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Uh oh… if you’re getting this on a Wednesday, you know what that means…I took on too much responsibility! Too many plates to juggle! Better late than never is what I’m *hoping you’re thinking right now.
🗞️ News That Actually Matters
🌡️ Temp Check - Is AI an Investment Bubble About to Pop?
🧩 Project Playground: Personal Brand Makeover Pt. 3
📣 Word of the Week
🗞️ News That Actually Matters
Summary: Google expanded AI Mode with more agentic, personalized responses and broader global availability (Aug 21).
Why it matters: Expect more zero‑click answers—rethink SEO/SEM, add structured data, and test “answer-ready” content for AI surfaces.
Summary: Klaviyo launched MCP Server (Aug 20) to connect first‑party data directly to AI platforms like Cursor, Claude, and Windsurf.
Why it matters: Even for B2B, this simplifies piping clean CRM/CDP data into agents for personalization and analytics without heavy ETL.
Summary: New General Assembly research (Aug 19) finds confidence in AI’s revenue impact lags, despite perceived CX gains.
Why it matters: Prioritize enablement and measurement—train teams, define AI use cases, and tie outputs to pipeline, not vanity metrics.
🌡️ Temp Check - Is AI an Investment Bubble About to Pop?
AI stocks like Nvidia, Microsoft, and Alphabet took a hit after an MIT study grabbed headlines saying 95% of AI pilots fail. The market treated that as a sign the tech isn’t living up to the hype, but the study itself tells a different story (if only people knew how to read). The real issue wasn’t that the models didn’t work; it’s that companies didn’t know how to use them properly or design NEW workflows that unlocked value.
Key Takeaways
95% of pilots showed no clear business impact.
Buying AI solutions succeeded ~67% of the time vs. ~33% for in-house builds.
Failures mostly came from poor integration and process design, not weak models.
Quiet wins are happening: companies saving $2–10M in back-office costs or trimming ~30% off agency spend.
Markets reacted to the scary headline, not the nuance. The study actually suggests AI can deliver, but only if companies stop treating it like plug-and-play magic and start rethinking how their workflows run. The bubble talk may be overblown; the real story is about execution, not technology.
🧩 Project Playground: Personal Brand Makeover Pt. 3
Step 3: Positioning & Differentiation
Goal: Define the point of view (POV) and core beliefs that make you worth paying attention to — the stuff that makes your audience think “Finally, someone said it.”
What this involves:
Contrarian Takes
What do you believe that goes against the grain in your industry?
Example: “The people who call themselves AI experts usually know the least. Expertise is a habit, not a destination.”
Lines in the Sand
What are the non-negotiables you’ll stand by and repeat?
Example: “AI should never remove humans from the loop.”
Repeatable Beliefs
Short, sticky statements that reinforce your POV.
Example: “Curiosity is the competitive edge.”
Proof of Difference
What about your background, approach, or voice makes you stand out?
Example: You’re not posturing as an “expert” — you’re “learning out loud” and cutting through overwhelm for your peers.
💡 The output from Step 3 should be a POV playbook with:
3–5 contrarian takes
3–5 lines in the sand
A few sticky one-liners you’ll repeat across posts, newsletter, talks
Use this prompt with ChatGPT (or your AI tool of choice) to uncover the beliefs and POV that make your brand stand out:
You are my personal branding strategy advisor.
Help me define my positioning and differentiation for LinkedIn.
Ask me:
What frustrates me about my industry or role?
What common advice do I secretly disagree with?
What do I believe that others don’t usually say out loud?
What principles or “lines in the sand” do I refuse to compromise on?
Based on my answers, generate:
3–5 Contrarian Takes (opinions that go against the grain but feel true to me).
3–5 Lines in the Sand (non-negotiable principles I’ll stand by).
A few short, repeatable Belief Statements I can use across posts and my profile.
Format the output as a POV Playbook I can keep referring back to when creating content.📣 Word of the Week
Hallucination: In AI, a hallucination is when a model confidently produces information that isn’t true or can’t be verified. For example, an AI might invent a source, make up statistics, or present guesses as facts. Even small differences in hallucination rates can make or break an AI business use case.
Thanks for tuning in this week! Next week, I’ll be back in the States after six weeks abroad, finishing my time with a week in London. The timeline of chaos seemingly never ends, though, as I will be moving to NYC two weeks after getting back. It’s never not entertaining around here.
As always, stay Human First ☝🏻 → AI Forward.
See you next Tuesday, in your inbox!
Skyler Neal
