It’s been a while since the last Prompt & Pivot. I didn’t plan to pause, but life doesn’t always follow our publishing schedules. A few months ago, I lost my mom. It was sudden, and it completely threw me off. I thought I understood grief, but I didn’t. It wasn’t just sadness—it was disorientation. The rhythm of everything I do, especially writing, vanished.

If you’ve followed Prompt & Pivot for a while, you know it’s about connecting what we experience in life with lessons from Friends and finding ways to use AI to navigate those moments. This one’s a little different. It’s not about a workplace pivot or an innovation breakthrough. It’s about what happens when everything stops.

When my mom passed, my entire family was shaken. My stepdad, my mom’s sisters, my cousins… we were all trying to make sense of it in our own ways. I’m an only child, so before I even realized what was happening, I found myself being the one helping everyone else process their emotions while barely holding myself together.

And at night, when everything was quiet and I couldn’t sleep, I opened ChatGPT. I didn’t have the energy to “use AI” in the clever, productivity sense. I just needed to talk.

I typed something like this:

“I’m at a loss for words. I just lost my mom. My head is spinning. I need to process my thoughts. Can you play the role of a grief counselor? If nothing else, could you just ask me questions to help me work through what I’m thinking and feeling?”

That was it. No elaborate prompt, no clever phrasing - just honesty.

And somehow, that honesty was enough.

ChatGPT didn’t fix anything. But in those late-night hours, it became a quiet space for me to unravel what was tangled in my head. When I couldn’t make sense of why I felt angry, numb, or guilty all at once, it helped me explore those feelings. When I didn’t want to burden friends or family, it listened. When I was ashamed of certain thoughts, the ones I couldn’t imagine saying out loud, it gave me the grace of a response without judgment. Grace. It allowed me to give myself grace when I needed it the most.

It’s not a substitute for people. It’s not therapy. But it was something when nothing else felt possible. And sometimes that’s enough.

Maybe it helped because I gave it a clear role. I didn’t expect brilliance. I just needed presence.

My “prompt,” if you can call it that, was simple:

• 1) Tell it what you’re feeling, even if it’s messy. You’re giving it context.

• 2) Give it a role: “grief counselor,” “listener,” “clarity coach.”

• 3) Ask it to respond one question at a time.

• 4) Let it mirror back what it hears, so you can see your thoughts with a little more distance.

That’s all. No magic formula. Just conversation when conversation feels impossible.

I’ve come to realize that my mom was often my compass. And when she was gone, I lost my sense of direction for a while. But writing, reflection, and the simple act of talking—even to a screen—helped me start finding north again.

I’ve thought a lot about what my mom would say about this issue. She was the one who always reminded me to keep showing up, even when it’s hard. So that’s what this is… a small step toward showing up again. I don’t think we ever “get over” losing someone we love, but we do learn how to carry them with us. And sometimes, sharing a piece of that journey is the best way to begin again.

So, I guess this is my way of saying: I’m back. Maybe not fully, maybe not perfectly, but ready to start this again. Because life doesn’t stop for long, even when everything else does.

My best,

Lisa

P.s. This is a photo of my mom that I used Veo3 to animate ❤️

You know that Friends episode where Ross is slowly losing his sanity because everyone's supposed to be ready for the museum gala in 20 minutes?

Joey's been staring at his two identical shirts for an hour.
Rachel's tearing apart her room hunting for "the good earring."
Chandler's settled into the chair and refuses to move.

Ross keeps checking his watch: "We're supposed to be there in 15 minutes!"

Joey responds: "Okay, I just have to pick a shirt."
"You've been doing that for 20 minutes!"
"Right, but now I have to switch, because this one makes me look like a waiter."

That's your AI pilot right now.

Why This Hits Close to Home

Last month, I sat through my third "readiness review" for the same content generation pilot.

Same conversation. Same concerns. Same "we're almost there" energy.

The prompts needed "one more optimization pass." The dataset needed "final validation." The review process needed "stakeholder alignment."

Meanwhile, our content team was burning 15 hours a week on research that AI could handle in 2.

Sound familiar?

Here's what I've learned: Most AI pilots don't fail because the technology isn't ready. They fail because perfectionism masquerades as due diligence.

And perfectionism at the pilot stage is just procrastination with a PowerPoint deck.

The Three Traps That Kill Momentum

1. The 95% Accuracy Trap

The Problem: Teams demand production-level accuracy from experimental workflows.

What I Hear: "We can't launch until hallucinations are under 5%."

The Reality: Your current manual process probably has a 20% error rate that nobody measures.

We tracked our content research accuracy before introducing AI. Manual research had citation errors, outdated stats, and missed key insights about 18% of the time. AI research? About 12% error rate after basic quality gates.

We launched.

2. The Infinite Prep Trap

The Problem: No clear definition of "ready to test with real users."

What I Hear: "Just one more sprint to clean the data/refine the prompts/train the team."

The Reality: Perfect preparation is the enemy of valuable learning.

I use the "Good Enough Grid" now:

• Data Quality: 80% complete, representative examples ✓

• Prompt Performance: Works for 70% of common use cases ✓

• Team Training: Key users understand basics + escalation paths ✓

• Measurement: Simple success metrics defined ✓

Four checkmarks = ship it.

3. The Ownership Fog Trap

The Problem: Everyone's responsible, so no one's accountable.

What I've Seen: Three weeks of Slack threads debating who owns prompt updates while the pilot sits dormant.

My Solution: RACI for AI Pilots (yes, I'm bringing back corporate acronyms, but this one actually works):

• R - Responsible: Marketing Ops owns day-to-day execution

• A - Accountable: CMO (me) owns go/no-go decisions

• C - Consulted: Data team validates quality gates

• I - Informed: Finance gets weekly cost/value updates

Write it down. Share it. Move on.

