. Next year, the submission gets even faster.

The depth of context determines output quality. The specificity of instruction determines whether that output is generic or sharp. This is not “give the AI your docs and hope for the best.” This is building a knowledge base with reasoning instructions that teach the agent how to think about the material.

Step 2: Map Questions to Sources

Have the agent read every question and identify:

• Which questions can be answered from existing materials

• Which questions need updated data (and from whom)

• Which questions require strategic decisions from leadership

This produces two things: a draft readiness score and a data request list with specific names attached to specific gaps.

But the real power is in how you ask for the data. The agent does not just flag “need financial data from finance.” It identifies the exact data point needed, frames the request so the stakeholder knows precisely what to provide, and removes much of the guesswork. No back and forth. No “can you clarify what you need?” They get a clear ask, they provide the answer, and they can see in real time how their input updates the response.

I vibe-coded a web application for this. A single-page app and Supabase where every stakeholder could see the full set of answers, read the simulated analyst feedback alongside each response, and provide their input. No meetings. No email chains. Everyone in one place.

That feedback loop completely changes the dynamic. Stakeholders are not filling out a spreadsheet and hoping it ends up in the right place. They can see the impact of their contribution immediately. It makes the whole process faster and dramatically reduces friction.

Step 3: Draft with Constraints

Set explicit rules before drafting:

• Terminology enforcement (product names, positioning language)

• Tone guidelines (no superlatives, evidence-based only)

• Character limits per question

• What counts as GA vs. planned

• Which claims need citations

The agent drafts all answers in one pass, applying constraints uniformly. A human team working across sections will inevitably have different interpretations of these rules. The agent does not.

But the agent is not doing the thinking. It is handling the pattern matching and the grunt work. I still had to bring the pieces together. The strategic framing, the positioning choices, and the judgment on how to present a nuanced story. That is human work.

What changed is the speed of iteration. In past submissions, I would read every answer in the full document and manually adjust the positioning so the narrative flowed naturally across sections. That editing pass alone could take days. With agents, I could describe the adjustment I wanted, and it would be applied immediately to every relevant answer. The same work that used to take days happened in about an hour. My judgment still drove every decision. The agent just made it possible to act on that judgment at scale.

One of the things that changed my mental model: Claude Code ran autonomously overnight. I would set it on a long job and go to sleep. I woke up to completed work. That is not how traditional project management works. You do not assign a task at 11 PM and get it back at 6 AM. But with agents, the clock is not a constraint. The project moved forward while I was sleeping.

Step 4: Cross-Reference Everything

This is the step most teams skip or do poorly. Every answer must be consistent with every other answer.

• Revenue figures

• Customer counts

• Capability claims

• Competitive framing

• Pricing logic

• Deployment model descriptions

The agent checks every answer against every other answer and flags contradictions. In a 146-question RFI, this is not optional. It is the difference between looking polished and looking disorganized.

Here is why this matters so much: analysts read the entire submission. They are looking for the story across all 13 sections, not evaluating each answer in isolation. If you claim 500 customers in Section 2 and reference “nearly 600” in a case study summary in Section 8, that inconsistency registers. If your competitive positioning in the product section emphasizes one differentiator but your innovation section tells a different story, the analyst notices. These are the kinds of errors that erode credibility, and in a traditional multi-author process, they are almost impossible to catch completely.

No single person on a traditional team can hold 146 answers in their head simultaneously. The agent can. When I was reviewing Section 9 on pricing, the agent could instantly check whether my framing was consistent with what I said in Sections 1 and 3. That cross-referencing at scale is something humans cannot replicate, no matter how good the team is.

When I ran this process with a 25-person team at my previous company, the consistency review was its own workstream. Someone had to read the entire document end-to-end multiple times, cross-checking numbers, language, and claims. It was tedious, error-prone, and always time-pressured because it happened at the end when the deadline was already breathing down your neck.

With agents, cross-referencing happens continuously. Not as a final pass. Not as a last-minute scramble. The agent holds every answer in context simultaneously and catches contradictions as they are introduced, not after they have been baked into the final draft for a week. That single capability alone justifies the entire approach.

