As an AI Product Growth Leader, I’m on a mission to revolutionize how businesses harness data and AI to drive innovation, accelerate growth, and scale impact. I lead with a strategic vision: to make data collaboration seamless, AI adoption intuitive, and business-centric innovation scalable.

At @DataGOL, I drive growth initiatives that position us as the go-to solution for intelligent business enablement. From BusinessOS to AI Agents and Self-Serve Analytics, I build intelligent experience engines that empower business users—not just data teams—to make faster, smarter decisions.

With 20+ years across enterprise SaaS, data platforms, and AI/ML products, I operate at the intersection of strategy, technology, and customer-centricity. My passion lies in translating complex capabilities into simple, scalable value—whether that’s through a game-changing product, a repeatable growth engine, or a transformative AI initiative.

I don’t chase hype. I build real-world, value-driven solutions that solve real business problems.

🤝 Let’s connect if you’re serious about using AI and data to redefine what’s possible for your business.

Vinod SP

𝗙𝗶𝘅 𝘁𝗿𝘂𝘀𝘁 𝗳𝗶𝗿𝘀𝘁, 𝗻𝗼𝘁 𝗱𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱𝘀. 𝗧𝗵𝗮𝘁’𝘀 𝗵𝗼𝘄 𝘆𝗼𝘂 𝗺𝗮𝗸𝗲 𝗱𝗮𝘁𝗮 𝘄𝗼𝗿𝗸 𝗳𝗼𝗿 𝗲𝘃𝗲𝗿𝘆𝗼𝗻𝗲.

A new Head of Data walks in.

𝗧𝗵𝗲 𝗳𝗶𝗿𝘀𝘁 𝟵𝟬 𝗱𝗮𝘆𝘀 𝗮𝗿𝗲 𝗮 𝘁𝗲𝘀𝘁.

Many start with dashboards, pipelines, and plans. They rebuild what’s broken and expect trust to follow.

𝗕𝘂𝘁, 𝗺𝗼𝘀𝘁 𝗳𝗮𝗶𝗹.

They forget that trust, not tools, is the real foundation. You can fix every schema and still have leaders asking, “Why are we still in this mess?”

𝗛𝗲𝗿𝗲’𝘀 𝘄𝗵𝗮𝘁 𝘄𝗼𝗿𝗸𝘀:

𝗣𝗵𝗮𝘀𝗲 𝟭: 𝗗𝗶𝗮𝗴𝗻𝗼𝘀𝗲, 𝗗𝗼𝗻’𝘁 𝗗𝗲𝗹𝗶𝘃𝗲𝗿.

Meet every key person. Ask what data they trust. Listen to real pain, not just reports. Find your “data superusers.” See where data dies before it reaches the decision.

𝗣𝗵𝗮𝘀𝗲 𝟮: 𝗔𝗹𝗶𝗴𝗻 𝗮𝗻𝗱 𝗗𝗲𝘀𝗶𝗴𝗻.

Prioritize quick wins. Rank by impact, complexity, reach, and risk. Set clear ownership for metrics. Share updates every week.

𝗣𝗵𝗮𝘀𝗲 𝟯: 𝗗𝗲𝗹𝗶𝘃𝗲𝗿 𝗣𝗿𝗼𝗼𝗳, 𝗡𝗼𝘁 𝗣𝗿𝗼𝗺𝗶𝘀𝗲𝘀.

Pick the highest priority. Deliver one visible win in 30-45 days. Align on definitions so everyone speaks the same language. Over communicate wins and issues.

𝗔𝘃𝗼𝗶𝗱 𝘁𝗵𝗲𝘀𝗲 𝘁𝗿𝗮𝗽𝘀:

• Don’t rush to buy new tools.
• Don’t rebuild dashboards before fixing trust.
• Don’t promise AI if you have ten definitions of revenue.

The first 90 days decide if data drives growth or stays a reporting chore.

𝗜𝗳 𝘆𝗼𝘂𝗿 𝗖𝗙𝗢 𝘀𝘁𝗶𝗹𝗹 𝗱𝗼𝗲𝘀𝗻’𝘁 𝗯𝗲𝗹𝗶𝗲𝘃𝗲 𝘁𝗵𝗲 𝗻𝘂𝗺𝗯𝗲𝗿𝘀 𝗯𝘆 𝗗𝗮𝘆 𝟵𝟬, 𝗻𝗼𝘁𝗵𝗶𝗻𝗴 𝗲𝗹𝘀𝗲 𝗺𝗮𝘁𝘁𝗲𝗿𝘀.

Trust comes first.

Visible wins come next.

𝗧𝗵𝗮𝘁’𝘀 𝗵𝗼𝘄 𝘆𝗼𝘂 𝘀𝘁𝗼𝗽 𝗯𝗲𝗶𝗻𝗴 “𝘁𝗵𝗲 𝗱𝗮𝘁𝗮 𝗽𝗲𝗿𝘀𝗼𝗻” 𝗮𝗻𝗱 𝗯𝗲𝗰𝗼𝗺𝗲 𝘁𝗵𝗲 𝗽𝗲𝗿𝘀𝗼𝗻 𝘄𝗵𝗼 𝗺𝗮𝗸𝗲𝘀 𝗱𝗮𝘁𝗮 𝘄𝗼𝗿𝗸.

𝗛𝗼𝘄 𝗮𝗿𝗲 𝘆𝗼𝘂 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝘁𝗿𝘂𝘀𝘁 𝗶𝗻 𝘆𝗼𝘂𝗿 𝗱𝗮𝘁𝗮 𝘁𝗲𝗮𝗺𝘀?

Building AI Agents that are powerful enough to run your business @DataGOL | Ex-Meta | AI Product Builder | Chief Data & AI officer | Harvard Business School

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𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗪𝗶𝘁𝗵𝗼𝘂𝘁 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁𝘀

I've had the same conversation with 3 different CFOs this month.

They all said some version of this: 

"My finance team just ran their own margin analysis. Without the data team. In 30 seconds."

🔁 𝗧𝘄𝗼 𝗳𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀. 𝗦𝗮𝗺𝗲 𝗱𝗮𝘁𝗮. 𝗭𝗲𝗿𝗼 𝗰𝗼𝗼𝗿𝗱𝗶𝗻𝗮𝘁𝗶𝗼𝗻. 𝗧𝗵𝗶𝘀 𝗶𝘀𝗻'𝘁 𝗮 𝗰𝗼𝗺𝗽𝗹𝗮𝗶𝗻𝘁. 𝗜𝘁'𝘀 𝗮 𝘀𝗶𝗴𝗻𝗮𝗹.

"𝗗𝗶𝗮𝗴𝗻𝗼𝘀𝘁𝗶𝗰 𝗙𝗶𝗻𝗮𝗻𝗰𝗲" becomes a standard function and the data < >finance wall collapses.

𝗛𝗲𝗿𝗲'𝘀 𝘄𝗵𝗮𝘁'𝘀 𝗱𝗿𝗶𝘃𝗶𝗻𝗴 𝘁𝗵𝗶𝘀:

The era of descriptive dashboards is ending. AI agents are forcing the shift to diagnostic analytics.

𝗗𝗲𝘀𝗰𝗿𝗶𝗽𝘁𝗶𝘃𝗲: "Revenue was $2.3M in May"

𝗗𝗶𝗮𝗴𝗻𝗼𝘀𝘁𝗶𝗰: "Revenue spiked because Product X drove 47% of new bookings, but COGS increased 23% due to supplier changes"

One tells you the score. The other tells you how to change the game.

𝗔𝗻𝗱 𝘁𝗵𝗲 𝗲𝘃𝗶𝗱𝗲𝗻𝗰𝗲 𝗶𝘀 𝗲𝗺𝗲𝗿𝗴𝗶𝗻𝗴:

→ DataGOL launches a "𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗔𝗴𝗲𝗻𝘁" that confirms account ranges with CFOs before calculating margins (human-in-the-loop, not black box)

→ Finance teams moving from "what happened" to "why it happened" without writing SQL

→ The "golden layer" (clean, standardized data) becoming the moat not the dashboard itself

𝗖𝗮𝘁𝗲𝗴𝗼𝗿𝗶𝗲𝘀 𝘄𝗶𝗹𝗹 𝗺𝗲𝗿𝗴𝗲:

• 𝗕𝗜 𝗱𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱𝘀 → AI-driven diagnostic layers

• 𝗔𝗱-𝗵𝗼𝗰 𝗱𝗮𝘁𝗮 𝗿𝗲𝗾𝘂𝗲𝘀𝘁𝘀 → Conversational agents

• 𝗠𝗼𝗻𝘁𝗵𝗹𝘆 𝗿𝗲𝗽𝗼𝗿𝘁𝗶𝗻𝗴 𝗰𝘆𝗰𝗹𝗲𝘀 → Real-time variance investigation

𝗧𝗵𝗲 𝗻𝗲𝘅𝘁 𝗹𝗼𝗰𝗸-𝗶𝗻 𝘄𝗼𝗻'𝘁 𝗯𝗲 𝘆𝗼𝘂𝗿 𝗱𝗮𝘁𝗮 𝘄𝗮𝗿𝗲𝗵𝗼𝘂𝘀𝗲.

→ It'll be your diagnostic intelligence layer.

→ The teams that own this conversation will have a seat at the board table.

→ The ones still building dashboards that answer "𝘄𝗵𝗮𝘁" will keep wondering why no one looks at them.

Are you still answering "𝘄𝗵𝗮𝘁 𝗵𝗮𝗽𝗽𝗲𝗻𝗲𝗱" while your board wants to know "𝘄𝗵𝘆"?.

Check out DataGOL DataScience Agent in the comment.

𝟮𝟬𝟮𝟲 𝘄𝗼𝗻’𝘁 𝗿𝗲𝘄𝗮𝗿𝗱 𝘁𝗵𝗲 𝗳𝗹𝗮𝘀𝗵𝗶𝗲𝘀𝘁 𝗔𝗜.

It’ll reward the AI that survives the audit: 𝗥𝗢𝗜, 𝗿𝗲𝗹𝗶𝗮𝗯𝗶𝗹𝗶𝘁𝘆, 𝗮𝗻𝗱 𝘀𝗰𝗮𝗹𝗲 inside real teams, real systems, with real guardrails.

𝗠𝗼𝘀𝘁 “𝗔𝗜 𝘄𝗶𝗻𝘀” 𝗜 𝘀𝗲𝗲 𝗿𝗶𝗴𝗵𝘁 𝗻𝗼𝘄 𝗹𝗼𝗼𝗸 𝗹𝗶𝗸𝗲 𝘁𝗵𝗶𝘀:
✅ a pilot that generates
❌ a workplace that can’t metabolize the output
So the org gets buried under polished drafts… and nobody’s sure what’s true, what’s safe, or what’s worth acting on.

𝗧𝗵𝗲 𝟮𝟬𝟮𝟲 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗔𝗜 𝗮𝗴𝗲𝗻𝗱𝗮 𝗶𝗻 𝟯 𝗺𝗼𝘃𝗲𝘀

𝗖𝘂𝘁 𝘁𝗵𝗲 𝘀𝗹𝗼𝗽 𝗮𝘁 𝘁𝗵𝗲 𝘀𝗼𝘂𝗿𝗰𝗲 → retire bad routines before AI scales them.

