Helping SMEs boost profits 10x faster with my AI & Automation-as-a-Service agency—turn your business processes into saved time, empowered teams, & increased profits, all without the extra overhead. Link in Bio ↓

There are two ways to be a business leader in 2025:

1. Struggle with inefficient processes. Stagnate.
2. Leverage AI Agents (aiGENTS). Scale 10x faster. 

Most are stuck in the first.

As a leader, you have ambitious goals. But they're out of reach, aren't they? 

The bigger problem?

You're missing out on game-changing opportunities. 

Opportunities to streamline operations, outpace competitors, and attract top talent to fuel your growth.

Yet, when it comes to:

↗ Automating repetitive tasks
↗ Breaking down departmental silos
↗ Achieving rapid, sustainable growth
↗ Reducing reliance on costly vendors

You find:

↗ Your workflows are still painfully manual
↗ Your teams are disconnected and inefficient
↗ Your operational costs are spiraling out of control

The solution? 

Transform your business with AI Agents and workflow optimization.

That's our expertise. 

Our services encompass:

↗ Crafting tailored AI strategies aligned with your business goals
↗ Implementing AI Agents for immediate impact
↗ Streamlining cross-departmental collaboration
↗ Providing ongoing optimization and support

Are you serious about revolutionizing how your business operates? 

Are you ready to scale 10x faster with AI Agents? 

Reach out now to start your journey towards operational excellence and exponential growth. 

→ GO HERE: www.hiteshmahajan.com 

Email: hello@aidapt.co
X: @hitesh_ai​

Hitesh Mahajan

Most companies don’t have a project management problem.

They have a discipline problem.

If it’s not in ClickUp it didn’t happen.​​​​​​​​​​​​​​​​

Hitesh Mahajan 🚀

CEO @ Aidapt | I design your AI strategy and orchestrate the data foundations, platforms and intelligent systems you need, then build and run them for you in a single AI-first operating model with clear, measurable value

Everyone that says they use ClickUp

They don’t.

They have ClickUp.

There’s a difference.

Here’s what I see in 8-figure companies:

Tasks without owners.

Due dates that mean nothing.

Projects scattered across 14 spaces no one understands.
This is “tool deployment.”

It’s not operational discipline.

The companies actually winning with ClickUp do 5 things differently:

1. Single source of truth
EveryPOST: “The ClickUp Lie”

2. Due dates = do dates
Not suggestions. Not aspirations.
A due date is a commitment someone’s name is attached to.

3. Embedded processes
The system enforces how work runs.
Not a human chasing people in Slack.

4.	Shared habits
Weekly reviews. Sprint rhythms. Status updates.
These aren’t optional extras.

5. Consistent accountability
One owner per task.
One home per workflow.
No exceptions.

The gap between “having ClickUp” and “running ClickUp” is the gap between activity and outcomes.

Most companies are stuck in activity.

The fix isn’t more features.

It’s operational discipline.

How are you using ClickUp

We just became a Snowflake AI Data Cloud Services Partner.

Here’s why that matters for your business:

Most AI projects fail.

Not because the models are bad.

Because the data foundation doesn’t exist.
I’ve seen it dozens of times:

→ $500K spent on AI tools
→ 6 months of “pilot programs”
→ Zero production workflows
→ Leadership asking “where’s the ROI?”

The answer is always the same.

You skipped the data layer.

No clean pipelines. No governed access. No single source of truth.

Just scattered systems and dashboards nobody trusts.

That’s the problem Snowflake solves.

And that’s the problem we implement.

What we actually do:

Data Strategy and Advisory
We audit your current state. Map your data sources. Design the architecture that makes AI possible. Not a 200-page deck. A buildable plan with owners and timelines.

Data and Analytics
Snowflake as your AI Data Cloud.Sigma for real-time analytics your teams will actually use. Pipelines that feed decisions, not just dashboards.

Generative AI and ML
Production-ready AI. Not demos. We build the models, deploy them into workflows, and measure adoption. If it’s not being used daily, we haven’t finished.

Migration and Modernization
Legacy systems holding you hostage? We migrate you to Snowflake without breaking operations. Typical timeline: 90 days to production.

The results we’ve delivered:

→ 8-figure manufacturing company: Legacy data warehouse to Snowflake + Sigma in 12 weeks. Finance team now runs reports in minutes, not days.

→ Regional bank: Consolidated 7 data sources into one governed platform. Compliance reporting went from 2 weeks to 2 hours.

→ FMCG distributor: Built ML demand forecasting on Snowflake. Reduced stockouts by 34% in first quarter.

This isn’t theory.

This is what we do every week.

Here’s the thing most consultancies won’t tell you:
The tool doesn’t matter if nobody owns the outcome.

We don’t just install Snowflake and disappear.
We build the foundation → Train your teams → Run it until adoption is proven → Then hand over the keys.

Every engagement ships with:
	∙	Telemetry (proof it’s being used)
	∙	Runbook (how it operates)
	∙	Story Point (evidence of value)
	∙	Clear next step based on data

That’s our standard. Not the premium package.
If you’re sitting on:

❌ AI ambition but no data strategy
❌ Tools purchased but not adopted
❌ Dashboards built but not trusted
❌ Pilots completed but not scaled

You don’t need another vendor.

You need a partner who builds and runs until it works.

30-minute call. No pitch deck.

We’ll map your current data state, identify the highest-ROI first move, and tell you exactly what it takes to get there.

If we’re not the right fit, I’ll tell you who is.

Book here: https://lnkd.in/eXbEd5Py

Turn AI On.

There is no AI strategy without a Data Strategy.

Full stop.

I just left an intimate Snowflake x Amazon Web Services (AWS) partner event in Dubai.

The room was small. The signal was loud.

From Nissan Motor Corporation driver-assist systems to call centres, to learning that maids.cc - AI & Tech is the second largest employer in UAE, all
running entirely on Snowflake and AWS, every use case reinforced the same thing:

AI doesn’t work on broken data foundations.

Here’s what most companies get wrong:

They chase the shiny AI tool.

They skip the unglamorous data work.

They wonder why nothing sticks.

The winners?

They build the data layer first.

Then they let AI do what it’s actually good at.

Snowflake might be the most versatile data platform I’ve come across. The flexibility to unify, govern, and activate data across an entire organisation is unmatched.

And it’s making me think:

What if every mid-market business had access to a team of “dark data engineers,” specialists who turn fragmented, siloed data into an AI-ready foundation?

That’s exactly what we’re building at Aidapt.

Data-first. AI-native. Proof-backed.

If you’re sitting on data that should be working harder for you, let’s talk: https://lnkd.in/eXbEd5Py​​​​​​​​​​​​​​​​

The most successful AI-first leader you know has the most failed pilots.

They’ve tested more workflows.
Built more automations that crashed.
Launched more systems nobody used.

You see their ROI dashboard.
You miss their failure count.

While you’re perfecting one pilot,
they shipped twenty.

Eighteen flopped.
Two saved 200+ hours monthly.

