Dear friends,
Some personal news: after 9 years of being a fractional CMO and, recently, a CMO advisor/wingman, I've decided to dive back in.
I was approached to join a company whose mission I wholeheartedly believe in, with incredible people, to work on my all-time favorite product (and maybe yours, too?). A product whose impact on how we work - and beyond - is mind-boggling. So I couldn’t pass up the opportunity to get involved and play a small role in making it an essential part of our personal and collective “work OS”.
So on May 26, I’ll be joining the Product Marketing team at Anthropic to help market Claude apps. That’s Claude Chat, Cowork, and Code.
After all, I was already studying them, using them, and writing about them, so why not do it on a greater scale and help shape what’s next?
As a result, I will pause writing this newsletter for a while. I'll share some updates on LinkedIn.
Thanks so much for reading these highlights and for all the support and feedback. I’ve learned a lot. And there is still so much to learn!!
I hope to see you on LinkedIn or at Anthropic’s events.
If you only check one highlight this week:
B2B CMO? How a DG leader drove AI adoption on her team
Everyone else: why you shouldn't feel bad if keeping up with AI seems exhausting
-François, a future “Ant”
The best playbook to engage the C-suite today
If you are a B2B marketer, how many times have you heard your CEO, CRO or CMO say, "We need to engage more senior decision-makers. Get me access to the C-suite. It will accelerate deals."
Thank you, CxO… Easy to say, but if you're a CISO, a CMO, or a CTO on the receiving end, how many vendors can you really pay attention to? We know CxOs rarely end up being our champions. They usually provide the final seal of approval on a deal. But they’re rarely the person the engagment starts with.
But that changed recently: just like us, they are on the hook to transform their processes with AI and accelerate AI adoption among their teams. They need to quickly report progress and results to the CEO and the board.
Like us, they have a large appetite for understanding how their peers drive that change, including:
What works?
What doesn't?
Who to partner with?
What tools to bring in?
What are the common mistakes?
More than ever, they are showing up and deeply engaging in peer discussions.
As B2B marketers, that is our opportunity: right now, the playbook of curating intimate events that bring these exec peers together is both effective and straightforward.
It builds reputation, trust, and a pipeline, as well as a deep library of knowledge and content.
The play is NOT new:
Plan intimate exec-only, invite-only sessions. In-person. 8 to 15 people. Virtual can work too.
Focus these events on a given use case or vertical. "How did we do this in manufacturing?" "How did we do this in retail?"
Lock in 1 or 2 anchor names first; ideally, customers (the playbook can even function without existing customers). Confirm them, then promote. The rest of the room shows up because they want to hear from those names.
Invite select prospects from that vertical or use case. Your sales team must provide these names from their top target accounts, especially the execs they struggle to engage on their own.
Bring two moderators from your company: an exec (ideally, from the same function as your target persona) + whoever on your solutions or FDE team worked the closest with your anchor customer
Let customers and your solutions architect/FDE describe how they tackled the project. Talk about strategy first, then specific implementation details. Encourage them to cover the bad and the ugly. That's what folks learn most from. It creates authentic conversation vs something that sounds like a promotion.
Then open up for questions and contributions from the rest of the group.
Keep sales pitches and Sales out of the room. You can invite your channel partners.
Transcribe and store everything and share with the group afterward (Chatham House Rules). You end up with a library of real implementation stories, pain points, and VOC to inform messaging, roadmap, and feed your content play. With every conversation, your team collects more insights and therefore sounds smarter and more credible at each event
Help the group connect after the event: share LinkedIn profiles and emails unless they opt out
Follow up with personal thank you notes and a summary of the key takeaways. Give your salespeople information about what their prospect learned, the questions they asked, and anything they shared about their own goals or challenges + a good offer, so they can follow up
Run the postmortem with your team. Iterate and repeat. Do this at scale and quickly to flood the market before someone else does.
My selection of tips, news and workflows
💡 How a Demand Gen leader drove AI adoption on her team
Non-technical team members feel most at risk of being left behind on AI, so Francesca Krihely-Price set out to drive adoption among non-technical marketers at dbt Labs through hackathons and structured constraints.
She explains how she did it in her 15-min talk:
dbt gave universal access to Claude Code Enterprise with unlimited credits for experimentation
Used Notion as a "hive mind" with structured databases containing all content and marketing materials
Implemented RunLayer as "Okta for MCPs" to connect AI tools with daily platforms like Notion, Omni, and Glean
Organized a Claude Hack Day specifically for non-technical female marketers who face bias (women using AI seen as "cheating" while men seen as "innovative")
Built workflows and systems thinking approaches rather than just content creation with AI
Team focuses on pipeline and revenue metrics, not just leads and MQLs
💪 The personal struggle to learn AI: yes, it very much feels like that.
Kieran, who's got a new AI-first role at HubSpot, captured very well the feeling of struggling to adopt AI and the nights-and-weekends work required to get there.
“There’s a period of time when it makes you less productive. It’s also near impossible to do your day job and keep up with AI.
.... unless your day job is AI.
Most people I talk to ask, “How do I carve out hours during my work to learn AI”
The reality is, you can’t depend on others to help with this. Sure, your company might offer hackathons, training, and more. But, the honest answer is, for a lot of people, AI is a nights and weekends thing until it’s deeply embedded into how you work.