Step 5: Build a Quality Gate with a Simulated Analyst

This was the move I did not expect to matter as much as it did.

I built a simulated analyst agent that reviewed every answer the way a real industry analyst would. It looked for unsupported claims, vague language, missing quantification, positioning that did not differentiate, and answers that contradicted other answers in the submission.

The feedback was sharp. Genuinely sharp. It caught things like: “You claim this capability is differentiated, but you have not explained what competitors lack.” Or: “This revenue growth narrative does not address the churn question the analyst will ask in follow-up.”

This quality gate meant that by the time I reviewed the answers, the obvious problems had already been flagged. I could focus on the strategic decisions instead of catching basic issues.

Step 6: Separate Decisions from Drafting

Create a clear list of every strategic decision the agent cannot make:

• Should we disclose this metric?

• How do we frame this gap?

• Which competitive story do we lead with?

• How aggressive is our roadmap commitment?

Present each decision with context, options, and tradeoffs. The decision-maker reviews 20 judgment calls instead of 146 full answers. That is a fundamentally different use of their time.

This is the step that changes what it means to be the person leading the submission. In the traditional model, the AR lead is drowning in logistics. Chasing contributors. Reformatting answers. Reconciling conflicting inputs from six different teams. By the time you get to the strategic decisions, you are exhausted, and the deadline is tomorrow. You make those calls with whatever mental energy you have left.

When the agent handles drafting, cross-referencing, and consistency, the strategic decisions surface cleanly. You are not hunting for them buried inside 80 pages of text. They are presented to you: here is the question, here is what we said last year, here is what the data supports, here are three options for how to frame it, and here is what the simulated analyst would push back on for each option.

That reframing is the whole point of Agentic PMM. The senior person’s time goes to the work that actually moves the dot on the quadrant. Not formatting. Not chasing stakeholders. Not reconciling inconsistencies. The 20 decisions that require real judgment get 100% of your attention instead of competing with the rest of the answers that just need someone to type them up.

The output is better because the decision-maker is sharper. They are not fatigued from the grind. They are fresh and focused on the choices that matter.

Step 7: Iterate on Quality

Because drafting is fast, you get multiple review passes:

• Terminology and consistency sweep

• Strategic positioning review

• Data accuracy verification

• Final tone and formatting pass

In the traditional model, you are lucky to get one full review before the deadline. The submission goes out, and you know there are things you would have caught with one more day. But the deadline does not care.

With agents, the economics of iteration flip completely. A full review pass that would take a human at least a day takes 45 minutes. You find something in Section 11 that changes how you want to frame the answer in Section 3. In the traditional model, that is a painful rework. With an agent, you describe the change, and it immediately propagates across every affected answer.

I ran three full review passes on this submission. Each one improved the quality materially. The first pass caught structural issues and positioning gaps. The second tightened the narrative and strengthened the competitive framing. The third was a final polish on tone and specificity, and on ensuring every claim was backed by evidence.

Three passes. On a 146-question submission. Before the deadline. In a traditional process, that would have required an extra week that the team did not have.

This is the part people underestimate about working with agents. Speed is not just about finishing faster. It is about having time to make the work better. When the grind takes 70% of your timeline, quality gets whatever is left over. When the grind is handled, quality gets the full runway.

A Note on Model Agnosticism

Mid-project, Opus went down. Not ideal when you are on a deadline.

I switched to GPT 5.2 and kept moving. The work continued. The quality held. When Opus came back online, I had it review the GPT 5.2 work and smooth out any stylistic inconsistencies.

This taught me something important: you cannot build a workflow that depends on a single model. Model agnosticism is not a nice-to-have. It is a survival skill. The best Agentic PMM workflows are designed so you can swap the underlying model without losing momentum.

What This Changes

The traditional analyst relations playbook assumes that the hard part is production. Gathering data, writing answers, coordinating reviewers, and managing timelines.

With agents, the hard part shifts to judgment. What to say, what to hold back, how to frame your narrative. That is what the analyst is actually evaluating. Not your ability to fill out a form. Your ability to articulate a coherent strategy.

When you remove the production bottleneck, the quality of your strategic thinking becomes the differentiator. That is where product marketers should be spending their time anyway.