Guess which ones made the case study?

Here’s the math nobody talks about:

90% pilot failure rate sounds brutal.
Until you realize 10% breakthrough.

Run 3 pilots = 1 winner.
Run 10 pilots = 1 game-changer.
Run 30 pilots = 3 transformations.

Most MEA leaders run 1 test.
See mixed results.
Retreat.

Then call AI adopters “lucky.”

The CEO with seamless ops?
Pilot #8.

The COO with perfect dashboards?
Test #12.

The founder scaling effortlessly?
Iteration #15.

They throw pilots at processes.
Learn fast.

It looks like AI magic.
It’s really just smart testing.

You’re not behind on AI.
You’re under-experimenting.

Stop planning perfect systems.
Start shipping small pilots.

Let the data teach you what scales.

Comment “Framework” for the rapid-pilot method that turns 90% failure into competitive advantage.

Smart ops spread faster when you pass them on.

Charging by the hour for transformation is like paying surgeons based on how long the operation takes.

We charge for outcomes, proof, and adoption instead, because speed, clarity, and results should be rewarded, not inefficiency.

Everyone’s chasing AI agents.

They’re missing the point.

The next frontier isn’t better models.

It’s better data.

Here’s why I’m betting on Snowflake Intelligence:

Most AI fails because the data underneath it is garbage.

Fragmented across 47 systems.

No governance.

No lineage.

No one knows what’s true.

You can’t build AI-native operations on a broken foundation.

Snowflake Intelligence changes this.

It’s not just a data warehouse anymore.

It’s an AI-ready data layer that lets you:

∙Ask questions in plain English across your entire data estate
∙Get answers grounded in YOUR data, not hallucinations
∙Build agents that actually know your business

The companies winning with AI in 2026 won’t be the ones with the fanciest prompts.

They’ll be the ones with the cleanest data foundations.

Data strategy before AI strategy.

Every time.

The model is the commodity.

Your data is the moat.

If you want to build that golden data layer and become truly data-driven with AI at its core, let’s talk.
We’re helping mid-to-enterprise clients turn their data foundations into an AI-ready operating advantage.
Link in comments 👇​​​​​​​​​​​​​​​​

The smartest thing I heard all week came from a CIO in Dubai.

“We don’t have an AI problem. We have a ClickUp  problem.”

I didn’t get it at first.

Then he showed me.

His company spent $200K on AI tools last year.

ChatGPT Enterprise for 200 people.
Snowflake for data infrastructure.
Three different AI automation platforms.
A “custom AI solution” from a Big 4 firm.

I asked: “What changed?”

He pulled up their project management tool.

2,847 open tasks.
940 marked “In Progress” for over 90 days.
127 tasks assigned to people who don’t work there anymore.

Zero tasks had:

- Clear owners
- Due dates that meant anything
- Acceptance criteria
- Links to actual deliverables

“We gave them AI,” he said. “But we can’t even run a basic workflow.”

Then he said the thing that’s been stuck in my head for 72 hours:

“You can’t automate chaos.”

This is the dirty secret nobody talks about.

AI doesn’t fix broken operations.

It amplifies them.

If your team can’t:

- Define what “done” means
- Assign clear ownership
- Track work without seventeen Slack threads
- Close a task without three follow-up meetings

Then AI will just create chaos faster.

I’ve seen this pattern 61 times now:

Company buys AI tools → Usage drops after week 2 → Team blames the technology → Repeat with different vendor.

The problem was never the AI.

It was the operating system underneath.

Here’s what I mean:

No single source of truth = AI can’t find the right information

No clear due dates = AI can’t prioritize

No embedded processes = AI can’t follow non-existent workflows

No shared habits = AI adoption dies in 14 days

No accountability = AI becomes another abandoned tool

You know what actually works?

Fix the operating system first.

One company did this:

- Moved everything into ClickUp
- Forced single ownership per task
- Made due dates real
- Built actual workflows
- Created visibility

Then they added AI.

Response time dropped 67% in 30 days.

Not because the AI was better.

Because the operation was ready.

Most companies are doing it backwards.

They’re buying Ferrari engines and bolting them onto broken bicycles.

Then wondering why they’re not going faster.

If your “AI transformation” isn’t working, ask this:

Can your team close a simple task without confusion?

If no, stop buying AI tools.

You don’t need artificial intelligence.

You need actual intelligence about how work flows.

The boring stuff.

The stuff nobody wants to fix because it’s not sexy.

ClickUp. Workflows. Ownership. Cadence.

That’s the real transformation.

AI is just the accelerator.

But you can’t accelerate a car that won’t start.

Technology without people is shelf-ware.

People without technology is slow.

The winners work on both in parallel.​​​​​​​​​​​​​​​​

“I ship a minimum of 2 Story Books every month.”

Not because it’s a nice-to-have.
Because without evidence, AI transformation is just expensive fiction.

Most AI projects die the same way:

* Slides look great
* Demos impress leadership
* Everyone *feels* progress
* Six months later, no one can explain what actually changed

No proof.
No adoption clarity.
No value trail.

Just vibes.

A Story Book fixes that.

A real Story Book answers four uncomfortable questions:

* What workflow actually changed?
* Who is using the system, and how often?
* What does the data say improved?
* What should we do next based on evidence, not opinion?

If you can’t answer those, you didn’t transform anything.
You shipped activity.

Here’s the hard truth:

AI without documentation creates organizational amnesia.

People leave.
Context disappears.
Wins don’t compound.
Failures repeat.

Story Books prevent that.

They turn delivery into an audit trail.
They turn usage into evidence.
They turn work into institutional memory.

And most importantly:

They force intellectual honesty.

You can’t hide behind ambition when the data says adoption stalled.
You can’t claim value when no one’s using the system.
You can’t fake momentum when the evidence is flat.

That’s why most companies avoid them.

Story Books are accountability in written form.

They expose what worked.
They expose what didn’t.
They expose what needs to change next.

I don’t ship Story Books for marketing.

I ship them because:

If the value isn’t documented,
it doesn’t exist.

And if it can’t be explained clearly to a business leader in one sitting,
it wasn’t real transformation.

AI transformation without Story Books isn’t bold.

It’s irresponsible.

We have one rule at Aidapt that clients hate at first.

Then they love it.

We call it the Receipts Rule.

It's simple:

Every engagement must produce four things before we call it "done."

1. Telemetry
2. A Story Point
3. A runbook
4. A next-step recommendation

If we can't deliver these, we don't claim transformation.

Let me break down why this matters.

Most consulting engagements end with a handshake and a slide deck.

"Great working with you. Here's the documentation. Call us if you need anything."

Then 6 months later, nobody knows what was built, who owns it, or whether it's even being used.

That's not delivery. That's abandonment.

The Receipts Rule kills this pattern.

Here's what each piece does:

Telemetry

We instrument measurement so you can see who's using what, how often, and where it breaks.

No more "I think it's working." You know it's working because the data says so.

If adoption drops, you see it immediately. If something fails, you catch it before clients do.