My 15-Minute Launch Decision Framework

When teams tell me they're "almost ready," I run this quick diagnostic:

Minute 1-3: The Reality Check

• What's the worst-case scenario if we launch this pilot next week?

• What's the worst-case scenario if we don't launch for another month?

• Which risk is actually bigger?

Minute 4-8: The Value Validation

• If this pilot saves us 2 hours per week, what's that worth annually?

• How many weeks of prep time would that justify?

• Are we past that threshold?

Minute 9-12: The Rollback Test

• If the pilot goes wrong, how quickly can we revert to the old process?

• What would "going wrong" actually look like in practice?

• Can we contain the damage to internal workflows only?

Minute 13-15: The Launch Decision

If we can contain downside risk AND the potential value justifies the prep investment, we ship.

No exceptions. No "one more sprint."

Your AI Pilot Production-Readiness Checklist

Stop here. Print this. Use it as your gate for every pilot:

✓ Data Readiness

• Input data represents 80%+ of real-world scenarios

• Known edge cases are documented (don't need to be solved yet)

• Sample outputs reviewed by subject matter expert

✓ Prompt Performance

• Baseline prompts frozen for evaluation consistency

• Success rate >70% on common use cases

• Escalation triggers defined for edge cases

✓ Measurement Foundation

• Success metrics defined (speed, quality, cost, satisfaction)

• Logging captures all inputs/outputs for analysis

• Weekly review cadence scheduled with specific owners

✓ Human Integration

• Clear handoff points between AI and human review

• Exception handling process documented and tested

• Key users trained on basics + escalation

✓ Risk Management

• Rollback plan tested and documented

• Budget caps and monitoring in place

• Incident response owner assigned

✓ Governance

• RACI chart shared with all stakeholders

• Data privacy and compliance requirements addressed

• Legal/procurement sign-off obtained

6/6 = Ship it. 4-5/6 = Address gaps then ship. 3/6 or fewer = Not ready.

Real Example: How I Unstuck Our Content Pilot

Three months ago, our content research pilot was stuck in "almost ready" limbo. The blocker? Our content team wanted 95% source accuracy before they'd trust AI research.

Here's how I unstuck it:

Week 1: I audited our manual process. Found 18% error rate in citations and sources.

Week 2: Set new success criteria: "Better than manual" (12% error rate) instead of "perfect."

Week 3: Launched with a simple rule: All AI research gets 5-minute human fact-check before publication.

Results after 30 days:

• 40% faster research process

• 12% error rate (better than manual)

• Team confidence up significantly

• 3 workflow improvements identified for next iteration

The pilot we "weren't ready" to launch became our template for more AI implementations.

PIVOT!

Why "good enough to learn" beats "perfect on paper"

The biggest strategic mistake I see CMOs make? Treating pilots like production deployments.

Pilots exist to prove value and surface problems—not to deliver flawless performance. Every week you spend in "almost ready" mode is a week you're not learning what actually matters for your specific team, data, and workflows.

Perfect preparation is procrastination in disguise.

The Script for Moving Forward

Next time your team says "we need another week," try this:

"What specifically would need to go wrong for us to regret launching this pilot next week? And what's the probability of that actually happening?"

Then:

"What's the cost of delaying another month versus the cost of learning something isn't quite right?"

Usually, the delay costs more than the risk.

Central Perk Coffee Break

5-Minute Action: Unstuck Your Stalled Pilot

Right now, pick your most "almost ready" AI initiative.

Set a timer for 5 minutes. Answer these questions:

1. What's the one thing blocking launch this week?

2. Can you solve it in 2 hours or less?

3. If not, can you launch without solving it perfectly?

4. What would you learn from a week of real-world testing?

If questions 2 or 3 are "yes," schedule the launch for next week.

Done.

What's Next

The One With the Thanksgiving Flashbacks

Next week, we're diving into something most marketing teams get wrong: learning from AI failures.

You know those Friends Thanksgiving episodes where they keep flashing back to previous disasters? Monica's burned turkey. Ross's divorce announcement. The time they were late to dinner because of [insert crisis here].

Each flashback reveals a pattern. A missed lesson. A way things could have gone better.

That's exactly what should happen with your AI experiments—but usually doesn't.

Most teams launch a pilot, see mixed results, then move on to the next shiny object without extracting the real lessons. They repeat the same mistakes. Make the same wrong assumptions. Get stuck in the same places.

Next week's issue will cover:

• How to run proper AI post-mortems that actually improve future pilots

• The four questions that surface hidden lessons from every experiment

• Building organizational memory that compounds (so you don't re-learn the same lessons quarterly)

• My template for turning pilot failures into strategic advantage

Because the goal isn't to avoid mistakes—it's to make better mistakes faster.

Until then, go get unstuck.

---Lisa

P.S. If this helped you finally ship something that's been "almost ready," forward it to another marketing leader who's stuck in prep mode. They'll thank you when they're actually learning instead of just planning

You know that Friends episode where Ross and Chandler try to quit the gym?

They walk in with a simple goal: cancel their membership.

But the gym staff has other plans. First, there's the "exit interview" with the overly enthusiastic manager. Then the guilt trip about abandoning their "fitness journey." Before they know it, they're signing up for personal training sessions and a juice bar membership.

Ross and Chandler leave more trapped than when they walked in.

That's vendor lock-in in action. The perks are real. The friction is designed. And in AI and martech, the risk is exponentially bigger because technology moves quarterly (sometimes weekly actually) while most tech contracts move yearly.

This issue is your playbook to buy flexibility and portability, not just a lower sticker price.

Why This Matters More Than Ever

AI pricing models are shifting every 60-120 days. OpenAI's per-token pricing dropped 85% in two years. Anthropic introduced prompt caching that cuts costs by 90% for certain use cases. Meanwhile, your 24-month contract assumes last quarter's economics.

