Stop Rebuilding Campaigns. Start Defining Outcomes.

Workflows Were Step One. Agentic AI Is Step Change.

For years, marketing teams have been told the same thing: “Build workflows.”

So we did. We stacked triggers. Layered logic. Connected dozens of tools.

And somewhere along the way, campaign execution became less about strategy… and more about repetitive steps and maintenance.

Today, there is an efficient way to do this. Not because there is a lack of tools, but because we have a pleather of them and they all play their part. Today we hook them all together to perform expected outcomes, accelerate conversion, and demonstrate ROI. We do this for every campaign manually entering each expected outcome. Manually monitoring intent, manually entering a response workflow, manually accessing dashboards to review success and retarget opportunities.

This is where Agentic AI changes everything.

Today’s marketing backend repetitive workflows have entered Agentic AI. This shift is proving extensive ROI for early adopters and the key to slashing campaign launch times from weeks of manual coordination to days of set up and hours of autonomous execution.

From Workflows to Outcomes

Traditional marketing automation is built on:

• If-this-then-that logic every time you set up a campaign

• Manual orchestration across platforms

• Static outcomes

Agentic AI flips that model.

Instead of telling the system how to execute every step, you define:

The goal.

And the system determines:

·       What actions to take

·       When to take them

·       Which channels to use

·       How to optimize in real time

This isn’t automation. This is autonomous execution.

What Agentic AI Actually Does in Campaigns

Agentic AI doesn’t replace your tools, it connects and activates them. There is a learning curve, a shift in how you use your tools more efficiently. You now get to strategize, test, adjust, and execute across platforms like:

• HubSpot

• 6sense

• Google Analytics

• Marketo

• Clay

• Constant Contact

• Bombora

• Claude

• OpenAI

• Canva

…it acts as the orchestrator layer.

For Example: A Webcast Campaign

Instead of manually building:

• Invite emails

• Reminder sequences

• Attendance branches

• Follow-ups

You define:

“Launch a 5-part webcast series targeting high-intent accounts.” By a defined Industry, segmentat, ICP.

The agent:

• Pulls intent data (6sense, Bombora)

• Builds segments (HubSpot, Clay)

• Generates emails (Breeze, OpenAI, Claude)

• Designs assets (Canva)

• Executes campaigns (HubSpot, Marketo)

• Tracks performance (Google Analytics)

• Optimizes in real time

Reducing your steps and automating the workflow.

The ROI of Agentic Campaigns - This is where it matters.

⏱ Time Savings

Traditional campaign build:

• 8–20+ hours per campaign

• Multiple tools, handoffs, QA cycles

Agentic AI:

• 60–80% reduction in setup time

• Campaigns launched in hours—not weeks

• You Build the strategy, Review, Execute

📈 Performance Lift

Because Agentic AI:

• Continuously optimizes timing and execution

• Adapts based on engagement signals (not manual lists)

• Prioritizes high-intent accounts

Teams are seeing:

• Higher conversion rates

• Better engagement across channels

• Faster pipeline velocity

💰 Operational Efficiency

Instead of:

• Manual repetitive workflows and activities

• Managing more dashboards

You:

• Scale campaigns with higher ROI

• Reduce tool friction

• Eliminate redundant manual work

Where This Shows Up Across Your Stack

Agentic AI thrives in complexity. Here’s how it activates your ecosystem:

🎯 Data + Intent Layer

• 6sense

• Slintel

• Bombora

• HG Insights

👉 Identifies who to target and when

⚙️ Execution Layer

• HubSpot CRM

• Salesforce

• Marketo

• Constant Contact

👉 Launches and manages campaigns

🎨 Content + Creative Layer

• Canva

• Adobe Creative Suite

• Figma

• HeyGen

👉 Generates assets at scale

🧠 Intelligence Layer

• ChatGPT

• Claude

• Perplexity

👉 Drives decisions, messaging, and optimization

📊 Analytics + Optimization

• Google Analytics

• SEMrush

• Hotjar

• Screaming Frog

👉 Measures and refines performance in real time

What This Means for Marketing Teams

This isn’t about replacing marketers. It’s about changing their role.

Before:

·       Build workflows

·       Manage tools

·       Build Create

·       Execute tasks

Now:

·       Define strategy

·       Set objectives

·       Orchestrate outcomes

The Human in the Loop: The Pilot, Not the Passenger

A common fear is that "agentic" means "uncontrolled”.  Agentic AI requires better marketing, not less of it.

There is still strategy, ICP definition, messaging, branding, and tone-of-voice training. You need to reason and have good judgement. The difference, however, is you are no longer the one moving the bricks; you are the architect designing the building. You set the guardrails, define the brand's soul, and let the agents handle the repetitiveness that used to take weeks to set up and more labor to optimize.