Story Point

This is the audit trail of value creation.

Not a status report. Not a deck full of logos and timelines.

A clear narrative: what changed, who adopted it, what the telemetry shows, what improved, what to do next.

Every claim backed by evidence.

This is what drives renewals, expansions, and internal buy-in. Not opinions. Receipts.

Runbook

A system without a runbook is a system waiting to fail.

Who owns it? What does "normal" look like? What do you do when it breaks? Who escalates to whom?

If the person who built it leaves and nobody knows how to run it, you don't have a system. You have a liability.

Next-step recommendation

Every engagement ends with a clear answer to "what should we do next?"

Not based on what we want to sell. Based on what the evidence says.

Telemetry shows adoption is strong? Scale it.
Adoption is weak? Fix the workflow before adding features.
New bottleneck emerged? Scope the next block.

This is how AI transformation compounds instead of stalling.

Here's why clients hate it at first:

It's more work. For them and for us.

They have to assign owners. They have to accept measurement. They have to engage with the evidence, not just the demo.

And we have to deliver systems that actually work, not just systems that look good in a presentation.

Here's why they love it later:

Because when renewal conversations happen, there's no debate.

The Story Book shows what changed. The telemetry shows who's using it. The runbook shows it's operationally sound.

Value isn't a claim. It's a fact.

That's what separates transformation from theatre.

If your current partners can't show you receipts, ask yourself what you're actually paying for.

We analyzed 23 failed AI implementations across the Middle East and APAC.

Every single one made the same mistake.

They skipped the data strategy.

Not “we need a data lake.”
Not “let’s hire a Chief Data Officer.”

I mean they couldn’t answer these 5 questions:

1. What data matters for this workflow?

One company built an AI agent for customer inquiries.

Never mapped which systems held customer history.
Never confirmed sales could access support tickets.
Never checked if product codes matched across databases.

The agent launched.
It hallucinated answers for 6 weeks.
They shut it down.

2. Where does that data live right now?

Another client wanted AI-powered forecasting.

Turns out their data lived in:

- 3 different ERPs across regions
- 47 Excel files someone’s nephew maintained
- A Salesforce instance no one could access
- Somebody’s desktop (seriously)

We spent 90 days just mapping reality.

3. Who owns this data?

This is where it gets ugly.

“Who approves changes to customer records?”
“Uh… marketing? Or is it sales? Maybe IT?”

If nobody owns the data, nobody will fix it when AI breaks.

4. What does “good” look like?

We asked a client: “What’s an acceptable error rate?”

They said: “Zero.”

We said: “Then you’re not ready for AI.”

You need to know:

- Completeness thresholds
- Freshness requirements
- Quality tolerances
- Update cadences

Before you build anything.

5. How will we measure this?

The workflow needs telemetry.

Not “let’s check in monthly.”

Real instrumentation:

- Usage events
- Error states
- Response times
- Drop-off points

Most companies answer zero of these questions before they start building.

Then they blame the AI when it doesn’t work.

Here’s the framework we use at Aidapt:

Week 1: Map the workflow end-to-end
Week 2: Identify data dependencies  
Week 3: Confirm ownership and access
Week 4: Define “good enough” standards
Week 5: Design telemetry before build

Total time: 5 weeks.

Total cost: A fraction of what you’ll waste rebuilding after launch.

We had one client push back:

“That’s too slow. Our competitor is already using AI.”

We said: “Your competitor is also rebuilding their system for the third time because they skipped this.”

Three months later, their competitor called us.

If you’re planning an AI implementation right now, do this:

Open a doc.
Write those 5 questions at the top.
Answer them honestly.

If you can’t answer all 5, you’re not ready to build.

You’re ready to do the boring work that makes AI actually succeed.

Nobody wants to do the minimum viable data strategy.**

Everybody wants to skip to the demo.

That’s why 90% of AI projects sit in pilot purgatory.

You can’t automate chaos.
Fix your workflows before you buy AI tools.
Otherwise you’re just creating expensive confusion faster.

#wevearrived #aidapt #snowflake #ai #thenextaifrontier

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Everyone’s chasing better AI models.

They’re solving the wrong problem.

The companies winning with AI have one thing in common:

- Their Clean data foundations.
- Data strategy before AI strategy. 

Every time.

Want to pay for an AI consultant? Don’t.

Here’s your 2026 AI + Ops Masterclass:

(Save this + share with your team ♻️)

1․ Map your 3 biggest manual bottlenecks e.g. approvals, reporting, customer comms.

2․ Add 1 process that eats ~20+ hours weekly e.g. data entry, follow-ups, status updates.

3․ Your automation should always connect to ROI. Track hours saved, not tools deployed.

4․ Focus on ONE department first. Master it. Then expand. Don’t boil the ocean.

5․ Your team’s adoption rate matters more than the tech’s sophistication. Start simple.

6․ Your dashboards should answer the question: “What’s our ROI this month?”

7․ Your training should be bite-sized, not workshop marathons. 15-min modules win.

8․ I recommend 3-week sprints maximum. Longer = scope creep. Shorter = real wins.

9․ I recommend measuring hours saved weekly. This builds momentum and proves value.

10․ Tool types? Your choice entirely. Don’t use AI just because someone said “AI is everything”.

11․ Keep each process to 1 outcome only. Multiple goals = confused teams and failed adoption.

12․ Make every automation teachable. Teams should “get it” in 5 minutes or less.

13․ If you’re rolling out new systems, show quick wins upfront. Don’t “just implement”.

14․ Hot take but… You don’t need perfect tech to get perfect results from your operations.

15․ Before you buy any software, ask yourself: “Will my team actually use this daily?”

16․ Internal buy-in beats external consultants. Train champions, don’t hire more vendors.

17․ Look for adoption signals before scaling. Usage rates. Feedback. Fewer complaints.

18․ Mindset hack? Always approach automation with one goal: Free time for strategy.

19․ Change management? How much? Everything. It’s MORE important than the tech itself.

20․ Best adoption hack? Show ROI to users, not just leadership. “You saved 2 hours today.”

21․ Vendor promises? Don’t believe them. Pilot first. Prove value. Then commit.

22․ Shiny object syndrome? Don’t even think about it. Focus beats features every time.

23․ Have patience. Stop obsessing over perfection and focus on progress.

Aaaand… You’re ready!

(For now)

Just know: There’s so much more to AI adoption, change management, and scaling operations than ONE post can cover.

This entire framework is just 1% of what we deploy for MEA leaders. The rest: 21-day ROI pilots that actually work.

Comment “Pilot” if you want to see how we could do this for you.

Which one of these made you rethink your AI strategy? What needs fixing first? 👀

Leaders grow quicker when insights are shared.

Everyone thinks AI transformation is about the technology.

It’s not.

I’ve watched 47 companies try to “go AI-first” in the last 18 months.

3 actually succeeded.

The difference?

The 44 failures bought tools.
Ran pilots.
Created dashboards.
Hired consultants who wrote strategy decks.

