Hey! I'm Aldis, founder of Second Voice.

We're a ghostwriting agency for coaches, consultants, and fractional executives.

You have the expertise. You know a newsletter would build trust and bring in clients. But you don't have 10+ hours a week to write, publish, and grow it yourself.

That's where we come in.

We handle everything:

- Daily Notes to grow your audience
- Substack setup, strategy, and analytics
- Weekly long-form posts written in your voice

The result? 

A newsletter that builds your reputation and warms up prospects while you focus on coaching and running your business.

High-ticket services need 15-20 touchpoints before someone buys. Your Substack delivers those touchpoints automatically.

My background is 13+ years in Agency and AdTech, building and measuring campaigns for some of the biggest brands in the world. That taught me how attention and distribution actually work.

I applied those skills to build my own newsletter. Now I build them for others.

Book a consultation. 

We'll map out your entire Substack strategy.

Aldis Ozols

Just got off a call with Taylin John Simmonds.

His Substack results are insane.

4,600 new newsletter subscribers in 90 days. 

And get this...

He didn't go viral on LinkedIn. Didn't run ads. Didn't even leverage his massive X audience. 

All he did was post organic Substack content.

Plus, he added $3.5k in revenue from paid subscribers. Again, with NO upsell funnel...literally just from posting on Substack.

I've been watching this closely because I've been grinding LinkedIn for over a year to grow my newsletter. 

And it works, but it's getting harder. 

Reach is down. The algorithm is stingier. Converting followers to subscribers takes more effort than it used to.

So what did I do? I started posting on Substack a few weeks ago.

And I got 200 subscribers. In a week. 

You better believe I'm going all in.

The built-in discoverability is wild right now. Substack actively pushes your content to people already looking for newsletters to subscribe to. You're not fighting the algorithm—it's actually working for you.

And here's what makes it different from other platforms:

You're not just collecting followers... 

You're collecting SUBSCRIBERS. 

An owned audience that lives in your email list that you can export at any time you want.

So now I'm implementing a two-prong strategy:

1/ LinkedIn builds your audience + grows your list through lead magnets. 

2/ Substack grows your newsletter through built-in discovery. 

Paid subscriptions add a revenue stream on top of whatever else you're selling.

I'm not abandoning LinkedIn….

But I'm convinced Substack is the biggest opportunity right now for long-form content and growing your newsletter. 

But the window won't last forever. These things never do.

"This feels like LinkedIn in 2021" is what I keep hearing...

The creators who move now will have a massive head start. But this window won't last. Maybe 3-12 months before it gets crowded.

---

PS. I've partnered with Taylin to help coaches and consultants do the same thing. Full setup. Weekly content. Daily Notes. Built-in discovery that grows your list by 100+ subscribers per month.

Opening 5 spots this month. DM me "Substack."

Substack ghostwriting for coaches, consultants, & fractional execs. VP @ InMarket | Founder @ Second Voice | 13+ years in Agency & AdTech | Audience building + email marketing tips 🧩

My favorite AI prompt to turn any fuzzy idea into a killer content angle in under 10 minutes:

(Even if you have no clue what your actual point is)

I shared this a few months ago and people are still asking me for it.

Here's why it works:

AI can't read your mind.

But it can interview you.

And that's way more valuable.

When you dump a topic into ChatGPT and say "write me a post," you get ChatGPT's thinking.

Generic. Vague. Sounds like AI. Who'da thought?

I used to do this exact thing when I first started writing online. It's ok. Pretty sure most of us did.

Here's how to fix it:

Instead of asking AI to write FOR you, have it ask YOU questions.

Suddenly, your own thinking comes out.

Clearer. Sharper. Actually yours. Your posts will "feel" better and perform better too.

(and THEN you can use Claude to write you a first draft)

Your bottleneck isn't writing. It's the clarity of your idea.

Once you've clarified your thinking through an interview, the first draft writes itself.

(Works for LinkedIn posts, Substack notes, newsletters, whatever)

Here's the process:

Step 1: Pick any rough idea

"I want to write about how curiosity gaps work"
"How to find ideas for newsletters"

Step 2: Run the "Pinpoint Writing" prompt (screenshot the image below - credit to Nicolas Cole and Dickie Bush)

Step 3: Let AI interview YOU

Answer out loud. Or type stream-of-consciousness.

Don't filter. Just respond.

After you've done the thinking, writing is simple.

And you get content that sounds like you.

Because it IS you.

Try this once and I promise you'll never go back to "write me a post about [topic]" again.

LMK what you think.

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My goal for 2026: 5,000 email subscribers.

Here's how I'm getting there:

1/ Going all in on Substack

I finally started writing on Substack and I'm obsessed.

I put it off for months because it felt new and scary. But now that I'm in? I'm extremely bullish on this platform.

The distribution engine actually works.

Plus...I'm working on something exciting related to Substack growth as a service :)

More on that later.

2/ Repurposing Substack Notes everywhere

I'm taking my best Substack Notes and turning them into newsletters, LinkedIn posts, and Threads posts.

If you haven't tried Threads yet, it's Instagram's version of Twitter...just with way less ragebait and sensationalism (and it's easier to grow).

It's also writing-focused, which I love.

3/ Doubling down on email

Email is my insurance policy against algorithm changes.

Every experiment I run, every piece of content I create, I'm asking myself: how can this drive more email subscribers?

Because email lets me own the relationship. No platform can take that away.

And I want to test using email to accelerate social traffic too. Make the whole flywheel spin faster.

4/ Doubling down on outreach and social selling

Loom videos. Connection requests. DMs.

I'm making outreach a daily habit and gamifying it so it's actually enjoyable.

The key is treating it like a game, not a chore.

5/ Updating my email course

The old email course served its purpose but isn't relevant to my current offer anymore.

I use the lead magnet rule: Does this asset make the paid offer feel like the obvious next move?

If it solves a smaller problem people hit before the main one, that's a winner.

So, I'm creating a completely new course this month that shows exactly how to position, start, and grow a revenue-driving newsletter.

6/ Creating one new lead magnet per week

I'm committing to creating one new lead magnet every week.

The strategy: Take my best-performing posts and expand them with AI prompts and deeper frameworks.

If a post resonates, why not turn it into something that captures emails?

That's my 2026 playbook.

If you're also diversifying your traffic strategy, let me know what you're trying.

"How often should I email my list?"

That's not actually what you're asking.

What you're really asking is "How much is too much before people get annoyed and leave?"

And that's coming from a place of fear.

Here's the reframe:

If your emails deliver value, people want more of them. Not less.

Think about the newsletters you actually look forward to. The ones you open every time. Do you wish they emailed you less?

Probably not.

The problem isn't frequency. The problem is most emails are boring, generic, downright spammy, or feel like obligations to open.

So the real limit isn't your audience's tolerance.

It's your ability to consistently create valuable content.

That's a completely different constraint.

Instead of asking "How much is too much?" ask "How often can I produce something genuinely worth reading?"

If that's once a week, email once a week.

If you can do twice a week and maintain quality, do that.

If you're cranking out daily emails that are mid, scale back.

Stop worrying about annoying your subscribers.

Worry about whether what you're sending is worth their time.

- Aldis

"I don't know what to write about in my newsletter."

False. Yes, yes you do.

The answer is already in your analytics.

Last year I let my newsletter fall off, mostly because I thought I needed "fresh ideas" every time.

And then I'd get overwhelmed and just wouldn't write it.

Meanwhile, I had 200+ LinkedIn posts just sitting there. Already tested. Some proven to resonate.

Every post that performed well is a potential newsletter.

Plus, most email subscribers never saw those LinkedIn posts anyway.

And the ones who did? They'll appreciate the expanded version.

Here's how to turn one LinkedIn post into a newsletter that hits:

1) Find your winner

Open your analytics. Find a post from the last 30 days that performed AND has clear structure.

Framework posts and step-by-step posts (like this one) work best.

Stream-of-consciousness posts are harder to repurpose.

2) Expand each section 2-3x

Add examples, stats, or personal stories to each point.

You're not repeating the post. You're making it stick.

3) Rewrite the opening for email

LinkedIn hooks are built for the scroll.

Email hooks are built for the inbox.

LinkedIn: "Stop making this mistake."

Email: "Quick question. When's the last time you opened your analytics?"

4) Write 10 subject lines

Keep them under 44 characters.

Your subject line determines if anyone reads this thing.

One high-performing post = one newsletter.

Do this weekly and you'll never run out of content.

P.S. That's a lot of steps. I know.

That's why I built an AI prompt that generates 10 client-attracting newsletter ideas for any LinkedIn post in minutes using the "10 Magical Ways" framework.

Comment "NEWSLETTER" and I'll DM it to you (make sure we're connected first)

I help leaders turn AI from “interesting” into measurable business leverage.

After 25+ years in marketing and digital marketing, including 4 years as a CRO, I’ve seen the same pattern over and over:

Most teams don’t have an AI problem.
They have a workflow + clarity + accountability problem.

