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.

I work on B2B LinkedIn GTM, combining branding and outreach systems to help teams turn visibility into pipeline.

Many B2B organizations are already active on LinkedIn. They publish content, build visibility, and see engagement.

However, that activity often doesn’t translate into consistent conversations or revenue impact.

In most cases, this comes down to two gaps:

- Limited buyer trust at the point of engagement
- Misalignment with where buyers are in their decision-making process.

My work focuses on addressing this by aligning three core elements:

- Authority and positioning - establishing a clear, credible point of view buyers recognise.
- LinkedIn-led GTM — coordinated inbound and outbound efforts that support each other. 
- Buyer intent signals — identifying when accounts are genuinely ready for conversation. 

Rather than optimizing for reach, the emphasis is on:
- Reaching the right accounts and stakeholders
- Enabling more relevant, warmer conversations
- Building a high-quality professional network that contributes to pipeline over time. 

I’ve worked with funded B2B SaaS founders (Series A–B), agencies, and enterprise B2B consulting teams / MNCs across APAC, the US, and Europe, supporting them in using LinkedIn as a structured, repeatable GTM channel.

If you’re looking to make LinkedIn more predictable and commercially aligned,
feel free to reach out.



Sunaina Ukil

I replaced Sales Navigator. Didn’t lose lead quality. Saved $1,188/year.

Here’s why:

Sales Nav gives you:
→ Filters
→ Public profiles
→ A fancy “save to list” button

That’s a $99/month search bar.

So I tried something else.

I searched in plain English: “Founders of high growth companies in the financial service industry who are based out of UK recently active ”

Results: Got 100s of leads without wasting hours on figuring which filter to use 

Repeatable. Consistent. $0.

Sales Nav thinks in filters.
Modern search thinks in intent. 

One gives you volume.
The other gives you buyers.

Comment “FREE” for access. 

P.S. A 10-person team spends ~$12k/year on Sales Nav.

PSS: For priority access.

Sunaina Ukil 

Helping B2B leadership teams turn LinkedIn into a pre-sales engine inside their GTM.

Age 18: went from a straight a student to an avg clg with English hons. 

Age 20: started selling imported cosmetics but had no clue about distribution, quit. 

Age 21: Graduated got my first job as an affiliate marketing. 

Age 22: took a course quit my job and started freelance and hustled as a beauty creator failed. 

Age 24: continued freelancing, launched a short freelancing bootcamp - hated the service bit - quit. 

Age 26: built 27k followers on linkedin, working as core team with GroundAI and building my linkedin agency with 4 clients.

I’ve had multiple “this is it” moments in life.

There are no shortcuts to wins, there’s always multiple this is it moments before things start compounding (only timelines differ).

What’s your take?

Here’s what meal prep taught me about B2B sales (it’s not even funny!).

The first was almost here, new year and new goals took over and one thing I realised since I started a week ago, is life just gets easier. 

But it takes a lot of planning, strategy and hoarding.. (all Monica traits!)

And while, I sharpened my cooking skills and dreamt of the perfect summer body, I noticed a few things, I didn’t before:

- your offer doesn’t always have to be the hero, sometimes the mint mayo is what clicks. 

- after the first week, when the repetition boredom clicks, your brain tricks you into stopping. 

-life gets easier when you measure, which seems tedious like weighing scale being the CRM. 

B2B sales can be a foodie’s foreplay … tedious, repetitive and boring but it works. 

So, do you meal prep or how do you hit the right protein spot?

PS: this breakfast spread at Flurys , is me bidding farewell to eating out, FOR sometime.

If I were to rewind to my 2025 self, I would ask myself to *not* go after leads and here’s why.

In Jan I had reached out to 200+ leads and the adrenaline rush was insane.. It was my first time with … automation and the Custom GPT mix that hit the right spot.

On paper, it looked like the perfect system to generate 1000s of dollars… but in reality it was just a bunch of fancy systems that sounded like a bot.

Hundreds of semi-warm leads, gone to waste.

Looking back, jumping in wasn’t the best decision - what would have shifted this was manual work.. 

(1) lower volume of outreach, less wastage and better focus on follow ups and urgency. 

(2) more room for testing, to switch and try different experiments with the same set of leads.

Automation might give you the illusion of volume and surface area but in reality, the only thing it does is leak your database. 

Learning when and how to test a system is what makes you golden. 

PS: what’s your biggest takeaway from last year? Comment below 👇🏼

She gets 10 leads a week from LinkedIn and that’s exactly why I wouldn’t copy her. Let me explain. 

Metrics can be convincing. 

They create the illusion that the method must be right but numbers don’t tell you what kind of reputation you’re building while you chase them.

I’ve seen strategies that are excellent at pulling replies and terrible at creating preference.

They rely on urgency, familiarity, and repetition. People respond because it feels easy, not because it feels necessary.

For a while, it works. Conversations happen. Calendars fill but ask this six months later: 

Would anyone struggle to replace you? If the answer is no, the strategy was never the asset.

There’s also the opposite camp.
People who refuse to play the game at all. 

They share ideas with depth and nuance, but no intention behind how they’re packaged or distributed. Their thinking is sharp. Their reach is not.

Both approaches stall.

One builds volume without gravity.
The other builds gravity without movement. The inflection point is knowing when to lean into each.

Your perspective needs structure.
Your structure needs intent.

When your content reflects how you think and how people actually consume, something changes.

You stop attracting attention and start attracting alignment.

If you’re building for the long arc, not just next week’s pipeline, then the question isn’t “how many leads did this bring?”