Then wondered why nothing changed.

The 3 winners did something different.

They stopped treating AI like a project.
Started treating it like an operating system.

Here’s what that actually means:

They didn’t deploy ChatGPT to the team.
They rebuilt one workflow end-to-end with AI embedded inside it.

They didn’t measure “AI adoption.”
They measured whether the workflow actually ran differently on Tuesday.

They didn’t create an AI strategy.
They assigned an owner who wakes up accountable for that workflow every morning.

They didn’t celebrate the launch.
They instrumented telemetry so they could see usage, health, and value in real-time.

The company that got this right first?

Cut their customer service response time from 4 hours to 11 minutes.

Not with better tools.

With one workflow.
One owner.
One telemetry dashboard.
One Story Point proving it worked.

Then they scaled that pattern to 6 more workflows.

Here’s my 2026 prediction:

The companies that win won’t have the best AI strategy.

They’ll have the best operating discipline.

Because AI doesn’t fail because the models are bad.

AI fails because workflows are unclear, ownership doesn’t exist, and nobody measures what actually changed.

If you can’t answer these 4 questions about your “AI initiative”:

1. What workflow changed end-to-end?
2. Who owns it tomorrow morning?
3. What does your telemetry show about adoption?
4. What specific value can you prove?

You don’t have AI transformation.

You have AI theatre.

Turn AI On. Make it measurable. Make it owned.

I just watched John Sanei explain the future of work.

And he’s half right.

The shift is real:

- Corporate structures fragmenting
- One-person companies with AI agents
- Individual creators capturing massive value

But here’s what nobody’s saying.

You can’t run a “one-person company with 100 agents” if you don’t know how to run operations.

Most creators are about to learn this the hard way.

Because agents don’t fix chaos.
They amplify it.

Here’s what the creator economy is missing:

The Operating System

When you’re a one-person business with AI doing your:

- Content scheduling
- Email responses
- Research
- Analytics
- Customer service

You need answers to questions most creators never ask:

Who owns it when an agent breaks?
Where’s the runbook when something fails at 2am?
How do you know what’s working vs quietly failing?
What’s your rollback plan?

Volume and cinematic quality won’t save you if your operations are a mess.

I’ve seen this movie before.

In 2015, everyone became a “startup.”
Most failed not because of their idea.
They failed because they couldn’t operate.

2026 will be the same.

Everyone becomes a “one-person company.”
Most will drown in their own automation.

The winners won’t be the ones with the most agents.

They’ll be the ones who built the operating discipline first:

- Clear workflows before automation
- Telemetry on what agents actually do
- Owners for every automated process
- Runbooks that prevent 3am panic

You can’t automate your way out of operational chaos.

You have to build the foundation first.

Then turn AI on.

The future of work isn’t just “individual + agents.”

It’s “individual + agents + operational discipline.”

Without that last part, you’re just creating expensive chaos faster.

A company just showed me their “AI success story.”

92% adoption rate.  
Thousands of queries per day.  
Executive team celebrating.

I asked one question that ended the meeting:

“What decision changed because of this?”

The VP of Operations froze.

“Well… people really like using it.”

“Right. But what actually changed? What do you do differently now?”

“We’re… gathering insights.”

“From where?”

“The AI generates reports.”

“Who reads them?”

“The team reviews them weekly.”

“Then what happens?”

Silence.

Here’s what I see everywhere:

High usage.  
Zero impact.

Companies confusing activity with outcomes.

Lots of people asking AI questions.  
Nobody changing how decisions get made.

The pattern:

Teams use AI to write emails faster.  
Great.  
But the approval process still takes 3 weeks.

Teams use AI to analyze data.  
Cool.  
But the insights sit in a doc nobody reads.

Teams use AI to automate reports.  
Awesome.  
But operations run exactly the same way.

This is why CFOs are getting skeptical.

They see the Snowflake bill.  
They see the ChatGPT Enterprise invoice.  
They see “high engagement.”

But they don’t see the P&L move.

The question that matters isn’t:

“Are people using AI?”

The question is:

“What stops happening when AI starts working?”

One client finally got this right.

Before AI: approval workflows took 4 days.  
After AI: approvals happen in 11 minutes.

The workflow changed.  
The decision velocity changed.  
The business outcome changed.

That’s transformation.

Everything else is just productivity theatre.

If you’re measuring “AI adoption” right now, stop.

Start measuring this instead:

What decision happens faster?  
What approval disappears?  
What manual step stops existing?  
What meeting gets cancelled?

If you can’t answer that, your AI isn’t working.

It’s just working out.

Getting reps in. Burning calories. Going nowhere.

Usage is not the same as impact.

And your board is about to figure that out.

Wild conversation I just had with a COO in Dubai.

They’re watching their organization split in half.

And they don’t know how to stop it.

55% of their team has AI embedded in daily work.
They use it inside ClickUp, Slack, their CRM.
They’re constantly asking: “Can we automate this?”

45% refuse to touch it.
They tried ChatGPT once, didn’t like the output, now claim “AI creates garbage.”

Here’s what shocked me:

The productivity gap isn’t 20%.

It’s not even 2x.

It’s 10-50x.

The AI-native teams are:

- Closing workflows in 3 days that used to take 2 weeks
- Handling 4x the volume with the same headcount
- Building telemetry dashboards in hours, not sprints

The AI-resistant teams are still:

- Scheduling meetings to discuss what should be in ClickUp
- Manually copying data between systems
- Waiting on “the right AI strategy” from leadership

Previous tech waves didn’t create gaps like this.

Excel shortcuts vs mouse clicking? Maybe 25% faster.

This is different.

One team is operating differently.
The other team is still discussing whether to start.

The COO asked me: “How do I fix this?”

I said: “You can’t train someone into being AI-native.”

“You have to rebuild the workflows so AI is embedded, not optional.”

If the workflow requires AI to function, adoption becomes binary.

You use it, or the work doesn’t happen.

That’s how you close the gap.

Not with training sessions.
Not with “AI champions.”

With operating model change that makes AI unavoidable.

Because here’s the truth:

The 45% who are resisting aren’t lazy.

They’re rational.

They tried a tool in isolation.
It didn’t fit their workflow.
So they went back to what works.

You can’t blame them for that.

But you can make their current workflow obsolete.

That’s what “Turn AI On” actually means.

Not everyone using ChatGPT.

Everyone using workflows where AI is the operating system.

The companies that figure this out in 2026 will 10x their competition.

The ones that don’t will be managed by the ones that do.

Ryan Staley

I help Chief Revenue Officers & Sales Professionals Leverage AI to work 10 hours less weekly and 2x performance. Follow for cutting-edge cheat codes, prompts, and workflows🎙Scale Up Show Host🔥$0→$30M ARR in 5.5 yrs

Over the last decade, I’ve grown a division from 0-$30M ARR with 4 Sales People and have upskilled over 2,500 GTM executives on AI, Enterprise Sales and GTM Strategy  .