That is Agentic PMM. Not AI writing for you. AI that gives you the infrastructure to think at scale.

I’m Shafiq Shivji. I’ve spent 15+ years in B2B product marketing. Auth0, mParticle, CloudBees. I’ve built positioning from scratch, run launches that moved markets, and sat in enough win/loss calls to fill a warehouse. I’ve seen the discipline evolve through every major shift: cloud-native, PLG, the rise of the buyer, the death of the funnel.

None of those shifts comes close to what’s happening right now.

The Moment That Changed How I Think About PMM

Six months ago, I started building AI workflows to monitor competitors. Pricing pages, changelogs, job postings. Not a dashboard. Not a one-time pull. A system that runs on a schedule, cross-references changes against our positioning, and surfaces a brief with recommended updates to our battlecards.

Within weeks, it surfaced a pricing restructure that our existing competitive intel process had missed. It flagged new job postings in a vertical we were about to target, suggesting a competitor was making the same bet.

AI was doing in hours what used to take my team weeks. And it was doing it continuously, not quarterly.

That’s not AI as a tool. That’s AI as a teammate. And it forced me to rethink what product marketing actually is when the tedious, time-consuming work that fills 60% of your calendar just... disappears.

The State of Play: PMM Is Having an Identity Crisis

Let’s be honest about where we are. The numbers tell one story. The reality tells another.

Gartner says 81% of marketing technology leaders are either piloting or have already implemented AI agents. That sounds like the revolution already happened. But dig deeper, and you find that most of those “implementations” are basic. Summarize this call. Draft this email. Generate three subject lines.

That’s generative AI. That’s the copilot era. And the copilot era is already over for anyone paying attention.

The next era is agentic. And it changes the job description of every PMM alive.

Here’s the difference. Generative AI waits for you to ask it something. An AI agent decides what to do, executes a plan, uses tools, checks its own work, and comes back with a result. It doesn’t need a prompt for every step. It operates.

Think about what product marketers actually spend their time on:

• Monitoring competitors across dozens of sources

• Synthesizing win/loss data into patterns

• Updating battlecards and sales enablement materials

• Researching market trends for positioning work

• Building launch plans from a template (again)

• Chasing down product teams for release details

• Making presentation slides pretty (yuck!)

Every single one of those workflows can be partially or fully handled by an AI agent today. Not in theory. Right now. I know because I’m building them.

What “Agentic PMM” Actually Looks Like

Let me get specific. This isn’t a thought experiment. These are workflows I’ve designed, built, and started running.

Competitive Intelligence on Autopilot

Old way: Quarterly competitive review. Someone spends two weeks pulling together a deck. It’s outdated by the time it ships.

Agentic way: An agent monitors competitor websites, G2 reviews, job postings, press releases, and social mentions daily. It cross-references changes against your positioning and battlecards. When something material shifts, it flags it and drafts recommended updates. You review and approve. Time investment: 20 minutes a week instead of 40 hours a quarter.

Competitive tracking tools have existed for years. But the agentic layer is what turns monitoring into action. It's the difference between a security camera and a security guard.

Win/Loss Analysis That Actually Works

Old way: Conduct 10-15 interviews a quarter. Transcribe them. Find themes. Build a presentation. Share it once. Nobody reads it again.

Agentic way: Every sales call is automatically transcribed and analyzed. An agent identifies win/loss patterns across hundreds of conversations. It can spot deals mentioning a specific theme in discovery and close faster. It can flag that a competitor objection appears in a majority of recent losses. These insights surface weekly, not quarterly. And they feed directly into updated talk tracks and enablement materials.

The insight cycle drops from 90 days to 7.

Positioning That Stays Current

Old way: Build positioning in a workshop. Document it. Revisit it annually (if you’re disciplined). Watch it slowly drift from market reality.

Agentic way: An agent continuously analyzes your market. It tracks how competitors describe themselves, how analysts frame the category, what language buyers use in reviews and calls. When the market shifts, it flags the gap between your current positioning and market reality. It drafts alternatives. You choose.