The future isn't about doing more with less, it’s about doing everything at once. Are you ready to stop manually repeating "if-this-then-that" and start following a goal?

The Shift Is Already Happening

Platforms like HubSpot Breeze are already:

• Generating workflows

• Recommending actions

• Automating execution layers

But this is just the beginning.

The Bottom Line

The future of marketing isn’t:

• More tools

• Better dashboards

• Smarter workflows

It’s:

Systems that execute campaigns for you. You work smarter not harder.

Agentic AI doesn’t just improve how you work. It accelerates entire categories of work.

Final Thought

If your campaign requires:

• 10+ tools

• 20+ steps

• Multiple dashboards

You don’t have a campaign problem. You have a system problem.

And Agentic AI is the solution.

What Cherie DeVaux’s Kentucky Derby Win Teaches Women About Leadership, Risk, and Going For It

Golden Tempo wasn’t supposed to win.

At 70–1 odds, sitting at the back of the pack with half a mile to go, the story was already written, just not the one that unfolded.

Then everything changed.

Golden Tempo surged from last to first.
And with that finish, Cherie DeVaux became the first female trainer to win the Kentucky Derby.

Let that sink in.

Against the Odds Isn’t a Metaphor It’s Reality

Cherie didn’t just win a race.
She broke through a decades old system!

And what struck me most wasn’t just the win, it was what she said:

“You can dream big, and you can pivot. You can come from one place and make yourself a part of history.” 

She started 22 years ago as an exercise rider.
At one point, she was at a crossroads, unsure, questioning, like so many of us have been.

Someone believed in her before she fully believed in herself.

And she chose to go for it.

This One Felt Personal

A lot of women will read this story and feel something deeper.

I know I did.

Over 20 years ago, I started my career in IT, another space where women are often underrepresented.

And while no one says it out loud, you feel it:

• The pressure to prove more

• The need to move faster

• The constant question of “Did I do enough?”

But here’s the truth:

👉 Yes, We show up!

Women Win!

Time and time again, women have shown up, not just to participate, but to lead, to build, to win.

We are:

• Leaders in our careers

• Leaders in our homes

• Navigators of complexity

• Masters of resilience

And yet, we still hesitate.

• “Am I ready?”

• “Is this the right time?”

What If You Don’t Go?

Cherie DeVaux said she never imagined she’d be sitting there as a Derby winner.

Most women don’t imagine themselves there until they are.

The real risk isn’t trying and failing.

👉 The real risk is never stepping into the race at all.

At the Back of the Pack Is Still in the Race

Golden Tempo was last.

Not behind.
Not out.
Just… not leading yet.

There’s a difference.

Representation Matters And So Does Action

Cherie said:

“I’m glad I could be a representative of women everywhere.”

Representation matters.

Seeing someone do it matters.

But what matters more is what we do with it.

👉 Do we watch?
👉 Or do we move?

I am at a Crossroads, and I’m moving!

I’m in a season where I’m being pushed and encouraged to take the next step.
To trust what I’ve built.
To go further.

And like Cherie said, sometimes others see something in us before we fully see it ourselves.

That makes it worth exploring.

Go For It! Even If You’re Not Sure

Here is what I have learned:

• There is no perfect timing

• You own your destination

• You don’t need permission

You need:
👉 Courage to take the Risk
👉 Conviction
👉 And the willingness to try

Final Thought

Golden Tempo didn’t win because the odds were in his favor.

He won because he was in and kept going.

And Cherie DeVaux didn’t make history because it was expected.

She made history because she stepped into the race!

To every person reading this:

You are not behind.
You are not out.
You are in the race.

And you never know—

👉 Your surge might be coming next.

The PMM AI Playbook You Can Actually Use

Now I can analyze my entire market before I finish my first cup of coffee. I have more time to strategize, deep dive into my technology, position, get in front of my customer during their inquiry. 

Gone are the days of chasing information, refreshing RSS feeds, 15 tabs open, digging through competitor announcements, hunting for pricing that may or may not be public, and drowning in PDFs, Analyst Reports, and hoping to find one usable insight that I can replicate across 20 different artifacts!

Don’t get me wrong, those skills still matter.
But now? I don’t go looking for answers. I don’t copy and paste, copy and past……

👉 My day starts with a prompt.

AI is redefining what great PMMs do day‑to‑day. Work that used to take months, research, positioning, launch planning, now happens in minutes.

But here’s the important part:

AI didn’t replace Product Marketing. It removed the friction.

