audience & revenue growth tips for B2B startups and creators

My mission is to build predictable pipelines for B2B startups. I share what I learn along the way. 

Read more if you are ready to build your pipeline 👇

💡 I am super relevant if you are:

→ Indie Hacker
→ Freelancer
→ Early Startup Founder
→ B2B Sales Person
→ Creator (any profession)

✍️ I write about:

→ B2B sales
→ Go to market strategies
→ Signal-based prospecting

Little more about me:

Cryptocurrency Investor. SaaS Enthusiast. Agriculture Business Owner. 

Lived in 3 countries.

I'm a T-shaped growth person who has experience in sales, content marketing, and growth.

Drop me a LinkedIn message if you need any help.

We will keep it simple.

Twitter: @sehaokudan

https://calendly.com/sehaokudan/30min

Seha Okudan

Is your sales team struggling? 
(The whole company is sinking.) 

No revenue. 
No growth. 
No jobs. 

If sales isn’t working, nothing else matters. 

You’re in the same boat. 

Even if you’re not in sales, you are still losing. 

When sales miss quota; 
(potentially) 

- Ops lose headcount 
- Marketing loses budget 
- Leadership loses investor trust 

Everyone loses momentum 

When sales struggle, it’s not their problem. 
It’s everyone’s problem. 

Because no revenue = no company to run. 

It’s everyone’s job. 

Agree or disagree?

5 ways to lose prospects:
(10 leads lost guaranteed)

5 ways to lose prospects

Salespeople lose deals they should’ve won.
Here’s how they kill it - without realizing:

How salespeople kill their deals

I Help Corporate Professionals Gain Career Clarity & Fulfillment | Founder @Amplify Your Home | Global AI Tax Copilot Strategist @EY

Don't be fooled by my appearance. Quiet, Asian, tiny girl.

In the last decade, I've...

Successfully navigated the corporate giants: Goldman Sachs, PwC, Deloitte and EY.

Built communities, tax and financial models, and automations. (Excel wizard here)

Empowered employees to own their damn careers,

Challenge their norms to do something special.

That's when Problems become Opportunities,

Strategies into Actions.

You are not Alone.

Ready to redefine your career? Let's make it happen.

Mary Fung

The 'AI wrapper' startups are dying. The market is finally figuring it out.

In 2023-2024, you could raise millions by putting a nice UI on top of GPT-4 and calling it a product. Those days are over.

OpenAI keeps shipping features that kill wrapper startups overnight.

Custom GPTs? Dead.

ChatGPT plugins? Dead.

The integrations announced last week? Another wave of startups just became features.

The surviving AI companies all have something the wrappers didn't:
- Proprietary data moats
- Deep workflow integration
- Vertical-specific fine-tuning
- Or actual ML teams building differentiated models

'We make ChatGPT easier to use' was never a defensible business. It just took 2 years for the market to figure that out.

RIP to the AI wrappers. 2023-2025. You will not be missed.

Own Your Damn Career | Founder @Amplify Your Home | Head of Synthetic Data & Simulations @EY

TCS just paid $700M for Coastal Cloud, a Salesforce consultancy. Becoming a top-5 Salesforce partner globally.

Why should you care about a consulting M&A deal?

Because it signals where the big players think enterprise AI is actually going.

TCS COO said the quiet part out loud: 'advancing our AI-led transformation agenda' through Salesforce expertise.

Not building custom AI. Not launching a foundation model. Embedding AI into the platforms enterprises already use.

This is the enterprise AI playbook that actually works:

1. Meet customers where they are (existing platforms like Salesforce)
2. Integrate AI into workflows that already exist
3. Don't ask enterprises to rip and replace - enhance what they have

The AI startups trying to replace Salesforce should be nervous. The big consultancies are betting that AI's value comes from integration, not disruption.

$700M is a lot of conviction in that thesis.

The consultant's dilemma: build vs. advise.

I've been on both sides.

As an advisor:
• You recommend the best solution
• The client nods enthusiastically
• You leave with a fat invoice
• The recommendation dies in a drawer
• You're hired again next year to 'refresh the strategy'

As a builder:
• You recommend the best solution
• Then you have to make it work
• Every shortcut comes back to haunt you
• Success is measured in production, not slides
• If it fails, you own it

The industry has an advice surplus and an execution deficit.

Every company has a strategy deck. Very few have working AI in production.

The consultants who win long-term are the ones who cross the line. Who stay for implementation. Who put their name on outcomes, not just recommendations.

'We advised them' is worthless if nothing shipped.

The market is figuring this out. Clients are tired of paying for slides. They want results. The firms that adapt will thrive. The ones that don't will wonder why revenue is flat.

My advice to consultants: learn to build. Not just 'understand' technology. Actually build things. Your credibility depends on it.

People ask why I’m okay sharing my “secrets.”

Why I teach everything I know.
Give away resources.
Tools.
Playbooks.

Because you can copy the output but
you can’t copy the process.

You won’t have my judgment.
My taste.
My reps.
My scars.

If sharing makes you afraid,
you were never the edge.

7 Lessons from My First Year Leading AI Transformation:

1. Nobody knows what they want until you show them something. Stop waiting for perfect requirements. Build a prototype and let them react.

2. Your biggest blocker isn't technology - it's the person two levels up who's afraid AI will make their team look redundant.

3. Quick wins buy you time. Deliver something useful in 30 days or leadership will move budget to whoever promises faster results.

4. The people closest to the work have the best AI use cases. Stop running executive workshops. Go sit with the analysts drowning in spreadsheets.

5. Governance isn't the enemy. The AI review board that feels like bureaucracy is actually protecting you from the lawsuit that kills the whole program.

6. You will be wrong about timelines. Multiply your estimate by 3, then add buffer for 'unknown unknowns.' You'll still probably be late.

