DataGOL

In my role as Head of Product at DataGOL, I led the efforts to define and execute on the product market fit for our data intelligence platform. By supporting presales, product operations, and product positioning, I played a key role in driving the success of our offerings to enhance product visibility and drive sales.

Head of Product & Marketing

Datagamz

- Managed a cross-functional product team of 21 people to deliver an AI-powered analytical platform, contributing to a 200% increase in ARR for the enterprise segment.
- Created and realised a future-forward vision and strategy, securing funding for business scaling and market penetration.
- Reduced user onboarding time by 45%, through streamlining the integration process, skill-fully aligning people, and developing efficient data connectors.

Head of Product Management

Principal Technical Product Manager

Avaya

- Created Avaya's strategic pivot in response to fast-changing CCaaS market conditions, steering 300K on-premise customers towards cloud solutions
- Directed a 150-member product team and effectively collaborated with stakeholders throughout the organization. This collaboration resulted in the successful launch of multiple SaaS  products including MPaaS, Avaya Analytics, Unified Management, and Composable APIs
- Cultivated a high-impact product management team and built the Bring your own AI initiative, aligning with tech giants such as Google, Amazon, and Microsoft to integrate cutting-edge AI capabilities into the product suite.

 Group Product manager, Digital Platform

- I launched products from 0 - 1 to solve customer experience problems including Avaya Notification Solution, Social Media Chatbot, Fleet Management, Business Routing Engine and One Touch Video. Managed their market research, product strategy, and execution.
- Delivered WebRTC product consolidation, saving over $5 million, while steering product strategy, roadmap development, architectural governance, and execution.

Technical Product Manager - Emerging Verticals

I led the consulting business and development of advanced contact center technologies, realising a $10M annual impact, and scaled Asia Pacific consulting team from 2 to 11 team members.

Lead Software Architect - Emerging Products & Technologies

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.

𝗜 𝗵𝗮𝗱 𝗼𝗻𝗲 𝗼𝗳 𝘁𝗵𝗼𝘀𝗲 𝗺𝗼𝗺𝗲𝗻𝘁𝘀 𝗹𝗮𝘀𝘁 𝘄𝗲𝗲𝗸 𝘁𝗵𝗮𝘁 𝗿𝗲𝗺𝗶𝗻𝗱𝗲𝗱 𝗺𝗲 𝘄𝗵𝘆 𝗜'𝗺 𝘀𝗼 𝗽𝗮𝘀𝘀𝗶𝗼𝗻𝗮𝘁𝗲 𝗮𝗯𝗼𝘂𝘁 𝘄𝗵𝗮𝘁 𝘄𝗲 𝗱𝗼.

Our 𝗖𝗗𝗢 𝗥𝗼𝘂𝗻𝗱𝘁𝗮𝗯𝗹𝗲 𝗼𝗻 𝗔𝗜 𝗶𝗻 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 brought together incredible leaders from across Asia and the Middle East, and honestly? The conversations were eye opening 👀

Here's the thing nobody talks about enough: Most organizations are still figuring this out. And that's completely okay.
What struck me most was the raw honesty in the room:
𝗧𝗵𝗲 𝗖𝘂𝗿𝗿𝗲𝗻𝘁 𝗥𝗲𝗮𝗹𝗶𝘁𝘆:
🔹 Most organizations are in the early stages of AI adoption, with the goal of operational efficiency being the primarily objective
🔹ROI demonstration remains to be a major challenge for AI initiatives, with CFO's asking for clear value metrics
🔹Data maturity varies significantly across organizations, creating challenges for successful AI implementations
🔹Organizations are struggling with the "hype" around AI, with unrealistic expectations from users about AI capabilities
🔹Data unification will be necessary to enable business users to easily and directly interact with data

𝗧𝗵𝗲 𝗧𝗿𝘂𝘁𝗵: 𝗬𝗼𝘂 𝗰𝗮𝗻'𝘁 𝗯𝘂𝗶𝗹𝗱 𝗮 𝘀𝗸𝘆𝘀𝗰𝗿𝗮𝗽𝗲𝗿 𝗼𝗻 𝘀𝗵𝗮𝗸𝘆 𝗴𝗿𝗼𝘂𝗻𝗱 
AI transformation isn't a technology problem – it's a foundation problem. Before you invite AI to revolutionize your business, you need your data house in order. Period.
This is exactly why we built DataGOL. We've seen too many brilliant AI initiatives crash and burn because the data infrastructure couldn't support them.

𝗧𝗵𝗲 𝗴𝗮𝗽 𝗯𝗲𝘁𝘄𝗲𝗲𝗻 𝗔𝗜 𝗽𝗼𝘁𝗲𝗻𝘁𝗶𝗮𝗹 𝗮𝗻𝗱 𝗔𝗜 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲? That's where we live. And after this roundtable, I'm more fired up than ever about bridging it 🔥

Huge gratitude to everyone who shared their challenges and insights so openly. 