What’s left is the real job:

·       understanding customers

·       making decisions

·       orchestrating GTM

This is a practical playbook for that new reality, what to do, which tools to try, and how to turn AI into visible, defensible value as a PMM.

The New PMM Operating Model

For years, PMMs were measured by output: decks, one‑pagers, messaging docs, launch plans. The work was execution‑heavy and often reactive.

AI flipped the script.

·       Old PMM → Execution
You personally write every asset and manually pull every insight.

·       New PMM → Orchestration
You design the narrative, the systems, and the experiments—then use AI to research, synthesize, and scale.

Your leverage is no longer how fast you can type; it’s how well you can direct your “AI model” toward meaningful outcomes.

1. Insight & Voice‑of‑Customer Intelligence: Leverage raw Customer data

PMMs are surrounded by signals: customer interviews, support tickets, call recordings, community threads, win/loss notes. The problem has never been too little data, it’s that no one had time to read it all.

Turn raw data into positioning and real customer speak.

What to do:

• Analyze interviews, tickets, win/loss, calls

• Extract patterns, objections, language

Tools:

• NotebookLM: ingest internal docs, extract themes

• Claude: best for long-form synthesis + structured thinking

• Gemini: strong for data + ecosystem integration

👉 These tools are considered among the most powerful AI assistants for research, writing, and analysis

Outcome:

👉 Messaging grounded in real customer truth—not assumptions

2. Messaging & Positioning: Build Systems, Not One‑Offs

AI can draft infinite copy. That’s not the hard part anymore. The hard part is defining a coherent narrative system that everything flows from.

Design narratives, not just copy

What to do:

• Define:

  • positioning pillars

  • differentiation

  • proof

Tools:

• Claude: best for structured narrative + tone control

• Microsoft Copilot: build decks, messaging frameworks

• Gemini: iterate variations quickly

Outcome:

👉 Scalable messaging across:

• personas

• industries

• channels

3. Competitive Intelligence: Always On, Never Overwhelming

Traditional competitive analysis is slow and goes stale quickly. In the AI era, the goal is a live radar.

From snapshots → continuous awareness

What to do:

• Monitor competitors continuously

• Update battlecards dynamically

Tools:

• Crayon

• Klue

• Claude → summarize + interpret

Outcome:

👉 Less time collecting
👉 More time strategizing

4. GTM Planning & Launch Orchestration: From Chaos to Simulation

Launches are where PMMs earn trust or lose it. AI helps you move from “spreadsheets and chaos” to something closer to simulation.

From chaos → simulation

What to do:

• Build launch plans

• Simulate demand scenarios

• Identify gaps early

Tools:

• Microsoft Copilot → planning, timelines, decks

• Gemini → structured workflows

• Claude → scenario modeling

Outcome:

👉 Faster launches
👉 Better alignment
👉 Accelerated Pipeline

5. Sales Enablement: See What Actually Wins

Most PMM enablement is built on gut feel and anecdotes. AI lets you see the patterns across hundreds of calls and opportunities extracting real relevant information to design enablement tools that directly relate to real customer data.

What wins deals

What to do:

• Analyze calls + CRM activity

• Identify winning messaging

Tools:

• Gong

• Seismic

• Claude

Outcome:

👉 Real-time enablement
👉 Data-backed talk tracks

6. Content & Campaigns: 10× Output Without 10× Headcount

This is the obvious use case, but it’s also the easiest to get wrong.

Use AI for:

First drafts of blogs, landing pages, nurture sequences, and ads.

Repurposing: turn one webinar into a recap article, social posts, outbound sequences, and internal enablement.

Channel‑specific tweaks: same core idea, tuned for email vs. LinkedIn vs. website.

Scale without losing quality

What to do:

• Create:

  • Blogs/white paper/core asset

  • landing pages

  • campaigns

• Repurpose across formats (Infographic, carousel, multi-channel)

Tools:

• Claude: long-form writing

• Gemini: fast drafts + variations

• Microsoft Copilot: document creation

• Adobe Firefly: visuals + brand assets

👉 AI tools now span writing, design, and business workflows, making content creation significantly faster and more scalable

Outcome:

👉 10x content velocity
👉 Consistent brand execution

7. Video Creation & Personalization

Human storytelling at scale, video is king, a moving visual is more interesting than lifeless words on a page all day long!

What to do:

• Build:

  • product explainers

  • demo videos

  • personalized outreach

Tools:

• Synthesia

• HeyGen

Outcome:

👉 High-impact storytelling
👉 Without production bottlenecks

8. Voice, Audio & Globalization

Scale across markets, ICPs, and localization with a few clicks.