AI just exposes it faster.

Today, I work with 7-figure business owners, CXOs, and marketing leaders who want to:

→ Upskill their teams without disrupting execution
→ Build practical AI workflows that actually get used
→ Protect brand voice, customer trust, and confidential data
→ Produce better content and campaigns with fewer bottlenecks
→ Turn “random tool usage” into a repeatable operating system
→ Develop an effective AI strategy and tool review that significantly shortens the learning curve

What you and I will build together

1) Your AI Strategy (built for how you actually work)
Clear priorities, use cases, guardrails, and success metrics—no fluff.

2) Your Team’s AI Enablement (adoption, not training theater)
Role-based playbooks, prompts, and workflows that stick after the workshop ends.

3) Your Content Engine (without losing your voice)
AI-assisted storytelling systems that scale thought leadership while staying human.

Why clients bring me in

→ 25+ years in marketing + digital
→ 4 years CRO experience (I care about revenue, not vanity metrics)
→ MBA (strategy + execution + operational discipline)
→ AI-certified trainer and hands-on builder across modern GenAI tools

If you’re a leader thinking, “We need to operationalize this,” send me a DM with “AI” and tell me what you’re trying to improve (content, pipeline, team productivity, or governance). I’ll reply with a quick starting point.

Carolyn Healey

95% of execs say they "use AI."
 
Most don’t know what kind.

And the confusion is costing them.

I sit in rooms with CXOs who use "generative AI" and "agentic AI" interchangeably. 

They're not the same. 

Treating them as if they are leads to bad strategy and misallocated budgets.

Here's the difference:

𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜: You give it a prompt, it gives you an output. One interaction. You're in control.

𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜: You give it a goal, it figures out the steps, executes them, and comes back with results. Multiple interactions. It operates autonomously within boundaries you set.

Let me make it concrete:

𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜:
→ You open ChatGPT
→ You type: "Write me a marketing email"
→ It writes the email
→ You copy, paste, send

You did the work of deciding what, when, and how. The AI helped with one step.

𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜:
→ You tell an agent: "Launch a campaign for our new product. A/B test two subject lines. Send the winner to our list. Report back on results."
→ The agent writes the emails, connects to your platform, runs the test, waits for data, sends the winner, compiles the report
→ You gave it a goal. It executed a multi-step workflow.

Use generative AI when:
→ output is the end product (drafts, summaries, ideation)
→ failure is cheap
→ the human stays as the workflow engine

Use agentic AI when:
→ output triggers actions (create ticket, ship campaign, reconcile invoices)
→ the workflow has repeatable steps
→ you can define guardrails + approvals

Why this matters for your strategy:

→ Generative AI augments tasks
→ Agentic AI automates workflows

Tasks are individual steps. Workflows are entire processes.

When you automate a task, you save minutes. When you automate a workflow, you change how work gets done entirely.

If you're planning your AI strategy based on generative AI alone, you're planning for the wrong future.

The shift from "AI as tool" to "AI as teammate" is happening now.

Save this and send it to your leadership team because alignment here prevents expensive confusion later

AI Strategy and Tool Coach for Leaders | Fractional CMO | Helping CXOs Upskill Marketing Teams | Content Strategist and Storyteller

Teams think: “Let's automate everything with AI.”

But they forget the one thing they can’t automate.

Their voice.

I watched a marketing team almost lose their brand's soul to automation.

They thought AI was the answer to everything. 

The result?

Engagement dropped 40%. Trust evaporated. Their audience said they sounded like robots.

The audience was right.

Leadership had a choice: Keep "optimizing" with AI or have the team edit everything with actual heart.

Engagement came back. So did the customer replies that actually meant something.

In a sea of AI-generated content, "human-premium" branding is now the ultimate trust differentiator.

Here are 9 ways to build a Human-AI Hybrid Strategy with your team:

𝟭/ 𝗦𝗽𝗲𝗲𝗱 𝘃𝘀. 𝗗𝗶𝗿𝗲𝗰𝘁𝗶𝗼𝗻
→ AI moves at 100mph
→ Your team chooses where to go

💡 Reality: Fast execution is worthless if you're running in the wrong direction.

𝟮/ 𝗗𝗮𝘁𝗮 𝘃𝘀. 𝗖𝗼𝗻𝘁𝗲𝘅𝘁
→ AI analyzes millions of data points in seconds
→ Your team understands the "why" behind the numbers

💡 Reality: AI sees the trend. Your people see the person behind the trend.

𝟯/ 𝗦𝗰𝗮𝗹𝗲 𝘃𝘀. 𝗖𝗼𝗻𝗻𝗲𝗰𝘁𝗶𝗼𝗻
→ AI blasts 1,000 messages
→ Your team builds 1 real relationship

💡 Reality: Customers don't connect with algorithms. They connect with vulnerability.

𝟰/ 𝗖𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝗰𝘆 𝘃𝘀. 𝗖𝗿𝗲𝗮𝘁𝗶𝘃𝗶𝘁𝘆
→ AI maintains the pattern perfectly
→ Your team breaks the pattern to create delight

💡 Reality: Hybrid campaigns consistently beat AI-only because they preserve context and voice.

𝟱/ 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆 𝘃𝘀. 𝗘𝗺𝗽𝗮𝘁𝗵𝘆
→ AI saves your team time on tasks
→ They spend that saved time on people

💡 Reality: Your team's empathy is now their most valuable currency. AI can't fake it.

𝟲/ 𝗟𝗼𝗴𝗶𝗰 𝘃𝘀. 𝗡𝘂𝗮𝗻𝗰𝗲
→ AI follows the rules of grammar and SEO
→ Your team knows when to break the rules for impact

💡 Reality: Perfect grammar often reads as "fake." Authentic voice reads as "trust."

𝟳/ 𝗢𝘂𝘁𝗽𝘂𝘁 𝘃𝘀. 𝗢𝘂𝘁𝗰𝗼𝗺𝗲
→ AI focuses on volume (more posts, more emails)
→ Your team focuses on value (better insights, deeper trust)

💡 Reality: Don't let your team use AI to make more noise. Train them to find the signal.

𝟴/ 𝗔𝗻𝘀𝘄𝗲𝗿𝘀 𝘃𝘀. 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀
→ AI gives the answer to any prompt
→ Your team asks the questions that actually matter

💡 Reality: The quality of your strategy depends on your team's curiosity, not their processing power.

𝟵/ 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 𝘃𝘀. 𝗔𝘂𝘁𝗵𝗲𝗻𝘁𝗶𝗰𝗶𝘁𝘆
→ AI handles the scheduling and sorting
→ Your team handles the soul

💡 Reality: 86% of consumers say authenticity is a key factor in which brands they support (Stackla consumer research).

The leadership rule I now follow:

Give your team AI to free up their time, not to replace their thinking.

AI should speed up drafting. Humans should own the voice.

Try my AI Upskilling Audit Coach, an interactive assessment that scores your marketing team’s AI readiness: https://lnkd.in/gQvQ4Ryf

Leveraging AI Tools to Build Brands | Fractional CMO | Helping CXOs Upskill Marketing Teams | Content Strategist and Storyteller

9 Ways to Build a Human-AI Hybrid Strategy

AI won’t scale you.

It’ll expose every weak process.

Don't ignore the signs.

We chased shiny AI demos but ignored the plumbing.

Campaigns stalled. Dashboards conflicted. The team got cooked.

The lesson: AI isn’t a feature. It’s infrastructure.

If any of these sound familiar, AI workflows and infrastructure should be your priority:

1/ The Integration Problem
→ Average marketing team uses 12+ tools
→ None of them talk to each other properly
→ AI that connects these systems is the fastest ROI lever

2/ The Data Quality Crisis
→ Most marketing data is unusable or incomplete
→ AI that cleans, enriches, and structures = competitive advantage
→ Clean data is more valuable than clever chatbots

3/ The Measurement Gap
→ Most companies still can’t answer “what’s actually working?”
→ AI-powered attribution you can defend in a budget meeting
→ CFOs care about proof, not conversations

4/ The Content Bottleneck
→ Teams drowning in content requests
→ AI that manages workflows, versioning, and distribution = sanity
→ Infrastructure scales. Headcount doesn’t.

5/ The Personalization Problem
→ Everyone wants 1:1 messaging
→ Few have the systems to deliver it without being creepy
→ AI infrastructure that respects privacy while personalizing = trust

6/ The Speed-to-Market Crunch
→ Campaign cycles are shrinking fast
→ Manual handoffs kill momentum
→ AI that automates approvals and deployment = competitive edge

7/ The Knowledge Drain
→ Institutional knowledge walks out the door with every departure
→ AI that captures, organizes, and surfaces tribal knowledge = resilience
→ Your best practices shouldn’t live in someone’s head

The companies winning right now aren’t obsessing over chatbots.

They’re building pipes, not props.

→ Clean data in. Clear signals out.
→ Fewer handoffs. Faster cycles.
→ Privacy-first personalization that earns trust.