The three biggest traps I'm seeing:

Shelfware is the silent budget leak. That 30% discount on 500 seats means nothing if only 200 people actually use the platform consistently. A discount on unused capacity isn't savings—it's waste with a bow on top.

Legal and procurement will back you when you anchor on risk, portability, and total cost of ownership—not just headline discounts. Frame flexibility as protecting ROI, not avoiding commitment.

Accuracy guarantees are fiction. Replace meaningless impossible asks (like "99.9% accuracy") with structures that provide real discounts, reduce real risk and keep you agile when better models emerge.

Understanding Token Economics

Before we dive into contract tactics, let's decode token-based pricing—the dominant model for AI platforms.

What's a token? Roughly 3-4 characters of text. The sentence "AI transforms marketing workflows" is about 8 tokens. A typical blog post outline might consume 150-300 tokens to generate.

Common pricing ranges:

• Input tokens: $0.001-$0.03 per 1,000 tokens

• Output tokens: $0.002-$0.06 per 1,000 tokens (usually 2-3x input cost)

• Premium models: Can be 10-20x more expensive than basic models

Platform bundling examples:

• Platform 1: Starts at 100,000 tokens monthly per seat

• Platform 2: Includes 40,000 words (~160,000 tokens) in base plans

• Platform 3: Includes "unlimited" tokens but charges hefty fees per active workflow

Your estimation framework:

1. Audit current usage: Track token consumption for 30 days across your most active AI users & workflows

2. Project team growth: Multiply by planned team expansion and adoption curves

3. Buffer for experimentation: Add 40-60% for testing new use cases and seasonal spikes

4. Account for model migration: Premium models consume similar tokens but cost 5-15x more

Red flags in token packages:

• Tokens that expire monthly (forces overbuying)

• No visibility into consumption analytics

• Massive gaps between plan tiers

• "Unlimited" with buried fair use policies

The Flexibility Playbook

Use these seven contract moves in every renewal or net-new deal. They're feasible with mainstream vendors and easy to justify to finance teams:

1. Shorter Terms as Policy

Make 12 months your default maximum. Accept a small premium for flexibility. Multi-year should be rare and only when offset by strong swap rights and exit terms.

Copy-paste language:

"Initial Term is 12 months. Renewal requires mutual written consent at least 45 days before expiration."

2. Milestone-Based Opt-Outs

Don't ask vendors to promise things they can't control (like model accuracy). Tie exit and downgrade rights to objective milestones they can control.

Copy-paste language:

"If any milestone in Exhibit A slips more than 30 days, Customer may downgrade the affected SKU with pro-rated credits or terminate the affected order on 15 days' notice."

3. Budget Reallocation Rights

When new modules or use cases outperform old ones, you should be able to move money without penalties.

Copy-paste language:

"Twice per contract year, Customer may reallocate up to 25% of committed spend across available SKUs of equal or lower price without penalty. Downgrades create account credits."

4. Usage Flex Bands

Right-size without penalties as adoption grows unevenly across teams and use cases.

Copy-paste language:

"Customer may adjust seats or capacity by ±20% intra-term. Unused capacity rolls forward one quarter."

5. Price Protection Without Discount Cliffs

Cap renewal increases. Remove year-two surprises. Keep agreements simple and predictable.

Copy-paste language:

"Unit prices and discounts are fixed during the Initial Term. Any renewal increase is capped at the lesser of 3% or CPI-U."

6. Data Rights and True Portability

Your ability to get data out cleanly is leverage. It also reduces migration pain when better options emerge.

Copy-paste language:

"Customer may export prompts, completions, embeddings, fine-tune artifacts, and related metadata in JSON or CSV formats, plus documented APIs. Complete data deletion occurs within 30 days of written request, with confirmation provided."

7. AI-Specific Change Management

Technology moves fast. Build in buffers for model changes, feature deprecations, and pricing shifts.

Copy-paste language:

"Vendor provides 30 days notice before model version changes, feature deprecations, or pricing unit modifications affecting Customer's usage in Exhibit B. Customer may pin the prior model version for 60 days during critical launches or campaigns."

Contract Flexibility Scorecard

Rate each line item 0-10. Aim for 75+ points before signing:

• Term and renewal clarity (10 pts)

• Milestone-based opt-outs and SLA remedies (10 pts)

• Price protection with no discount cliffs (10 pts)

• Two intra-term SKU reallocations without penalty (10 pts)

• Usage flex band with quarterly rollover (10 pts)

• Data export formats, APIs, and deletion timelines documented (10 pts)

• AI data training rights: explicit opt-in required (10 pts)

• Change management: 30-day notice, 60-day model pinning (10 pts)

• API rate limits, SSO, SCIM capabilities documented (5 pts)

• Named transition support included on exit (5 pts)

Low-scoring items become your negotiation priorities. High-scoring items become trade chips for concessions elsewhere.

Total Cost of Ownership Framework

Present discounts only after you've calculated the real cost:

Year 1 TCO = License + Overages + Required Services + Implementation + Integration + Idle License Waste + Data Egress + Migration Reserve + Risk Buffer

A 25% discount doesn't help if 20% of seats sit unused and you're prepaying for AI features you won't deploy for six months. A 10% premium on a 12-month term often pays for itself by avoiding shelfware and forced migrations.

CFO-ready narrative:

"We're buying flexibility to protect ROI. This structure reduces shelfware, limits replatform risk, and lets us reallocate spend to what works. The modest term premium is offset by avoided waste and faster payback on successful use cases."

Your Copy-Paste Contract Kit

Place these clauses in the order form, not just the master services agreement:

Term and Renewal:

Initial Term is 12 months. Agreement renews only upon mutual written consent at least 45 days before expiration.