What to do:

• Create:

  • voiceovers

  • podcasts

  • localized content

Tools:

• ElevenLabs

👉 Known for highly realistic speech synthesis and rapid adoption in AI workflows

Outcome:

👉 Faster global expansion

9. Demand Generation & Personalization

Right message. Right account. Right time. Feed the LLM! Your customers are making decisions before they even come to your webpage, knowing, publishing, and amplifying your ICP purchasing patterns, FAQs and inquiry strings will roll you up into LLMs.

What to do:

• Build ICPs

• Personalize campaigns

• Align messaging/FAQs/Inquiries to buying signals

Tools:

• 6sense

• HubSpot

• Nanobanana

Outcome:

👉 Higher conversion
👉 Accelerated/Qualified pipeline

10. Analytics, Forecasting & AI Discovery

Measure, optimize, and get served up

What to do:

• Track funnel performance

• Predict outcomes

• Optimize for AI discovery

Tools:

• Google Analytics

• Tableau

• Gemini

• Microsoft Copilot

• HubSpot/SEMrush LLM Graders

Outcome:

👉 Smarter decisions
👉 Visibility in AI-driven buying journeys

Getting Started:  Proving Value and Time‑to‑Impact

All of this is nice in theory. As a PMM, you’re judged on results. A simple way to make AI value visible:

·       Pick 1–2 areas as pilots (for example: win/loss analysis and launch planning).

·       Document “before” baselines: time spent, asset quality, launch slippage, win‑rate data quality, etc.

·       Run your AI‑powered workflow for a quarter.

Report back in the language your execs care about:

·       Cycle time: “We cut launch planning from 6 weeks to 3.”

·       Coverage: “We analyzed 10× more calls for win/loss without adding headcount.”

·       Impact: “Sales is using the new battlecards in 60% of opps; win‑rate against Competitor X is up 5 points.”

You’re not selling “we used AI.” You’re showing we changed outcomes.

The Reality: Power and Risk

The upside is real:

·       Massive productivity gains

·       Smaller teams, bigger output

·       Insight from data you never had time to read

And so are the risks:

·       Generic messaging

·       “AI sameness”

·       Skill gaps

PMMs who never learn to direct AI are getting left behind

The New PMM: Strategy Amplifier

The best PMMs don’t rely on AI.

They direct it.

They:

• own the insight

• define the POV

• make the decisions

And let AI:

• summarize

• draft

• scale

Final Takeaway

AI is not your replacement.

👉 It’s your multiplier.

The future PMM is not:
👉 the fastest writer

The future PMM is:
👉 the best decision-maker

Your New Tools

·       Fast.

·       Relentless.

·       Always on.

And still needs direction.

And that’s where you win.

AI Isn’t Replacing Your Marketing Team, Bad Strategy Is!

There’s a quiet shift happening in marketing organizations right now—and it’s not what most people think.

AI didn’t suddenly make ten-person marketing teams obsolete.
But job descriptions would have you believe it did.

Today, I’m seeing roles posted that expect one person to do the work of:

• a demand gen lead

• a content strategist

• a product marketer

• an ABM marketer

• a marketing ops specialist

• a field marketer

• a partner marketer

• an events planner

• a designer

• and a publisher

All wrapped into one.

And here’s the catch:
👉 No requirement for actual AI experience
👉 No mention of agentic workflows
👉 No operational understanding of how AI integrates into execution

Just an assumption:
“AI will make this possible.”

The Fantasy: AI as a Force Multiplier

Yes, AI is a force multiplier.

But only when:

• strategy and goals exist

• workflows are designed intentionally

• tools are integrated properly

• teams are trained to use them

• and expectations are grounded in reality

AI doesn’t magically replace execution.
It accelerates it, if you know what you’re doing.

What I’m seeing instead is this:
Organizations are reducing headcount first……and figuring out how AI fits later.

That’s not transformation.
That’s cost-cutting with a narrative.

The Reality: AI Requires More Structure, Not Less

The irony is that AI actually increases the need for:

• clear processes

• defined roles

• strong editorial oversight

• performance measurement frameworks

Without that, AI doesn’t scale output, it scales disfunction.

You don’t get:
👉 better campaigns
👉 stronger messaging
👉 higher conversion

You get:
👉 more content
👉 more activity
👉 less clarity

And ultimately worse outcomes

The Talent Mismatch No One Is Talking About

Here’s where it breaks down even further.

These “AI-enabled” roles:

• expect full-funnel ownership

• demand cross-functional execution

• require both strategy and hands-on delivery

…but don’t require the one skill set that actually makes it work:

👉 AI workflow design and orchestration

Agentic AI, automation pipelines, content systems, data integration—
this is the real unlock.

And it’s rarely mentioned.

