Product Manager | UI/UX Designer | passion for products

Transforming product management in the AI era. I specialize in telecom, automation, data analytics, workflow management, and AI agents.

My focus is on helping startup founders, CEOs, product managers, and VPs tackle challenges with a data-driven and automation-first mindset. By leveraging cutting-edge AI tools and agents, I guide teams to approach problem-solving from fresh angles.

The results speak for themselves. My clients consistently see a 30% boost in productivity, customer satisfaction, and acquisitions. This isn't by chance. It's the product of a carefully crafted methodology that combines the best of Agile, Lean, Design Thinking, Jobs-to-be-Done, and other proven frameworks.

I don't just apply these methods blindly. I tailor my approach to each unique situation, ensuring that every solution fits the specific needs of the business and its customers. From mapping user journeys to implementing Kanban systems, I provide end-to-end support that drives real, measurable change.

My expertise spans the entire product lifecycle. Whether you're at the ideation stage or looking to optimize an existing product, I can help you make data-informed decisions that lead to better outcomes.

If you're ready to revolutionize your approach to product management and see tangible improvements in your business metrics, let's connect. I'm here to help you navigate the complex world of AI-driven product development and turn your vision into reality.

Reach out to discuss how we can work together to elevate your product strategy and achieve remarkable results.

Your AI didn’t make you messy.
Your document setup did.

Last week I watched a smart PM “research” with Claude Code.

End result:
	•	7 markdown files
	•	3 PRDs
	•	2 decision notes
	•	1 half-used prompt
	•	0 clarity

Nothing was wrong.
Everything was too much.

That’s document sprawl — and AI accelerates it fast.

The quiet problem no one talks about

Claude Code is incredibly good at:
	•	explaining
	•	structuring
	•	expanding
	•	“just one more option”

But thinking ≠ shipping.

When every thought becomes a document,
your system collapses under its own intelligence.

The rule that fixed this for me

Claude can generate unlimited text.
I’m allowed to keep only what changes a decision.

Everything else is temporary.

A sprawl-free Claude Code setup (simple & brutal)

1️⃣ One permanent context doc
Only:
	•	problem
	•	constraints
	•	decisions

No research dumps. No history novels.

2️⃣ One running worklog
Daily:
	•	what changed
	•	what was decided
	•	what’s next

This replaces Slack archaeology and Notion graveyards.

3️⃣ One scratch space
Claude can think loudly here.
You delete or summarize it weekly.

No emotions attached.

4️⃣ One deliverables folder
If another human can’t use it next week — it doesn’t belong here.

Prompt tweaks that prevent sprawl

Instead of:
“Research this…”

Use:
“Start with a recommendation.
Give 3 reasons, 2 risks, and the next action.
Keep it under 200 words.”

Instead of:
“Explore all options…”

Use:
“Given these constraints, what are the 2 viable paths and trade-offs?”

And my favorite kill-switch:
“If this doesn’t change a decision, stop.”

The mindset shift

Stop treating AI like:

a research assistant

Start treating it like:

a consultant you pay per outcome

If a doc wouldn’t be worth paying for —
it doesn’t deserve to live.

Docs are not knowledge.
Docs are interfaces for decisions.

Clean inputs → clean thinking → clean shipping.

Curious —
what’s your biggest document sprawl right now?
(Notion, Google Docs, or Markdown chaos? 😄)

Packia lakshmi Maharajan ✨

Transforming Telecom with Gen AI-Driven Product Innovation || Product Management || Product Designer || AI Enthusiast || Data Analyst

Another big update from Claude ! 

Claude cowork ! Ultimately you can give full control to your system to Claude , i see mind blowing explainer videos 

https://lnkd.in/g8zH5dgN


#claude #claudecode

The network didn’t fail.
It drifted.

Latency crept up slowly.
Packet loss appeared only at peak hours.
A few cells behaved oddly.

Nothing crossed a hard threshold.
So everyone said the same thing:

“Let’s monitor.”

And the network kept drifting.

This is how most telecom issues start.
Not as outages.
As weak signals spread across layers.

Humans are good at spotting failures.
We’re bad at spotting slow change.

That’s where AI actually helps.

AI doesn’t wait for alarms to scream.
It connects patterns early:
• regions + recent changes
• KPIs that move together
• behavior that degrades quietly

Not certainty.
Early confidence.

The teams that recover fastest
don’t wait for things to break.

They act while the system
is still whispering.

Today’s Thought

Technology didn’t just make work faster.
It made iteration cheaper.

What once needed approval, resources, and weeks
now needs clarity and a first step.

The people moving ahead aren’t the ones with perfect plans.
They’re the ones who test early, learn fast, and adjust calmly.

Progress today is less about certainty
and more about curiosity.