Start with one workflow. Instrument it. Prove one win.

Then scale what works and delete the rest.

Save this for future reference when you are creating AI workflows.

Most people say they “have AI workflows.”

What they really have is a prompt they liked once.

A prompt is a single instruction.

A workflow is a repeatable engine: input → process → output.

Here are 5 AI workflows that gave me my time back:

EMAIL & COMMUNICATION

1/ 𝗧𝗵𝗲 "𝗖𝗹𝗼𝗻𝗲 𝗬𝗼𝘂𝗿𝘀𝗲𝗹𝗳" 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄
→ Workflow: Build a Voice Pack once (tone rules, structure, phrases you use/avoid) from 50 sent emails. Then paste any inbound email to generate replies in your voice.

→ Prompt: “You are my email co-writer. Use my Voice Pack exactly. Draft 3 reply options. Each must include: 1-line summary, the decision, the next step, and one question. Keep each under 120 words.”

MEETINGS

2/ 𝗧𝗵𝗲 "𝗜𝗻𝘀𝘁𝗮𝗻𝘁 𝗔𝗰𝗰𝗼𝘂𝗻𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆" 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄
→ Workflow: Transcribe the call, then convert it into a decision log and action plan you can send immediately.

→ Prompt: “From the transcript, produce: (1) decision log, (2) action table with Owner, Due date, Definition of Done, (3) risks/blockers, and (4) a crisp follow-up email I can send.”

DECISION-MAKING

3/ 𝗧𝗵𝗲 "𝗥𝗲𝗱 𝗧𝗲𝗮𝗺" 𝗦𝗶𝗺𝘂𝗹𝗮𝘁𝗶𝗼𝗻
→ Workflow: Paste your strategy and pressure-test it against real-world objections before you commit resources.

→ Prompt: “Critique my plan as 3 roles: Aggressive Competitor, CFO, and Skeptical Customer. For each, list the top objections, the assumption behind each objection, and the best rebuttal plus proof.”

RESEARCH & ANALYSIS

4/ 𝗧𝗵𝗲 "𝗥𝗮𝗱𝗮𝗿" 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄
→ Workflow: Collect competitor updates (news, hiring, product changes) and turn them into signals and next actions.

→ Prompt: “You are my competitive intelligence analyst. Using the updates I paste, create an Executive Brief with: what changed, why it matters, and what it signals strategically. Remove duplicates.”

CONTENT & BRAND

5/ 𝗧𝗵𝗲 "𝗖𝗼𝗻𝘁𝗲𝗻𝘁 𝗖𝗮𝘀𝗰𝗮𝗱𝗲" 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄
→ Workflow: Start with one pillar input (video, memo, voice note) and convert it into a full distribution pack.

→ Prompt: “You are my content repurposing engine. Take the input and produce: (1) two LinkedIn posts, (2) one short email, (3) five hook options, and (4) one CTA to a lead magnet. Keep the tone authoritative.”

These workflows are only useful if you can find it again.

Here's what I use to store them:

→ 𝗖𝘂𝘀𝘁𝗼𝗺 𝗚𝗣𝗧𝘀 (ChatGPT Plus): Build a dedicated GPT for each workflow. Upload your files and instructions once. It remembers everything.

→ 𝗖𝗹𝗮𝘂𝗱𝗲 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀: Create a project with your context and docs. Every conversation starts with the same foundation.

→ 𝗚𝗲𝗺𝗶𝗻𝗶 𝗚𝗲𝗺𝘀: Save your instructions as a Gem. One tap to launch a workflow with your preferences baked in.

The leaders who aren't using AI aren't just losing time.

They're competing against leaders who have smart systems.

Need help getting started on your AI journey? I can help significantly reduce the learning curve. DM me to discuss!

Most people treat AI tools like clones.

Same prompts, same expectations, same disappointment.

But each model has a different “personality,” and if you use them interchangeably, you’re leaving speed, quality, and ROI on the table.

Here’s a simple framework I use to match the right tool to the right job, so you stop guessing and start producing.

1. The Genius Strategist: ChatGPT (GPT-5.2)

Think of this as your high-IQ creative partner.

While other models are catching up, GPT-5.2 still dominates in raw logic and complex reasoning.

Best For: 
→ Solving hard logic puzzles and math 
→ Building complex marketing funnels 
→ "Deep Research" that requires connecting distant dots

The Superpower: The Ecosystem. With the new "Operator" features, it doesn't just plan the strategy; it can go execute parts of it for you.

2. The Deep Thinker: Claude (Opus 4.5)

This is your thoughtful writer and senior engineer.

When I need to write something that sounds human, or code something that actually works, I go to Claude.

Best For: 
→ Coding and debugging (it currently holds the highest score on SWE-bench) 
→ Writing nuanced blogs that don't sound like a robot 
→ Managing complex "Agent" workflows

The Superpower: "Artifacts & Projects." You can see code and documents side-by-side. It creates a workspace, not just a chat. It feels like collaborating with a human expert.

3. The Workhorse: Google Gemini (Gemini 3 Pro)

This is your productivity assistant.

If your company lives in Google Workspace, this is non-negotiable.

Best For: 
→ Analyzing massive files in your Google Drive 
→ Processing video and audio (it watches hour-long meetings for you) 
→ Multimodal tasks (mixing text, video, and code)

The Superpower: The Context Window. Gemini 3 can "read" millions of tokens at once. You can upload entire quarters of financial reports, and it won't forget the first page.

4. The Fact-Checker: Perplexity

This is your truth filter.

Stop "Googling" market research. Perplexity reads the articles for you, cites them, and gives you the answer.

Best For: 
→ Real-time market research 
→ Fact-checking competitors 
→ Finding citations for presentations

The Superpower: "Deep Research." It doesn't just search once. It searches, identifies missing info, and searches again until the report is complete.

5. The News Junkie: Grok (Grok 4.2 / 5)

This is your trend spotter.

It has direct access to X (Twitter) data in real-time.

Best For: 
→ Real-time sentiment analysis 
→ Catching breaking news before it hits Google 
→ "Unfiltered" content ideas and edgy drafts

The Superpower: Now. It sees what the world is talking about right this second. It is the fastest way to spot a viral trend before it peaks.

When you match the right AI personality to the right task, you stop fighting the tool and start compounding output:
→ Strategy gets sharper
→ Content sounds more human
→ Research gets cleaner
→ Execution gets faster

Save this post if you want a quick reference guide.

I promoted my best performer by helping her leave.
 
Because she’d outgrown us and I didn’t have the next step

She had been with us for 3 years. 

She crushed every KPI. She managed our biggest accounts.

I looked at our 24-person org chart and department budget.

Then I looked at her.

The truth? 

There was no next step. Not here. Not now.

I could have offered a small raise.

I could have made empty promises about "future opportunities."

You can buy time with a raise.

You can buy hope with vague promises.

Instead, I told her: "You have outgrown this room. It is time to find a bigger one."

It hurt.

Losing her cost us in the short term. Recruiting is expensive.

But keeping her in a box? That costs more.

High performers don’t quit on a bad day. 

They quit after months of stalled growth. 

Here are the signals you should watch for.

Save this: it’s the checklist your best people run before they resign.

1/ The Plateau Pattern

What you’ll see: They’re still delivering, yet the stretch is gone. Same type of work. Same playbook.

Meaning: You’ve turned them into a machine, not a builder.

Move: Give them a bigger problem, not a bigger title (new scope, new ownership, higher stakes).

2/ The “Later” Loop

What you’ll see: Promotion chats keep getting deferred with timelines that move.

Meaning: They’re hearing “not a priority.”

Move: Set a decision date. If it’s a yes, write a 90-day progression plan. If it’s a no, be honest.

3/ The Investment Signal

What you’ll see: You negotiate comp like they’re a cost line.
 
Meaning: They’ll go where they’re valued as an asset.
 
Move: Tie comp to outcomes and leverage. Show the math.

4/ The Feedback Gap

What you’ll see: They stop asking for feedback. 

Meaning: They’ve stopped believing growth happens here.

Move: Give specific, frequent calibration: “Here’s what ‘next level’ looks like.”

5/ The Trust Tightening

What you’ll see: You ask for more updates. More approvals. More status.

Meaning: You’ve reduced autonomy, which is oxygen for top talent.

Move: Swap reporting for outcomes. Define the “what”, let them own the “how”.

6/ The Energy Shift

What you’ll see: Their pace drops. They go quiet in meetings. Fewer ideas, less edge.

Meaning: Engagement is leaking. Exit planning starts soon after.

Move: Ask directly: “What would make this role exciting again?” Then change one thing quickly.

7/ The Future Repulsion

What you’ll see: They don’t want your job, or their manager’s job.

Meaning: There’s no aspirational path in your org.
 
Move: Create lateral “bigger rooms” (lead a new function, own a new market) or help them move on.

I wrote her a glowing recommendation.

Three weeks later, she landed a Director role at a larger firm. 

Double the salary.

She is thriving.

And me? I slept well.