This turns positioning from a periodic exercise into a living system. That’s not a nice-to-have. In a market where AI-native competitors can reposition in days, annual positioning reviews are a death sentence.

Sales Enablement That Writes Itself

Old way: PMM creates battlecards, one-pagers, talk tracks. Sales ignores half of them. The ones they use go stale within weeks.

Agentic way: Agents generate enablement materials dynamically based on current competitive data, recent win/loss patterns, and the specific deal context. Before a sales call, the rep gets a briefing that includes the prospect’s tech stack, relevant case studies, the competitor most likely in the deal, and updated objection responses. Not a generic deck. A customized brief.

Highspot reports that 82% of B2B sales teams using AI-driven enablement platforms saw increased rep productivity. But most of that is still human-triggered. The agentic shift is when the system knows what sales needs before sales asks.

Why Most AI-in-Marketing Content Misses the Point

I’ve read dozens of “AI for PMMs” articles, playbooks, and courses over the past year. Nearly all of them fall into one of three buckets:

The Vendor Pitch. “Our tool uses AI to do X.” Useful for evaluating software. Useless for understanding how the discipline changes.

The Analyst Overview. “Here are 10 trends in AI marketing.” Informative but detached. Written by people who observe marketing, not people who do it.

The Fear Piece. “AI will replace marketers!” or “AI won’t replace marketers!” Binary takes that miss the actual story.

The actual story is this: AI agents don’t replace product marketers. They replace the product marketing model that’s 70% data gathering and 30% strategy. What’s left is the 30%. And that 30% becomes your entire job.

The PMMs who thrive in this shift won’t be the ones who learn to write better prompts. They’ll be the ones who learn to design, manage, and orchestrate agent-based workflows. They’ll think like systems architects, not just storytellers.

That’s a fundamental identity shift. And almost nobody is talking about it with the specificity it deserves.

The Gap Nobody’s Filling

Here’s what I noticed when I started looking for people writing seriously about this intersection:

Tool vendors write about their tools. Consultants write about frameworks. Analysts write about trends. Training companies sell courses on prompting.

Nobody is writing from the practitioner’s seat. Nobody is saying, “I run AI agents for my actual PMM job, here’s what works, here’s what breaks, here’s what it means for how we organize teams and define the role.”

That’s the gap. And that’s what Agentic PMM exists to fill.

I’m not building this publication because I think AI in marketing is interesting. I’m building it because I believe product marketing is about to split into two tracks. PMMs who operate with agents. And PMMs who get outpaced by those who do.

What’s Coming (and What Matters)

Forrester reported that in 2025, 30% of all B2B buyers viewed GenAI tools as a meaningful interaction during their final purchase decision. More meaningful than talking to a product expert. Read that again.

Buyers are already using AI agents on their side of the table. They’re researching your product with AI. They’re comparing your positioning against competitors using AI. They’re making decisions faster because AI compresses their evaluation cycle.

If the buyer is agentic and the PMM isn’t, you lose. Not because your product is worse. Because your go-to-market is slower.

Gartner predicts that by 2028, 33% of enterprise software will include agentic AI, up from less than 1% in 2024. That’s not a gradual shift. That’s a phase change. Marketing organizations that aren’t redesigning their workflows around agent orchestration will find themselves running yesterday’s playbook in tomorrow’s market.

The Thesis

Here’s what I believe, and what this publication will explore:

Product marketing’s next evolution isn’t about using AI. It’s about becoming agentic.

That means PMMs who design systems, not just deliverables. Who build agent workflows that run competitive intel, win/loss, enablement, and positioning as continuous processes, not periodic projects. Who think about their work as orchestration, not execution.

The best PMMs have always been systems thinkers. They connect product to market to sales to customer. Agentic AI doesn’t replace that skill. It amplifies it beyond anything we’ve had before.

But only if you build for it. Intentionally. Starting now.

Agentic PMM is the publication for product marketers who want to lead this shift, not react to it. Every post will include real workflows, real results, and real talk about what works and what doesn’t. No vendor pitches. No analyst distance. Just a practitioner building in public.

If that sounds like what you need, subscribe. The shift is already underway. The question is whether you’re designing the system or stuck inside someone else’s.