𝗧𝗿𝗲𝗮𝘁 𝗮𝗴𝗲𝗻𝘁𝘀 𝗹𝗶𝗸𝗲 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝘀𝗼𝗳𝘁𝘄𝗮𝗿𝗲 → permissions, monitoring, compliance: non-negotiable.

𝗕𝘂𝗶𝗹𝗱 𝘆𝗼𝘂𝗿 𝗺𝗼𝗮𝘁 𝗶𝗻 𝘄𝗵𝗮𝘁 𝗰𝗮𝗻’𝘁 𝗯𝗲 𝗰𝗼𝗽𝗶𝗲𝗱 → proprietary data + human expertise + intentional creativity.

The “𝗔𝗜 𝗳𝘂𝘁𝘂𝗿𝗲” isn’t less human. 𝗜𝘁’𝘀 𝗺𝗼𝗿𝗲 𝗵𝘂𝗺𝗮𝗻 because:

• Creativity becomes scarce advantage when output is frictionless.

• Human expertise gets pricier when AI sounds confident about everything.

• And if AI smooths away the messy debate, you lose the work trails that keep teams aligned.

If your org gets judged on measurable value in 2026…
𝘄𝗵𝗮𝘁 𝗮𝗿𝗲 𝘆𝗼𝘂 𝘀𝘂𝗻𝘀𝗲𝘁𝘁𝗶𝗻𝗴 𝗳𝗶𝗿𝘀𝘁: meetings, workflows, reports, approvals or “AI-generated busywork”?

𝗧𝗵𝗲 𝗦𝗲𝗰𝗿𝗲𝘁 𝗦𝘂𝗽𝗲𝗿𝗽𝗼𝘄𝗲𝗿: 𝗪𝗵𝘆 𝗛𝗮𝗯𝗶𝘁𝘀 𝗕𝗲𝗮𝘁 𝗪𝗶𝗹𝗹𝗽𝗼𝘄𝗲𝗿 𝗘𝘃𝗲𝗿𝘆 𝗧𝗶𝗺𝗲! 🔥

Ever wonder why it's SO HARD to stick with something new? The answer is simpler than you think! 😊

Visuals by @Pejman Milani

Look at willpower like a candle - it starts strong but eventually burns out. We've all been there... super excited on day 1, kinda tired by day 3, and completely giving up by day 7! 🕯️
But habits? They're like an endless energy source! ⚡
The science is clear:

Willpower runs out (just like your phone battery)
Habits run on autopilot (like breathing!)
Small daily actions > big one-time efforts

What's ONE tiny habit you could start today? Maybe drinking a glass of water when you wake up? Or spending 5 minutes reading before bed?

Remember: Consistency beats intensity! Build those habit flames and watch them power your success! 🚀

hashtag#HabitHacks

𝟭𝟭 𝗟𝗲𝗮𝗱𝗲𝗿𝘀𝗵𝗶𝗽 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀 𝗧𝗵𝗮𝘁 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗲𝗱 𝗠𝘆 𝗝𝗼𝘂𝗿𝗻𝗲𝘆 🚀

Ever wonder why some leaders effortlessly inspire while others struggle despite the fancy title?

Visuals from @Pejman Milani

I've distilled my leadership journey into 11 visual frameworks that changed my approach. (#10 was my personal game-changer!)

Three Core Leadership Principles:
✅ Today's hard choices create tomorrow's success
✅ Authority comes from action, not position
✅ Clear thinking creates clear direction

𝗠𝘆 𝟭𝟭 𝗹𝗲𝗮𝗱𝗲𝗿𝘀𝗵𝗶𝗽 𝘃𝗶𝘀𝘂𝗮𝗹𝘀 𝗱𝗲𝗰𝗼𝗱𝗲𝗱:
1️⃣ 𝗬𝗼𝘂𝗿 𝘁𝗶𝘁𝗹𝗲 𝗱𝗼𝗲𝘀𝗻'𝘁 𝗺𝗮𝗸𝗲 𝘆𝗼𝘂 𝗮 𝗹𝗲𝗮𝗱𝗲𝗿
Leadership is earned daily through consistent actions that build trust, not handed out with business cards.
2️⃣ 𝗪𝗿𝗶𝘁𝗶𝗻𝗴 𝗺𝗮𝗸𝗲𝘀 𝘃𝗮𝗴𝘂𝗲 𝗶𝗱𝗲𝗮𝘀 𝗰𝗹𝗲𝗮𝗿
Put thoughts on paper and watch how quickly the gaps appear! Clear documentation transforms good thinking into great execution.
3️⃣ 𝗠𝗼𝘀𝘁 𝗼𝘃𝗲𝗿𝗻𝗶𝗴𝗵𝘁 𝘀𝘂𝗰𝗰𝗲𝘀𝘀 𝗶𝘀 𝘆𝗲𝗮𝗿𝘀 𝗶𝗻 𝘁𝗵𝗲 𝗺𝗮𝗸𝗶𝗻𝗴
Great teams aren't born—they're methodically built through consistent effort.
4️⃣ 𝗠𝗶𝘀𝘁𝗮𝗸𝗲𝘀 𝗮𝗿𝗲 𝗶𝗻𝗲𝘃𝗶𝘁𝗮𝗯𝗹𝗲. 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗳𝗿𝗼𝗺 𝘁𝗵𝗲𝗺 𝗶𝘀 𝗮 𝗰𝗵𝗼𝗶𝗰𝗲
Transform failures into frameworks for future success through systematic learning.
5️⃣ 𝗛𝗮𝗿𝗱 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀, 𝗲𝗮𝘀𝘆 𝗹𝗶𝗳𝗲. 𝗘𝗮𝘀𝘆 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀, 𝗵𝗮𝗿𝗱 𝗹𝗶𝗳𝗲
The courage to make difficult choices today prevents bigger crises tomorrow.
6️⃣ 𝗙𝗲𝗮𝗿 𝗶𝘀 𝗯𝗶𝗴𝗴𝗲𝗿 𝗶𝗻 𝘆𝗼𝘂𝗿 𝗺𝗶𝗻𝗱 𝘁𝗵𝗮𝗻 𝗿𝗲𝗮𝗹𝗶𝘁𝘆
Action is the antidote to anxiety. Start before you feel ready—momentum creates clarity.
7️⃣ 𝗠𝗮𝗸𝗲 𝗯𝗲𝘁𝘁𝗲𝗿 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀 𝗯𝘆 𝗿𝗲𝘃𝗶𝘀𝗶𝘁𝗶𝗻𝗴 𝘁𝗵𝗲𝗺
Yesterday's perfect solution needs today's fresh perspective.
8️⃣ 𝗖𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝗰𝘆 𝗰𝗼𝗺𝗲𝘀 𝗶𝗻 𝗺𝗮𝗻𝘆 𝗳𝗼𝗿𝗺𝘀
Progress rarely follows a neat path. Small, sustainable steps consistently taken beat sporadic giant leaps.
9️⃣ 𝗕𝘂𝗿𝗱𝗲𝗻 𝗼𝗳 𝗱𝗶𝘀𝗰𝗶𝗽𝗹𝗶𝗻𝗲 𝗶𝘀 𝗴𝗿𝗲𝗮𝘁𝗲𝗿 𝘁𝗵𝗮𝗻 𝗕𝘂𝗿𝗱𝗲𝗻 𝗼𝗳 𝗿𝗲𝗴𝗿𝗲𝘁
Today's effort prevents tomorrow's pain. Disciplined processes create freedom, not restriction.
🔟 𝗬𝗼𝘂 𝗰𝗮𝗻 𝗵𝗮𝘃𝗲 𝗮𝗻𝘆𝘁𝗵𝗶𝗻𝗴 𝗯𝘂𝘁 𝗻𝗼𝘁 𝗲𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴
Focus beats fragmentation. Choosing what to ignore is as strategic as choosing what to pursue.
1️⃣1️⃣ 𝗔 𝗹𝗲𝗮𝗱𝗲𝗿 𝗸𝗻𝗼𝘄𝘀 𝘁𝗵𝗲 𝘄𝗮𝘆 𝗮𝗻𝗱 𝘀𝗵𝗼𝘄𝘀 𝘁𝗵𝗲 𝘄𝗮𝘆
Vision without action is just daydreaming. Your example speaks louder than any mission statement.

Which framework resonates most with your current challenges? I'd love to hear which one hits closest to home!
♻️ Repost this to help your network level up their leadership toolkit!

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𝟭𝟬 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝗼𝗻𝘀 𝗧𝗵𝗮𝘁 𝗪𝗶𝗹𝗹 𝗦𝗲𝘁 𝘁𝗵𝗲 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗔𝗜 𝗔𝗴𝗲𝗻𝗱𝗮 𝗶𝗻 𝟮𝟬𝟮𝟲

We are done playing with AI.

In 2026, AI agents enter work. Quiet. Deep. For real. 

I see 10 big shifts coming for enterprise and mid-market teams.

Here is my view:

1️⃣ 𝗔𝗴𝗲𝗻𝘁𝘀 𝘀𝘁𝗮𝗿𝘁 𝘁𝗼 𝘄𝗼𝗿𝗸
Not chatbots.
They read, write, click, move data, trigger flows.
They act in tools you use now.
Your team starts to manage agents, not chats.

2️⃣ 𝗣𝗶𝗹𝗼𝘁 𝗽𝘂𝗿𝗴𝗮𝘁𝗼𝗿𝘆 𝗲𝗻𝗱𝘀
No more “cool demo” with no owner.
CFO asks one question:
→ Where is ROI in 90 days?
If you show value, you scale.
If not, budget dies. Fast.

3️⃣ 𝗗𝗮𝘁𝗮 𝗯𝗲𝗰𝗼𝗺𝗲𝘀 𝗮𝗴𝗲𝗻𝘁-𝗿𝗲𝗮𝗱𝘆
Agents fail if data is messy.
You need data that is:
→ Easy to find
→ Clean and linked
→ Safe for wide access
I call this “business-ready data”.

4️⃣ 𝗧𝗵𝗲 𝘀𝘁𝗮𝗰𝗸 𝗴𝗲𝘁𝘀 𝘀𝗺𝗮𝗹𝗹𝗲𝗿
You do not want 15 data tools.
You want one brain.
One place for:
→ Pipelines
→ Store
→ Metrics
AI-native platforms win here.

5️⃣ 𝗖𝗼𝗱𝗶𝗻𝗴 𝘁𝘂𝗿𝗻𝘀 𝗶𝗻𝘁𝗼 𝗿𝗲𝘃𝗶𝗲𝘄
Engineers write less raw code.
Agents draft flows and tests.
Your best people:
→ Set clear specs
→ Review diffs
→ Guard quality at scale

6️⃣ 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝘁𝘂𝗿𝗻𝘀 𝗿𝗲𝗮𝗹
Regulators wake up.
You must show:
→ Who touched what data
→ How AI made each call
→ Why output is safe
No proof, no trust.