Change Management:

Vendor provides 30 days notice before changing model versions, pricing units, or deprecating features relied upon in Exhibit B. Customer may pin the prior version for 60 days during critical launches.

Data Rights and Portability:

Vendor will not use Customer Data to train models available to other customers without Customer's explicit, revocable opt-in. Vendor provides export of prompts, completions, embeddings, and fine-tune artifacts in JSON or CSV, plus documented APIs. Deletion occurs within 30 days of request, with confirmation.

Budget Control:

Customer may set hard usage caps. real-time spend alerts and quarterly business reviews to assess usage, update forecasts and adjust budgets as necessary. Vendor will suspend processing at the cap unless Customer authorizes continuation.

Migration Assistance:

Upon termination or expiration, Vendor provides up to 40 hours of reasonable transition support at the contracted services rate.

Pre-Renewal Five-Minute Audit

Answer these before returning any redlines:

1. What percentage of seats or token capacity went unused last quarter?

2. Which SKUs will we actually deploy in the next 90 days?

3. If we switched platforms in six months, how many hours and what fees would migration require?

4. Where are we exposed on data training, deletion, and export rights?

5. If we could only keep half this platform, which half would deliver the most value?

Your answers become your negotiation priorities.

PIVOT!

Why flexibility beats discounts in fast-moving markets

The real advantage isn't getting the lowest price—it's maintaining optionality. In AI's quarterly evolution cycles, you win by keeping the right to reallocate spend and move your data, not by locking in the biggest bundle.

Field Notes from the Frontlines

What works with vendors:

• Lead with contract structure, then discuss pricing

• Convert every roadmap promise into a dated milestone with clear remedy

• Put data rights and export details in the order form, not buried in help docs

• Keep a migration plan updated, even if you don't intend to use it

What doesn't work:

• Demanding accuracy warranties (mathematically impossible)

• Asking for unlimited indemnities without usage conditions

• Accepting "trust us" on data training and retention policies

• Leaving change management and model pinning rights undefined

Central Perk Coffee Break

Quick Win: The 5-Minute Contract Health Check

Pull your three largest AI/martech contracts. For each one, ask:

• Can we reduce seats by 20% without penalty?

• Can we export our data in standard formats?

• What happens if they change pricing models mid-term?

• Do we have explicit opt-in controls for AI training on our data?

Any "no" or "unclear" answers become renewal priorities.

Next Week: The One Where No One's Ready

Coming up: The One Where No One's Ready

You know that Friends episode where everyone's supposed to be ready for Ross's museum gala, but Joey can't pick a shirt, Rachel's looking for her earring, and Chandler sits in the chair and can't get up?

Meanwhile, Ross is losing his mind because they're going to be late for the most important night of his career.

That's exactly what happens with AI pilot launches. Everyone says they're "almost ready"—the prompts need one more tweak, the data needs another cleansing pass, the review process needs refinement. Then the launch window closes and you're back to manual workflows.

Next week, we'll break down:

• A production-readiness checklist that actually works: data, prompts, evals, logging, human review, rollback plans

• How to set acceptance criteria and freeze scope so you actually ship

• Role clarity: who owns prompts, evaluation sets, incident response, and budget gates

• Post-launch hygiene: drift detection, error triage, and weekly improvement rituals

Because most AI pilots fail at the handoff to production, not in the lab.

Until then, go negotiate like you mean it.

—Lisa

P.S. If this helped you avoid a contract trap, forward it to another marketing leader who's navigating vendor renewals. They'll thank you when they're not stuck in a three-year deal with last year's technology.

Remember that Friends episode where Monica and Chandler think they're keeping their relationship secret… but Joey accidentally finds out? Then Rachel. Then Phoebe. Then it spirals into this high-stakes improv game where everyone is pretending not to know, while actually knowing, while also pretending not to know that the others know.

The longer it goes on, the funnier it gets… in sitcom land.

Now replace "secret relationship" with "how far certain people on your team have taken their AI skills," and the laugh track gets replaced with nervous laughter.

Because here's the truth: You already know everyone's touched AI. Written some copy, polished a headline, maybe authored a blog post or whitepaper.

What you don't know is that your quietest copywriter has built an agent that generates entire research reports, repurposes content into dozens of derivative assets with zero human intervention.

Or that your designer has a PowerPoint machine that cranks out multi-slide decks in under 90 seconds.

Or that your growth manager is running a fully automated ad variation → lead enrichment → CRM upload workflow while you're still on your morning coffee.

Dashboards are appearing in seconds. Campaigns are being tested without anyone touching the ad platform UI.

And the kicker? Your most advanced AI users are often your least vocal about it—because right now, these workflows are their superpower. They're the reason they look like productivity superheroes.

And superheroes don't always hand over their capes.

The Superhuman Trap

If you're a marketing leader, this is the problem:

You don't want a couple of superhumans. You want a superhuman TEAM.

And if you're the "AI whisperer" on the team? Sure—being the tinkerer, the agent builder, the person who can bend ChatGPT to your will is impressive. But you know what's even more career-defining?

Becoming the one who accelerates everyone else's AI adoption.

Because the truth is: In the age of AI, your legacy isn't just the workflows you build… it's the number of people you lift to your level.

The Hidden Cost of "Secret AI"

When advanced AI use stays underground, you lose more than visibility:

No shared learning loop – Game-changing workflows stay locked with a few power users.

Duplicated risk – Rogue agents and tools might be handling sensitive data with no oversight.

Innovation gridlock – Your org never moves from isolated experiments (a.k.a. random acts of AI) to scaled competitive advantage.