Because leadership isn't about owning people. 

It is about launching them.

Your reputation as a leader is built on what happens to people after they work for you.

7 Signals Your Top Performer Is Leaving

AI rollouts fail for one reason: 

They start with software, not people.

Leadership announces AI initiative.

No context. No conversation. 

No consideration for the people currently working at the company.

The pattern:
→ AI gets positioned as "efficiency play"
→ Workforce hears "headcount reduction"
→ Trust erodes before the first tool is deployed
→ Top performers start taking recruiter calls
→ Adoption fails because nobody wants to train their replacement

The cost savings you projected? Gone. 

Here's the 7-step human-centered framework I use with CEOs who want AI adoption that actually works:

𝟭/ 𝗦𝘁𝗮𝗿𝘁 𝗪𝗶𝘁𝗵 𝘁𝗵𝗲 𝗪𝗼𝗿𝗸𝗳𝗼𝗿𝗰𝗲 𝗠𝗮𝗽

Before you evaluate any AI tool, map your workforce:
→ Which roles will be augmented?
→ Which roles will be transformed?
→ Which roles are at risk?

Be honest. Your employees are already asking these questions. 

2/ 𝗜𝗻𝘃𝗲𝘀𝘁 𝗶𝗻 𝗧𝗿𝗮𝗻𝘀𝗶𝘁𝗶𝗼𝗻, 𝗡𝗼𝘁 𝗝𝘂𝘀𝘁 𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆

For every dollar you spend on AI tools, budget for workforce development.
→ Upskilling programs for roles being transformed
→ Reskilling pathways for roles at risk
→ Clear career trajectories that show people their future

The companies with the highest AI adoption rates invested equally in people and technology.

3/ 𝗔𝗱𝗱𝗿𝗲𝘀𝘀 𝘁𝗵𝗲 𝗙𝗲𝗮𝗿 𝗗𝗶𝗿𝗲𝗰𝘁𝗹𝘆

"Will AI take my job?" 

Answer honestly.

Uncertainty is more damaging than hard truths. People can plan around reality.

4/ 𝗕𝘂𝗶𝗹𝗱 𝗛𝘂𝗺𝗮𝗻 𝗖𝗵𝗲𝗰𝗸𝗽𝗼𝗶𝗻𝘁𝘀 𝗶𝗻𝘁𝗼 𝗘𝘃𝗲𝗿𝘆 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄

Define where humans must stay in the loop:

→ Decisions that affect customers
→ Decisions that affect employees
→ Anything with ethical implications
→ Anything that requires judgment, not just data

AI handles execution. Humans own judgment.

5/ 𝗠𝗲𝗮𝘀𝘂𝗿𝗲 𝗣𝗲𝗼𝗽𝗹𝗲 𝗠𝗲𝘁𝗿𝗶𝗰𝘀, 𝗡𝗼𝘁 𝗝𝘂𝘀𝘁 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆 𝗠𝗲𝘁𝗿𝗶𝗰𝘀

Most AI dashboards track:
→ Time saved
→ Cost reduced
→ Output increased

Add these:
→ Employee confidence with AI tools
→ Voluntary turnover in AI-affected roles
→ Internal mobility into new AI-enabled positions
→ Employee sentiment about the company's AI direction

If efficiency goes up but trust goes down, you haven't won. 

6/ 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗲 𝗖𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀𝗹𝘆

 AI strategy requires ongoing communication:
→ Monthly updates on what's changing
→ Quarterly town halls for questions
→ Visible leadership using the tools themselves

Transparency builds trust.

𝗧𝗵𝗲 𝗕𝗮𝗹𝗮𝗻𝗰𝗲 𝗧𝗵𝗮𝘁 𝗪𝗼𝗿𝗸𝘀

The math:
→ AI deployed without people strategy = short-term savings, long-term talent drain

→ AI deployed with people strategy = sustainable efficiency + workforce that grows with you

The CXOs who get this right don't choose between cost savings and people.

They build strategies where both win.

Which role in your company will feel this first and what are you telling them?

"I feel like I'm faking it every time AI comes up in a meeting."

A VP told me this last month.

She's not alone.

I train leaders on AI tools. And the pattern I see over and over is this: 

There's a growing gap between leaders who feel confident with AI and leaders who feel like they're falling behind.

I call it the AI Anxiety Gap.

It's not about intelligence. It's not about age. It's not even about technical ability.

It's about exposure.

The AI Anxiety Gap isn't closed by reading articles.

It's closed by using the tools.

Here's the 6-step Familiarity Framework I use with every leader I train:

𝟭/ 𝗣𝗶𝗰𝗸 𝗢𝗻𝗲 𝗧𝗼𝗼𝗹

Don't try to learn everything. Pick one AI tool and use it daily for two weeks.

Start with ChatGPT or Claude. Both are conversational. Both are general-purpose. Both have free versions.

💡 Reality: Leaders who try to learn five tools at once learn none of them well.

𝟮/ 𝗦𝘁𝗮𝗿𝘁 𝗪𝗶𝘁𝗵 𝗧𝗮𝘀𝗸𝘀 𝗬𝗼𝘂 𝗔𝗹𝗿𝗲𝗮𝗱𝘆 𝗗𝗼

Don't look for "AI use cases." Look at your calendar.

→ Summarizing a long document
→ Drafting an email response
→ Preparing for a meeting
→ Creating a first draft of anything

💡 Reality: The best AI use cases aren't exotic. They're boring tasks you do every week.

𝟯/ 𝗘𝗺𝗯𝗿𝗮𝗰𝗲 𝗕𝗮𝗱 𝗢𝘂𝘁𝗽𝘂𝘁𝘀

Your first prompts will produce mediocre results. That's normal.

When the output is bad, don't give up. Ask: Was my prompt specific enough? Did I give it enough context?

Then try again.

💡 Reality: Leaders who expect perfect outputs on the first try quit too early.

𝟰/ 𝗕𝘂𝗶𝗹𝗱 𝗮 𝗩𝗼𝗶𝗰𝗲 𝗣𝗮𝗰𝗸

AI outputs feel generic because AI doesn't know how you communicate.

Create a simple document with 5-10 examples of your writing, phrases you use, phrases you avoid. Paste it in before asking AI to write for you.

💡 Reality: AI doesn't have your voice until you give it your voice.

𝟱/ 𝗦𝗰𝗵𝗲𝗱𝘂𝗹𝗲 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲 𝗧𝗶𝗺𝗲

Block 30 minutes, three times a week, for AI practice.

Not "when I have time." Scheduled. Protected.

💡 Reality: Leaders who schedule AI practice close the gap. Leaders who "fit it in" don't.

𝟲/ 𝗧𝗲𝗮𝗰𝗵 𝗦𝗼𝗺𝗲𝗼𝗻𝗲 𝗘𝗹𝘀𝗲

Once you've used AI for a few weeks, show one person on your team how you're using it.

Teaching forces clarity. And it positions you as someone learning and leading, not hiding.

💡 Reality: The anxious leader hides their learning. The confident leader shares it.

The gap closes faster than you think.

Pick one tool. Use it daily. Embrace the bad outputs. Build your voice pack. Schedule the practice. Teach someone else.

AI isn't slowing down. Neither should you.

Save this framework for when you're ready to start.

The best leadership coach you'll ever have already exists.

It's you.

Most leaders pay for executive coaches, read leadership books and attend conferences.

But they overlook the decades of wisdom they've already accumulated.

Your past presentations. Your interviews and speeches. Your best emails. 

That's your leadership playbook. And you can turn it into an AI GPT that thinks just like you.

I use mine to:

→ Prepare for high-stakes presentations
→ Gut-check decisions against my own values
→ Draft communications in my authentic voice
→ Pressure-test strategies

It reflects back to me my best work.

𝗕𝘂𝗶𝗹𝗱 𝗬𝗼𝘂𝗿 𝗟𝗲𝗮𝗱𝗲𝗿𝘀𝗵𝗶𝗽 𝗖𝗼𝗮𝗰𝗵 𝗚𝗣𝗧

𝗦𝘁𝗲𝗽 𝟭: 𝗚𝗮𝘁𝗵𝗲𝗿 𝗬𝗼𝘂𝗿 𝗟𝗲𝗮𝗱𝗲𝗿𝘀𝗵𝗶𝗽 𝗔𝗿𝘁𝗶𝗳𝗮𝗰𝘁𝘀

Pull together 10-20 documents that represent how you think and communicate at your best:

→ Presentations you're proud of
→ Speeches or keynotes (transcripts work)
→ Strategic memos or board decks
→ Emails where you navigated something difficult
→ LinkedIn posts or articles

The more variety, the better.

𝗦𝘁𝗲𝗽 𝟮: 𝗗𝗲𝗳𝗶𝗻𝗲 𝗬𝗼𝘂𝗿 𝗩𝗼𝗶𝗰𝗲 𝗮𝗻𝗱 𝗩𝗮𝗹𝘂𝗲𝘀

Write a short brief that captures:

→ Your leadership philosophy
→ Your communication style
→ Phrases you use often / phrases you avoid
→ How you make decisions (frameworks you rely on)
→ Your blind spots (what do you want the AI to challenge you on?)