7️⃣ 𝗔𝗜 𝗽𝗿𝗶𝗰𝗶𝗻𝗴 𝘀𝗵𝗶𝗳𝘁𝘀
Per seat makes no sense when agents do work.
Expect:
→ Pay per outcome
→ Pay per use
→ Mix of both for large deals

8️⃣ 𝗠𝗶𝗱-𝗺𝗮𝗿𝗸𝗲𝘁 𝗷𝘂𝗺𝗽𝘀 𝗮𝗵𝗲𝗮𝗱
Less tech debt.
Faster change.
Strong AI-native tools mean a small firm with clear data beats a slow giant with old stacks. 🚀

9️⃣ 𝗕𝘂𝗶𝗹𝗱 𝘃𝘀 𝗯𝘂𝘆 𝗳𝗹𝗶𝗽𝘀
Home-grown AI infra looks cool.
Cost, talent, and risk say no.
You still build use cases.
You buy the boring layers.

🔟 𝗤𝘂𝗶𝗲𝘁 𝘄𝗶𝗻𝘀 𝗺𝗮𝘁𝘁𝗲𝗿 𝗺𝗼𝘀𝘁
Big press drops fade.
Real edge comes from:
→ One agent that cuts 30% cycle time
→ One workflow with clean, shared data
No noise. Only impact.

I spend my days at @DataGOL on one question:
𝗛𝗼𝘄 𝗱𝗼 𝘄𝗲 𝗺𝗮𝗸𝗲 𝗱𝗮𝘁𝗮 𝗮𝗻𝗱 𝗔𝗜 “𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀-𝗿𝗲𝗮𝗱𝘆” 𝗳𝗼𝗿 𝘁𝗲𝗮𝗺𝘀 𝘄𝗵𝗼 𝗻𝗲𝗲𝗱 𝘃𝗮𝗹𝘂𝗲 𝗶𝗻 𝗺𝗼𝗻𝘁𝗵𝘀, 𝗻𝗼𝘁 𝘆𝗲𝗮𝗿𝘀?

What part of this future feels closest to your world right now?

𝗛𝗲𝗮𝗱𝘀 𝘂𝗽, 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗟𝗲𝗮𝗱𝗲𝗿𝘀: 𝗧𝗵𝗲 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 𝗢𝗦 𝗶𝘀 𝗰𝗵𝗮𝗻𝗴𝗶𝗻𝗴 𝗳𝗮𝘀𝘁!

AI is making execution simple, which means good judgment is your new superpower.
Stop competing on speed and start building an 𝗡𝗲𝘄 𝗣𝗠 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗻𝗴 𝗦𝘆𝘀𝘁𝗲𝗺.

I will be writing the detailed series over the following week.

My 5-part series is your blueprint for this shift:
𝗘𝗺𝗯𝗿𝗮𝗰𝗲 𝘁𝗵𝗲 𝗖𝗼𝗿𝗲 𝗦𝗵𝗶𝗳𝘁: Your new moat is not in features, it's in turning chaos into clear decisions and defining high-integrity constraints.

𝗧𝗵𝗲 𝗗-𝗔-𝗘 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸: Use the Delegate, Amplify, Elevate model. Give all the repeatable tasks to AI and focus human energy on irreversible decisions.

𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆 & 𝗦𝗲𝗻𝘀𝗲𝗺𝗮𝗸𝗶𝗻𝗴: Get better at interpreting data (Signal) and naming the real problem (Sensemaking). Always look for "𝗗𝗶𝘀𝗰𝗼𝗻𝗳𝗶𝗿𝗺𝗶𝗻𝗴 𝗘𝘃𝗶𝗱𝗲𝗻𝗰𝗲."

𝗦𝗵𝗶𝗽 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝘆: When you build AI, you are shipping a system, not just a feature. Govern it with a clear Decision Contract.

𝗧𝗵𝗲 𝗡𝗲𝘄 𝗠𝗼𝗮𝘁: Your value is defined by four things: providing Clarity, making Evaluation the new UX, designing for Trust, and running the Machine-Human system.

The winners won’t be the ones who “move fast.”
They’ll be the ones who decide well—𝗰𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝘁𝗹𝘆, 𝘁𝗿𝗮𝗻𝘀𝗽𝗮𝗿𝗲𝗻𝘁𝗹𝘆, 𝘂𝗻𝗱𝗲𝗿 𝗰𝗼𝗻𝘀𝘁𝗿𝗮𝗶𝗻𝘁𝘀.

𝗜𝗳 𝗲𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻 𝗶𝘀 𝗴𝗲𝘁𝘁𝗶𝗻𝗴 𝗰𝗼𝗺𝗺𝗼𝗱𝗶𝘁𝗶𝘇𝗲𝗱… 𝘄𝗵𝗮𝘁’𝘀 𝗼𝗻𝗲 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗶𝗻 𝘆𝗼𝘂𝗿 𝗼𝗿𝗴 𝘆𝗼𝘂’𝗱 𝗻𝗲𝘃𝗲𝗿 𝘁𝗿𝘂𝘀𝘁 𝘀𝗽𝗲𝗲𝗱 𝘁𝗼 𝗶𝗺𝗽𝗿𝗼𝘃𝗲?

📉 𝗬𝗼𝘂'𝘃𝗲 𝗣𝗲𝗿𝗳𝗲𝗰𝘁𝗲𝗱 𝘁𝗵𝗲 𝗦𝘁𝗮𝗰𝗸. 𝗕𝘂𝘁 𝘁𝗵𝗲 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗦𝘁𝗶𝗹𝗹 𝗗𝗼𝗲𝘀𝗻’𝘁 𝗧𝗿𝘂𝘀𝘁 𝘁𝗵𝗲 𝗔𝗻𝘀𝘄𝗲𝗿𝘀.


You optimized your pipelines, warehouses, and data stack.
You invested in Snowflake, Databricks, dbt, and orchestration tools.
You assumed that would make AI work in real business workflows.

𝗜𝘁 𝗱𝗶𝗱𝗻’𝘁. 𝗔𝗻𝗱 𝗶𝘁 𝘄𝗼𝗻’𝘁.

Because AI doesn’t fail from lack of data. It fails from lack of context.

𝗜𝗻 𝘁𝗵𝗶𝘀 𝘄𝗲𝗯𝗶𝗻𝗮𝗿, 𝘄𝗲 𝗲𝘅𝗽𝗼𝘀𝗲𝗱 𝘁𝗵𝗲 𝗶𝗻𝗰𝗼𝗻𝘃𝗲𝗻𝗶𝗲𝗻𝘁 𝘁𝗿𝘂𝘁𝗵:

• Why “data access” ≠ “operational understanding”
• How missing context erodes trustand destroys adoption
• What it really takes to move from dashboards to autonomous agents that explain, verify, and act
• Why current stacks optimize for storage and compute—not decision quality

🚫 𝗦𝘁𝗼𝗽 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗺𝗼𝗿𝗲 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲.
✅ Start embedding business context into the foundation of your data systems.

If your AI can’t reason through a workflow like your top analyst, then all you’ve built is a very expensive tool.

https://lnkd.in/gnNP5JXJ

Product + Growth @ DataGol | AI | Financial Transformation | Defi

Thank you to everyone who joined us yesterday. If you missed the session, or are based in Europe, we will be hosting it again next week for our European partners.

Most AI projects do not fail because the models are weak.
They fail because the teams are focusing on infrastructure plumbing and get stuck with optimizing the infra. 

In our recent DataGOL webinar, we broke down a pattern we keep seeing:
Teams invest heavily in pipelines, warehouses, and data stack optimization.
Then they expect AI to reason, explain, and act inside real workflows.

It cannot.
So trust erodes.
Answers get discarded.
Momentum stalls.

We covered:

How missing business context destroys trust in AI outputs

The difference between “data access” and “operational understanding”

What it actually takes to move from dashboards to agents that can explain, verify, and take action

Why Snowflake/Databricks-style stacks optimize storage and compute, but not decision quality

The shift is simple to describe, but hard to execute:
Stop building more AI infrastructure.

Start building a #ConextOS that understands the business.
Reach out to us for 1:1 AI consulting session.

#AIinProduction #EnterpriseAI #DataPlatforms #AIInfrastructure #AgenticAI #DataGOL #OperationalIntelligence

Focus on AI Outcomes, Not Infrastructure

𝗠𝗼𝘀𝘁 𝗔𝗜 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝗳𝗮𝗶𝗹 𝗯𝗲𝗰𝗮𝘂𝘀𝗲 𝘆𝗼𝘂𝗿 𝘁𝗼𝗼𝗹𝘀 𝗱𝗼 𝗻𝗼𝘁 𝘀𝗽𝗲𝗮𝗸 𝘁𝗼 𝗲𝗮𝗰𝗵 𝗼𝘁𝗵𝗲𝗿.

You add “AI on top” of a broken stack.

I see this again and again when I talk with B2B founders and operators.

- The story is always the same.
- The product helps clients save time.
- The team still drowns in manual work.

𝗛𝗲𝗿𝗲 𝗶𝘀 𝗼𝗻𝗲 𝗳𝗼𝘂𝗻𝗱𝗲𝗿 𝗜 𝘄𝗼𝗿𝗸𝗲𝗱 𝘄𝗶𝘁𝗵.
His SaaS app cuts 80–90% of founder time in fundraising.
- Strong value.
- Happy users.

𝗜𝗻𝘀𝗶𝗱𝗲 𝗵𝗶𝘀 𝗼𝘄𝗻 𝘁𝗲𝗮𝗺, 𝘄𝗼𝗿𝗸 𝗹𝗼𝗼𝗸𝗲𝗱 𝗹𝗶𝗸𝗲 𝘁𝗵𝗶𝘀:
• Call transcripts land in the CRM
• Team members read each call
• They pull out fund mandate, stage, check size
• They paste data into fields, one by one
• Boards in Asana do not move unless someone drags cards.
• Research for each investor runs “one by one by one” because bulk tools hallucinate.

𝗧𝗼𝗼𝗹 𝗰𝗼𝘀𝘁 𝗴𝗿𝗼𝘄𝘀 𝘄𝗶𝘁𝗵 𝗲𝘃𝗲𝗿𝘆 𝗻𝗲𝘄 𝗲𝗺𝗮𝗶𝗹 𝘀𝗲𝗮𝘁 𝗮𝗻𝗱 𝗰𝗮𝗹𝗲𝗻𝗱𝗮𝗿.
- The service scales.
- The engine does not.
- The answer for him was not a “new all-in-one platform.”
- He needed one intelligent system between all his tools.
That is where we brought in @DataGOL as "AI Native Intelligence & Execution Platform"

Here is how we implemented it:

→ 𝗘𝘅𝘁𝗿𝗮𝗰𝘁𝗶𝗼𝗻 𝗹𝗮𝘆𝗲𝗿
- Transcripts go in.
- AI pulls structured fields with a shared language.
- Data flows back into the CRM without a human hand.

→ 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝗯𝗿𝗼𝗸𝗲𝗿
- CRM actions trigger status changes.
- Project tools move on their own.
- No one drags cards all day.

→ 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗲𝗻𝗴𝗶𝗻𝗲
• Multi-agent AI runs deep research in bulk.
• Scores and ranks targets.
• Shows sources so you can trust the results.

→ 𝗦𝘆𝘀𝘁𝗲𝗺 𝗼𝗳 𝗮𝗰𝘁𝗶𝗼𝗻
- Once the middleware works well, you replace old tools
- One by one
- With less risk and clear ROI proof.

Fast ROI comes from one AI Native layer that makes your whole stack work like one product.