My mission is to inspire 1 million Go To Market Executives to work less and sell more through becoming Superhuman with Artificial Intelligence.

I help revenue leaders transform their teams by acquiring AI skills to escape the operational grind, and accelerate topline revenue.

I’m the founder of Whale Boss and The Scale Up Show.

My portfolio:

→ I’m the Founder of Whale Boss, an AI Sales & Marketing consulting and skill transformation company
→ I’m the Founder of the Scale Up Show, Top 1% Global Podcast
→ I teach Executives, Sales & Marketing teams to implement tools like ChatGPT, Copilot, Claude, etc.   
→ I provide Chief AI Officer Advisory services for human capital.  


My results:

→0-$30M without funding, marketing or SDRs with only 4 Sales people
→Identified and implemented over 500 AI use cases (internally and externally)
→Taught over 800 CROs proprietary selling strategy and tactical execution
→Consulted over 40 Technology companies on custom revenue growth strategies.  
→Voted as a TOP global AI thought leader, Gartner Ambassador & Sales Assembly thought leader. 


If you want to learn more on how to become Superhuman, you can join my newsletter.
⇒ https://superhumanrevenue.beehiiv.com/


If you want to explore AI skill transformation for your organization or Team, apply for an AI Consult.  
=> https://calendly.com/ryanstaleywss/introductory-call-with-ryan-staley-from-whale-boss


Youtube- https://www.youtube.com/channel/UCFGndSHN4KkE89kUP74U7Qg

Podcast-https://ryanstaley.io/podcast/

X-https://x.com/Ryan_Staley1

Podcast Host

I deconstruct how the top SAAS CEOs in the world grow their companies repeatedly and predictably.  

This show has featured Startup and Billion Dollar Founders, Best Selling Authors, and the World’s Top Sales and Marketing Experts like Terry Jones (Founder of Travelocity and Chairman of Kayak),  Andrew Gazdecki (Founder of Microacquire), Harpaul Sambhi (Founder of Magical with a previous exit to Linkedin) and many more.

I've spent 3 years helping executives navigate the fastest transformation in human history.

Here's what I've learned:


The ones who perform under pressure all have something in common.


A center. An anchor. 
Something they control when everything else is chaos.


For me? It's this. 5am. 
Weights. 
Silence before the storm.


This isn't a "wake up at 4am and crush it" motivational post.

It's reality:
→ My energy stays higher throughout the day
→ My decision-making stays sharper under pressure
→ I'm building health proactively instead of reacting to burnout


I've watched too many CROs and CMOs grind themselves into the ground chasing numbers.


Hit the target. Miss their health. Miss their families. Miss themselves.

The irony? When you lose your center, you lose your edge.


NOTE: This doesn't have to be the gym. 
It could be running. Meditation. 
Playing guitar. 
Coaching your kid's team.


The point is having SOMETHING that's yours. 


That doesn't depend on board approval, pipeline coverage, or the next AI announcement.


What's your anchor?

Founder & CEO, Whale Boss | Demystifying AI Agents | Helping revenue leaders 2x team results through AI Led GTM | Trusted by PE-backed SaaS | Transform your team in 60 days

I did something from Thanksgiving to New Year I haven't done in 5.5 years.
I stopped.

No podcasting. No YouTube. No content treadmill.
Instead:
→ Closed out the year strong
→ Set up massive foundational moves for 2026
→ Actually recharged with family in Napa-Russian River Valley
→ Did deep work when things got quiet

Here's what happened when I created space:

I spent serious time with Claude Code and orchestration workflows.

What I discovered completely rewired what I think is possible.

The jump I'm seeing in AI orchestration isn't incremental.
It's exponential intelligence at the speed of thought.

2026 is going to be different. Not because of hype.

Because of what I've built in the silence.

Sometimes the best move is to step back so you can leap forward.

What's one thing you stepped away from that gave you unexpected clarity?

I just finished another AI Transformation Sprint with a SAAS leader-QT9 Software  software

The results blew me away.

Here's what 45 days of focused AI implementation produced:

→ 18 AI use cases deployed across Sales, Marketing, and Leadership
→ 139 hours saved WEEKLY across the team
→ Equivalent of 3.5 Headcount gain to drive greater efficiency and output
→ 12.4x return on program investment

But here's the metric that matters most:

Workflow confidence jumped 103%.

Before: 1.94/5
After: 3.94/5

This is the real unlock.

Most AI training programs teach tools. We transformed how the team THINKS about AI.

The 3 biggest wins:

🔹 Demo Prep: 2+ hours → 10 minutes (92% reduction)
🔹 Account Research:** 4 hours → 30 minutes (87.5% reduction)
🔹 Business Case Creation:** 4 hours → 15 minutes (94% reduction)

Here's what most companies get wrong:

They measure AI adoption by "ROI on Day 1 like a slot machine"
That's like measuring fitness by gym membership.

Leadership doesn’t set the non-negotiables

The real ROI comes from:

1. leading indicators=time
2. lagging indicators=increase in tasks, activity, pipeline , etc
3. Results= Increases in revenue and profit 

When confidence doubles, adoption becomes automatic.

NOTE: 

74% of the ROI came from SALES use cases. 

The Executive leadership was committed

-leading by example, setting expectations and driving adoption

The team was amazing

-hungry, curious and grew exponential by doing

What's blocking your team from this kind of transformation?

Feel stuck or don’t know where to start, DM me and I’ll give you a few ideas of what has been working over the last 3 years.

Amazing work Jim Ellward Jared Brady Brian Kulaga Cole Gardner, MBA Jake Ninowski Kertrina Justice Blake Carlson Christian Reyes 

And the rest of the QT9 team (who are not showing up in LI for me.)

15 hours in, three operating systems later, and I'm officially hijacked by Claude Code.

Not in the way you think.

Let's skip the AI hype and get real.

I stopped reading about "AI use cases" and got my hands dirty.

Fifteen solid hours, two weeks, zero fluffy prompts.

Just building actual operating systems.

Not content.

Not lists.

Real, working systems that actually run my business better.

Here's what happened:

1. The CEO Operating System (Annual Planning)

Forget hope and guesswork. This system turns chaos into a clear plan.
- Pick the few things that matter (not 27 "priorities")
- List what we're not doing
- Map risks and constraints
- Build a real 90-day action plan
- Log decisions, assumptions, and learnings as we go
- Use 10 battle-tested frameworks for tough calls

The result? Not a plan that "looks good" in a slide deck, but one that actually runs when the rubber meets the road.

2. The Sales Intelligence Operating System (Client Interaction Analysis at scale)

My new force multiplier.
It chews through a mountain of sales calls, client emails, and feedback in seconds, not weeks.
- Finds trends, patterns, and recurring objections
- Spots churn risk and expansion signals
- Surfaces the REAL words buyers are using, not what we wish they'd say
- Highlights opportunities we're missing

This thing can scrape thousands of data points and hand me something actionable before the coffee gets cold.