This becomes the instruction set for your AI coach.

𝗦𝘁𝗲𝗽 𝟯: 𝗖𝗵𝗼𝗼𝘀𝗲 𝗬𝗼𝘂𝗿 𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺

Here's where to build it, with strengths for each:

𝟭/ 𝗖𝗵𝗮𝘁𝗚𝗣𝗧 𝗖𝘂𝘀𝘁𝗼𝗺 𝗚𝗣𝗧𝘀

Strengths:
→ Best ecosystem: connects to web browsing, DALL-E, code interpreter
→ Easy to share with your team later if you want
→ "Operator" features can execute tasks, not just advise

𝟮/ 𝗖𝗹𝗮𝘂𝗱𝗲 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀

Strengths:
→ Best for nuanced, human-sounding writing and thinking
→ Handles very long documents well (massive context window)
→ "Artifacts" feature shows documents side-by-side with chat

𝟯/ 𝗚𝗼𝗼𝗴𝗹𝗲 𝗚𝗲𝗺𝗶𝗻𝗶 𝗚𝗲𝗺𝘀

Strengths:
→ Native Google Workspace integration (pulls from Drive, Docs, Slides)
→ Processes video and audio (upload recordings of your speeches)
→ If your work lives in Google, this is the fastest path

𝟰/ 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗖𝗼𝗽𝗶𝗹𝗼𝘁 𝗚𝗣𝗧𝘀

Strengths:
→ Native Microsoft 365 integration (pulls from Outlook, Teams, SharePoint)
→ Enterprise-grade security and compliance
→ If your org runs on Microsoft, this keeps everything in one ecosystem

Once it's built, use it for:

→ "I'm presenting to the board next week on X. Help me structure my argument the way I typically would."
→ "Read this strategy doc. What questions would I ask if someone brought this to me?"
→ "Draft a response to this difficult email in my voice."

Your decades of experience shouldn't live only in your head.

Capture it. Structure it. Make it accessible.

Save this for when you build yours.

p.s. - turn on the don’t use my data to train/improve the model to keep data private.

Executives keep canceling 1:1s.

They don't realize they're canceling revenue.

I tracked 1:1 patterns across a leadership team for 6 months. 

Over 6 months, the exits we saw were almost always preceded by repeated 1:1 cancellations.

Here are 9 ways 1:1s are actually revenue meetings:

1/ Retention Starts Here

→ Employees who have regular 1:1s are 3x more likely to stay
→ Replacing one employee costs 50-200% of their salary
→ That "30 minutes you don't have" prevents a $150K replacement cost

💡 Reality: Skip the 1:1, pay the recruiter.

2/ Problems Surface Before They Explode

→ Small frustrations become resignations when ignored
→ 1:1s catch issues at $1,000, not $100,000
→ The warning signs are always there — if you're listening

💡 Reality: Every surprise resignation had signals you missed.

3/ Top Performers Need Them Most

→ Your best people have the most options
→ They leave when they feel invisible
→ One competitor DM and they're gone

💡 Reality: Top performers don't quit companies. They quit being ignored.

4/ Pipeline Intelligence Lives in Your Team

→ Your reps know why deals are stalling
→ Your marketers know which campaigns are actually working
→ 1:1s extract insights that dashboards can't show

💡 Reality: The best market research is already on your payroll.

5/ Engagement Drives Productivity

→ Disengaged employees cost companies 18% of their salary in lost productivity
→ Regular 1:1s are the #1 driver of engagement
→ Engaged teams outperform by 21%

💡 Reality: 30 minutes of attention returns hours of output.

6/ You Build Leaders or You Lose Them

→ High-potentials need development conversations
→ Without growth, they find it elsewhere
→ Your next VP is deciding right now if they have a future here

💡 Reality: Leadership pipelines are built in 1:1s, not succession planning docs.

7/ Trust Compounds Over Time

→ Consistent 1:1s build psychological safety
→ Safe teams innovate, take risks, speak up
→ That breakthrough idea? It only surfaces when people trust you

💡 Reality: Trust isn't built in all-hands. It's built in 1:1s.

8/ Alignment Prevents Expensive Mistakes

→ Misaligned priorities waste entire quarters
→ 1:1s keep everyone rowing the same direction
→ One misunderstanding can derail a $500K initiative

💡 Reality: Alignment is cheap. Rework is expensive.

9/ Culture Is Set in Private Conversations

→ What you say in 1:1s defines your real culture
→ Employees remember how you showed up for them
→ Your reputation as a leader is built here

💡 Reality: Culture isn't your values poster. It's your 1:1 calendar.

The executives who "don't have time" for 1:1s are spending 10x more time on:
→ Recruiting replacements
→ Managing preventable crises
→ Rebuilding teams that fell apart

The math is simple:

30 minutes of prevention beats 30 hours of damage control.

If you cancel more than one 1:1 a month, you’re signaling 'you’re optional'.

Save this and share to make sure leaders understand the importance or 1-1s.

9 reasons 1:1s are actually revenue meetings

Spent $40k on an AI chatbot. 

6 months later: almost no one uses it. 

A client told me this last month. 

She was frustrated. Her team still emails each other the same questions they always did.

The chatbot works fine. The team just doesn't use it.

The pattern I often see:

→ Leadership gets excited about AI
→ Budget gets approved fast
→ Vendor gets selected
→ Tool gets deployed
→ Enablement gets skipped

The assumption? "It's intuitive. People will figure it out."

They don't.

Here's what skipping training actually costs:

→ Adoption rates under 15%
→ Shadow workflows where people use old methods anyway
→ Resentment toward "another tool leadership forced on us"
→ Wasted budget that's impossible to justify next year

One CFO told me: "We can't get budget for AI tools anymore because the last three flopped."

The tool isn't the problem.

The rollout is the constraint.

When I audit failed AI implementations, I ask 3 questions:

𝟭/ 𝗗𝗶𝗱 𝗮𝗻𝘆𝗼𝗻𝗲 𝗲𝘅𝗽𝗹𝗮𝗶𝗻 𝘁𝗵𝗲 "𝘄𝗵𝘆"?

Most teams hear: "We're implementing an AI chatbot."

What they don't hear: "This will save you 3 hours a week on repetitive questions so you can focus on strategic work."

Without the "why," people see the tool as extra work, not a time-saver.

𝟮/ 𝗗𝗶𝗱 𝗮𝗻𝘆𝗼𝗻𝗲 𝘀𝗵𝗼𝘄 𝘁𝗵𝗲𝗺 𝗵𝗼𝘄 𝘁𝗼 𝘂𝘀𝗲 𝗶𝘁 𝗳𝗼𝗿 𝗧𝗛𝗘𝗜𝗥 𝗷𝗼𝗯?

Generic training doesn't work.

A marketing manager and a finance analyst use the same chatbot completely differently. 

When training is one-size-fits-all, everyone walks away thinking: "That doesn't apply to me."

𝟯/ 𝗗𝗶𝗱 𝗮𝗻𝘆𝗼𝗻𝗲 𝗳𝗼𝗹𝗹𝗼𝘄 𝘂𝗽?

Most implementations have a launch day.

Few have a "30 days later" check-in.

By day 30, the people who tried the tool once and got confused have already given up. The people who never tried it have forgotten it exists.

Here's a simple “adoption sprint” that’s easy to execute:

Week 0: Workflows + success metric
Week 1: Role training + examples + manager reinforcement
Week 2: Office hours + champions
Week 4: Usage review + friction removal + wins shared

If you’ve bought AI software and usage is flat, you’ve got an enablement problem, not a tooling problem.

Ready to find out how much time and money AI can save your team when they actually use it? My free GPT breaks it down: https://lnkd.in/gdMizmRH

Save this if your AI tool “works” but nobody uses it.

Most teams buy AI agents like they buy software. 

Plug it in. Expect ROI.

Then spend weeks cleaning up the output.

I've watched marketing teams throw agents at "content creation" and "campaign launches" without ever mapping what those workflows actually look like.

The result? 

Agents running in circles. Humans cleaning up messes. 

Leadership asking why the expensive AI isn't delivering ROI.

The fact is if the workflow is invisible, the agent guesses.

Execution collapses.

Here's what I mean:

𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝟭: 𝗖𝗼𝗻𝘁𝗲𝗻𝘁 𝗖𝗿𝗲𝗮𝘁𝗶𝗼𝗻

Most teams say: "We want AI to create content."

That's not a workflow. That's a wish.