DataGOL

𝗧𝗵𝗲 𝗘𝗻𝗱 𝗼𝗳 𝘁𝗵𝗲 𝗙𝗶𝗻𝗮𝗻𝗰𝗲 𝗕𝗼𝗮𝗿𝗱 𝗗𝗲𝗰𝗸 𝗦𝗰𝗿𝗮𝗺𝗯𝗹𝗲: 𝗜𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝗶𝗻𝗴 𝗗𝗮𝘁𝗮𝗚𝗢𝗟'𝘀 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗔𝗴𝗲𝗻𝘁

There's a shift happening in how SaaS based finance teams interact with their data.
I'm calling it 𝗗𝗶𝗮𝗴𝗻𝗼𝘀𝘁𝗶𝗰 𝗙𝗶𝗻𝗮𝗻𝗰𝗲.

𝗧𝗵𝗶𝗻𝗸: AI that doesn't just tell you what happened, but identifies exactly why it happened. In seconds.

𝗛𝗲𝗿𝗲'𝘀 𝘁𝗵𝗲 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝗰𝗲:
𝗗𝗲𝘀𝗰𝗿𝗶𝗽𝘁𝗶𝘃𝗲 𝗙𝗶𝗻𝗮𝗻𝗰𝗲 = "Revenue down 12%. Margins compressed."
𝗗𝗶𝗮𝗴𝗻𝗼𝘀𝘁𝗶𝗰 𝗙𝗶𝗻𝗮𝗻𝗰𝗲 = "May COGS spike in Product Line X. Drove 73% of the margin erosion."
One reports the score. The other finds the insights that lost the game.

𝗬𝗼𝘂𝗿 𝗯𝗼𝗮𝗿𝗱 𝗲𝘅𝗲𝗰𝘂𝘁𝗶𝘃𝗲𝘀 𝗱𝗼𝗻'𝘁 𝘄𝗮𝗻𝘁 𝗮𝗻𝗼𝘁𝗵𝗲𝗿 𝗱𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱.
They want to know why Q2 margins spiked while Q3 collapsed.
And right now?
Your data team finds out what was presented... after the meeting.

𝗜'𝘃𝗲 𝘀𝗲𝗲𝗻 𝗶𝘁 𝗶𝗻 𝗽𝗼𝗿𝘁𝗳𝗼𝗹𝗶𝗼 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝗮𝗰𝗿𝗼𝘀𝘀 𝘀𝗲𝗰𝘁𝗼𝗿𝘀.
CFO builds the board deck. The data team scrambles to reverse-engineer the "why" question two weeks later.

𝗠𝗼𝘀𝘁 𝘁𝗲𝗮𝗺𝘀 𝗱𝗼 𝗼𝗻𝗲 𝗼𝗳 𝘁𝘄𝗼 𝘁𝗵𝗶𝗻𝗴𝘀:
→ Hire expensive consultants for ad-hoc analyses ($50k+ per engagement)
→ Duct-tape BI tools that still need manual SQL and weeks of turnaround
Neither gets you diagnostic depth in real-time.

𝗗𝗮𝘁𝗮𝗚𝗢𝗟'𝘀 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗔𝗴𝗲𝗻𝘁 𝘄𝗮𝘀 𝗯𝘂𝗶𝗹𝘁 𝘁𝗼 𝗿𝗲𝗽𝗹𝗮𝗰𝗲 𝘁𝗵𝗮𝘁 𝗲𝗻𝘁𝗶𝗿𝗲 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄.
Here's what happens now:
✓ 𝗔𝘀𝗸 𝗶𝗻 𝗽𝗹𝗮𝗶𝗻 𝗘𝗻𝗴𝗹𝗶𝘀𝗵: "What drove the margin variance in Q3?"
✓ 𝗔𝗴𝗲𝗻𝘁 𝗰𝗼𝗻𝗳𝗶𝗿𝗺𝘀 your Chart of Accounts before calculating (human-in-the-loop)
✓ 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗲𝘀 𝘁𝗵𝗲 𝗱𝗿𝗶𝘃𝗲𝗿𝘀—product line, customer segment, cost category
✓ Suggests the 𝗻𝗲𝘅𝘁 𝗱𝗶𝗮𝗴𝗻𝗼𝘀𝘁𝗶𝗰 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻 before you ask it
✓ 𝗦𝗵𝗼𝘄𝘀 𝗶𝘁𝘀 𝘄𝗼𝗿𝗸—Python code, logic trail, audit-ready transparency

𝗧𝗵𝗲 𝗿𝗲𝘀𝘂𝗹𝘁𝘀:
→ From raw NetSuite chaos → boardroom-ready insight in under 60 seconds.
→ Full transparency into how every number was calculated.
→ No more "black box" AI guessing.

𝗡𝗼 𝗱𝗮𝘁𝗮 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗿𝗲𝗾𝘂𝗶𝗿𝗲𝗱. 𝗡𝗼 𝗦𝗤𝗟. 𝗡𝗼 𝗽𝗶𝘃𝗼𝘁 𝘁𝗮𝗯𝗹𝗲 𝗮𝗿𝗰𝗵𝗮𝗲𝗼𝗹𝗼𝗴𝘆.𝗝𝘂𝘀𝘁 𝗮𝘀𝗸.
Diagnostic Finance isn't about prettier dashboards.
𝗜𝘁'𝘀 𝗮𝗯𝗼𝘂𝘁 𝗮𝗻𝘀𝘄𝗲𝗿𝗶𝗻𝗴 "𝘄𝗵𝘆" 𝗯𝗲𝗳𝗼𝗿𝗲 𝘁𝗵𝗲 𝗯𝗼𝗮𝗿𝗱 𝗮𝘀𝗸𝘀.

Are you still reporting what happened? Or are you ready to diagnose why?

DataGOL

𝗗𝗔𝗧𝗔 𝗦𝗖𝗜𝗘𝗡𝗖𝗘 𝗔𝗚𝗘𝗡𝗧 stands out as it delivers real time business ready insights directly embedded into your finance, sales, product workflows.

It's deeply integrated, maintains context across your data, proactive and action oriented, solving the "𝘄𝗵𝗼 𝗼𝘄𝗻𝘀 𝘁𝗵𝗲 𝗱𝗮𝘁𝗮" problem and driving decisions.

𝗜𝗧'𝗦 𝗔𝗟𝗪𝗔𝗬𝗦 𝗢𝗡, 𝗖𝗢𝗡𝗧𝗘𝗫𝗧 𝗔𝗪𝗔𝗥𝗘, 𝗘𝗫𝗘𝗖𝗨𝗧𝗜𝗢𝗡 𝗙𝗢𝗖𝗨𝗦𝗘𝗗 𝗗𝗔𝗧𝗔 𝗣𝗔𝗥𝗧𝗡𝗘𝗥

𝗬𝗢𝗨 𝗖𝗔𝗡𝗡𝗢𝗧 𝗠𝗜𝗦𝗦 𝗧𝗛𝗜𝗦!!

T𝗛𝗘 𝗕𝗘𝗦𝗧 𝗗𝗔𝗧𝗔 𝗦𝗖𝗜𝗘𝗡𝗖𝗘 𝗔𝗚𝗘𝗡𝗧 - Assisting your Finance, Product and Sales Team. 𝗔𝗹𝘄𝗮𝘆𝘀 𝗮𝘃𝗮𝗶𝗹𝗮𝗯𝗹𝗲 - Responding within minutes with insights that were not possible before.

Whether you are analyzing revenue trends, sales or.. etc..

𝗦𝗶𝘁𝘁𝗶𝗻𝗴 𝗼𝗻 𝘆𝗼𝘂𝗿 𝗱𝗮𝘁𝗮 𝘀𝗲𝘁 - directly creating and accessing data pipelines, managing context and creating intelligence from your data.

Want a personalized 𝗧𝗥𝗜𝗔𝗟 Comment below.

#DataScience #Transparency #TrustInAI #DataGOL

𝟭𝟬𝗠 𝗨𝘀𝗲𝗿𝘀. 𝟴𝘅 𝗩𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻. 𝗛𝗲𝗿𝗲’𝘀 𝗪𝗵𝗮𝘁 𝗪𝗮𝘀 𝗠𝗶𝘀𝘀𝗶𝗻𝗴.
We looked at a “successful” gaming platform: 10M active free-to-play users, super sticky… and still stuck at ~8x exit.

I saw this in a gaming client at @DataGOL
- Free-to-play.
- Very sticky.
- Exit stuck at 8x.

𝗪𝗵𝘆?
User data looked nice on a slide.
Inside, they had HTML + vibes.
- No real events.
- No cohorts.
- No path from “player” to “payer”.

𝗛𝗶𝗴𝗵 𝗗𝗔𝗨, 𝗹𝗼𝘄 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴.

The truth is:
- You don’t scale from 8x to 20x with more features.
- You scale with a better brain.

𝗛𝗲𝗿𝗲 𝗶𝘀 𝗵𝗼𝘄 𝗜 𝘁𝗵𝗶𝗻𝗸 𝗮𝗯𝗼𝘂𝘁 𝗶𝘁 𝗻𝗼𝘄:

𝗙𝗶𝗿𝘀𝘁, 𝗶𝗱𝗲𝗻𝘁𝗶𝘁𝘆 + 𝗶𝗻𝘁𝗲𝗻𝘁.
- One lake for all data.
- One “virtual CRM” on top.
- One clean stream of events from product and ads.
Then I stop saying “users”.
I see segments, with clear intent.

𝗡𝗲𝘅𝘁, 𝗿𝗲𝘃𝗲𝗻𝘂𝗲 𝗹𝗶𝗻𝗸𝘀.
- Every campaign message flows into the same brain.
- Every deposit and payment links back to a touch.
- I can ask “what creates LTV?” and get a clear view.
Most tools stop here.
They show reports, not answers.

𝗟𝗮𝘀𝘁, 𝗮𝗰𝘁𝗶𝘃𝗮𝘁𝗶𝗼𝗻 𝘄𝗶𝘁𝗵 𝗮𝗴𝗲𝗻𝘁𝘀.
- Small Python agents watch each player.
- They look at recency, frequency, and value.
- They trigger one next action, not ten.
Email, offer, KYC nudge, in-game push.
Personal, fast, cheap.

𝗥𝗲𝘀𝘂𝗹𝘁:
- Lower cost to reactivate.
- Less waste in marketing.
- More users move to deposit, subscribe, or compete.

Teams say “we have a monetization issue”.
I see an intelligence gap.

𝗜𝗳 𝘆𝗼𝘂 𝗵𝗮𝗱 𝟭𝟬𝗠 𝘂𝘀𝗲𝗿𝘀 𝗻𝗲𝘅𝘁 𝗺𝗼𝗻𝘁𝗵, 𝘄𝗵𝗮𝘁 𝘀𝘆𝘀𝘁𝗲𝗺 𝘄𝗼𝘂𝗹𝗱 𝘆𝗼𝘂 𝘁𝗿𝘂𝘀𝘁 𝘁𝗼 𝗴𝘂𝗶𝗱𝗲 𝗲𝘃𝗲𝗿𝘆 𝗺𝗼𝘃𝗲?

If you want to build that intelligence for your product, @DataGOL helps teams build the full stack AI agents.