These conversations are how we all get better.
If you're wrestling with data unification or trying to make your AI initiatives actually deliver results, let's talk. Because transforming AI from buzzword to business impact? 

cc: Jonathan D. Ahn, Manikandan Balasubramanian, Nandini Bagit, Utkarsh Barasia, Ghassan Chkaiban, Anees Fareed, James Fong, Shobha Joshi, Jayesh Kammili, Marziyat Mamalaeva, Hiroaki M., Abdul Hakim, Sundaramoorthy M., Karthikeyan P. (Karthik), Jacques Rahi, Rajesh Khanna, Ravindra Rampuria (Ravi), Srinivasa Ruttala, Alaa Salem, Yuluarjo Sentosa, Dr. Henry SOO, Jaime Syjuco, Kevin Tay, Tit Ho Tan, Victor Tan, Rino Thomas, Arockiya Raj Venmani Arul, Boon Yip Yee, Yugandhar Vutti, Carolyn Goh, Tara Dutt, Ramesh Ramasubbu, Shruti T., Govindan Srinivasan, Ashish Singh, Vishal Singh, Vishal Singh, Neal Shah, Mark Seaton, Jeremy Hamerman, Christian Rice, 
 
#DataTransformation #AIStrategy #CDORoundtable #DataUnification #AIInnovation

Vinod SP

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We make data business-ready@DataGOL | Ex-Meta | Chief Data & AI officer | Harvard Business School

𝗠𝗼𝘀𝘁 𝗽𝗶𝗹𝗼𝘁𝘀 𝘀𝗵𝗼𝘄 𝗔𝗜 𝗰𝗮𝗻 𝘄𝗼𝗿𝗸, 𝗯𝘂𝘁 𝗳𝗲𝘄 𝗽𝗿𝗼𝘃𝗲 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝘃𝗮𝗹𝘂𝗲.

Twelve months ago, pilots and PoCs were the safe bet.

They gave teams a way to “try AI” in small, controlled pieces.
They made it look like progress.
But behind that comfort, the real work of adoption never started.

𝗣𝗶𝗹𝗼𝘁𝘀 𝗮𝗿𝗲 𝗲𝗮𝘀𝘆 𝘁𝗼 𝗿𝘂𝗻 𝗶𝗻 𝗮 𝗯𝘂𝗯𝗯𝗹𝗲.
They show a tool works - but not that it delivers value in the wild.
They skip the hard questions:
→ Will the whole team use it?
→ Can we plug it into our legacy systems?
→ Who owns the permissions and governance?

𝗜𝗻 𝘁𝗵𝗲 𝗹𝗮𝘀𝘁 𝘀𝗶𝘅 𝗺𝗼𝗻𝘁𝗵𝘀, 𝗜’𝘃𝗲 𝘀𝗲𝗲𝗻 𝗮 𝗯𝗶𝗴 𝘀𝗵𝗶𝗳𝘁.
Enterprise buyers are asking less about pilots.
They want to know:
→ Where is the business impact?
→ What does “good” look like for our teams?
→ How do we go beyond the demo?

𝗢𝘂𝗿 𝗮𝗻𝘀𝘄𝗲𝗿:
1️⃣ Define the outcome first
→ Not “does it work?” but “does it move the needle?”
2️⃣ Invest in role-based enablement
→ Every user gets the training and support to succeed
3️⃣ Build your network of AI champions
→ Early adopters who model, teach, and spread best practices

𝗡𝗼 𝗺𝗼𝗿𝗲 𝗽𝗶𝗹𝗼𝘁 𝗽𝘂𝗿𝗴𝗮𝘁𝗼𝗿𝘆.

• We do our research up front.
• We perform tailored demos.
• We partner with customers to show them proof, not just promises.

Then we commit - even if we start with one team.

The goal is always scale.
AI embedded into real business processes, not locked away in a POC.

Pilots can give you comfort.
Anchoring to business outcomes gives you results.

Where are you focusing your AI investments right now?


#AI #EnterpriseAI #DigitalTransformation #BusinessOutcomes #Leadership #DataGOL

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The secret to being data-driven?
It’s not a secret.

It’s 3 steps and zero BS.

𝗦𝘁𝗲𝗽 𝟭: 𝗘𝗧𝗟 (𝗮𝗸𝗮 “𝗷𝘂𝘀𝘁 𝗺𝗼𝘃𝗲 𝘁𝗵𝗲 𝗱𝗮𝗺𝗻 𝗱𝗮𝘁𝗮”)
DataGOL — unlimited data ingestion.
Python script — For people who are either clever… or broke.
Goal: one place for your data. Not duct tape. Not Excel exports.