A workflow looks like this:

𝗦𝘁𝗲𝗽 𝟭: 𝗧𝗼𝗽𝗶𝗰 𝗜𝗱𝗲𝗻𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻
→ Input: Content calendar, trending topics, audience questions
→ Output: Prioritized topic with angle and target audience
→ Human checkpoint: Approve topic before proceeding

𝗦𝘁𝗲𝗽 𝟮: 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 & 𝗢𝘂𝘁𝗹𝗶𝗻𝗲
→ Input: Approved topic + brand guidelines + competitor content
→ Output: Structured outline with key points and sources
→ Human checkpoint: Review outline for strategic alignment

𝗦𝘁𝗲𝗽 𝟯: 𝗙𝗶𝗿𝘀𝘁 𝗗𝗿𝗮𝗳𝘁
→ Input: Approved outline + voice pack + example posts
→ Output: Complete draft matching brand voice
→ Human checkpoint: Edit for accuracy and tone

𝗦𝘁𝗲𝗽 𝟰: 𝗩𝗶𝘀𝘂𝗮𝗹 𝗔𝘀𝘀𝗲𝘁𝘀
→ Input: Final copy + brand templates
→ Output: Formatted graphics, carousel, or video brief
→ Human checkpoint: Approve visuals

𝗦𝘁𝗲𝗽 𝟱: 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻
→ Input: Final content + channel specs + scheduling parameters
→ Output: Scheduled posts across platforms
→ Human checkpoint: Final review before publish

Without this map, an agent doesn't know:
→ Where to start
→ What inputs it needs
→ When to pause for human review
→ What "done" looks like

💡 Reality: "Create content" isn't a workflow. It's five workflows stitched together with decision points.

𝗧𝗵𝗲 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝗠𝗮𝗽𝗽𝗶𝗻𝗴 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸

Before you deploy any agent, answer these questions for each workflow:

→ What triggers this workflow?
→ What are the discrete steps?
→ What inputs does each step require?
→ What outputs does each step produce?
→ Where do humans need to review or approve?
→ What does "done" look like?
→ How do we measure success?

Save this for your next AI planning session.

I build with these AI tools daily. 

Keeping up still breaks your brain.

ChatGPT 5.2 dropped.

Gemini released "Nano Banana Pro".

Midjourney is updating again.

It is overwhelming.

I stopped "playing" and did a deep dive test. 

I ran 5 identical prompts across 4 tools.

Scored them this month on text accuracy, realism, and speed.

Here is the cheat sheet I wish I had last week.

1. Midjourney (The Artist)
This is for when you need "wow" factor. It is not for logic. It is for beauty.

Strengths:
→ Creates the most beautiful, artistic photos.
→ Understands lighting and texture better than anyone.
→ Great for abstract ideas or mood boards.
→ Huge community to learn from.

Weaknesses:
→ It is hard to use.
→ It (really) struggles with specific text inside images.
→ You cannot "edit" easily (like moving a cup to the left).
→ It is slow to set up.

2. ChatGPT 5.2 (The Assistant)
This is your smart coworker. It listens well but isn't a trained artist.

Strengths:
→ You can talk to it like a human ("Make it pop more").
→ It creates graphs and charts accurately.
→ It is amazing at understanding complex instructions.
→ It is safe and generally work-appropriate.

Weaknesses:
→ The images often look "like AI" (shiny and plastic).
→ It refuses to generate many copyright-style images.
→ It struggles with photorealism.
→ It can be too literal (boring).

3. Gemini Nano Banana Pro (The Professional)
This is the new heavy hitter for business. It is built for work, not just play.

Strengths:
→ Best at putting readable text and creating fairly complex infographics.
→ Connects to Google apps (Slides, Docs).
→ Very fast processing speed.
→ Understands current events and logos well.

Weaknesses:
→ Great at clean, weaker at edgy.
→ It can feel "stiff" or corporate compared to Midjourney.
→ Detailed artistic control is still limited.
→ Some advanced features are hidden in expensive plans.

4. Canva (The Marketer)
This is for speed. If you need a post done in 5 minutes, go here.

Strengths:
→ It is a feature of a robust design tool.
→ You can easily add your logo or headline on top.
→ Extremely easy to use (no coding needed).
→ Great for creating carousels.

Weaknesses:
→ The image quality is lower than the others.
→ It often messes up faces and hands.
→ It struggles with very long, detailed prompts.
→ It lacks the "magic" creative spark.

My Rule of Thumb

Do not try to master all of them.

→ Need a hero image / vibe: Midjourney
→ Need charts / logic / iteration: ChatGPT
→ Need readable text layouts / work output: Gemini
→ Need fast social packaging: Canva.

The tool doesn't matter if the story is bad.

Focus on your story first.

Save this if you make carousels, infographics, ads, or landing pages.

Are you a leader committed to improving your authoritative presence on LinkedIn in 2026? DM me - I can help.

I got laid off last year.

I took 6 months to figure out what came next.

Not because I lacked skills. 

Because I lacked adaptability.

I'd spent years building expertise in one direction. 

When that direction disappeared, I spent weeks looking at job boards, waiting for certainty.

I kept waiting for the "right" opportunity.

Those 6 months taught me something I wish I'd learned earlier:

Adaptability and resilience aren't soft skills. They're survival skills.

Especially now, with AI reshaping entire industries.

These are the 10 adaptability muscles leaders need in the AI era:

𝟭/ 𝗔𝗜 𝗜𝘀 𝗘𝗹𝗶𝗺𝗶𝗻𝗮𝘁𝗶𝗻𝗴 𝗥𝗼𝗹𝗲𝘀 𝗙𝗮𝘀𝘁𝗲𝗿 𝗧𝗵𝗮𝗻 𝗜𝘁'𝘀 𝗖𝗿𝗲𝗮𝘁𝗶𝗻𝗴 𝗧𝗵𝗲𝗺

→ The job you have today may not exist in 3 years
→ Entire departments are being restructured around AI capabilities

💡 Reality: The leaders who thrive will be the fastest to evolve.

𝟮/ 𝗬𝗼𝘂𝗿 𝗘𝘅𝗽𝗲𝗿𝘁𝗶𝘀𝗲 𝗛𝗮𝘀 𝗮𝗻 𝗘𝘅𝗽𝗶𝗿𝗮𝘁𝗶𝗼𝗻 𝗗𝗮𝘁𝗲

→ What made you valuable in 2020 may be automated in 2025
→ The half-life of skills is shrinking every year

💡 Reality: Learning how to learn matters more than what you currently know.

𝟯/ 𝗥𝗲𝘀𝗶𝗹𝗶𝗲𝗻𝗰𝗲 𝗗𝗲𝘁𝗲𝗿𝗺𝗶𝗻𝗲𝘀 𝗥𝗲𝗰𝗼𝘃𝗲𝗿𝘆 𝗦𝗽𝗲𝗲𝗱

→ Setbacks are inevitable. Bounce-back time is optional.
→ Leaders who've practiced recovery recover faster

💡 Reality: You can't prevent disruption, but you can shorten the downtime.

𝟰/ 𝗥𝗶𝗴𝗶𝗱 𝗣𝗹𝗮𝗻𝘀 𝗕𝗿𝗲𝗮𝗸. 𝗙𝗹𝗲𝘅𝗶𝗯𝗹𝗲 𝗣𝗹𝗮𝗻𝘀 𝗕𝗲𝗻𝗱.

→ Annual strategies are obsolete before Q2
→ The best leaders plan in 90-day sprints

💡 Reality: The ability to pivot quickly is essential in today's market.

𝟱/ 𝗬𝗼𝘂𝗿 𝗡𝗲𝘁𝘄𝗼𝗿𝗸 𝗔𝘁𝗿𝗼𝗽𝗵𝗶𝗲𝘀 𝗜𝗳 𝗬𝗼𝘂 𝗗𝗼𝗻'𝘁 𝗨𝘀𝗲 𝗜𝘁

→ When I got laid off, 90% of "let's keep in touch" meant nothing
→ The time to build your network is before you need it

💡 Reality: Resilient leaders invest in relationships when they don't need anything.

𝟲/ 𝗜𝗱𝗲𝗻𝘁𝗶𝘁𝘆 𝗧𝗶𝗲𝗱 𝘁𝗼 𝗧𝗶𝘁𝗹𝗲 𝗜𝘀 𝗙𝗿𝗮𝗴𝗶𝗹𝗲

→ "So what do you do?" becomes terrifying without a role
→ Your value exists independent of your employer

💡 Reality: If losing your job means losing yourself, you've built on sand.

𝟳/ 𝗔𝗜 𝗥𝗲𝘄𝗮𝗿𝗱𝘀 𝘁𝗵𝗲 𝗔𝗱𝗮𝗽𝘁𝗮𝗯𝗹𝗲

→ Leaders who learn AI tools gain leverage
→ The gap between AI-fluent and AI-resistant leaders is widening

💡 Reality: AI is replacing leaders who won't adapt.

𝟴/ 𝗖𝗼𝗺𝗳𝗼𝗿𝘁 𝗭𝗼𝗻𝗲𝘀 𝗦𝗵𝗿𝗶𝗻𝗸 𝗬𝗼𝘂𝗿 𝗢𝗽𝘁𝗶𝗼𝗻𝘀

→ Discomfort is the price of growth
→ Leaders with the most options are the ones who've tried the most things

💡 Reality: Staying comfortable is the riskiest strategy in a changing market.