You’re trying to do your day job, keep up with AI, and learn a new way of working, so you become less productive. You’re also working more hours than before, because you’ve added in learning an entirely new way of doing things.
This is the low point for most people. How the f*ck am I meant to do this.
The upside is, when you get through the ‘struggle’ phase, you’ve embedded AI into how you work, likely changed how you work in a lot of ways, and now you’re in the compounding gains stage. It’s more fun.
Here’s the truth: no one is coming to help you do this. A lot of this will be down to personal choice. Do I want to sacrifice a lot of my personal time to learn this because it’s critical to the future I want to build?
And it’s hard. Everyone has personal stuff going on in their lives. At times, working late nights and weekends is a brutal commitment. Believe me, I’ve got s**t going on in my personal life right now, making that choice is brutal. But, I know I have to.
Like anything good, there is gold at the end of the rainbow (or U-Curve), you just need to struggle your way there, even if it means crawling at times :)
🔍 Product marketing at AI speed - Learnings from Lovable
Solid AI-native PMM best practices in this post by the Head of Product Marketing at Lovable. Fully agree with her main point that product marketing now needs to build infrastructure and guidance that adjusts with new products, market intel or changes in strategies. Infra and guidance that others can tap, easily, to move fast and then adjust on the go.
tl;dr:
“Key Takeaway: PMM is now infrastructure. Provide ICP, self-serve launches, and findable, askable, usable info. Get out of the way.
1 - Enable your team to launch stuff, themselves - Lovable launches Tier 1 updates almost every other week. PMM tiers releases, lead tier 1 and tier 2 and lets builders self-launch the rest.
2 - Define your ICP based on actual data - Pull from CRM, usage, surveys, interviews, and Slack-based AI agents. Review weekly. Lovable's ICP is genuinely "everyone."
3 - Constantly interview customers - Two PMMs ran back-to-back interviews using five fixed questions. They captured exact phrases, emotional moments, and recurring objections.
4 - Create a simplified messaging framework - Audit every existing piece of copy. Find patterns. Resolve conflicts. Build one hierarchy with variants, use cases, and proof points.
Bonus: put it where they can ask it - Docs in one place is table stakes, but often clunky. Build LLM agents to answer people's queries in natural language about ICP, messaging, and upcoming launches.”
Their three-tier launch system
Tier 1: Big launches. Big releases or roundups with active coordination. PMM still runs these.
Tier 2. Run by PMM alongside a PM, more fluid bill of materials. Amplified post-announcement if traction is good.
Tier 3: Everything else. Builders launch it themselves. PMM gives them the resources.
You may also remember this lightning talk by Deedi, Head of Content at Bubble, who created a system to receive alerts when Bubble’s competitors go through a meaningful positioning change (incl. major launches) and recommends next steps for their own messaging or thought leadership.
🛠️ The internal forward-deployed engineer role
The new roles that Aaron Levie from Box describes in his post (excerpts below) have already started popping up. You need them in your organization
“We’re in the early phases of starting to hire and retrain for new agent engineering roles for internal functions to help get more powerful agents working well on critical business processes. I expect this type of role to be a very big deal over time at Box and other companies.
It looks something like an internal FDE, whose job it is to wire up internal systems and get agents working with them effectively. The person will be extremely technical and capable of building secure, governed agents for internal workflows that connect to business systems (like Box, Salesforce, etc.), and codify workflows in skills.
In some cases this person may understand the business process well enough to do it fully, but in most cases I expect them to work with the business directly in an embedded fashion. Ironically, that may introduce another new role on the business side that is more akin to agent product management for internal processes.
How does the agent get access to the data it needs, where do humans show up in the workflow, how does the agent communicate with the users, and so on.”
AEO is a recent discipline. Yet, it’s already evolved.
After interviewing 12 of the best AEO minds, Kyle Poyar detailed the new AEO playbook:
Measure AI influenced $$ (helps justify growing AEO budgets)
Get described well by AI (visibility doesn’t matter if AI engines say the wrong things)
Point AI to the right content (many are putting key links in their footers)
Keep core pages fresh (pro-tip: Kevin Indig sees a 15% citation uplift after adding a “last updated” date)
Go after specialized, long-tail queries
Go big on YouTube (now cited more often than Reddit)
Optimize for readability
🔍 Want to create non-commodity content?
In this “6 rules for content marketing in 2026”, Kip Bodnar, CMO at HubSpot, walks us through his criteria and shares a cool grader (built with Perplexity) to avoid creating commodity content and quickly rise to the top of search results.
His 6-Checkpoint Content Quality Test:
Proprietary Evidence check. Identify one data point or proof that only your brand can provide. No proprietary evidence = commodity content.
Firsthand experience check. Confirm the author has direct experience with the topic. Researched advice doesn't count; "I did" beats "experts say."
Specificity scan. Find every adjective in the content. Replace each with a number, name, or date. Forces real expertise to surface.
Point-of-view check. Locate where the author takes a clear stand. If there's no opinion, add one. Neutral content ranks lower in AI search.
LLM test. Ask: Could ChatGPT have written this? If yes, rewrite with something only a human with direct experience could provide.
Information gain test. Google the content's own headline. Read the top 3 results. Confirm your content says something none of those pages say.
Final Words
The problem [with agents] is that the work no longer drains you through typing. It drains you through judgment. More attention, more context switching, more verification, more decisions per hour. The agent can't keep working 24/7. The human still has a hard limit.