Instead, companies are hiring for:
“10+ years of experience, full-stack marketer, operates across everything”

With 500+ applicants per role.

Why This Matters

This isn’t just a hiring issue.

It’s a signal that many organizations:

• don’t yet understand how AI changes marketing

• are underestimating the operational lift required

• are overestimating what one person can realistically deliver

And in the process, they risk:

• burning out top talent

• slowing down execution

• and missing the very growth they’re trying to accelerate

What High-Performing Teams Are Actually Doing

The teams that are getting this right look very different.

They’re not eliminating roles—they’re redefining them.

They are:

• building AI-assisted workflows, hiring the right people

• pairing operators with systems, not expecting unicorns

• investing in process before scale

• measuring output and outcomes, not just activity

They understand:
👉 AI is not a shortcut
👉 It’s an operating system upgrade

The Bottom Line

AI will absolutely reshape marketing organizations.

But not in the way these job descriptions suggest.

It won’t replace five people with one.
It will enable better performance from the right team structure.

The companies that win won’t be the ones that cut the deepest.

They’ll be the ones that:

• understand the work

• design the system

• and build teams that can execute within it

A Final Thought

If your hiring strategy depends on finding one person to do the job of ten….
AI isn’t your solution.

It’s your excuse.

🥚 The Easter Egg Hunt of ABM

Agentic AI Is Helping Teams Find the Right Buyers Faster

Enterprise ABM has always felt like an Easter egg hunt.

o   The right accounts are there

o   The buying group is there

o   The signals are happening

However, they are scattered, across CRM, intent data, web visits, event lists, purchased lists, and incomplete contact records.

The problem is not data. It is finding the data with the right contact information

The Real ABM Bottleneck

Most ABM programs don’t fail on strategy; they stall on execution.

You know the account, but:

·      Do you have the full buying group?

·      Are contacts accurate?

·      Who changed roles?

Instead of precision, teams end up searching, cleaning, and guessing.

Onboard Agentic AI: From Hunt to Workflow

Agentic AI changes ABM by doing what humans take months to or simply can’t do:

·      Continuously scanning

·      Connecting signals

·      Organizing insights

·      Suggesting next actions

Less hunting. More engaging.

Where It Drives Immediate Impact

1. Buying Group Discovery

• Finds missing stakeholders

• Maps personas to roles

• Expands coverage across decision-makers

👉 From one lead → full buying committee

2. CRM Hygiene (The Silent Killer)

• Flags duplicates, stale data, missing fields

• Continuously cleans, not quarterly panic

• Limits additional licensing fees

👉 Better targeting = better outcomes

3. Signal Unification

Brings together:

• Web activity

• Intent data

• Campaign engagement

• Sales interactions

• Event lists

👉 One view of who is in-market

What Improves

• Prioritization: focus on accounts that matter

• Speed: faster research, faster execution

• Personalization: better data = better messaging

• Alignment: Sales + marketing operate from the same data points

The hunt becomes precision.

Where Teams Get It Wrong

Agentic AI is powerful, It certainly is not plug-and-play.

• Garbage data = faster bad decisions

• Over-automation kills nuance

• Generic prompts = generic messaging

• Buying committees are still human

👉 AI accelerates while humans decide

The New ABM Model

Not autonomous. Not manual.

Human-led. Agent-assisted.

• AI finds and organizes the signals

• Humans define strategy, narrative, and judgment

A Simple Where to Start (Practical Playbook)

Don’t boil the ocean. Start with one workflow:

• Contact discovery

• CRM cleanup

• Buying group mapping

• Account research briefs

Measure:

• Time saved

• Data quality

• Account coverage

• Pipeline impact

👉 If it improves one of those—you have a real use case.

🛠️ A Simple Starter Stack

• Clay: contact discovery & enrichment

• 6sense / Demandbase: account intelligence

• Insycle / Clearbit: CRM hygiene

• Crayon / Klue: competitive insights

• Claude / AI assistants: synthesis & drafting

• Highspot / Seismic: enablement

Final Thought

ABM was never about finding more data.
It was about finding the right signal at the right time.

Agentic AI doesn’t replace the marketer! It makes the marketer efficient and removes the friction.

Focus on what increases revenue:

• Understanding the account

• Engaging the buying group

• Moving with HUMAN precision

Event Season Is Not for the Faint of Heart

And here we are, heavy into event season! Looks exciting from the outside…
The booths. The keynotes. The energy. The connections.

But if you’ve been in it, you know. Double, triple duty—for all!