Check out the infographic for all 10.

Remember, you don't have to wait for a crisis to build these muscles.

Start now. AI isn't slowing down. Neither should you.

Save this post for a reference if you or a colleague needs it this year.

Get a high-res pdf copy of this infographic here: https://lnkd.in/gEJFp4rk

Your employees are about to become AI managers.

Most aren't ready.

One of my clients deployed 15 AI agents across their marketing org. Their human team? 11 people.

Week 3, output volume doubled. 

Delivery speed improved. Quality became the bottleneck.

Their best performers thrived. 

Their traditional managers who managed people via meetings, approvals, and context in their heads struggled.

When AI agents can execute multi-step workflows autonomously, your employees stop doing the work and start managing the work.

They become:
→ Goal-setters (defining what the agent should accomplish)
→ Quality controllers (reviewing agent outputs)
→ Exception handlers (stepping in when the agent fails)
→ Feedback providers (training the agent to improve)

This requires completely different skills than doing the work yourself.

The skill stack your people need now:

𝟭/ 𝗖𝗹𝗲𝗮𝗿 𝗚𝗼𝗮𝗹 𝗔𝗿𝘁𝗶𝗰𝘂𝗹𝗮𝘁𝗶𝗼𝗻
Agents need precise objectives. Vague instructions produce vague results. Your people need to translate business goals into agent-executable tasks.

𝟮/ 𝗢𝘂𝘁𝗽𝘂𝘁 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻
Knowing when output is good enough. Agents will produce work that looks right but isn't. Your people need judgment to catch what's off.

𝟯/ 𝗗𝗲𝗯𝘂𝗴𝗴𝗶𝗻𝗴 𝗔𝗯𝗶𝗹𝗶𝘁𝘆
Figuring out why the agent failed. Did it misunderstand the goal? Lack context? Hit an edge case? Your people need to diagnose and fix.

𝟰/ 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸 𝗟𝗼𝗼𝗽𝘀
Training the agent to improve. Every failure is a learning opportunity—if your people know how to capture and apply it.

𝟱/ 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝗗𝗲𝘀𝗶𝗴𝗻
Breaking complex goals into agent-executable steps. Agents don't think in strategy. They think in sequences. Your people need to architect the path from goal to outcome.

𝟲/ 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗧𝗿𝗮𝗻𝘀𝗳𝗲𝗿
Agents don't know what's in your head. Your people need to document the unwritten rules, brand nuances, and institutional knowledge that make outputs actually usable.

𝟳/ 𝗥𝗶𝘀𝗸 𝗔𝘀𝘀𝗲𝘀𝘀𝗺𝗲𝗻𝘁
Knowing which agent outputs need human review before going live. Not everything requires approval. But some things absolutely do. Your people need to know the difference.

𝟴/ 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻
Coordinating multiple agents working on related tasks. When you have 15 agents and 11 humans, someone needs to ensure the outputs connect. That's a human job.

Most organizations are investing heavily in AI technology.

Few are investing in the human capability to manage it.

The companies that win with agentic AI won't have the smartest agents. 

They'll have the workforce best equipped to direct them.

Many leaders on LinkedIn sound the same.

AI made it worse.

If you're just blending in, you're probably losing trust.

I know because I made this mistake.

8 months ago, I started using ChatGPT for LinkedIn posts. 

The output was fast. It was also lifeless. No engagement.

I rebuilt my entire approach.

I stopped treating AI like a content vending machine and started treating it like a junior writer who needed to learn my voice.

Here are 8 ways to leverage AI for LinkedIn content while keeping your voice intact:

1/ Build a Voice Document Before You Write a Single Prompt

Most people open ChatGPT and start typing. That's backwards.

Before you ask AI to write anything, create a document that captures how you actually communicate:

→ 10-15 of your best-performing posts (copy the full text)
→ Your sentence rhythm (short and punchy? Long and flowing?)
→ How you start posts (questions? Bold statements? Stories?)
→ How you end posts (CTA style, sign-off patterns)

This becomes your "brand bible." 

2/ Create Custom AI Tools Trained on Your Voice

Generic ChatGPT produces generic content.

Custom GPTs, Claude Projects, and Gemini Gems let you bake your voice into the tool itself.

Pre-load each one with your voice doc, 20 example posts, and strict instructions on tone (at a minimum).

The setup takes 30 minutes. The payoff is permanent.

3/ Feed It Your Actual Writing

Don't describe your voice. Demonstrate it.

→ Upload real emails you've sent
→ Paste transcripts from talks you've given
→ Include Slack messages that capture your casual tone

AI learns from examples, not instructions. Give it the raw material.

4/ Use the 80/20 Rule: AI Drafts, You Finish

AI writes the first 80%. You do the last 20%.

Never publish AI output without a human pass. Not because AI is bad but because AI is generic until you make it specific.

5/ Create a "Do Not Use" List

Every AI model has favorite words. They're usually the same words everyone else's AI is using.

Build a banned list and enforce it.

Add this list to your custom GPT instructions. Tell the AI: "Never use these words. If you're tempted to use them, find a more specific alternative."

6/ Read It Out Loud Before You Post

Read your AI-assisted post out loud.

If it doesn't sound like something you'd say in a conversation, rewrite it.

Your voice probably has something AI doesn't:
→ Incomplete sentences
→ Casual transitions

The out-loud test catches what your eyes miss.

7/ Anchor Every Post in a Specific Experience

Generic AI content happens when you give generic prompts.

"Write a post about leadership" produces garbage.

"Write a post about the time I canceled three 1:1s in a row and lost my best employee" produces something real.

The more specific your input, the more specific your output.

People don't follow generic advice. They follow specific voices.

Train your AI. Protect your voice. Stand out.

Get a high-res pdf of the infographic: https://lnkd.in/gCEkWjjg

Save this for future reference.

AI isn't a goal. It's a tool.

Without a plan, it can become a $50K mistake.

It starts with the CEO saying: “I want AI by Q2.” 

With no detailed plan or strategy.

One question stopped the train.

"What exact friction point is this solving for the customer?"

The room went quiet.

That’s the insight: the business has a clarity problem, not a tech problem.

Here's the Problem-First Framework I use to stop building expensive solutions for problems that don't exist:

𝟭/ 𝗧𝗵𝗲 𝗙𝗿𝗶𝗰𝘁𝗶𝗼𝗻 𝗔𝘂𝗱𝗶𝘁

→ Don't look for places to put AI
→ Look for places where people give up
→ If the process is broken, AI just makes it faster at being broken

💡 Reality: We found customers were confused by our pricing, not our speed.

𝟮/ 𝗧𝗵𝗲 "𝗪𝗵𝘆" 𝗜𝗻𝘁𝗲𝗿𝗿𝗼𝗴𝗮𝘁𝗶𝗼𝗻

→ Ask "Why?" five times before you mention technology
→ Peel back the layers to the root cause
→ Symptoms are tempting. Root causes are profitable.

💡 Reality: We didn't need a bot. We needed a clearer landing page.

𝟯/ 𝗧𝗵𝗲 𝗗𝗮𝘁𝗮 𝗙𝘂𝗲𝗹 𝗖𝗵𝗲𝗰𝗸

→ AI is an engine. Data is the fuel.
→ Is your data clean and structured?
→ Or is it a mess of spreadsheets and notes?

💡 Reality: Our data was fragmented. The AI would have hallucinated answers.

𝟰/ 𝗧𝗵𝗲 𝗛𝘂𝗺𝗮𝗻 𝗩𝗮𝗹𝘂𝗲 𝗧𝗲𝘀𝘁

→ Will this replace drudgery or creativity?
→ Use AI to remove repetitive tasks
→ Keep humans for empathy and strategy

💡 Reality: Our customers wanted to talk to a person, not a smarter robot.

𝟱/ 𝗧𝗵𝗲 𝗣𝗶𝗹𝗼𝘁 𝗥𝘂𝗹𝗲

→ Never go "all in" on day one
→ Build a messy prototype first
→ Test it manually before you automate it

💡 Reality: We tested new messaging via email first. It converted 20% better.

𝟲/ 𝗧𝗵𝗲 𝗦𝗰𝗮𝗹𝗶𝗻𝗴 𝗖𝗵𝗮𝗼𝘀 𝗧𝗿𝗮𝗽

→ AI scales everything. Even the bad stuff.
→ Automating a bad process creates disaster at speed
→ Fix the manual workflow first

💡 Reality: We streamlined the workflow manually and saved 10 hours a week before adding tech.

𝟳/ 𝗧𝗵𝗲 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗟𝗲𝗻𝘀

→ Does the customer care if it's AI?
→ They want results, not buzzwords
→ If AI doesn't improve their experience, skip it

💡 Reality: Clients preferred our simple weekly PDF over a dynamic AI dashboard.