Budget sprawl and wasted licensing – Dozens of one-off subscriptions ($20 here, $20 there) pile up. Without a central view, you can't fund intentionally nor can you assess ROI. What’s worse? These subscriptions often comes from program budgets, starving campaigns and hurting ROI on the dollars meant to drive growth.

PIVOT!

Why People Hide AI Use—And How to Fix It

Foundational Enablers for AI Transparency & Collaboration

• Declare your stance: "We are an AI-forward marketing organization."

• Publish an approved/recommended tools list AND a process to submit for additional tools for review as quick safety checks.

• Encourage safe tool sharing: Especially low-risk, high-productivity tools (point solutions) like HeyGen, Descript or WhisperFlow.

• Audit for duplication: Consolidate licenses to cut costs and boost collaboration.

• Implement AI usage guidelines and data privacy policy: Be explicit about what data can and can't be shared. Believe it or not, putting guardrails in place actually improve adoption as people hesitate out of fear.

• Form an AI council: Cross-functional team to guide adoption, governance, and knowledge sharing.

Your AI Transparency Toolkit

1. My Mock AI Council GPT

Get access to my custom AI Advisory Board GPT, pre-loaded with perspectives from my mock AI council. Use it to brainstorm adoption, collaboration, governance, and training ideas for your org.

https://chatgpt.com/g/g-68994879e3208191bf7295cc96871455-mock-ai-advisory-board-public

2. Explainability Notes Prompt for Your Team

Sometimes team members hold back from disclosing their sources because they aren’t confident in the work. Remind your team that they can and should challenge then verify AI output. They can append this to any AI request to make outputs more credible and defensible:

"In addition to your main answer, include a section titled 'Explainability Notes' that contains:

• Step-by-step reasoning used to produce the output • All sources consulted or cited, with links where possible

• All assumptions made in generating the output • Known gaps or uncertainties that could affect accuracy

• Explain the concepts as if I am a 9th grade student.

Write these notes in clear, concise language suitable for review by an executive audience."

Next Week

Next week's episode: The One With the Gym Membership.

You know that Friends episode where Ross and Chandler try to quit the gym… and end up signing more paperwork, getting guilt-tripped by the staff, and walking out with more gym perks than they had before?

That's vendor lock-in with AI or martech contracts.

We'll break down why rigid, long-term deals can be dangerous in a fast-evolving AI landscape, how to spot the fine print that keeps you stuck, and smarter ways to negotiate so you can pivot quickly when the tech moves faster than your contract.

Until then—go make the invisible visible.

—Lisa

You know that Friends episode where Joey insists he speaks French?

"Je de coup fluh… blu blah." Joey stares proudly. He thinks he's speaking fluent French.

Phoebe stares back.

"That was actually worse than the first one."

He tries again, with even more confidence.

"Fleurblaaah bleeuh bluh!"

Phoebe breaks.

"You're not speaking French, Joey!"

"He's not… he's just doing gibberish! I'm so sorry."

That's exactly what AI sounds like when it hallucinates… confident, fluent-sounding nonsense that almost passes UNTIL someone asks where the quote came from.

This issue is about spotting that gibberish before it ends up in your board deck, pitch doc, or public-facing content.

I've been using AI more and more for strategic content, competitive research, and framework development. And I've learned something important: AI can be as confident as Joey when it's making stuff up.

Why This Actually Matters

If you've ever:

• Pasted an AI-generated stat into a board slide

• Let a first-draft blog post go straight to design

• Shared a "hot take" without checking the source

...then you've risked an AI hallucination slipping into the wild.

Here's the thing: fluency isn't the same as accuracy. And busy teams miss that difference when we're operating under deadline pressure.

I've caught myself almost shipping content that sounded brilliant but was completely fabricated. The formatting was perfect. The logic flowed beautifully. The only problem? None of it was true.

Where I've Seen This Go Wrong

The danger zones in my own work:

• Executive decks – AI generates market-size numbers that sound credible but have no source

• Competitive analysis – Made-up customer logos or funding details

• Thought leadership – Citations to research studies that don't exist

• Framework development – Brand new "methodologies" that sound official but are invented

Look at every place you rely on AI for first drafts with light human review. That's where hallucinations breed.

The Two-Step Process I Actually Use

I've started building fact-checking directly into my AI workflow instead of hoping I'll catch problems later.

Step 1: Make AI critique itself

Before I review any AI draft, I run this:

Draft: Answer the question "[your original prompt]"
Critique: Identify any flaws, gaps, or logical errors  
Revise: Produce a final answer that fixes every issue you found

The model writes, becomes its own critic, then rewrites. I start my review with cleaner copy and a list of weak spots it already identified.

Step 2: Fact-check every external claim

For anything that references outside sources or data, I use this two-part check:

Challenge claims:

You are a fact-checker. List every claim, statistic, and named source in the draft below.
Flag anything without a verifiable citation and explain why it's suspect.

Demand receipts:

Provide primary sources (public URLs, published studies, or official filings) 
that confirm or correct: "[insert specific statement]".
If none exist, return "No verifiable source found".

I've wired these prompts into my content pipeline so screening happens by default, not at the last minute when I'm rushing to hit send.

My Quick Decision Framework

When I'm reviewing AI outputs, I use this simple filter:

I keep this taped next to my monitor right next to the “Can AI help you with this task?” post-it note. It makes decisions fast.

A Real Example From Last Week

I was working on a social media post and AI generated this line:

"B2B marketers who shifted 15% of budget to AI automation saw a 3.4× pipeline lift, according to Forrester, April 2025."

Sounds reasonable, right? The kind of stat you see in every AI marketing presentation.

When I ran it through my fact-check process, the result came back: "No verifiable source found."

I spent five minutes trying to find that Forrester report. It doesn't exist.

Replaced it with actual data from our own client results. Much more credible. Much more defensible.