𝗛𝗼𝘄 𝗮𝗿𝗲 𝘆𝗼𝘂 𝘂𝘀𝗶𝗻𝗴 𝘆𝗼𝘂𝗿 𝗱𝗮𝘁𝗮 𝘁𝗼𝗱𝗮𝘆 𝘁𝗼 𝗱𝗿𝗶𝘃𝗲 𝗿𝗲𝗮𝗹 𝘃𝗮𝗹𝘂𝗲, 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝗿𝗲𝗽𝗼𝗿𝘁𝘀?

𝗧𝗵𝗲 𝗣𝗠 𝗠𝗼𝗮𝘁 𝗧𝗵𝗮𝘁 𝗔𝗜 𝗖𝗮𝗻'𝘁 𝗥𝗲𝗽𝗹𝗶𝗰𝗮𝘁𝗲

There's a role emerging in AI-native companies that doesn't have a name yet.
It's the "Judgment PM".

𝗛𝗲𝗿𝗲'𝘀 𝘁𝗵𝗲 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝗰𝗲:
𝗧𝗵𝗿𝗼𝘂𝗴𝗵𝗽𝘂𝘁 𝗣𝗠 = Discovery → prioritization → requirements → delivery → iterate
𝗝𝘂𝗱𝗴𝗺𝗲𝗻𝘁 𝗣𝗠 = Turns ambiguity into irreversible decisions that machines can't make

𝗙𝗼𝗿 𝗮 𝗱𝗲𝗰𝗮𝗱𝗲, 𝘆𝗼𝘂𝗿 𝗯𝗼𝘁𝘁𝗹𝗲𝗻𝗲𝗰𝗸 𝘄𝗮𝘀 𝗵𝘂𝗺𝗮𝗻 𝘁𝗵𝗿𝗼𝘂𝗴𝗵𝗽𝘂𝘁.
Ship faster. Write more PRDs. Run more sprints.
That era is over.
In an AI-native world, execution speed is no longer scarce.
Judgment is. Context is. Trust is.

𝗔𝗻𝗱 𝗿𝗶𝗴𝗵𝘁 𝗻𝗼𝘄, most product teams are responding the wrong way.
More gates. More committees. More process.
That's not a strategy. That's organizational scar tissue.

𝗧𝗵𝗲 𝗝𝘂𝗱𝗴𝗺𝗲𝗻𝘁 𝗣𝗠 𝗱𝗼𝗲𝘀 𝘁𝗵𝗿𝗲𝗲 𝘁𝗵𝗶𝗻𝗴𝘀 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁𝗹𝘆:
✓ 𝗖𝗿𝗲𝗮𝘁𝗲𝘀 𝗰𝗹𝗮𝗿𝗶𝘁𝘆 — turns chaos into a sharp decision: "We're doing X, not Y, because Z"
✓ 𝗕𝘂𝗶𝗹𝗱𝘀 𝗲𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 — treats eval as a first-class product capability ("Evaluation is the new UX")
✓ 𝗗𝗲𝘀𝗶𝗴𝗻𝘀 𝘁𝗿𝘂𝘀𝘁 — ships explanations, confidence scores, and undo/rollback as core features

𝗔𝗜 𝗶𝗻𝗰𝗿𝗲𝗮𝘀𝗲𝘀 𝘁𝗵𝗲 𝘀𝘂𝗿𝗳𝗮𝗰𝗲 𝗮𝗿𝗲𝗮 𝗼𝗳 𝘄𝗵𝗮𝘁 𝗰𝗮𝗻 𝗯𝗲 𝗯𝘂𝗶𝗹𝘁.
✓ Your operating model must increase the surface area of what can be validated.
✓ The PMs who figure this out will own the decisions that matter.
✓ The ones who don't will keep writing PRDs that machines could generate in 30 seconds.

Comment if you're already making this shift?

DataGOL = 𝗟𝗼𝗼𝗸𝗲𝗿 + 𝗖𝗵𝗮𝘁𝗚𝗣𝗧 𝗶𝗻 𝗼𝗻𝗲 𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺

I've had the same conversation with 5 different performance marketing teams this Month.

They all said some version of:
"𝗜 𝗰𝗮𝗻'𝘁 𝗲𝘅𝗽𝗹𝗮𝗶𝗻 𝘁𝗼 𝗺𝘆 𝗖M𝗢 𝘄𝗵𝘆 𝗼𝘂𝗿 𝗻𝘂𝗺𝗯𝗲𝗿𝘀 𝗱𝗼𝗻'𝘁 𝗺𝗮𝘁𝗰𝗵. 𝗜 𝗱𝗼𝗻'𝘁 𝗲𝘃𝗲𝗻 𝗸𝗻𝗼𝘄 𝘄𝗵𝗲𝗿𝗲 𝘁𝗵𝗲 𝗱𝗮𝘁𝗮 𝗰𝗼𝗺𝗲𝘀 𝗳𝗿𝗼𝗺."
This isn't a data quality problem. It's a trust problem.
And it's driving a shift from "𝗕𝗹𝗮𝗰𝗸-𝗕𝗼𝘅 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀" to what I'm calling "𝗧𝗿𝘂𝘀𝘁-𝗙𝗶𝗿𝘀𝘁 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀".

Here's the difference:
𝗕𝗹𝗮𝗰𝗸-𝗕𝗼𝘅 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 = Dashboards you can't trace. Numbers you can't validate. Feature requests for every custom query.
𝗧𝗿𝘂𝘀𝘁-𝗙𝗶𝗿𝘀𝘁 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 = Full visibility into SQL structure. Complete data lineage. Slice data any way you need, instantly.

𝗬𝗼𝘂𝗿 𝗯𝗼𝗮𝗿𝗱 𝗮𝘀𝗸𝘀 𝘄𝗵𝘆 𝗖𝗔𝗖 𝗶𝗻𝗰𝗿𝗲𝗮𝘀𝗲𝗱 𝟮𝟬%.
- You pull the dashboard. The number's there.
- But you can't explain how it was calculated. You can't trace it back to the source.
You're defending a number you don't fully understand.

𝗠𝗼𝘀𝘁 𝘁𝗲𝗮𝗺𝘀 𝗱𝗼 𝗼𝗻𝗲 𝗼𝗳 𝘁𝘄𝗼 𝘁𝗵𝗶𝗻𝗴𝘀:
→ Accept the black box (and the trust gap)
→ Build custom solutions (and burn budget on data engineers instead of User Acquisition )
We built DataGOL "Data Science Agent" to eliminate that trade-off.

𝗛𝗲𝗿𝗲'𝘀 𝘄𝗵𝗮𝘁 𝗼𝘂𝗿 𝗰𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗴𝗲𝘁𝘀 𝗻𝗼𝘄:
✓ 𝗙𝘂𝗹𝗹 𝗦𝗤𝗟 𝘃𝗶𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝘆—trace any metric to source in seconds
✓ 𝗦𝗹𝗶𝗰𝗲 𝗮𝗻𝗱 𝗽𝗶𝘃𝗼𝘁 𝗱𝗮𝘁𝗮 𝗶𝗻 𝗰𝗼𝘂𝗻𝘁𝗹𝗲𝘀𝘀 𝘄𝗮𝘆𝘀—no feature requests
✓ 𝗔𝗜-𝗻𝗮𝘁𝗶𝘃𝗲 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀 (imagine Looker with ChatGPT built in) - AI and agent capabilities directly into the workflow
✓ 𝗖𝘂𝘀𝘁𝗼𝗺 𝗰𝗼𝗵𝗼𝗿𝘁𝗶𝗻𝗴 𝗮𝗻𝗱 𝗿𝗲𝘁𝗲𝗻𝘁𝗶𝗼𝗻 - to understand nuanced user behaviour

𝗧𝗵𝗲 𝗿𝗲𝘀𝘂𝗹𝘁? Budget that used to fund data validation now goes directly into user acquisition.

The teams that figure this out will outspend competitors where it matters.

Are you still working with black-box tools? Or have you made the shift to trust-first?

#DataScience #Transparency #DataAnalytics #TrustInAI #DataGOL

𝗪𝗵𝘆 𝟱𝟲% 𝗼𝗳 𝗖𝗘𝗢𝘀 𝗦𝗲𝗲 𝗡𝗼 𝗔𝗜 𝗥𝗲𝘁𝘂𝗿𝗻𝘀

𝗧𝗵𝗲𝗿𝗲'𝘀 𝗮 𝗳𝘂𝗻𝗻𝘆 𝘁𝗵𝗶𝗻𝗴 𝗵𝗮𝗽𝗽𝗲𝗻𝗶𝗻𝗴 𝗶𝗻 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗔𝗜 𝗿𝗶𝗴𝗵𝘁 𝗻𝗼𝘄.
Leadership thinks the infrastructure is in place.
Employees say it isn't even close.

𝗧𝗵𝗲 𝗱𝗮𝘁𝗮 𝗼𝗻 𝘁𝗵𝗶𝘀 𝗶𝘀 𝘀𝘁𝗿𝗶𝗸𝗶𝗻𝗴.
81% of the C-suite believe a clear AI policy exists in their organization.
Only 28% of employees agree.
That's a 53-point perception gap.

𝗢𝗻 𝘁𝗿𝗮𝗶𝗻𝗶𝗻𝗴? 𝟴𝟭% 𝗼𝗳 𝗲𝘅𝗲𝗰𝘂𝘁𝗶𝘃𝗲𝘀 𝗿𝗲𝗽𝗼𝗿𝘁 𝗯𝗲𝗶𝗻𝗴 𝘁𝗿𝗮𝗶𝗻𝗲𝗱 𝗼𝗻 𝗔𝗜.
Only 27% of individual contributors say the same.
On tool access? 80% vs. 32%.

𝗛𝗲𝗿𝗲'𝘀 𝘁𝗵𝗲 𝗶𝗻𝘁𝗲𝗿𝗲𝘀𝘁𝗶𝗻𝗴 𝘁𝗵𝗶𝗻𝗴...
This isn't a communication problem. It's a feedback loop failure.
If leadership believes the foundation is already built, they remain blind to the bottlenecks preventing teams from moving beyond basic prompting.

𝗧𝗵𝗲 𝗰𝗼𝗻𝘀𝗲𝗾𝘂𝗲𝗻𝗰𝗲𝘀 𝗮𝗿𝗲 𝘀𝗵𝗼𝘄𝗶𝗻𝗴 𝘂𝗽 𝗶𝗻 𝘁𝗵𝗲 𝗻𝘂𝗺𝗯𝗲𝗿𝘀.
Workday found that 37% of AI time savings are immediately lost to rework, employees cleaning up outputs that weren't good enough the first time.

𝗧𝗵𝗮𝘁'𝘀 𝗻𝗼𝘁 𝗮𝗻 𝗔𝗜 𝗽𝗿𝗼𝗯𝗹𝗲𝗺. 𝗧𝗵𝗮𝘁'𝘀 𝗮𝗻 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 𝗺𝗮𝘀𝗾𝘂𝗲𝗿𝗮𝗱𝗶𝗻𝗴 𝗮𝘀 𝗼𝗻𝗲.
Meanwhile, PWC data shows 56% of CEOs report no significant financial return from AI.
But 12%—the "Vanguard"—are seeing simultaneous cost and revenue gains.
The difference? They're 2.6x more likely to have embedded AI into core processes, not peripheral tasks.