Build → observe → refine.
That loop beats overthinking every time.

What’s one small thing you can test today?

So… I almost built a new feature.

And if you’re a Product Manager, you already know what that means.
It wasn’t because users asked for it clearly.
It wasn’t because we had strong data.

It was because I felt uncomfortable.

Users were saying things like,
“Hmm… this is a bit confusing.”
Not angry.
Not blocked.
Just… confused enough to make me nervous.

And when PMs get nervous, we don’t ask questions.
We add things.

My brain immediately went into roadmap mode:
“Okay, what if we add a new module?”
“Maybe a new flow?”
“We can clean this up with another screen.”

I didn’t say it out loud,
but somewhere inside me was that thought:
If we build something new, at least it looks like progress.

So I opened my laptop, ready to ask the agent for mock-ups.

Then I paused.
Not a dramatic pause.
More like that awkward moment where you stare at the screen and think,
“Wait… am I actually solving this?”

Instead of asking AI to build, I asked it to think with me.

I typed:
“Show me five different ways this data could be understood.”

That’s it.
No UI.
No requirements.
No acceptance criteria.

AI did what AI does best.
It didn’t give me answers.
It gave me possibilities.

A timeline view.
Which looked impressive, but felt like homework.

A hierarchy view.
Very structured. Also very slow.

A flow diagram that somehow had arrows pointing back to themselves.

Buckets.
Lots of buckets.
Everyone loves buckets until they have to explain them.

And then one option that felt… quiet.
A simple matrix.

Nothing fancy.
No clever interaction.
Just a layout that made the data easier to compare.

That’s when it clicked.

The problem was never missing functionality.
It was that we were making users think too hard before they could decide.

So I did the most PM-ish thing possible.

I didn’t pick one idea.
I stole small parts from a few.
I dropped anything that needed explanation.
And I kept asking myself one question:

“Would a user understand this in five seconds without a walkthrough?”

If the answer was no, it was gone.

No new backend work.
No new feature flag.
No sprint planning drama.

Just a cleaner way to look at what we already had.

And here’s the strange part.

After we shipped it:
	•	Fewer questions came in
	•	Decisions happened faster
	•	Nobody asked, “Can we add another feature?”

Which, honestly, is the loudest compliment a PM can get.

So yeah… I didn’t out-build the problem.
I out-removed it.

What I learned:

AI is incredible at going wide.
PMs still matter when it’s time to go narrow.

Not by adding more.
But by choosing what not to build.

And that’s it.
No punchline.
Just a lesson I had to learn the slow way.

Prompt  engineering patten that actually matters !

Last year, this would’ve been a long night for a PM.

The roadmap review was in the morning.
Stakeholders wanted clarity.
Engineering wanted certainty.
And I had… fragments.

Half-formed ideas.
Loose Jira notes.
A few risks I felt but couldn’t fully articulate yet.

In the old PM world, this is where panic quietly sets in.

You start stitching slides.
Guessing dependencies.
Hoping no one asks that question.

This time, I did something different.

I opened my notes
and started explaining the problem — not cleanly, not perfectly — just honestly.

What’s the user pain.
Where the system breaks.
What we think the constraint is.

No formatting. No polish.

Something interesting happened.

The thinking came back clearer.

Trade-offs surfaced.
Hidden dependencies showed up.
The “small feature” revealed itself as a roadmap fork.

Not because AI decided anything.

But because it reflected my thinking back — structured.

That’s when it clicked.

AI isn’t a PM replacement.
It’s not a roadmap owner.
It’s not a decision-maker.

It’s the space where PM thinking gets sharpened faster.

The real shift for PMs isn’t speed.
It’s confidence.

Confidence to:
	•	explore ideas before they’re ready
	•	pressure-test assumptions early
	•	walk into meetings already aligned with reality

Roadmaps stop being promises.
PRDs stop being heavy.
Decisions stop being delayed by ambiguity.

This isn’t PM vs AI.

It’s PM intuition × AI clarity.

And the PMs who’ll thrive aren’t the ones chasing tools.

They’re the ones who learn how to think out loud with machines
before they ever speak in a meeting.

Question:
What part of your PM thinking do you still keep locked in your head —
when it could be explored faster out loud?

The night the network didn’t wake anyone up.

At 2:17 AM, a network engineer used to get the call.
 • Traffic dipping.
 • Energy spiking.
 • Complaints loading.
Dashboards → alarms → tickets → guesswork.

Except this time, the call never came.
Because the network already acted.
That’s what Verizon quietly changed.
They didn’t deploy AI for reports.
They put AI inside the live network.

𝗔𝗜 𝗻𝗼𝘄:

Watches real-time signals
Adjusts power based on demand
Optimizes performance before degradation shows up
No ticket.
No escalation.
No human waiting to react.