Here is a shout out to those who organize, support all sides—in the office, outside the office, and at the home office. You all know:

It’s:

✈️ early flights + delays, boarding group 9… again, and the herculean expectations… you want me to check what and get it where? by myself?
🏨 hotel rooms that all look the same (was I just in this one… or is this every Marriott ever?)
💧 $10 bottles of water that suddenly feel like a luxury purchase, and coffee that somehow becomes your primary food group
☕ breakfasts with strangers (that sometimes turn into great conversations)
📞 panic phone calls regarding a forgotten lunch box, homework, missing dog… did you take the car keys?

And then there’s the organization that looks like wedding planning on steroids:

👉 the planners coordinating months in advance
👉 the teams building booths—and the almost always missing components
👉 the teams running demos, answering the same question 50 times… and somehow still sounding like it’s the first while staying “on” all day
👉 the presenters refining content late into the night, only to change it again five minutes before going on stage—and somehow an old version still gets presented
👉 the constant juggling of your actual job while you’re on the road

Because nothing stops.
Not the emails.
Not the deadlines.
Not the business.

And then there’s the part we don’t talk about enough…

The ones at home… managing everything—and probably wondering why we packed three pairs of shoes for a two-day trip

The ones:

• keeping everything running

• holding down schedules

• making sure life stays on track

While we’re out there doing our thing.

That’s double duty—and it matters more than we say.

So here’s to everyone making event season happen:

👏 the planners
👏 the organizers
👏 the teams on the ground
👏 the presenters
👏 the behind-the-scenes operators
👏 and the people at home supporting it all

It takes all of it—teamwork, resilience… a village, and more coffee than we’d like to admit.

Safe travels to all—and thank you to those who make it possible.

Your Website Isn’t the Front Door Anymore

AI Just Moved the Lobby

For more than twenty years, the website was the center of gravity. The homepage was the front door of buyers’ discoveries. Traffic came from search, social, and campaigns, and the goal was simple:

Get people to land on your page and convert.

But something fundamental has changed. Large Language Models (LLMs) have quietly moved the lobby.

Today, buyers often ask AI systems questions before they ever visit your website. By the time they arrive at your page, much of their research and their vendor shortlist has been formed by what AI systems told them. Your website is still important. But it’s no longer the beginning of the journey. It’s a reference source inside a much larger knowledge ecosystem.

What Has Changed in Buyer Discovery

When buyers research solutions today, they increasingly start with AI tools such as:

• ChatGPT

• Perplexity AI

• Google Gemini

Instead of clicking through ten search results, buyers ask direct questions like:

• “What are the best hybrid cloud managed service providers?”

• “What is the difference between mainframe modernization and cloud migration?”

• “What companies specialize in data platform modernization?”

These AI systems synthesize answers using information from across the web.

Your website is ONLY one source among many.

Which means discovery has shifted from website navigation to AI-mediated knowledge retrieval. Read that again!

What LLMs Are Actually Scraping

When an AI system generates an answer, it doesn’t just read your homepage.

It looks across the entire digital ecosystem for corroborating information.

Typical sources include:

Your owned content

• product documentation

• technical explainers

• blog posts and guides

• FAQs and knowledge bases

External validation sources

• analyst reports

• industry publications

• research papers

• podcasts and webinar transcripts

Market proof signals

• review platforms

• comparison sites

• partner blogs

• developer forums

Community discussions

• GitHub issues

• Reddit threads

• Stack Overflow

• LinkedIn discussions

In other words:

Your website is no longer the destination.

It is one node in a knowledge network that AI systems analyze to produce answers.

The “Thin Page” Problem

Many marketing pages were built for search engines rather than knowledge extraction.

They include:

• vague hero messaging

• minimal technical detail

• gated assets behind forms

• keyword-stuffed landing pages

These pages were designed to capture traffic.

But LLMs are not looking for marketing slogans. They are looking for answers.

AI systems prefer content that is:

Specific
Who is the solution for? What problem does it solve?

Structured
Clear headings, lists, definitions, and logical sections.

Authoritative
Consistent claims supported by citations and external sources.

Thin landing pages built only for conversion rarely contain enough information for AI systems to reference or summarize.

The New Role of the Website

The modern website must now serve two audiences simultaneously:

1. Human buyers

2. AI systems summarizing your expertise

That means marketing pages must function as answer hubs, not just conversion funnels.

Effective pages now include:

• clear problem definitions

• precise explanations of how solutions work

• natural-language FAQs

• examples and use cases

• links to deeper documentation

• comparison tables

• implementation guidance

If an AI system lifted two paragraphs from your page, would the reader understand your value proposition?

That is the new benchmark.

How to Be Seen by AI Systems

Organizations that want to appear in AI answers need to optimize content for extractability, authority, and corroboration.

1. Structure Content for Extraction

AI models work best with structured information.