𝟴/ 𝗧𝗵𝗲 𝗦𝗸𝗶𝗹𝗹 𝗚𝗮𝗽 𝗖𝗵𝗲𝗰𝗸

→ Who is going to drive this car?
→ Buying the tool is easy. Learning it is hard.
→ Assess if your team can manage the output

💡 Reality: We avoided a tool that required coding knowledge we didn't have.

𝟵/ 𝗧𝗵𝗲 𝗠𝗮𝗶𝗻𝘁𝗲𝗻𝗮𝗻𝗰𝗲 𝗥𝗲𝗮𝗹𝗶𝘁𝘆

→ AI isn't "set and forget"
→ Models drift. Prompts break.
→ Calculate the cost of upkeep, not just the purchase price

💡 Reality: We saved $2,000 a month by sticking to a simpler stack.

We canceled the $50,000 contract. Fixed the messaging manually. Revenue went up.

If we had added AI too early, we would have scaled a disaster.

Save this for the next time someone says: 'We need AI.'

Get a high-res copy of the infographic: https://lnkd.in/gnFdWTwM

“Boards have stopped asking 'What’s our AI strategy?' 

They now ask: 'Where does the data go?'

I recently sat in a board meeting where a Director couldn't answer data flow questions on their AI roadmap.

The silence was deafening.

What the board was really asking: “Who owns the data, what can we use, and how do we stop it leaking into places it shouldn’t?”

That moment taught me something I wish more leaders understood:

You need the ability to map AI to workflows, data, risk, and decision rights without getting lost in tools.

Here's the 3-Tier AI Skill Ladder I use to map what each level actually needs to know:

Level 1: The Manager (Architect of Time)

Your job isn't to build AI. It's to clear the path for it.

The biggest mistake managers make? Trying to be the "super-user."

Stop. Focus on workflow augmentation instead.

→ The 10% Rule: Which 10% of work gets automated first, and who signs it off?

→ Psychological Safety: Your team is scared. They think robots are coming for their jobs. Your #1 skill right now is EQ and navigating that fear.

→ Output QA: AI hallucinates. Your judgment is the final firewall. You're the editor-in-chief now.

Level 2: The Director (System Integrator)

Managers see tasks. Directors see systems.

You're the bridge between execution and strategy.

→ Data governance & flow: “What data is allowed, where it lives, who owns it, and how it’s audited.”

→ Cross-Functional Fluency: You need to speak "Marketing" and "Data Science" in the same sentence. Silos kill AI projects.

→ Risk Radar: You spot the ethical cracks before they become legal canyons. Compliance isn't someone else's job anymore.

Level 3: The C-Suite (Visionary Governor)

Stop asking "How much will this save us?"

Start asking "What new value does this create?"

The C-Suite role has shifted from oversight to architecting strategy.

→ Value Thesis: Where does AI create new revenue, new margin, or new moat in the next 12 months and what gets stopped to fund it.

→ AI Portfolio Strategy: Your roadmap is a portfolio: quick wins, core upgrades, and asymmetric bets and each with owners, metrics, and kill criteria.

→ Decision Rights & Operating Model: Who can deploy AI, on what data, with what approvals, and who owns outcomes when it goes wrong.

The leaders who win in 2026 won't have the best algorithms.

They'll understand that AI is a tool.

Humanity is the strategy.

Don't let the tech outpace your leadership.

Save this. Most AI roadmaps fail because leaders don’t know what “good” looks like at each level.

AI rollouts fail for the same reason gym memberships fail: 

Behavior never becomes automatic.

People don’t build habits around AI tools. They build habits around outcomes.

I’ve seen this pattern across marketing teams.

The internal GPT works but adoption died quietly after launch.

No one talks about it. No one remembers it exists. 

And leadership assumes the team “just isn’t into AI.”

Adoption fails for three reasons: people forget, people don’t trust outputs, people can’t see where it fits.

Here’s the framework I use to bring dead AI tools back to life:

𝗦𝘁𝗲𝗽 𝟭: 𝗗𝗶𝗮𝗴𝗻𝗼𝘀𝗲 𝘁𝗵𝗲 𝗥𝗲𝗮𝗹 𝗣𝗿𝗼𝗯𝗹𝗲𝗺

Send a 3-question survey:
→ Have you used [tool] in the past 30 days?
→ What task were you trying to do when you didn’t use it?
→ What would make you use it?

Don't assume. Ask.

𝗦𝘁𝗲𝗽 𝟮: 𝗥𝗲𝗯𝘂𝗶𝗹𝗱 𝘁𝗵𝗲 "𝗪𝗵𝘆"

Create a simple one-pager:
→ Without the tool: [specific pain point]
→ With the tool: [specific benefit]
→ Real example: [actual use case from someone on the team]

Use it when: you’re creating first drafts, variants, rewrites.

Guardrails: positioning, legal claims, crisis comms = human-led.

𝗦𝘁𝗲𝗽 𝟯: 𝗥𝘂𝗻 𝗥𝗼𝗹𝗲-𝗦𝗽𝗲𝗰𝗶𝗳𝗶𝗰 𝗢𝗳𝗳𝗶𝗰𝗲 𝗛𝗼𝘂𝗿𝘀

Forget the all-hands training webinar.

Run 30-minute sessions by function. Let people bring real questions. Solve them live.

The goal is to leave with 3 prompts / workflows saved per person.

𝗦𝘁𝗲𝗽 𝟰: 𝗖𝗿𝗲𝗮𝘁𝗲 𝗖𝗵𝗮𝗺𝗽𝗶𝗼𝗻𝘀

Find 2-3 people per department willing to be the "go-to" for questions.

They don't need to be power users. Just approachable and willing to help.

Give them a title + 30 mins/week carved out + visibility.

𝗦𝘁𝗲𝗽 𝟱: 𝗠𝗮𝗸𝗲 𝗜𝘁 𝗩𝗶𝘀𝗶𝗯𝗹𝗲

→ Pin the link in Slack/Teams.
→ Add it to your intranet homepage and highlight it.
→ Mention it when relevant questions come up in meetings.

𝗦𝘁𝗲𝗽 𝟲: 𝗠𝗲𝗮𝘀𝘂𝗿𝗲 𝗮𝗻𝗱 𝗦𝗵𝗮𝗿𝗲 𝗪𝗶𝗻𝘀

Track usage weekly. When it goes up, celebrate it.

The marketing team used the chatbot 47 times last week and saved an estimated 8 hours.

Calculate using sessions × avg minutes saved × fully loaded hourly cost.

AI adoption isn’t a launch problem. It’s a leadership habit problem.

If your tool is sitting idle, don’t replace it. Re-activate it.

First 7 days mini-plan:
Day 1: survey
Day 2: pick 1 team + 3 use cases
Day 3–5: role office hours
Day 6: publish top workflows
Day 7: share 1 quantified win

The goal isn’t “more AI.”

It’s more outcomes with less friction.

Save this for your next AI rescue mission.

Marketing leaders say they want to “leverage AI.”
 
Few actually know where to start.

I've been there. I burned $30K learning what not to do with AI.

So, I created the AI Time and Cost Savings Evaluator, an interactive GPT that acts like a virtual consultant for senior marketers.

Try it: https://lnkd.in/gdMizmRH

Here's what it does in under 10 minutes:

Maps Your Reality 
→ Analyzes your actual team structure 
→ Identifies your recurring tasks 
→ Spots the time drains

💡 Reality: Most teams waste 40% of time on automatable tasks.

Calculates Real Savings 
→ Uses realistic benchmarks (not hype) 
→ Shows time saved per task 
→ Translates to actual dollar amounts

💡 Reality: Average marketing team can save 15-20 hours/week with targeted AI use.

Recommends Specific Tools 
→ Matches tools to YOUR tasks 
→ No generic "best AI tools" lists 
→ Prioritizes based on ROI

💡 Reality: You need 3-5 tools max. Not 30.

Flags the Risks 
→ Compliance concerns 
→ Brand voice consistency 
→ Implementation challenges

💡 Reality: Knowing what could go wrong prevents expensive mistakes.

Delivers Executive-Ready Output 
→ Clear action steps 
→ Prioritized roadmap 
→ Budget justification

💡 Reality: Your CFO cares about numbers, not possibilities.

Here's what early users discovered about time saved:

✓ Content creation: 12 hours → 3 hours/week
✓ Campaign analysis: 8 hours → 1 hour/week 
✓ Email personalization: Manual → Fully automated 
✓ Social media scheduling: 5 hours → 30 minutes/week

One CMO texted me: "This found $127K in savings. My CEO approved our AI budget in one meeting."

I've been where you are.

→ Overwhelmed by options. 
→ Skeptical of ROI. 
→ Worried about wasting budget.

That's exactly why I built this.

Because the question isn't "Should we use AI?"

It's "Where do we start to see immediate impact?"

The AI Savings Evaluator answers that in 10 minutes.

No hype. 

Just data-driven recommendations based on YOUR specific situation.

Ready to find out what AI could actually save your team?
Try it here: https://lnkd.in/gdMizmRH

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