7. Celebrate the failures publicly. When a pilot doesn't work, tell everyone why. You'll build more trust than a hundred success stories.

What Big 4 AI people know that tech companies desperately need.

I've worked with both worlds. Here's what Big 4 people have that tech often lacks:

1. Governance fluency. We know how to get AI approved, not just built. Tech companies build amazing products that die in procurement. We know the 47 stakeholders who can say no.

2. The 'no' navigation skill. Every enterprise has blockers. Legal. Compliance. Risk. Security. IT. We've learned to map them, sequence them, and neutralize them one by one.

3. Executive translation. We can explain transformer architecture to a CFO without their eyes glazing over. We speak business value, not technical capability.

4. Scope discipline. Tech wants to boil the ocean. We've learned that shipping a smaller thing that works beats a bigger thing that never launches.

5. Documentation-as-strategy. Tech sees docs as a chore. We see them as the approval pathway. Good documentation is how you scale without being in every room.

6. Political patience. Enterprise deals take 18 months. We know how to keep momentum through leadership changes, budget cycles, and priority shifts.

The best AI teams in 2026 won't be pure tech or pure consulting. They'll be hybrids who can build AND navigate.

Prompt engineering is just Googling with extra steps. It's not a career.

Hear me out. In 2024, we created job titles around 'prompt engineering' like it was some mystical new discipline.

Every time a new technology emerges, we create artificial scarcity around basic skills. Remember when 'social media manager' was exotic? Or when knowing HTML made you a 'webmaster'?

Prompt engineering is the same pattern. Within 2 years, it'll be assumed competency - like knowing how to use a search engine. The people building careers around it are standing on melting ice.

The real skill isn't writing prompts. It's knowing what problems to solve and having the domain expertise to evaluate the outputs. That's not prompt engineering. That's just being good at your actual job.

The “AI experiment” phase is over.

New data out of Wharton shows 75% of companies already see positive ROI from AI.

Less than 5% see negative returns.
Nearly half of business leaders use AI daily themselves.

That matches what we see on the ground.

This isn’t about prompts anymore.
It’s not “improve this email” or “summarize this doc.”

That was Phase 1.

Phase 2 has started and it’s bigger:
• “Solve this customer’s problem”
• “Handle this contract end-to-end”
• “Optimize CSAT”
• “Run this workflow”

AI is moving from accelerating steps to owning processes.

And that’s when productivity actually compounds.

The companies still stuck debating pilots and guardrails are about to feel very behind, not because AI is new, but because process always beats tools.

We’re just exiting the first act.
The second one is about systems, not shortcuts.

And once AI is embedded in how work flows - not how tasks are typed - there’s no going back.

What a time to be alive!

Why does every job posting say 'AI experience required' now?

I've been watching job boards. Finance roles want 'AI skills.' Marketing wants 'AI experience.' HR wants 'familiarity with AI tools.' Even admin assistant postings are asking for it.

But 90% of these roles don't actually need AI skills. They need people who can think critically about when NOT to use AI.

What's actually happening:
- Recruiters are adding 'AI' because everyone else is
- Hiring managers don't know what AI skills actually means
- Companies want to signal they're 'modern'
- Nobody has defined what 'AI experience' looks like

My advice to job seekers: Don't panic. 'AI experience' usually means 'can use ChatGPT without supervision.' That bar is not high.

My advice to hiring managers: Stop adding buzzwords you can't evaluate. If you can't test for it in an interview, don't put it in the job posting.

AI isn’t failing because it’s complicated.
It’s failing because no one owns it.

McKinsey’s data says it all -
most companies have no dedicated AI teams, no incentives, no feedback loops, no accountability.

Everyone wants the upside of AI.
No one wants to take responsibility when it breaks.

This is the corporate paradox:
You can’t automate what you refuse to own.

Until enterprises stop treating AI like an experiment and start treating it like infrastructure,
95% of “pilots” will keep dying in PowerPoint.

What Executives Get Wrong About GenAI:

> They think it's plug and play. 'Just add AI to our existing processes.' That's like adding a jet engine to a bicycle and wondering why it doesn't fly.

> They underestimate the data problem. Your AI is only as good as your data. And your data has been accumulated across 47 systems with no consistent formatting for 15 years.

> They expect immediate ROI. GenAI is infrastructure, not a feature. You don't ask 'what's the ROI on electricity?' You ask 'what can we build now that we have power?'

> They compare themselves to tech companies. Google has 10,000 ML engineers. You have Dave who took a Coursera course. Calibrate expectations accordingly.

> They skip the change management. The AI works. Your employees are terrified it will replace them. Guess which problem kills your project?

> They buy platforms before defining problems. 'We need an AI strategy' leads to expensive enterprise licenses collecting dust. Start with 'we waste 400 hours/month on X.'

I wish I could ask Copilot about a meeting I forgot to record.

Here's the trap:

> Option 1: 'Only during the meeting' - Copilot uses temporary speech-to-text. Meeting ends, data deleted. You can't ask questions later.

> Option 2: 'During and after' - Requires saving the full transcript. Now you have retention, compliance, and legal implications.

There's no middle ground.

Missed the meeting? Too bad. Forgot to enable transcription? Too bad. Your organization restricts transcript storage for compliance? Too bad.

What I wish existed:

> A 24-hour summary window. Copilot gives you the highlights, then the raw data is deleted.

> Personal-only transcripts that don't create discoverable records.

> Retroactive summary requests. 'I was in a meeting at 2pm, can you recap?' should work.

> Graceful degradation. If full transcript isn't allowed, at least give me action items.

The 'catch up on meetings I missed' use case - the one Microsoft demos constantly - only works if someone remembered to hit record.

That's not AI. That's a tape recorder with extra steps.

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