Use:

• descriptive H1/H2 headings

• short paragraphs

• bullet lists

• step-by-step explanations

This makes it easier for AI systems to quote or summarize content.

2. Target Real Questions

Traditional keyword research is no longer enough.

Instead, analyze:

• customer support tickets

• community forums

• industry Q&A

• “People Also Ask” search results

Turn these into question-based headings and FAQ sections.

3. Provide Clear Answers First

Each section should start with a concise explanation.

Example structure:

Definition → context → example → additional nuance.

AI systems often quote the first clear answer they encounter.

4. Implement Structured Data

Schema markup helps search engines and AI tools interpret content.

Common schema types include:

• FAQ Page

• How-To Page

• Article

• Product

• Organization

This structured metadata improves machine readability.

5. Demonstrate Authority (E-E-A-T)

Search and AI systems prioritize sources that show:

Þ    Experience

Þ    Expertise

Þ    Authority

Þ    Trust

Ways to strengthen these signals include:

• author bios

• case studies

• customer examples

• citations to reputable sources

6. Build Topic Clusters

Instead of isolated pages, create topic ecosystems.

Example structure:

Þ    Pillar Page

Þ    Supporting articles

Þ    Use cases

Þ    Industry applications

Þ    FAQs

Internal linking reinforces topical authority.

7. Publish AI-Friendly Knowledge Formats

Some companies now create simplified content views designed for machine ingestion.

Examples include:

• Markdown documentation

• technical knowledge hubs

• AI-friendly sitemap files such as /llms.txt

These versions remove visual clutter and provide structured knowledge directly to AI systems.

Why External Data Sources Matter

Appearing in AI answers requires more than optimizing your website.

Models look for corroboration across multiple domains.

That means your expertise must appear in:

• industry publications

• analyst research

• partner blogs

• research papers

• customer reviews

• community discussions

• conference talks, webcasts, and podcasts

When your message appears consistently across trusted sources, AI systems treat your perspective as more credible.

In other words:

Authority is now distributed across the internet, not confined to your website.

The AI-First Action Plan: From Traffic to Knowledge

Audit for "Answer Hubs"

Turn thin pages into definitive resources. Use clear problem statements, natural language FAQs, comparisons, and implementation guides to ensure you are the source of truth for your category.

Kill the Gate

If it’s gated, it’s invisible. LLMs cannot fill out forms. By hiding your best insights behind a lead-gen wall, you are choosing a handful of emails over being the primary recommendation in an AI’s answer.

Deploy /llms.txt

Create a "fast lane" for crawlers. A dedicated Markdown sitemap provides a clean, "no-noise" version of your value prop that AI agents can ingest in milliseconds without getting lost in HTML bloat. Even just your pillar pages would benefit.

Format for Machines

Structure is authority. Use descriptive H2s, Schema markup, and tight lists. If an AI lifted just two paragraphs from your site, the reader should have everything they need to make a confident decision.

Expand Web Authority

AI looks for consensus. Encourage third-party reviews, industry mentions, and community threads. The more sources that echo your value, the higher the "trust score" the LLM assigns you.

Monitor AI Citations

Track your "Share of Model." Shift your KPIs from "Clicks" to "Citations." Use tools to track where and how AI systems are referencing your brand to find (and fill) knowledge gaps.

If you want your brand to survive and show up in the "moved lobby," you need to stop hoarding information and start distributing it in formats machines take into consideration when serving up your buyers inquiries.

The New Marketing Mandate

Your website hasn’t disappeared.

It has evolved.

Instead of acting as the front door of discovery, it now serves as the reference library that powers AI answers.

Marketing is no longer just about attracting visitors.

It is about ensuring your knowledge appears wherever buyers are asking questions. It always has, the who is the same, the where and how has changed. Because the lobby has moved. And it now lives inside AI.

Now go be the answer in the LLMs your buyer is researching in.

The Funnel Is Dead. Long Live The Prompt.

The Funnel is Dead. Long Live the Prompt!

Over a decade of working in SEO and digital marketing, I’ve watched the search landscape evolve through several eras.

The early 2010s marked a pivotal shift in how businesses and buyers engaged online. Social platforms rapidly scaled, Facebook surpassed 500 million users, while LinkedIn emerged as the dominant B2B networking platform. At the same time, the mobile revolution reshaped how people accessed information. By 2020, digital marketing had evolved into a highly sophisticated, data-driven discipline powered by marketing automation, account-based marketing (ABM), analytics, and intent signals giving marketers remarkable visibility into buyer behavior and control over the funnel. But another shift is now underway. As AI and large language models reshape how information is discovered, evaluated, and delivered, the traditional marketing funnel is being rewritten once again.