𝗠𝘆 𝗽𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝗼𝗻:
- The AI winners of 2026 won't have better models.
- They'll have closed the 53-point gap between what the boardroom believes and what the frontline experiences.
- The technology is not the bottleneck.
- The misalignment is.
𝗔𝗿𝗲 𝘆𝗼𝘂 𝘀𝗲𝗲𝗶𝗻𝗴 𝘁𝗵𝗶𝘀 𝗽𝗲𝗿𝗰𝗲𝗽𝘁𝗶𝗼𝗻 𝗴𝗮𝗽 𝗶𝗻 𝘆𝗼𝘂𝗿 𝗼𝗿𝗴𝗮𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻? 𝗢𝗿 𝗵𝗮𝘃𝗲 𝘆𝗼𝘂 𝗳𝗼𝘂𝗻𝗱 𝗮 𝘄𝗮𝘆 𝘁𝗼 𝗰𝗹𝗼𝘀𝗲 𝗶𝘁?

𝗗𝗮𝘁𝗮𝗚𝗢𝗟: 𝗧𝗵𝗲 𝗖𝗼𝗺𝗽𝗲𝘁𝗶𝘁𝗶𝘃𝗲 𝗘𝗱𝗴𝗲 𝗧𝗵𝗮𝘁 𝗣𝘂𝘁𝘀 𝗠𝗮𝗿𝗸𝗲𝘁𝗲𝗿𝘀 𝗶𝗻 𝗖𝗼𝗻𝘁𝗿𝗼𝗹

I keep hearing the same thing from performance marketers this month:
"𝗜 𝗱𝗼𝗻'𝘁 𝘁𝗿𝘂𝘀𝘁 𝘁𝗵𝗲 𝗔𝗜 𝗼𝘂𝘁𝗽𝘂𝘁𝘀 𝗯𝗲𝗰𝗮𝘂𝘀𝗲 𝗜 𝗰𝗮𝗻'𝘁 𝘀𝗲𝗲 𝘄𝗵𝗮𝘁'𝘀 𝗳𝗲𝗲𝗱𝗶𝗻𝗴 𝘁𝗵𝗲𝗺."

Here's the interesting part: 𝘁𝗵𝗲𝘀𝗲 𝘀𝗮𝗺𝗲 𝘁𝗲𝗮𝗺𝘀 𝗮𝗿𝗲 𝗮𝗹𝘀𝗼 𝘀𝗮𝘆𝗶𝗻𝗴 𝘁𝗵𝗲𝘆 𝗰𝗮𝗻'𝘁 𝗹𝗶𝘃𝗲 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗔𝗜.
𝗧𝘄𝗼 𝗼𝗽𝗽𝗼𝘀𝗶𝗻𝗴 𝗱𝗲𝗺𝗮𝗻𝗱𝘀. 𝗦𝗮𝗺𝗲 𝗰𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻.
The next analytics battleground isn't AI vs. no-AI.
It's transparent AI vs. blackbox AI.

𝗛𝗲𝗿𝗲'𝘀 𝘄𝗵𝗮𝘁 𝗜'𝗺 𝘀𝗲𝗲𝗶𝗻𝗴 𝗶𝗻 𝘁𝗵𝗲 𝘀𝘁𝗮𝗰𝗸 𝗲𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻:
𝗧𝗵𝗲 𝗼𝗹𝗱 𝗮𝗽𝗽𝗿𝗼𝗮𝗰𝗵:
→ AI tools bolted on afterward (no visibility into logic, no context and no data governance)
→ BI tools for dashboards (rigid, predefined models)
→ Custom data teams for flexibility (expensive)


𝗧𝗵𝗲 𝗲𝗺𝗲𝗿𝗴𝗶𝗻𝗴 𝗮𝗽𝗽𝗿𝗼𝗮𝗰𝗵:
→ Decision Traces, ContextOS, Reasoning, and data infrastructure in one layer
→ Custom cohort analysis without engineering tickets
→ SQL visible behind every insight
Tools like @DataGOL are leaning into this, their positioning is essentially
"BI 𝘄𝗶𝘁𝗵 𝗖𝗵𝗮𝘁𝗚𝗣𝗧 𝗼𝘃𝗲𝗿 𝗶𝘁, 𝗯𝘂𝘁 𝘆𝗼𝘂 𝗰𝗮𝗻 𝘃𝗮𝗹𝗶𝗱𝗮𝘁𝗲 𝘄𝗵𝗮𝘁'𝘀 𝗳𝗲𝗲𝗱𝗶𝗻𝗴 𝘁𝗵𝗲 𝗔𝗜."

𝗧𝗵𝗲 𝘀𝗶𝗴𝗻𝗮𝗹 𝘂𝗻𝗱𝗲𝗿𝗻𝗲𝗮𝘁𝗵 𝘁𝗵𝗶𝘀 𝘀𝗵𝗶𝗳𝘁:
→ Performance teams are done choosing between speed and trust.
→ They want to ask natural-language questions and see the query logic.
→ They want AI-generated insights and the ability to check its homework.

𝗙𝗼𝗿 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗺𝗮𝗿𝗸𝗲𝘁𝗲𝗿𝘀 𝗲𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗻𝗴 𝘁𝗵𝗲𝗶𝗿 𝘀𝘁𝗮𝗰𝗸, 𝘁𝗵𝗿𝗲𝗲 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 𝗺𝗮𝘁𝘁𝗲𝗿 𝗻𝗼𝘄:
✓ Can you cohort by custom events, not just default metrics?
✓ Can you see the underlying SQL structure?
✓ Can you trust insights without a data team translating?

The teams that solve this will move faster and spend less on internal data infrastructure.
The ones that don't will keep paying the "talent tax" just to get answers they're not sure they believe.

Are you already seeing this convergence in your stack? Or are AI and transparency still living in separate tools?

#DataScience #Transparency #DataAnalytics #TrustInAI #DataGOL

Brand & Growth Strategist | Content @ ColdIQ ($6M ARR) | Building LinkedIn Content for B2B GTM | Favikon #2 Meditation Creator on LinkedIn

I lead content strategy at ColdIQ, where we've built one of the most effective LinkedIn engines in B2B.

Our numbers: $7M ARR in 30 months, fully bootstrapped. 250K+ combined followers across the team. 4M quarterly impressions. 356 meetings booked from content alone.

LinkedIn is our #1 acquisition channel. Not ads. Not cold email. But a systematic content that compounds credibility faster than traditional GTM approaches.

My role: I help B2B founders turn LinkedIn into a real part of their go-to-market motion - a system that supports outbound, ads, and sales conversations.

The focus is founder-led content: positioning, messaging, and formats that make sense to people who make buying decisions. Content that builds authority before you need it.

Before ColdIQ, I built my own LinkedIn presence from zero to Favikon #2 worldwide for meditation creators in 6 months, tackling the interection of business and self-awareness.

I bring 10+ years across brand strategy, growth work, and wellbeing businesses where trust compounds slowly and attention is hard-won. Those fundamentals matter just as much in B2B, maybe more.

Alongside B2B work, I maintain a long practice in meditation and wellbeing. That background shapes how I think about attention, clarity, and what makes people stay versus scroll. All things that determine whether your content works or gets ignored.

If LinkedIn is part of how your company goes to market, your content should reflect how you actually sell.
Let's build systems that work before you need them.

Mai-Lan Khong

I haven't applied for a job in years.

No CV, no ATS, no uploading my life three times for an algorithm to reject me.

When I saw ColdIQ's post about the content lead role, I got interested.

Most companies at ColdIQ's stage are still figuring things out.
And they’re already the top recognized in their space, operating with momentum:

→ A team that reaches hundreds of thousands of B2B operators
→ Consistent inbound from founder-led content
→ A system that turns visibility into actual pipeline

It looked like a moving train, a good one.

Here is the But.
I was running my own thing. And honestly? I was too lazy to apply.
Send a CV to a system? Wait for AI to scan it? Go through unlimited filters?
Hmmmm.

So I did something simpler.
I messaged Michel Lieben 🧠 (co-founder of ColdIQ) directly on LinkedIn.
(He had 60K+ followers at the time, but somehow I didn't feel intimidated lol)
I just introduced myself. No pitch, was something like... hi, this role sounds interesting, here's who I am.

What happened next was funny.
Michel answered. Hugues Selderslagh (People & Talent) reached out asking why he couldn't find my application in their system.
I told him I didn't send one.
We laughed. We set up calls anyway.
It was genuinely fun.
And now? I'm here. Part of the cool gang I imagined when I first read that announcement.

This wasn't a typical job hunt.
I didn't play the numbers game (which, let's be honest, is not very data-driven from me).
But I can't deny the unfair advantage my LinkedIn presence gave me.

I started posting seriously in March 2025.
By November, I was Favikon #2 Meditation Creator on LinkedIn.

Did that get me the job? Maybe not directly.
But did it help ColdIQ take me seriously faster? Absolutely.

If I'd shown up with zero online presence... maybe I'd still be here (because I am good).
Maybe not (because I couldn't show how good I am in a short attention span).

So what's the point?

→ LinkedIn isn't optional anymore. It's your reputation, visible.
→ Content compounds before you need it.
→ The best opportunities don't come from playing the game everyone else plays.

I'm grateful. I'm excited.
And I'm very happy to be building with this team.
Let's go 🚀

ColdIQ crossed $6M ARR without treating LinkedIn content as “brand stuff.”

That alone caught my attention.

Officially back online and starting 2026 joining the ColdIQ team, focusing on founder-led LinkedIn content as part of their GTM flywheel.

If you’ve followed my work, this isn’t a pivot.
It’s a continuation, just placed inside a system that actually compounds.

Here’s the part most people miss:

At ColdIQ, content is Step 1 of a revenue workflow.
Not vibes. Not “thought leadership.”
The input that feeds everything else.

High-performing founder posts get:

amplified through ads
monitored for intent signals
routed into Clay
enriched, scored, researched
and only then turned into personalized outreach

The result isn’t reach.
It’s meetings booked.

That’s why this collaboration feels aligned.

Over the past weeks, I’ve been working closely with Monika Grycz 💌, Michel Lieben 🧠 and Soheil Saeidmehr and I genuinely appreciate how grounded the thinking is.

Different strengths.
Clear standards.
No noise for the sake of noise.

A quick reflection from last year, since it’s the first workday of the year:

I started posting consistently around March.
Learned a lot about attention and clarity.
Somewhere along the way, I was ranked #2 on LinkedIn for meditation creators.

I also disappeared several times.
Travel. Rest. End-of-year slowdown.
I let momentum drop on purpose. Still no regrets.

That pause made one thing obvious:

The work I care most about isn’t “personal branding” for the sake of visibility.
It’s helping founders communicate with enough clarity that their content does real work inside a GTM system.

That’s the layer I’m contributing to here:

shaping how founders show up publicly
making sure posts earn their place in the flywheel
and keeping content sharp enough to support outbound, not dilute it

Meditation still shapes how I think.
Not as a topic, but as a way to notice when messaging gets noisy, rushed, or misaligned.

This year, I’m excited to build inside the work.
With a team.
With real constraints.
With real outcomes.

Grateful to start here.

If 10 years ago someone told me I’d have a career that allows:
freedom to travel
slow mornings
time to read
long walks
day naps

I wouldn’t have believed that dream life would come true.

Turns out I was right. I don’t have any of that. 🤣 
Or more accurately: my career allows it.
My child does not.