This isn’t GenAI explaining what broke.
This is agentic AI doing something telcos rarely allow:
acting first.

For years, telco AI meant:
“Help me analyze the outage.”

Now it means:
“Prevent it from happening.”

That shift matters more than any model upgrade.

Because the hardest part of AI in telecom isn’t accuracy.
It’s trust.
Trusting machines with:
 • Energy controls
 • Network behavior
 • Decisions that used to require approval chains

Operator didn’t remove humans. 
They moved them out of the way.
The future network won’t be AI-assisted.
It’ll be AI-operated — with humans supervising, not firefighting.

That 2:17 AM call?
It’s already disappearing.

Read the Full article 

https://lnkd.in/gt3bAJdZ

#AIOPS #viavi

Yesterday I opened my project folder and genuinely laughed.
Not because something shipped.

Because my AI agent had created a small city of Markdown files:
 • problem.md
 • context.md
 • user_stories.md
 • edge_cases.md
 • options.md
 • risks.md
 • architecture.md
 • final_v2_REAL_FINAL.md

It looked… responsible. Mature. “Product-led.”
But then I noticed something scary.

I had more documentation than decisions.
And my brain was doing this toxic loop:
Open doc
Feel productive
Find another doc
“Let me read this too”
Close laptop with zero commitment

AI wasn’t slowing me down.
Reading was.

Because every new doc adds a new job:
“Now reconcile this with the other 7 files.”

That’s not strategy.
That’s mental debt.

So I changed one thing, and it fixed everything:

I stopped asking agents to write docs.
I asked them to write decision receipts.
Now every doc must end with a stamp like this:

Decision Needed: one sentence
Options: A / B / C
Recommendation: pick one
Why: 3 bullets
Risks: 3 bullets
What would change my mind: 2 bullets
Owner + Date: who decides, by when

If the agent can’t fill that…
the doc is not “done.”
It’s just beautiful noise.

And the funniest part?

Once I forced a decision stamp, the number of files dropped.
And the speed of shipping went up.

Because in the AI world:

You’re not paid for documentation.
You’re paid for clarity under uncertainty.

Anyone else dealing with “Markdown explosion”?
How are you keeping decisions crisp?

I stopped blaming AI agents for “misunderstanding” my PRDs.

They weren’t confused.
They were following my instructions perfectly.

That’s when I realized something uncomfortable 👇

PRDs need to be written so agents can’t misunderstand them.

Here are 5 quick tips that actually work:


1️⃣ Write behavior, not intention

❌ “Make the widget flexible”
✅ “When Region changes → reset Devices → reload data”

If it can be interpreted, it will be.

2️⃣ Assume zero context

Write like:
	•	No meetings happened
	•	No follow-up questions allowed

Because agents read PRDs exactly like that.

3️⃣ Replace adjectives with structure

Agents don’t understand:
“clean”, “optimized”, “intuitive”

They understand:
lists, limits, formats.

4️⃣ Acceptance should be pass/fail

If someone can’t say yes or no in 5 seconds,
rewrite the acceptance.

Hope is not a requirement.

5️⃣ Constraints are kindness

Max limits.
Defaults.
What NOT to assume.

Boundaries make execution faster.

The shift that changed everything

PRDs are no longer just for humans.
They are instructions for machines.

Write them that way —
humans will understand them even better.

👇 What’s one PRD line you’ve written that sounded right but broke later?

Here are 3 prompt tweaks that make AI more useful:

1️⃣ Start with context, not tasks
Bad: “Give recommendations.”
Better:

“Here’s the situation, constraints, and what we’ve tried so far…”

2️⃣ Ask for structure before answers
Bad: “What should we do?”
Better:

“Break this into options, risks, and trade-offs.”

3️⃣ Use AI to challenge you, not agree with you
Bad: “Validate this idea.”
Better:

“What assumptions could make this fail?”

That’s the shift most people miss.

AI isn’t there to decide for you.
It’s there to sharpen your thinking before you decide.

When the prompt improves, the thinking improves.

What’s one prompt tweak that changed how you work?

“Coding is dead.”

Not really.

What’s dead:
• boilerplate
• syntax memorization
• low-leverage implementation

What matters now:
• problem clarity
• system design and scalablity
• trade-offs
• ownership

For Engineers
AI writes code.
You decide if it works, scales, and survives reality.

For PMs
AI accelerates delivery.
You decide what’s worth building.

For Founders
AI lowers cost.
You decide direction, timing, and risk.

The shift is simple:
typing → thinking
execution → intent

Coding didn’t die.
Unclear thinking did.

The winners won’t be the fastest typers.
They’ll be the clearest thinkers.

AI is learning from us.
And unknowingly… we’re learning from AI.