It’s “what am I training the market to associate with my name?” That distinction changes everything.

If you’re a founder or operator thinking about this shift, my DMs are open.

As a fresher, I made $12k a year working 10 hours a day, without a break.

In India, in 2021, for a remote role, that combination mattered more than the number itself. At the time, it felt like a rare kind of access. Not just to money, but to a different way of working.

On paper, the job was the perfect launch package. I had steady income, global exposure, and real responsibility early on. I learned how Facebook communities are built and maintained, how webinars actually convert, and how online businesses think about closing conversations rather than just collecting interest.

At 22, sitting in India, the idea that I could work remotely and earn in dollars was surreal.. It made the internet feel less theoretical and more operational.

A year in, something shifted.

It felt different, same things didn’t excite me anymore.. the learning curve flattened, and ambition has a way of noticing that before logic does.

So I pivoted to freelancing. My income dropped to roughly 60% of what I was making before, which on paper looked like a step backward. In practice, it felt like the right kind of friction.

The money changed, but the process became more engaging. I was learning faster, making more decisions, and owning the outcomes in a way I hadn’t before.

That first role was never meant to be permanent. It was a launch pad.

One message to anyone building here, early roles are not there to optimize for income. They’re there to compress learning.

Money keeps moving but the experience compounds with time :)

graphical user interface, text, application

It’s Jan 2, and I’ve seen more certainty about LinkedIn’s future than LinkedIn itself has shared publicly.

Which is interesting, considering how few people have actually read the 360Brew report end to end, and even then, the takeaway isn’t a prediction- more like a constraint. 

What the report hints at is that the system ranking your content is no longer a blunt algorithm optimizing for surface signals.

It’s a reasoning engine trained deeply on the professional world - 
job titles, seniority, skills, industries, and how people actually interact.

Which also means two uncomfortable things can be true at once:

1. The model understands nuance far better than most creators assume.

2. The system itself is in constant flux, because the infrastructure running it keeps evolving.

That combination makes “2026 hacks” a dead idea, because the engine underneath is being rebuilt continuously.

The most interesting part of the report is in the implication. 

Your content is being evaluated less like data and more like a document an expert is reading in context.

Which means:

- clarity beats cleverness
- coherence beats volume
… and generic content quietly loses, even if it performs for a week. 

If there’s a strategy hiding in all this, it’s definitely not predicting the future.

It’s about aligning with what stays stable: clear professional identity, precise language, and content that holds up when read closely.

Everything else is just commentary.

(I’ll link the full nerdy breakdown in the comments for anyone who wants to go deep ☕️)

Sunaina Ukil ✨

Helping B2B founders build a clear narrative so their LinkedIn content and outreach actually convert.

If you want to stay relevant as a startup read this: 

I work in 2 very fast paced roles in the startup ecosystem, one with startups as a part of socialsparkx.co and the other as a part of an early stage team at GroundAI..

Across both roles, I keep seeing the same problem show up in different disguises. 

Startups are becoming invisible while being “busy” and they don’t realise this until there’s pressure from the board for numbers or are at the brink of fund-raise. 

Founders think relevance comes from banger launches, new features and sponsoring events..but what truly keeps them relevant is: 

- Clear narrative across the team, if you don’t own your narrative someone else will. 

- Repitition of the message that actually clicks and resonates with their TA (can only be found when you put in the reps) 

- Being remembered for something specific - eg: as a sales tech tool is it your CRM, tool integration or scale? 

I see teams with category shifting products struggling to find clients, and smaller teams with average products win mindshare because their story travels. 

Relevance is not a PR blast, it’s a system of how often and clearly your audience sees you. 

If you’re scaling but feel invisible, start with the way you communicate. 

PS: will breakdown my framework in the next post, stay tuned. 

What’s your startup narrative, let’s chat in the comments!

As a growth experiment, I spent 10 hours last week doing one thing on LinkedIn. It changed how my feed, reach, and conversations work.

For some context.. I’ve been on this platform for 3 years. For most of that time, I had an engagement manager.

So, I grew..and climbed the ladder quick, but the community always felt.. “just managed”. This year, I decided to do it myself… and  last week, I spent 10 hours intentionally engaging.

These are my three cents: 

- Engagement styles are brand signals.. there is no such thing as a “good” comment length.

I’ve seen founders build visibility by:
→ Memes
→ Custom GIFs
→ One-liners
→ Thoughtful breakdowns

- Familiarity happens in comments. 

Comments create recognition. DMs create relationships like referrals, intros, and real conversations don’t happen randomly.  

They happen after repeated public touchpoints. My personal goal..5 new people every day. 

- Goal-oriented engagement is underrated

Most people engage reactively:
→ Whatever’s on their feed
→ Whatever’s on a list

It works.. but here’s the miss: Your engagement curates your feed. So engage with intent:

→ If the goal is connection: compliment, relate, explain
→ If the goal is attention: add value, entertain, share opinions
→ If the goal is conversion: wait it out

Dropping links in comment sections kills credibility faster than silence.

Low-key result:

My feed now serves me:
→ Job opportunities
→ Growth marketing insights
→ Founders actively raising

Used correctly, LinkedIn is a gold mine.

If you want to understand why this works from an algorithm perspective,
I’ll link the article below 👇🏼

taplio

The Battle

Hitesh Mahajan

Sunaina Ukil

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taplio

The Battle

Hitesh Mahajan

Sunaina Ukil

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Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Hitesh Mahajan's LinkedIn Performance

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