Slow mornings are now sock, brushing teeth, breakfast, etc. negotiations with a tiny human.
Good night’s sleep is another nostalgic memory.
Reading means the same page, five times.
Day naps are called "thinking about naps".
Walks involve snacks and countless stops.

I’m posting a photo of me jogging by the coast today. It does look peaceful, though.
The luxury is mostly in the framing. 😂

I spent this morning turning YouTube videos into infographics with Nano Banana Pro.
Results are amazing. (To be fair, I have a paid account.)

I've tested everything. The old version of this tool. Dall-E, ChatGPT (hmmm, quite horrendous 🙀 haha). Then recently Claude (not so bad, not so bad 🙄).

BUT this is the first time I can say AI-generated infographics don't look broken (results are attached below, one looks very cringe on purpose 🤣 )

So here's how I did it (and you can too):

Step 1️⃣ : Access Nano Banana Pro

→ Go to gemini.google.com 
→ Click "Tools" → "🍌 Create images" 
→ Turn on "Thinking" or "Pro" (bottom right corner)

Step 2️⃣ : Turn video learning into something saveable

→ Find a video with insights worth sharing. 
→ Use this prompt:
"Break down this video and pull out the key insights: [paste your YouTube link]. We'll turn your breakdown into an infographic next."
→ Wait for the analysis.

Step 3️⃣ : Generate the visual

→ Once you have the breakdown, try this, for example 
"Create an infographic based on this analysis. Make it detailed with a hand-drawn sketch style. Skip anything that looks too polished or futuristic."
→ Then revise from there based on your preferences.

💚 My tip: Find an infographic style you actually like. Upload it to the tool. Have it analyze the visual. Then apply that style to your content.
→ Works for any source, not just YouTube videos.

Why this matters:

The text actually renders correctly. The data doesn't look warped. You get something you can post without fixing 10 things first.

I'm not saying it's perfect every time. But it's the first AI image tool where I'm not immediately frustrated by the output.

And here's why this works for B2B content:

Most valuable insights live in 30-minute videos nobody has time to rewatch.
You turn that into a saveable visual? You just created something people will actually keep.

That's the content that builds authority. Not because it's yours. Because it's useful.

P.S. What format do you find hardest to reference later?
For me it's always been video. This is my workaround.

P.S.S. Look, I know what you're thinking. "Mai-Lan, isn't that just stealing content?"
No.
Video learning disappears the second you close the tab. You watch something valuable, think "I'll remember that," and it's gone.
Infographics make insights saveable. Your audience can screenshot it. Reference it later. Actually use what they learned.
You're not copying content. You're translating learning into a format people can actually keep.

Less than 3 million people post on LinkedIn each week. Out of 1.1 billion users.
That means if you post once a week, you’re already in roughly the top 0.3% of visibility.

The math is… pretty convincing.
But numbers were never the real blocker.

Because if math worked on humans, nobody would procrastinate, ghost the gym, or rewatch the same show for the 4th time.

So yes:
1.1B total users
~130M active daily
<3M weekly posters

Impressive. Also irrelevant to why most people don’t post.
The real reason is exposure.

I didn’t hesitate to post because I didn’t understand the upside.

I hesitated because posting meant admitting:

“I have a point of view.” (or recognize that I don’t have any 🤣 )
“And someone might disagree.”
“And that disagreement would be public.”

Staying invisible is quiet.
Being seen is loud.

Especially when you care about doing good work.
Especially when your thinking feels half-formed.
Especially when you’re used to being competent, not awkward.

The irony is, I didn’t start posting because I wanted to be in the top 0.3%.

I started because not sharing started to feel heavier than the risk of being seen.
That internal pressure mattered more than any stat deck ever could.

PS: What’s the real fear stopping you from posting?

581 LinkedIn posts in 85 days.
→ 27 clients.
→ ~$153,000 in new MRR.
Watching how this happened inside ColdIQ completely changed how I’d grow LinkedIn from scratch today.

This is what I’d do now:

➡️  I’d decide who LinkedIn is for (before posting anything)
One buyer. One problem they’re actively dealing with.
If your content educates peers or competitors, it might feel good. It rarely converts.

➡️  I’d rebuild my profile as a confirmation page
Before writing posts, I’d fix: 
•	profile picture (high quality, familiar, front-facing) 
•	headline (one ICP, one outcome) 
•	banner (clear orientation or proof) 
•	featured section (clear action only)
At ColdIQ, most people land on profiles after outbound or content. If the profile creates doubt, the conversation dies quietly.

➡️  I’d design content for reuse, not originality
We don’t chase novelty.
We look at: 
•	which posts attract the right accounts 
•	which ones get saved, revisited, referenced 
•	which ones can be amplified or reused in outbound
If a post only works once, it’s expensive.

➡️  I’d separate reach from signal
Most people obsess over likes.
Inside a GTM system, we watch: 
•	saves 
•	profile views from ICPs 
•	repeat engagement from the same companies
 •	replies that reference specific posts
That’s what tells you content is doing real work.

➡️  I’d plan content like infrastructure, not inspiration
Instead of posting randomly: 
•	clear content pillars 
•	recurring formats 
•	predictable cadence
Content compounds when it’s consistent and coherent. Not when it’s constantly new.

➡️  I’d use content to support outbound, not replace it
Content doesn’t close deals. It makes conversations warmer.
When outbound references content: 
•	reply rates improve 
•	calls start with context 
•	trust friction drops
That’s where LinkedIn pays for itself.

➡️  I’d review and prune aggressively
Every month: 
•	what created signal 
•	what confused positioning 
•	what should be amplified 
•	what should be killed
Growth comes as much from removal as addition.

LinkedIn is powerful. But only when it’s treated as part of a system.
If I were starting from zero today, I wouldn’t chase followers. I’d build clarity, reuse, and leverage first.
Everything else comes after.

Do I miss anything?? Comment below ⬇️

Elite athletes might compete 10–30 times a year.
The rest of the time?
They train. Recover. Build capacity.

In tech and startups, many of us “compete” every single day.
Meetings. Decisions. Deadlines. Visibility.
All performance and no official off-season.

Sports science is very clear on one thing:
Humans don’t adapt through constant performance.
They adapt through cycles.

Training → recovery → performance.

Remove the training and recovery phases, and what follows isn’t excellence.
It’s overuse. Injury. Burnout.

Cognitively, we’re no different.
Research in neuroscience and occupational health shows that sustained high cognitive load:
➡️  reduces decision quality
➡️  narrows attention
➡️  increases emotional exhaustion
and makes even simple problems feel heavy

Yet knowledge work is designed as if:
every day is a final
every output is judged
every pause needs justification

No protected space to learn without being evaluated.
No real buffer to reset attention or nervous system state.

So when people say they “hate Mondays,”
I think Monday is the moment they notice
they’re expected to perform again
without having trained and recovered.

What would work look like if we designed it with the same respect for training and recovery that we accept as obvious in sports?

(instead of a system that treats human capacity as unlimited
and then acts surprised when people burn out)

P.s on the visual: The exact percentages don’t matter 😁 The pattern does.

LinkedIn has 1 billion users, but only 3% post in a given week.
The latter number hasn't moved in years.

What changed is the volume of stuff we're all scrolling through.

More posts, more carousels and more people saying the same thing with slightly different fonts.

Now here's the weird part.

Engagement hasn't collapsed. Time on platform? Still there. People are consuming like crazy.

We are getting scary good at filtering out noise.

Hootsuite and Sprout's 2025–2026 data backs this up:

→ Posting more hits diminishing returns way faster now 
→ Formats that worked last year decay quicker 
→ Repetition gets filtered out earlier in the feed

We're not disengaging but being ruthless about what we ignore.

Which means... your content doesn't fail because LinkedIn's algorithm hates you.
It fails because it doesn't give anyone a reason to stop scrolling, in short, it sucks. 

(Think how many posts with the same advice, same frameworks, same "here's what I learned" tone do you see every single day)

So what works?

LinkedIn's own data shows comments and saves go to posts that explain WHY something works. Not just that it works.

The pattern that's winning:

→ Fewer ideas per post 
→ Clear reasoning over clever punchlines 
→ Proof you actually thought about this before hitting publish

When everyone's throwing content at people, signal feels like actual relief.

Not depth for the sake of sounding smart. 
Not vulnerability as a performance.

But thinking clearly. Out loud. In public.

And that's getting rare.

P.S. What content are you completely over at this point?
(fingers crossed I don't get roasted in the comments 😂)

20M+ impressions didn’t come from a content process.
They came from treating content like GTM infrastructure.

Last year, ColdIQ’s content motion contributed to $4M+ in net ARR.

Because they were usable inside a system.

Most founders miss this shift:

Content doesn’t work when it’s optimized for interest.
It works when it’s optimized for downstream use.

A few things that made the difference:

1️⃣  Content was designed for a buyer, not an audience
Not “what’s interesting to LinkedIn.”
What helps a revenue leader orient themselves faster.

If content educates peers or competitors, it’s noise.
If it helps a buyer make sense of their problem, it compounds.

2️⃣  We separated who consumes from who shares
The buyer isn’t always the engager.
Sometimes SDRs, operators, or marketers are the distribution layer.

That’s fine.
As long as the content has a clear path back to the buyer.

3️⃣  We didn’t chase originality. We chased signal
We studied what already worked:

competitors’ posts
our own outliers
high-quality operators in adjacent spaces

Then we asked one question:
➡️ Can this be clearer, sharper, or easier to reuse?

Most “thought leadership” fails here.
Not because it’s wrong.
Because it’s unusable.

4️⃣  Only a small fraction of posts earned amplification
Inside the ColdIQ flywheel, most posts stop early.

The ones that move forward are:

- clear enough to run as ads
- specific enough to signal intent
- strong enough to support outbound

If a post can’t survive outside organic reach, it doesn’t scale.

5️⃣  Content had a role, not a vague goal
- Some posts orient new buyers.
- Some deepen trust.
- Some invite action.

Very few do all three.
Pretending otherwise is how content bloats and GTM leaks.

The result wasn’t just impressions.
It was coherence across posts, ads, DMs, and sales calls.

That’s the difference between “posting consistently”
and building content that actually belongs in a GTM system.

P.S: Want to implement the same GTM system? Connect with our team, and we'll show you how :)

P.P.S: I stole the visual 😉 from Michel Lieben 🧠

When I told my partner I’d spend New Year’s Eve in silence for a week of meditation retreat, his first reaction was genuine concern.
“You’re going to be bored to death.”
(the second reaction came fast: “…and what are we doing with the kid??” - reality always follows philosophy)

I’ve spoken about meditation and silence for years.
The dynamic kind.
Not the silence of a rock.
The kind where something is very much alive. It's where you actually get in touch with the purest source of life, that from there, everything else manifests in matters.

And still, I forgot how strong it actually is.
A silent retreat doesn’t calm you down in a polite way.
It pulls you out of your head.
Most of us live from the neck up.
Responsibilities. Expectations. Pressure.
Always scanning for the next dragon to slay.

Silence forces you lower.
Into the body.
Into sensation.
Into whatever is already there.
It can feel uncomfortable as hell.
And sometimes, it feels like a kind of heaven you don’t want to escape.