In the early days of SEO Google search results was a marketer’s superpower. If you understood keywords and content structure, you could dial up visibility, drive traffic to landing pages, and convert search into revenue.

Fast forward to today, and the landscape has shifted again, dramatically.

Search no longer simply sends buyers to information. Now, information comes directly to the buyer. That shift is powered by Large Language Models (LLMs). Traditional SEO connects people to information. LLMs serve the information directly.

SEO is still important, but a new force has arrived, and it is fundamentally reshaping how buyers research, evaluate, and choose solutions.

And in doing so, it has flipped the marketing funnel upside down.

The Funnel We Grew Up With

For decades, marketers operated within a fairly predictable structure.

The classic funnel:

Þ    Top of Funnel (TOFU): Awareness

Þ    Middle of Funnel (MOFU): Research and evaluation

Þ    Bottom of Funnel (BOFU): Decision

With the right combination of targeting, keywords, landing pages, and budget, marketers controlled the awareness path.

·       We tracked every click.

·       Every form fill.

·       Every email open.

Lead scoring systems told us where buyers were in their decision journey. We nurtured them with content, emails, demos, and offers until they raised their hands.

And for many years, that system worked.

But it relied on one critical assumption:

Þ    Buyers came to us early in their journey.

That assumption is no longer true.

The Funnel Has Flipped

Today’s buyers often begin with a prompt, not a search query.

Instead of scrolling through ten blue links, they receive a curated answer, often including summaries, comparisons, reviews, and product recommendations.

In a single interface, they can:

·       Compare vendors

·       Review capabilities

·       Read third-party opinions

·       Evaluate trade-offs

·       Explore communities and documentation

Many buyers now complete 70–80% of their decision journey before ever visiting a vendor website.

The “middle” of the funnel, research and comparison, has effectively moved outside your website into AI interfaces and third-party sources.

It’s invisible. And often much shorter.

Asking an LLM today is like test driving a car before ever stepping on the dealership lot. Buyers can evaluate comfort, performance, and features without a salesperson in sight.

By the time they arrive at your website, they may already know exactly what they want.

Do Funnels and Lead Scoring Still Matter?

So, the natural question becomes:

Is the funnel dead?

Not exactly.

The buyers still exist, of course, but the experience is nonlinear and buyer-controlled.

Discovery and shortlisting now happen through:

·       LLM prompts

·       Community discussions

·       Comparison sites

·       Reviews and peer recommendations

Marketing often enters the process much later than before.

Lead scoring also still matters, but traditional models miss most of the high-value journey.

Page visits, form fills, and email opens no longer tell the full story.

Today’s meaningful signals look more like:

·       Product trials

·       High-intent content engagement

·       Community participation

·       Peer validation

·       Direct outreach

Buyers are increasingly self-directed decision makers.

Research consistently shows that most buyers prefer to conduct the majority of research independently, and many have a preferred vendor before speaking with a salesperson.

In reality, buyers are deciding on their own.

They’re just using everyone else’s data about you to do it.

We Didn’t Lose the Funnel

We changed our monopoly on the information that powered it.

For years, companies controlled the narrative.

Your website.

Your content.

Your lead forms.

Your funnel.

Today, that information is distributed across the internet, and LLMs aggregate it instantly.

But that doesn’t mean marketers are powerless.

It simply means our job has changed.

The New Job of Marketing: Feed the Bots

In an AI-first discovery environment, the brands that win will be the ones that make their information easiest to understand, verify, and surface.

Here are five emerging rules.

1. Make Your Content Easy for LLMs to Understand

Structure information clearly across documentation, FAQs, and product pages so both humans and machines can interpret it easily and accurately. And publish your Mark Downs for LLMs!

2. Publish the Details Buyers Actually Want

Transparency now accelerates decisions. Share implementation guidance, comparisons, pricing context, and even who your product is not designed for.

3. Write for Humans and Machines

Clear headings, structured explanations, and balanced perspectives help LLMs surface accurate answers.

4. Invest in Authority, Not Just Promotion

LLMs trust signals beyond your website. Reviews, Analyst mentions, industry pubs, case studies, and user-generated validation increasingly carry more weight than polished marketing campaigns.

5. Measure What Actually Correlates to Revenue

Focus less on top-of-funnel vanity metrics and more on signals like product usage, trial engagement, and high-intent inquiries.

The New Marketing Superpower

Twenty years ago, keywords were the superpower.

Today, transparency is.

In an LLM-first world, the brands that win will not be the ones guarding their best information.

They will be the ones who explain their value so clearly, and publish it so openly, that both buyers and bots can't help but choose them.