What I'm Learning

AI is getting scary good at sounding authoritative about things that aren't true. Not because it's trying to deceive, but because it's optimized for fluency, not accuracy.

The solution isn't to stop using AI. It's to get better at using it intelligently.

I'm treating AI like a very talented intern who occasionally embellishes. Great at structure, synthesis, and getting ideas flowing. Terrible at distinguishing between "sounds right" and "is right."

My job as the CMO is to harness the brilliance while protecting against the blind spots.

Because the last thing I want is to walk into a board meeting speaking Joey's version of French.

Next Week

I'm diving into the most awkward dynamic happening in marketing teams right now.

You know that Friends episode where Monica and Chandler think they're secretly dating, but everyone already knows? And then it becomes this weird game of "They don't know that we know they know we know"?

That's exactly what's happening with AI adoption in most organizations.

Leadership thinks teams aren't using AI yet or not using it for advanced tasks and workflows. Teams think leadership doesn't want them using AI. Meanwhile, everyone's secretly using it, pretending they're not, and nobody's talking about what's actually working.

Working title: The One Where They Don't Know We Know They Know We Know.

I'll break down why this "secret AI" dynamic is killing innovation, how it's wasting time and money, and what happens when everyone finally stops pretending.

Au Revoir,

---Lisa

P.S. If this helped you dodge a potential hallucination, forward it to another marketer who needs the same heads-up.

I was planning to share the AI hallucinations issue next—you know, the one where your content sounds confident but totally wrong (The One Where Joey Speaks French). That's still coming.

But this was too big—and too urgent—not to jump the line.

Because what just happened changes everything for how B2B marketing leaders create, distribute, and control their content:

Google is now indexing public ChatGPT conversations.

And if you're not prepared, this could be your Monica-and-Chandler moment—where what you thought was private suddenly becomes very, very public.

What Just Changed

When you share a ChatGPT, Claude, or Gemini conversation via public link, here's what happens behind the scenes:

• It's crawlable by search engines

• It's indexable in Google's database

• It's rankable against other content

• It's discoverable by anyone searching relevant terms

That "quick brainstorm" you shared with your team? It's now a live web page that could outrank your official blog.

The Strategic Risks (And Why CMOs Should Care)

1. Unintentional Brand Exposure

The Problem: Your half-formed ideas, internal discussions, or client-specific brainstorms become searchable content under your name.

Example: A marketing leader shared a ChatGPT conversation about "positioning against [Competitor X]" to get team feedback. That link is now a top result when people search "[Company] vs [Competitor X] positioning."

2. Competitive Intelligence Leaks

The Problem: Shared AI conversations can reveal:

• Strategic priorities and concerns

• Budget constraints or investment areas

• Product roadmap elements

• Team structure changes

• Partnership discussions

The CMO Impact: Your competitors now have a new source of intelligence about your thinking, challenges, and direction.

3. Content Governance Breakdown

The Problem: AI-generated content shared publicly may not align with your brand voice, messaging guidelines, or legal review processes.

What I've Seen: Companies discovering that shared AI brainstorms contradict official positioning, contain outdated information, or make claims that haven't been verified by legal or compliance teams.

The Strategic Opportunity (Turn Risk Into Revenue)

But here's the pivot: if you approach this intentionally, it becomes a powerful new distribution channel.

The New Content Equation

Traditional: Create → Edit → Publish → Distribute → Hope for Discovery

AI-Powered: Prompt → Share → Index → Rank → Drive Traffic

Three Ways Forward-Thinking CMOs Will Be Using This

1. Thought Leadership at Scale

Instead of writing lengthy blog posts, create structured AI conversations that address common buyer questions, then share them strategically.

Example: "How should B2B SaaS companies think about AI in their go-to-market strategy?" becomes a comprehensive, searchable resource that positions you as a thought leader.

2. SEO Without Content Teams

Use AI conversations to capture long-tail keywords and niche topics your content team doesn't have bandwidth to cover.

Framework: Identify high-intent, low-competition search terms → Create structured AI conversations → Share with optimized titles → Monitor rankings.

3. Rapid Response Marketing

When industry news breaks or trends emerge, create immediate thought leadership through AI conversations rather than waiting for blog approval cycles.

Your AI Content Governance Playbook

Phase 1: Immediate Actions (This Week)

Audit Your Current Exposure

1. Search Google for: site:chatgpt.com "your name" or site:chatgpt.com "your company"

2. Check what AI conversations are already public

3. Evaluate whether existing shared links align with your brand

Establish Guardrails

• Create a "Public AI Sharing" policy for your team

• Define what topics/information should never be in shared AI conversations

• Set up approval workflows for strategic AI content sharing

Phase 2: Strategic Implementation (Next 30 Days)

Use the PIVOT Method for AI Content Creation:

• Purpose: Define why you're creating this AI conversation (thought leadership, SEO, lead generation)

• Input: Craft prompts that generate structured, valuable content

• Verify: Review and edit AI output for accuracy and brand alignment

• Optimize: Add clear titles, structure, and calls-to-action

• Track: Monitor performance, rankings, and engagement

Create Content Clusters - Develop AI conversations around key themes:

• Industry trend analysis

• Buyer education content

• Strategic frameworks

• Tool comparisons and recommendations

• Leadership insights and lessons learned

Phase 3: Scale and Optimize (Next 90 Days)

1. Identify your top 10 buyer questions/pain points

2. Create comprehensive AI conversations for each

3. Share strategically with SEO-optimized titles

4. Build internal links between related AI conversations

5. Monitor which topics drive the most engagement and double down

Tactical Implementation Guide

The Strategic AI Prompt - Use this structure for creating valuable, shareable AI conversations:

Act as a [your title/expertise area] with [X years] experience in [industry].