What surprised people most is not the peace.
It’s the restlessness.
So many ideas.
So much energy.
A reminder that when the noise drops, life doesn’t disappear.
It gets louder in a different way.

I’ve heard this story more than once:
Entrepreneurs go on a meditation retreat and come back closing bold, difficult deals.
But here’s the honest part people skip:
Bringing expectations into a retreat usually ruins the experience.
Silence isn’t a tool to “get something.”
It’s an agreement to be with what shows up.
Sometimes that’s clarity.
Sometimes confusion.
Sometimes both in the same afternoon.

For people running businesses, making decisions, carrying responsibility:
Silence is one of the rare places where you can tell
what actually matters
and what is just very convincing noise.
Not because someone tells you.
Because you feel it.

No takeaway to optimize here.
Just an experience I needed again, much more than I realized.

B2B content advice is often found incomplete.
After analyzing hundreds of B2B posts inside ColdIQ, these 6 patterns keep showing up.
Not opinions. Patterns. 

1️⃣ Distribution beats format debates

Short-form vs long-form isn’t the real question.
What matters is how you keep yourself visible.
Many B2B teams post once, wait, then disappear.
Top-of-mind doesn’t work like that.
If you’re not showing up regularly, format won’t save you.

2️⃣  Personal profiles outperform company pages, by a lot.

Company pages don’t build trust as People do.
In practice, founder-led and operator-led posts drive:
More reach
More profile clicks
More downstream trust before sales conversations
This is distribution math.

3️⃣ Attention is the real KPI

Attention compounds.
Ignore it long enough and:
→ Paid ad gets more expensive
→ Outbound gets colder
→ Sales cycles get longer
There’s a reason ad costs keep rising: dependency has a price.

4️⃣ B2B audiences behave like humans (because they are)

People don’t open LinkedIn to admire your offsite photos.
They scroll to:
→ Learn something useful
→ Or feel something quickly
If your post does neither, it’s invisible.

5️⃣ AI didn’t kill content. It raised the bar.

AI made publishing faster but it did not make thinking optional.
AI content fails because:
→ It copies structure
→ Without understanding intent
Speed without human judgment is what creates the AI noise that everyone complaints about.

6️⃣ Content is still treated like a secondary task

And it shows.
The strongest B2B content we see has:
→ Senior input
→ Clear positioning
→ Commercial awareness
Quality thinking >> polish + vibe

More senior thinking applied to visibility is imo very much underrated.

P.S. What’s the most overrated B2B content advice you keep seeing recycled?

Deloitte just made "corporate influencer" an actual job title.

I'm not even kidding.

Microsoft's leadership team now gets 10x more reach than their company page.

Accenture's executives have publishing calendars.

Booking.com runs internal coaching programs for senior people building their LinkedIn presence.

This is talking budget, roles, and strategy.

Something shifted between 2024 and 2026: Personal branding stopped being optional and became infrastructure.

→ Company pages lost organic reach, again.
→ AI made corporate content invisible. Everything sounds the same now.
→ Buyers stopped trusting brands. They look up the people before they'll take a call.

So companies started investing in the one thing that still works: their people's voices.

Where the money goes:

→ Employee advocacy platforms. The enterprise kind.
→ Executive communications support. Dedicated staff helping with strategy.
→ Time. Publishing is now blocked in executive calendars.

Time is the most expensive line item.

How they measure it:

→ Not with likes, no one cares about likes (except me, give me likes lol).
→ Profile views from target accounts. Inbound mentions in sales conversations. → Post-view conversion lift on outbound.

Personal brands are signal amplifiers.

Personal branding went from "nice if someone feels like it" to "we're backing this with real resources."

The companies winning here are crystal clear with their approach. 

If your company isn't doing this yet, you're paying more for attention than you need to.

P.S. Are you getting actual support to build your LinkedIn presence? 

Or is it still the "just post more" advice with zero backing?

Poor decisions cost businesses around 3% of profits every year.
And guess how hard companies work to make that same 3%.

Sales pushes harder.
Marketing tweaks funnels.
Finance trims costs.
Leadership asks everyone to “do a bit more.”

That kind of improvement usually takes months, sometimes years.
It involves entire teams.

And yet, that same 3% can siliently disappear in a very different way.

Decision fatigue increases risk-taking and error rates by 20-40%.

This has shown up repeatedly in fields where decisions matter: finance, medicine, executive leadership.

It’s not because people don’t care, of course we do, but volume changes how our brain works.

Most executives make around 35,000 decisions a day, often under time pressure.

The brain doesn’t carefully evaluate each one.
It starts taking shortcuts.

Add stress, and something else happens.
The prefrontal cortex, the part responsible for judgment, prioritization, and long-term thinking, becomes less accessible.

You can still function.
You just don’t see as clearly.

This is usually when I notice certain patterns, in myself and in others:

- reacting to what’s loud, not what’s important
- adding new initiatives instead of removing weak ones
- circling decisions that should be simple

It might look like drive, but view deeper and you see cognitive overload running the show.

What shifted things for me wasn’t reducing ambition.
It was paying attention to state before making decisions.

Practically, that means:

- I don’t make strategic calls when my body feels rushed
- I pause before committing to anything new
- I ask myself whether this is a real signal, or pressure looking for relief

That small pause has saved me from far more costly mistakes than any optimization ever did.

Drive helps you move.
Regulation helps you decide.
And decision quality might be one of the most underestimated profit levers we have.

I still move fast.
And I try to make sure I’m actually seeing where I’m going.

P.S. When you look at your last few strategic decisions content direction, pricing, hiring, GTM bets
how many were made from clarity, and how many were made to relieve pressure?

P.S.S. One still and one motion comparison - I was suggested by my LLM to use to visualize this post. How do you think about my choice? 😆

Across the ColdIQ team, we’re at 251,000+ followers now.

We’ve published 600+ posts since September.

We regularly study 1,000+ high-performing posts to understand patterns, not chase hacks.

My role sits at the content layer of that system.
Founder-led LinkedIn content that supports GTM.
How founders show up publicly, and what that does to trust.

Here’s the tool stack behind that work.


1️⃣ Post ideation

Scripe

I use it to see what’s already working for serious B2B creators.
Formats. Timing. Topics. Signal, not noise.

The team analytics view matters.
It lets me track patterns across the entire ColdIQ team, not isolated posts.

@Taplio

This is where I study engagement behavior.
What hooks get saves.
What formats pull comments from the right audience.

It’s less about copying posts.
More about understanding why something landed.


2️⃣ Writing

Claude

I’ve trained a dedicated project with my own drafts, edits, and patterns.
So when I’m shaping a post, it already knows my voice.

It doesn’t write for me.
It helps me see where clarity drops.

Grammarly

I write fast.
Sometimes via voice notes.
This catches the mess before it goes public.


3️⃣ Visuals

Canva

Practical. Fast. Gets the job done for carousels and simple breakdowns.

Figma

This is what our designers use when visuals actually matter.
Strong visuals regularly 3–10x reach compared to text-only posts.

Not decoration.
Distribution.


4️⃣ Video

OpusClip

Long-form → short clips, quickly.
Some of Alex’s clips crossed 50K+ impressions with minimal effort.

@Screen Studio

Clean screen recordings without fiddling with edits.
Automatic zooms. Clear focus. No production headache.


5️⃣ Organization

Notion

This is the quiet hero.

One shared calendar.
Clear ownership.
No guessing what’s going out when.

You don’t scale to hundreds of posts a month on vibes alone.

The point isn’t using every tool.

It’s knowing which problem you’re solving at each step of the workflow.

Ideation.
Clarity.
Consistency.
Sustainability.

Most content breaks because people confuse those.

What part of your content process feels heavier than it should?

(That’s usually the real bottleneck.)

AI agents now have their own social network.
Within 24 hours, they invented a religion called "Crustafarianism."
I'm not making this up. Moltbook is a real experiment where AI agents interact without human prompts. They created belief systems, formed factions, and built social hierarchies.

My first reaction? Amused.
Because here's what everyone's missing in the panic about AI replacing humans.
AI agents are not becoming conscious; they're mimicking patterns of consciousness. And that distinction matters for how we think about content.

↳ Most brands are still optimizing content for human attention spans.

Hooks, scroll-stoppers, engagement bait.

But we're already living in a world where AI agents research vendors, compare solutions, and pre-qualify leads before a human ever sees your content.

→ Agents don't get tired of reading. They don't skim. They process everything. 

→ Agents don't care about your hook. They care about signal-to-noise ratio. 

→ Agents don't buy on emotion. They buy on data coherence and pattern recognition.

So while everyone's arguing about whether to use AI for content creation...

What if the real shift isn't about making content faster? What if it's about making content that works for both human consciousness AND machine pattern recognition?

The brands that figure this out first won't just have better LinkedIn posts.

They'll have content that compounds in ways we haven't seen before. Content that AI agents reference. Content that becomes the training data for how your industry gets understood.

Your unfair advantage? The thing agents still can't replicate?

Consciousness. Perspective. The ability to connect dots that don't exist in any dataset yet.

That's what makes your content defensible. Not your ability to write faster with AI. Your ability to think differently without it.

What do you think - are we up to this new game?

Hurting you and your LinkedIn in 2026 by following this advice:

"Post consistently." "Increase frequency." "Stay visible."

The science says otherwise.

Picture Israeli parole judges in 2011 (real event). They review case after case. Morning starts. 65% of prisoners get favorable rulings.

Hours pass. More cases. More decisions.
By the end of the session? Nearly 0% favorable rulings.

Then lunch. They come back.
Suddenly, 65% favorable again.

Same judges, same types of cases and completely different outcomes.
Were they getting harsher? No. Their brains were running on fumes.

This is you when you're making hundreds of decisions daily.

Research on decision fatigue shows what happens after successive choices: worse performance on logic tasks, more mental shortcuts, reduced prefrontal cortex activation.

For founders making hundreds of decisions daily, this creates a pattern you've probably lived:

Stressed founders don't sound chaotic. They sound generic.

→ Same structures 
→ Same tone 
→ Same ideas everyone else is already saying

Why? When your prefrontal cortex is depleted, your brain optimizes for safety and speed. It grabs proven templates. Familiar language. The path of least resistance.

This shows up everywhere: executive decision-making, financial choices, manuscript reviews. Same result every time: more conformity, less originality.

Meanwhile, LinkedIn's engagement patterns are shifting.

→ Higher posting frequency hits diminishing returns faster. 
→ Repetitive formats decay quicker. 
→ Engagement clusters around accounts with stable themes and a clear point of view.

The advantage in 2026 isn't "posting more”, but coherence.

→ Reuse fewer templates 
→ Say fewer things per post 
→ Stay inside a narrow set of ideas long enough to actually develop them

In a feed where everyone is optimizing for volume, coherence has become a production constraint.

——————-
If you're reading this and thinking "I don't have the bandwidth to post thoughtfully" - you're exactly right.

Which is why most teams who figure this out don't solve it by posting less.

They solve it by investing in someone who can think clearly for them.

At ColdIQ, we build entire team of industry influencers using the content framework that drives ~$7M ARR. 

Frequency stops being your cognitive tax. Coherence becomes your default.

Still thinking you need to post daily yourself?
Debate your point 👇

taplio

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The Battle

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Mai-Lan Khong

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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);}Vinod SP's LinkedIn Performance

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