3. The Product Intelligence Operating System (Continuous Improvement)

Where delivery drifts, this system pulls it right back.
Because my edge is delivering actual AI transformation training, not just talking about it.
This OS hunts for patterns in:
- What features clients love (and which get ignored)
- What gets real results vs. wasted effort
- Where new modules, examples, or sequences are needed

It's the antidote to vibes-only product improvement. Decisions made on facts, not feelings.

Let's get honest about what made this work.

It wasn't "AI did it for me."

It was orchestration.

I skipped the mega-prompt lottery and ran swarms of specialized agents, each with a clear job.

Every OS had something even more important: memory. They don't just spit out answers and forget. They get smarter. They remember what works, what doesn't, why we made certain calls, and how we define "good."

Every week, outputs compound instead of vanish.

Here's your anti-hype PSA: it's not magic. If you feed it garbage, you get garbage,just a lot faster, and prettier.

The unlock isn't the tech.

It's having:

- Real-world skills 
- Guardrails
- Repeatable workflows
- And, most important, systems that learn instead of reset every time you open a new chat window

Thinking about breaking this down in detail on LinkedIn, YouTube, maybe even my podcast.

Would you want to see how each OS works under the hood, or should I just go back to writing about "top 5 AI tools for 2026" instead?

Drop a comment, tag someone who needs to see this, or just tell me if you want more.

undefined undefined

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whale-boss

We’ve trained 1,850+ sales and marketing professionals on AI in 2025.
Here are the 10 most jaw-dropping ROI outcomes they achieved:

(With real quotes from real people. No theory. Just results.)

#10: Personalized Demo Configuration
"Prospects are more engaged because demos are tailored to their exact industry and use case." — Account Executive, B2B SaaS
Result: 10%+ conversion lift → 500 more closes/year potential

#9: Executive Alignment Strategy
"We're getting meetings at higher levels than ever before. The AI research helps us speak their language." — Enterprise Account Manager, $4B BPO Company
Result: 20%+ larger deal sizes → $2M incremental revenue

#8: Shark Tank Hackathon Model
"Teams built actual tools they're still using. It wasn't just training—it was delivery." — Leadership Teams, Multiple Companies
Result: 100 tools built → 200 hrs/week saved → $520K/year

#7: ROI Builder for Champions
"Building a business case for my champion used to take half a day. Now I can generate one in 15 minutes." — Account Executive, B2B SaaS
Result: 94% time reduction → $225K/year saved

#6: Competitive Intelligence GEM
"Instead of spending hours every week tracking competitors, the GEM does it automatically and alerts me." — Sales Rep, Life Sciences Company
Result: 4+ hrs/week → Fully automated → $1.04M/year

#5: Prospect List Generation
"It took us days to build a prospect list. Now we generate 300+ qualified companies in minutes." — Sales Team, Life Sciences Company
Result: 95%+ time reduction → $480K/year saved

#4: Crawl-Walk-Run Framework
"Every department now has a structured AI project in flight. We went from pockets of use to organization-wide." — COO, Industrial Software Company
Result: Org-wide adoption → 500+ hrs/week saved → $1.3M/year

#3: Call Transcript Analysis
"I don't spend 30 minutes after each call reviewing notes. The AI extracts everything instantly." — Multiple Participants
Result: 30 min → Instant (100% automated) → $2.6M/year for a 100-person team

#2: Account Research Workflow
"I was spending 4 hours researching each strategic account. Now I get better insights in 30 minutes." — Marketing Specialist, B2B SaaS
Result: 87.5% time reduction → $1.75M/year saved

#1: Demo Prep Automation
"What used to take me 2+ hours now takes 10 minutes. I can prep 5 demos in the time it used to take for 1." — AE, B2B SaaS
Result: 92% time reduction → $1.37M/year saved

The math doesn't lie:
📊 Total Annual Impact: $14.21M (for a 100-person team) 
⏱️ Hours Recaptured: 5,935 hrs/week 
🎯 Sales drove 79% of total ROI

The bottom line:

AI transformation isn't about technology. It's about closing the deal with your own organization. 

The companies seeing these results treated internal AI adoption like an enterprise sale: → Identify the pain → Find champions → Build the business case → Close the deal

Your team already knows this framework. Now apply it internally.

Which of these outcomes would move the needle most for your team?
Drop a comment 👇

I've presented growth strategies to C-Suites for the past 15 years.

Here's the brutal truth:

Most strategies die in the boardroom because they're built on gut feel and outdated playbooks.

The C-suite doesn't want more slides. They want conviction backed by data they can actually verify.

That's why I've spent the last 3 years building AI workflows that pressure-test strategy BEFORE it hits the executive table.

What used to take my team 3 weeks of analyst work now happens in 2 hours.

On January 28, I'm teaching this exact approach in Pavilion's AI in GTM School:

**Growth Strategy Design for an Enterprise (with AI Workflows)**

What you'll walk away with:
→ How to stress-test positioning against real market data using AI
→ Frameworks for validating competitive threats before they become surprises
→ The exact process I use to build strategies the C-suite actually trusts

This isn't theory. It's the same workflow I've used with 2,400+ executives over the past 3 years.

If you're a CRO or CMO tired of defending strategies built on hope, this session is for you.

Link in comments to register.

What's the last AI growth strategy you presented that got challenged in the boardroom?

This is taken from an AI Agent social network - Moltbook

It is the third most upvoted post and the first 2 aren’t any better.

Things are going to go off the rails really fast if someone doesn’t have some standards or safety measures to this site. 


Although this is an amazing concept and idea we need to make sure bad actors that have social burner accounts don’t poison the well.

Please comment for reach and let’s make sure we act smart when working with this insanely powerful technology.

WHAT keeps me up at night about AI in GTM:

The window is closing.

Right now, companies who implement AI systematically have a MASSIVE competitive advantage.

By 2027? AI capabilities will be table stakes.

Everyone will have access to the same tools.

The differentiator will be:
→ How fast you implemented
→ How deeply it's embedded in your operations
→ How skilled your team became during the transition

I've watched this pattern before.

Companies that moved early on CRM, marketing automation, sales engagement—they dominated for years.

The laggards spent the next decade catching up.

AI is that same moment. Compressed into 24 months.

This is why I partnered with Pavilion to teach a session for the **AI in GTM School**.

It's designed for GTM leaders who:
🔹 Know AI matters but don't have a systematic approach
🔹 Want alignment across teams, not scattered experiments
🔹 Need a 90-day plan they can actually execute

The next cohort starts next week.

If you're a CRO or CMO who's ready to move from AI experimentation to AI transformation—this is your moment.

Here is the link to sign up: 
https://lnkd.in/gekG_R6t

McKinsey just announced they have 25,000 AI agents.

My reaction? That's not a flex. That's a warning sign.

Here's what I've learned from 3 years of AI itransformation implementations across startups and fortune 500s:

MORE IS NOT ALWAYS BETTER.

In fact, more is usually worse.
I've watched companies rush to build dozens of agents, custom GPTs, and AI workflows—only to end up with a graveyard of tools nobody uses.