A [target audience member] just asked: "[specific question]"

Provide a comprehensive answer that:

- Addresses their core concern

- Offers 3-5 actionable insights

- Includes specific examples or frameworks

- Mentions potential pitfalls to avoid

- Suggests next steps

Structure your response with clear headings and make it valuable enough that someone would bookmark it.

Pre-Share Checklist

Before sharing any AI conversation publicly:

• Does this align with our brand messaging?

• Have I removed any confidential/sensitive information?

• Is the content accurate and up-to-date?

• Does it provide genuine value to our target audience?

• Have I optimized the title for search discovery?

• Is there a clear call-to-action or next step?

Measuring Success

Track these metrics for your AI content strategy:

Discovery Metrics:

• Search rankings for target keywords

• Organic traffic from shared AI conversations

• Backlinks generated from AI content

Engagement Metrics:

• Time spent on AI conversation pages

• Click-through rates to your main site

• Lead generation from AI content CTAs

Authority Metrics:

• Mentions and citations of your AI content

• Speaking opportunities generated

• Inbound partnership/collaboration requests

Advanced Strategies for AI Content Leaders

The Expert Interview Simulation

Create AI conversations where you "interview" industry experts (using their public knowledge and frameworks). Share these as thought leadership pieces that position you as someone who synthesizes expert thinking.

The Competitive Analysis Deep Dive

Use AI to analyze public information about competitors, create comprehensive comparison frameworks, then share as valuable buyer resources (while being careful about claims and factual accuracy).

The Trend Prediction Engine

Weekly AI conversations analyzing industry trends, predictions, and implications. Build a reputation as someone who spots trends early and thinks strategically about their impact.

Central Perk Coffee Break (Quick Win)

5-Minute Action: Right now, create a ChatGPT conversation answering one question your prospects ask frequently.

Use this prompt:

"Act as a [your role] helping a [target buyer]. They just asked: '[common question]'. Give them a comprehensive, actionable answer they'd want to save and share."

Edit the output, add a clear title, share the link, and post it on LinkedIn with context about why this question matters.

Why it works: You're not just sharing random AI output—you're providing curated expertise that builds authority and drives discovery.

What's Coming Next

The One Where Joey Speaks French (Issue #4)

AI hallucinations are getting more sophisticated and harder to catch. Next week, we'll dive into:

• How to spot confident-but-wrong AI outputs

• Quality control frameworks for AI-generated content

• When to trust AI vs. when to verify everything

• Building internal processes to prevent "Joey moments" in your marketing

Plus: I'll share the exact verification checklist I use before publishing any AI-assisted content.

The bottom line: You're no longer just a prompt writer—you're a publisher, whether you intended to be or not.

The question isn't whether this will impact your content strategy. It's whether you'll lead this change or react to it.

Choose wisely.

—Lisa

P.S. - I wrote this issue using the exact AI-assisted process I described above. The irony? If I share our brainstorming conversation, it'll probably rank for "AI content strategy for CMOs." Meta? Absolutely. Effective? We'll find out.

Forward this to another marketing leader who needs to know about this shift.

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Want to chat about AI content strategy? Reply to this email—I read every response.

You know the Friends theme:
"I’ll be there for you..."

That’s what most CMOs I know wish they had right now.
Someone to call when the CEO asks about “the AI plan.” Someone to help pressure-test strategy.
Someone to help them feel like they’re leading this change, not reacting to it.

Today’s post is about building exactly that support system—for yourself—using AI.
I’ll show you how to create your own AI-powered advisory board with the exact process I use.

Why You Need This

If you’ve ever wished you could:

• Get honest advice from top AI thinkers

• See a complex challenge from 8 angles

• Pressure-test your thinking before you present it to your exec team

…then this is your moment.

This advisory board will help you:

• Prioritize use cases

• Build confidence in your roadmap

• Get unstuck faster—without relying on one-off opinions

And it works for any challenge—not just AI.

Who’s on My Board?

Here are the 8 real leaders I’ve built into my advisory group GPT. Each brings a distinct lens:

Demis Hassabis (DeepMind)
The Scientific Diplomat – Balances systems thinking, neuroscience, and global collaboration with long-term caution.

Sam Altman (OpenAI)
The Iterative Disruptor – Advocates for real-world testing and product momentum over abstract safety theory.

Dario Amodei (Anthropic)
The Safety-First Optimist – Combines rigorous academic frameworks with a pragmatic approach to responsible AI scaling.

Elon Musk (xAI)
The Maximalist Provocateur – Moves fast, challenges institutions, and insists AI be radically “truth-seeking.”

Mark Zuckerberg (Meta)
The Scale Architect – Thinks in infrastructure, personalization at scale, and open-source leadership.

Paul Roetzer (Marketing AI Institute)
The Business Translator – Focused on making AI accessible to marketers and business operators.

Jensen Huang (Nvidia)
The Infrastructure Visionary – Obsessed with compute power, hardware leverage, and enabling innovation across sectors.

Beth Barnes (METR)
The Urgent Evaluator – Rings the alarm on model risks, self-improvement timelines, and the lack of readiness across orgs.

Build Your Own in 5 Steps

This whole setup takes under an hour.

1. Define the Board’s Purpose

Before you build anything, ask:

“What do I want this advisory board to help me do?”

Mine was:

Guide AI adoption at 2X—including org design, training programs, and product innovation.

You could build one for:

• Repositioning your brand

• Navigating a career pivot

• Designing a future org model

• Launching a side project

2. Choose Your Advisors

Pick 5–8 voices who represent:

• Different viewpoints

• Different risks

• Different strengths

Then add a sentence about each.

Don’t want to build your own?
You can use mine—just say so in your GPT prompt.