Here’s something subtle but fascinating.

Recent research that analyzed millions of words from podcasts before and after ChatGPT went mainstream found a shift in how people speak.

Certain words and expressions started appearing more often.
Not because humans suddenly changed overnight , but because those words were commonly used by AI models.

In other words 👇
Language didn’t just flow into AI.
It flowed back into us.

We didn’t “decide” to copy AI.
It happened naturally:
	•	We read AI responses
	•	We internalized the structure
	•	We reused the phrasing
	•	We changed how we express ideas

That’s the real shift most people miss.

AI isn’t just a tool we operate.
It’s a system we interact with, repeatedly —
and interaction always reshapes both sides.

This isn’t about losing originality.
It’s about co-evolution.

Humans shape AI with data.
AI reshapes humans through exposure.

Not consciously.
But consistently.

The future isn’t human vs machine language.
It’s a shared one.

What’s one word, phrase, or thinking pattern you’ve noticed yourself using more since working with AI?

#AI #HumanAI #Language #Learning #ProductThinking #FutureOfWork #LLM #CoIntelligence

I write PRDs at the end—because I hate rewriting lies.

9:30 AM: I open my notes and do something that looks messy.
I create multiple solutions at once.

Not because I’m confused.
Because I’ve learned one thing:

When you fall in love with the first idea, you stop seeing reality.

By noon, I’m comparing options like it’s a tournament:
Fastest impact. Lowest risk. Cleanest UX. Cheapest to maintain.

Then I mock it up—rough and ugly—just to see the truth.
And every time, the mockup reveals the trapdoors:
permissions, empty states, filter collisions, “what if user does X first?”

Only after that… I write the PRD.
Because now it’s not a fantasy document.

It’s a decision that survived the day.

What’s your order: ideas → mockups → PRD or PRD → everything else?

TM Forum didn’t appear overnight.
It emerged in 1988, when telecom operators were running
 multi-vendor networks with limited interoperability.
Over time, TM Forum introduced:
 • shared process models
 • common data definitions
 • application classification frameworks
 • standardized APIs
 • and a reference digital architecture
Across decades of change, one theme has remained consistent:
alignment before automation.

TMF evolution 

A quiet telco planning moment.

No buzzwords.
No future slides.
Just a simple question on the table:

“If we touch this part of the network… what else changes?”

No one answered immediately.

Not because people didn’t know the network.
But because everyone knew a different part of it.

One team thought about capacity.
Another thought about routing.
Another remembered a similar change from years ago.

All valid.
All partial.

Earlier, this is where planning slowed down.

Whiteboards filled.
Assumptions crept in.
The safest option usually won.

This time, the approach was calmer.

We looked at a single network view —
not perfect, not predictive —
just what exists today.

Nodes.
Links.
Traffic paths.

We changed one thing.
And watched what moved.

Not to decide yet.
Just to understand.

A small adjustment showed effects further away than expected.
Nothing broke.
But the picture widened.

That was the value.

Not a forecast.
Not a promise about 6G.

Just fewer surprises later.

Digital twins sound futuristic.
In practice, they’re quiet tools.

They don’t tell you what to do.
They show you what you’re touching.

For a telco PM, that changes the job.

Less guessing.
Less defending decisions later.
More confidence before saying “yes.”

The future will come when it comes.

But seeing the network clearly today
is what makes tomorrow manageable.

Question:
Where do you still rely on experience alone—
because the full picture isn’t easy to see?

Yesterday, I didn’t write a PRD.
I had a conversation.

I was about to start a new feature.

You know the routine:
	•	Open a doc
	•	Write objectives
	•	Add acceptance criteria
	•	Sprinkle some “non-functional requirements”
	•	Call it progress

Instead, I did something different.

I explained the problem to an AI agent like I would to a junior PM sitting next to me.

Not tasks.
Not instructions.
Just context.

“Here’s what users are struggling with.
Here’s what we’ve already tried.
Here’s what cannot break.
And here’s what success feels like, not just what it measures.”

Then I asked one question:

“What am I missing?”

The response didn’t give me answers.
It gave me angles:
	•	Edge cases I ignored
	•	Assumptions I didn’t realize I was making
	•	Trade-offs I hadn’t written down yet

That’s when it clicked.

AI isn’t replacing PM thinking.
It’s forcing us to think more clearly.

The quality of output didn’t change because the model got smarter.
It changed because my explanation got better.

👉 The real skill now isn’t “prompting.”
👉 It’s articulating problems without hiding behind templates.

If you can explain a problem clearly to an AI,
you can explain it clearly to:
	•	engineers
	•	designers
	•	stakeholders
	•	and yourself

That’s the new PM leverage.

Tomorrow, before you open a PRD—
try having a conversation instead.

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