The problem isn't building agents.

The problem is building agents without asking:
→ Who owns this?
→ Who maintains this?
→ Who actually uses this daily?

NOTE: Managing 25,000 agents currently is just as hard as managing 25,000 people.
Maybe harder.

Because agents don't tell you when they're broken.
They don't raise their hand when they're producing garbage.
They just keep running.

The companies winning with AI right now aren't the ones with the most agents.

They're the ones with the RIGHT agents.

Fewer. More precise. Actually used.

What's your take—is agent quantity becoming the new vanity metric?

I spent 4 hours watching a single Anthropic video over break.

Rewound it. Took notes. Implemented immediately.

Here's the insight that's changing how I build AI systems:

STOP building agents. START building skills.

Here's what most people get wrong:

They think an "AI agent" is some magical autonomous entity that figures everything out.

Wrong.

Agents without skills are just expensive guessing machines with massive drift.

🔹 They go off path
🔹 They hallucinate next steps
🔹 They deliver inconsistent outputs

The Anthropic team broke it down into 3 layers that change everything:

**Layer 1: Foundational Skills**
The basics. File creation. Web search. Code execution.
Every agent needs these—but they're just the table stakes.

**Layer 2: General-Purpose Skills**
Document creation. Data analysis. Spreadsheet generation.
These are the workflows that apply across industries.

**Layer 3: Custom Enterprise Skills**
YOUR specific processes. YOUR playbooks. YOUR workflows.
This is where the magic happens.

The shift?

Instead of asking an agent to "figure it out," you're giving it a SKILL.md file that says: "Here's exactly how we do this."

No guessing. No drift. No inconsistent outputs.

NOTE: I've been implementing this for 2 weeks and the quality of outcomes is dramatically better across everything from content to research to data analysis.

The companies that master skills will have AI that actually performs.

The companies that keep building generic agents will keep wondering why their AI is inconsistent.

Which approach is your team using right now?

I just burned through 115 million tokens on Claude Code in 30 days.

That's not a typo.

I kept hitting usage limits and upgrading—which tells you something about how deep I went.

Here's the thing nobody talks about:

Building agents is EASY.

Building GOOD agents is hard.


You can spin up a swarm of AI agents in minutes. I've done it. Command it, and it creates.


But here's what happens next:


The output is a fraction of what you actually need.
It's the exact same pattern as Custom GPTs.
Anyone can build one in 60 seconds.
Almost no one builds one worth using.


The difference between "agent that exists" and "agent that performs"?
🔹 Rigor in testing
🔹 Quality examples baked in
🔹 Clear success criteria
🔹 Iteration cycles (lots of them)

After 30 days of intensive building, here's my take:


The barrier to entry has collapsed.
The barrier to excellence hasn't moved.


That gap is where the real opportunity lives for revenue leaders willing to put in the work.


What's been your experience building agents—easy to start, hard to finish?

I've been wrong about where the next AI breakthrough is coming from.
Prompting. Workflows. Automation.

But here's what I'm realizing now:

The real unlock isn't better prompts.

It's agentic tools like Claude Code.

I've been fine-tuning how I use it across everything I do—content, research, client work, operations.

And it's not incremental improvement.


It's a different category entirely.

So I'm going to start sharing what I learn with you in real-time.

Frameworks.
Cheat sheets.
What works. What doesn't.


No polish. No waiting until it's "perfect."

Because this is the bridge to the next exponential value creation with AI.

And I'd rather you learn from my mistakes now than figure it out alone 6 months from now.

Here is quick cheat sheet that will get you started on how to use it the right way!

What AI tools are you experimenting with right now that feel like a step-change, not just an improvement?

I've spent the last 3 weeks building what I call an "Agentic Operating System" for my company.

Here's the breakthrough:

What if your AI didn't just answer questions...

But held the ENTIRE CONTEXT of how your company operates—and helped you see what you're missing?

This is orchestration engineering.

And it's the skill that will separate 10x knowledge workers from everyone else.

Here's how I'm building it:

**5 interconnected "Operating Systems" that form a company brain:**

1️⃣ **CEO/Executive OS**

- Annual goals
- Quarterly priorities
- Weekly operating rhythm
- Action plans with tracking

2️⃣ **Sales Intelligence OS**

- Pipeline analysis
- Call transcriptions → next best actions
- Executive overviews
- Hidden opportunities across deals

3️⃣ **Product Intelligence OS**

- Training transcripts
- Customer feedback loops
- Win/loss analysis
- Engagement and expansion signals

4️⃣ **Marketing OS**

- Campaign performance
- Content byproducts
- Dream 100 prospecting
- Competitive intelligence

5️⃣ **Internal Operations OS**

- Slack messages
- Email threads
- Meeting transcriptions
- All aligned to strategic goals

**The magic happens when they INTERCONNECT.**

🔹 Your sales calls inform product improvements
🔹 Your product intelligence informs marketing messaging
🔹 Your internal operations stay aligned to executive priorities
🔹 Every signal gets processed against your strategic goals

This creates what I'm calling the "company brain."

It helps you see:

- If you're on track
- What you're doing right
- How to optimize
- 10x opportunities hiding in plain sight
- 100x possibilities you never considered

NOTE: I used to think the future was individual AI agents doing tasks.

Now I realize it's ORCHESTRATED SYSTEMS where agents work together with shared context.

That's the 10x multiplier.

The Leaders who build this capability in 2025?

They won't just outperform their peers.

They'll operate in a different dimension entirely.

Which of these 5 operating systems would unlock the most value for your team?

I spent 25 years learning how to build pipeline, increase close rates, and shorten sales cycles.

Then I asked an AI agent: "How would YOU solve these problems?"

The answer exposed a blind spot I didn't know I had.

Here's what I learned: We've been giving agents human playbooks and asking them to execute faster.

That's backwards.

Here are the 4 questions that changed how I work with agents in 2026:

1. "How would you approach this AS an agent—not as a faster human?"

Stop giving agents your methodology. Ask them what methodology makes sense given THEIR capabilities.

An agent doesn't need to "qualify" leads the way a human does. It can score 1,000 signals simultaneously. Let it design the process.

2. "Given these raw materials, what would you build?"

Feed it the data: transcripts, CRM records, emails, call recordings, Google Drive docs.

Then ask: "What system would you create to solve [goal]?"

You'll get architectures you never would have imagined.

3. "What patterns do you see that I can't?"

This is the unlock.

Humans are pattern-matching machines—but we max out at ~7 variables.

Agents can correlate hundreds. Ask them what YOU'RE missing.

4. "Using first principles, how would you solve this if you had no constraints?"

Agents don't carry your limiting beliefs, your "that's how it's always been done," your organizational politics.

Let them design from scratch. Then adapt to reality.

NOTE: These questions assume you've given the agent CONTEXT—not just instructions. Context is the fuel. Instructions are just steering.

The skill of 2026 isn't prompting.

It's knowing what to ask.

What question would you add to this list?