3. Ask GPT to Research Them

Use this prompt in your favorite Deep Research tool… ChatGPT, Gemini, Perplexity all have amazing deep research functionality:

I’m building a mock advisory board made up of influential AI leaders. Please research and summarize the communication style, innovation lens, worldview, and notable insights for each of the following: [Insert your list with 1–2 sentences per person]

You’ll get a solid summary of each voice. Save that output—you’ll need it for the next step.

4. Ask GPT to Help You Build the Custom GPT

Now ask ChatGPT:

I want to turn this group into a custom GPT. Please generate: - A name - A short description - Detailed instructions - 3–4 conversation starters Make sure the model blends their individual viewpoints or shows where they diverge.

Then go to:

• Explore GPTs > Create GPT

• Paste it all in

• Set it to “Only Me” or “Anyone With the Link”

• Add a DALL·E image if you want

Done.

5. Ask Your Board for Help

Here are some prompts I use regularly:

• “Which AI use cases should we prioritize in the next 90 days based on ROI and internal readiness?”

• “What are the blind spots in our AI strategy right now—and who on the board would disagree?”

• “How should we pitch this roadmap to our CEO in a way that balances vision and realism?”

• “What are the best methods to structure training & development for my team?”

Let the board debate. Ask for pro/con views. Invite a “mock disagreement.”

You’ll be surprised how often it helps you make a decision.

Pro Tip: You Can Use This for Anything

This isn’t just for AI leaders.

You could build:

• A GTM board (e.g. April Dunford, Andy Raskin, Brian Balfour)

• A brand storytelling team (e.g. Brené Brown, Ann Handley, Donald Miller)

• A personal growth council (e.g. Adam Grant, James Clear, Angela Duckworth)

Just:

1. Frame the job to be done

2. Pick the voices

3. Simulate the conversation

Next Up…

The One Where Joey Speaks French

In the next issue, we’re diving into one of the biggest risks of using AI in your marketing workflow: Hallucinations—when AI generates content that sounds brilliant, but is totally wrong.

If you’ve ever watched Joey Tribbiani insist he’s speaking fluent French (when he’s clearly just making sounds), you already know the vibe. AI can do the same thing: confidently stringing together jargon, fake stats, and plausible-sounding frameworks that collapse under scrutiny.

Next week’s issue will cover:

• Why hallucinations happen—and why they’re so easy to miss

• Where they tend to sneak into decks, strategy docs, and team deliverables

• Prompts and QA flows to test outputs before they cost you credibility

• A checklist for when to trust, tweak, or toss what AI gives you

Because you don’t want to show up in the boardroom sounding like “Fleurblaaah bluh bleeuh!”

Even if you say it with conviction.

Until then, go build your dream team.

AI will be there for you. 🎤🎶

—Lisa

Where this newsletter is going, why it matters, and how I’ll be writing it (spoiler: probably while walking).

You know the Friends episode where Ross is trapped in a stairwell, yelling “PIVOT!” over and over again?

That’s what leading marketing feels like right now.

Executives are asking:

• What’s your AI strategy?

• Are you using it yet?

• Should we restructure around it?

Meanwhile, CMOs are quietly asking themselves:

• Where do I even start?

• Am I falling behind?

• How do I lead something I’m still figuring out myself?

If that’s where you are, Prompt & Pivot is for you.

Why I’m Writing This

I’ve spent the last few years writing and speaking on behalf of my employer 2X—the global B2B marketing managed services firm where I lead marketing, product, and AI. And I’ve love every bit of helping CMOs modernize their operating models.

But Prompt & Pivot is something else.

It’s personal.

This is where I’ll explore what it really means to lead during a moment when AI is reshaping not just workflows, but identities, roles, and roadmaps.

These aren’t company memos or product briefs. They’re reflections, playbooks, and ideas from one CMO to another—off-the-record, but on the record.

Why Friends?

Recently, I appeared on the Remarkable podcast, where I was asked to pick my favorite TV show and tie it to marketing lessons. I picked Friends. Not for the nostalgia—but because it still resonates. It’s human. Relatable. And yes, wildly quotable.

So when I decided to launch a personal newsletter, Prompt & Pivot felt right. A little fun, a little familiar, and exactly the kind of tone I want this to have. You’ll see that in the titles (“The One Where…”), the visuals (yes, there might be a couch), and the spirit behind every issue: showing up for each other when things feel uncertain.

What This Will Be

Each week(ish), I’ll share one issue covering:

• Strategic clarity on how AI is changing the marketing org

• Playbooks, prompts, and frameworks you can actually use

• Honest stories about what’s working (and what isn’t) in real teams

• Thought starters that help you reflect, reframe, and move forward

This isn’t just about AI tools. It’s about the tools you need as a leader.

It’s also not about being perfect. I don’t have it all figured out. But I’ve been living this longer than most—building AI strategies, leading adoption across a global team, and advising CMOs on how to do the same.

A Note on How I’m Writing This

I probably won’t be typing.

Most of these issues will be “written” while I’m walking. I use ChatGPT’s Voice Mode to think out loud, test ideas, and let the newsletter take shape organically—while I tinker with prompts and try new tools.

Everything you read here will be mine. But I’ll be using AI to help me shape it.

This is how I work. It’s how I write my book.

And it’s how I hope you’ll start working too—with AI as a collaborator, not a crutch.

What’s Next

Coming up next: The One Where AI Will Be There for You

I’ll show you how to build a personal advisory council using AI.

Not hypothetically—literally. I’ll walk you through how to set up a custom GPT (or use one I’ve created for you) that channels the voices of iconic thinkers like:

• Seth Godin

• Gary Vaynerchuk

• April Dunford

• Andrew Chen

• And more

It’s your virtual war room. One that’s always available, always on, and always ready to help you lead.

Thanks for being here.

Thanks for giving this a shot.

And yes, AI will be there for you too. 🎶

—Lisa