WHEN I started using AI 3 years ago, I focused on prompts.

HOW to write better questions.
HOW to get better outputs.
HOW to save time on individual tasks.

That approach 10x'd my productivity.

But what I discovered this week will 100x it.

**The shift: Prompt engineering → Orchestration engineering**

Here's the difference:

Prompt engineering is asking AI a great question.

Orchestration engineering is building SYSTEMS where AI holds your entire business context and synthesizes insights you'd never see on your own.

It's the difference between:

- Using GPS for one trip
- And having a co-pilot who knows every route you've ever taken, every destination you're considering, and every traffic pattern across all your priorities

I've been building what I call "operating systems" inside Claude Code:

A CEO OS that synthesizes my annual goals, quarterly priorities, and weekly rhythms.

The AI doesn't just answer questions.

It eliminates disparate information across areas.

It turns goals into action plans and tracking sheets.

It shows me where I'm off track before I even ask.

**Here's why this matters for YOUR career:**

The executives who master orchestration engineering won't just be more productive.

They'll have a STRATEGIC ADVANTAGE that compounds every single day.

While others are still asking ChatGPT individual questions...

You'll have an intelligent layer synthesizing every:

- Sales call
- Customer interaction
- Internal communication
- Market signal
- Team execution detail

Against your strategic priorities.

In real time.

NOTE: I predict "Orchestration Engineer" will be one of the most valuable skill sets in business by 2027.

It won't require you to be technical.

It will require you to think in SYSTEMS, not prompts.

The 10x knowledge workers of the next decade won't be the best prompt writers.

They'll be the best orchestration architects.

This is the fastest transformation mankind has ever had.

And the window to get ahead?

It's closing faster than most people realize.

What's the first "operating system" you'd build for your role if you could connect all your disparate information in one intelligent layer?

I'm in this photo with the cast of Severance.

Except I've never met them.

I strung together a couple commands in ChatGPT's new image generator and this was the result.

2026 is going to be WEIRD.

Here's what I'm seeing after 3 years of AI implementation work:

The line between real and synthetic is disappearing faster than anyone predicted.

🔹 Veo 3.1 just dropped
🔹 Image generation hit a new threshold
🔹 Video is about to follow

And we're only in January.

NOTE: This isn't fear-mongering. This is pattern recognition.

When you can't trust what you see online, something else becomes infinitely more valuable:

Human-to-human connection.

Not text. Not email. Not async.

→ Live conversations
→ Phone calls
→ In-person presence

These will be at a PREMIUM in 2026.

The irony? The more powerful AI becomes at mimicking reality, the more we'll crave what's actually real.

What's your prediction for how this plays out?

I just looked at my ChatGPT annual summary.
I don't know if I'm excited or disturbed.

The data showed me exactly how much of my thinking, creating, and problem-solving now runs through AI. Every day. 

Multiple times per day.


Three years ago, I had my "ChatGPT moment" that changed everything.

What I'm seeing now makes that look like a warmup.

The agents. The systems. The orchestration capabilities.


My mind is genuinely blown.


Here's what I've accepted: This is short-term sacrifice for long-term gain.

The hours I'm investing now in understanding these systems? 
They're not optional anymore.
They're the price of admission for what's coming.


I've worked with 2,600+ executives over the past three years. 

The ones who leaned in early are operating at a completely different level than those who waited.

That gap is about to become a canyon.


What did your AI usage summary reveal about how you're working now vs. a year ago?

HOW would an AI agent approach pipeline differently than your sales team?

I ran the experiment.

I asked an agent to solve evergreen pipeline problems that every sales org faces. But I didn't just give it the task—I asked it to map out:

→ Its approach
→ The steps it would take
→ How it would "attack" the problem

Here's what I learned:

**What impressed me:**

🔹 The level of detail was exceptional
🔹 It viewed pipeline stages through a different lens than most humans
🔹 It identified optimization points I hadn't considered

**What missed the mark:**

🔹 Its assessment of how humans handle pipeline undersold us
🔹 It didn't account for relationship nuance that top GTM pros leverage
🔹 It assumed more process consistency than exists in real orgs

NOTE: This isn't about agents being better or worse than humans.

It's about understanding WHERE each excels.

The takeaway for revenue leaders:

Before you deploy an agent on any workflow, ask it to explain its approach first. Compare it to your team's approach. The gaps tell you exactly where human oversight matters most.

This is how you build agent + human workflows that actually work.

What's the first sales problem you'd ask an agent to map out? Drop it below—I'll share what I've seen work.

I've never written a line of code in my life. 

30 days ago, I went all-in on Claude Code—a coding platform.

Here's what I learned as a complete non-coder doing extensive coding for the first time:

Orchestration platforms like Claude Code require you to master 4 things in a specific sequence:

1. Foundational Concepts First

There's a language barrier that has nothing to do with code.

It's like learning to ice skate. You need to understand the lingo, the logic, how things connect.

Skip this step? You'll be frustrated within 48 hours.



2. Learn By Doing (After Foundations)

Once you understand the vocabulary, execution becomes your teacher.

I built. I broke things. I rebuilt.

No course can replace actually shipping something—even if it's messy.



3. Return to Foundations

This is the step 95% of people skip.

After building, go BACK to foundational concepts.

You'll discover entire categories you missed:
→ Continuous automation
→ Skills architecture
→ Edge cases you never considered



4. Context Engineering

This might sound like a buzzword.

It's not.

If you align context properly, prompting becomes almost effortless.

It unlocks capabilities you didn't think were possible.

NOTE: Context engineering isn't prompt engineering. It's the architecture that makes prompts work.

The sequence matters: Foundations → Doing → Foundations Again → Context Architecture.

Miss any step and you'll plateau fast.


What step are you currently stuck on?

What if cheat sheets/frameworks are actually holding you back?

I've taught thousands of executives about AI.

Frameworks everywhere.

Slides. Decks. Cheat sheets.


And here's what I've realized after 3 years of implementation work:

Frameworks are the START.
Systems are the SCALE.


Last week I built something I'm calling Atomic Claude Code.


It takes James Clear's Atomic Habits—one of the best frameworks ever created—and turns it into a living, breathing agentic system inside Claude Code.


Not a document I reference.
Not a poster on my wall.
Not a "note to self."


An actual system that:
→ Tracks my 1% daily improvements
→ Compounds learning over time
→ Holds me accountable without willpower


Here's the honest truth:
I've been teaching AI transformation for 3 years.
But the biggest unlock in the last 90 days?
Realizing I can BUILD the frameworks I teach INTO the AI systems I use.


The framework becomes the infrastructure.
The concept becomes the code.
The theory becomes the transformation.


NOTE: We're at an inflection point where the gap between "people who know frameworks" and "people who implement frameworks as systems" will determine who wins the next 12 months.


So here's my question:

Is this crossover from frameworks to agentic implementations something you're interested in?


Something you've already done?


Or something you're waiting to explore?


Drop your answer below—I'm genuinely curious where you are in this journey.

taplio

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The Battle

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