𝗦𝘁𝗲𝗽 𝟮: 𝗗𝗮𝘁𝗮 𝗦𝘁𝗼𝗿𝗮𝗴𝗲
@DataGOL. That’s it.
Cheap. Scales forever. Doesn’t cry when you throw SQL at it.
Storage = pocket change.
Queries = fine even though you scale with millions of records

𝗦𝘁𝗲𝗽 𝟯: 𝗗𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱𝘀 𝗧𝗵𝗮𝘁 𝗗𝗼𝗻’𝘁 𝗦𝘂𝗰𝗸
@DataGOL. That's it
Designed for business users.
Make it visual. Make it clear. Make it clickable.
That’s how you get decisions, not headaches.

𝗕𝗼𝗻𝘂𝘀: 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀?
DataGOL. That’s it.
Agents that understands your business context.
𝗙𝗼𝗿 𝗖𝗙𝗢𝘀 (FinTech & SaaS): Agents track cash flow, spot revenue leakage, and surface actionable financial KPIs
𝗙𝗼𝗿 𝗖𝗢𝗢𝘀 (E-commerce & Supply Chain): Agents monitor operational bottlenecks, detect fulfillment delays, and recommend fixes before they impact customers.
𝗙𝗼𝗿 𝗖𝗠𝗢𝘀 (Digital & Retail): Agents tie campaign spend to business outcomes, flag underperforming creatives, and suggest optimizations in real time.
𝗙𝗼𝗿 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗟𝗲𝗮𝗱𝗲𝗿𝘀 (SaaS & Tech): Agents analyze usage telemetry, identify churn risks, and propose features that drive adoption.

They’re decision agents —business-aware, context-driven, and always working behind the scenes to get you to answers faster.

That’s the starter pack.

No enterprise fluff. No shiny $100K tools
Just a stack that works

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Marketing teams waste 50% of their time on reporting. AI ends this now.

Every week, I meet leaders and teams buried in reports.

Half their week is spent pulling data.
Audits stretch into days.
Dashboards show what happened, but never why.
No one gets the answers they need, only more numbers.

Creative teams split across too many tools.
Ad creative volume grows fast, but humans cannot keep pace.
No system ties ad spend to creative to business results in one place.

Social platforms focus on their own channels.
Agencies patch together spreadsheets, dashboards, and still miss real-time signals.

Everyone wants a smarter dashboard.
But what we need is an AI strategist-in-a-box.

Here is what our customers now expect:
→ Full ad account audits in minutes (not days)
→ 90% of reporting automated (no more manual updates)
→ Answers for WHY performance shifts (not just what changed)
→ Next-step recommendations, not just last week’s results
→ Learning from human strategy, not only numbers

Ad platforms automate delivery.
But the real value is in decision-making.

AI now connects the dots:
• Ad spend → Creative → Business impact
• Real-time intelligence for creative pivots
• Human playbooks, scaled by machine learning

The future is not another dashboard.
The future is answers, clarity, and action-delivered in minutes, not days.

Curious how this works at scale?
Let’s talk about how to build smarter marketing systems and give time back to the teams who need it most.

#MarketingAutomation
#AIinMarketing
#CreativeStrategy
DataGOL

𝟴𝟴% 𝗼𝗳 𝗲𝗮𝗿𝗹𝘆 𝗔𝗜 𝗮𝗱𝗼𝗽𝘁𝗲𝗿𝘀 𝘀𝗲𝗲 𝗿𝗲𝗮𝗹 𝗥𝗢𝗜. 𝗬𝗼𝘂𝗿 𝗖𝗙𝗢 𝘄𝗮𝗻𝘁𝘀 𝗽𝗿𝗼𝗼𝗳-𝗵𝗲𝗿𝗲 𝗶𝘁 𝗶𝘀.

The numbers are no longer projections or they are results.
→ 53% of businesses report 6-10% annual revenue growth from AI
→ 31% see revenue gains above 10%
→ Most get to production in 3-6 months (not years!)

𝗪𝗵𝗲𝗿𝗲’𝘀 𝘁𝗵𝗲 𝗺𝗼𝗻𝗲𝘆 𝗳𝗹𝗼𝘄𝗶𝗻𝗴?
The data tells a clear story:
→ 39% drive ROI through AI-powered productivity
(email automation, document generation, meeting optimization, your knowledge workers get a boost)

→ 37% profit from AI-driven customer experience
(chatbots, support, field service-customer satisfaction climbs, costs drop)

→ 33% see revenue from AI in sales & marketing
(content creation, lead optimization-growth you can measure)

The early adopters are pulling ahead.

𝗜𝗳 𝘆𝗼𝘂 𝗶𝗻𝘃𝗲𝘀𝘁 𝟱𝟬%+ 𝗼𝗳 𝘆𝗼𝘂𝗿 𝗔𝗜 𝗯𝘂𝗱𝗴𝗲𝘁 𝗶𝗻 𝗮𝗴𝗲𝗻𝘁𝘀:
→ 82% deploy over 10 AI agents in production
→ 78% see ROI across many use cases
→ 39% of IT spending is now AI-related

But here’s the real edge-executive alignment.

𝗖𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝘄𝗶𝘁𝗵 𝗖-𝘀𝘂𝗶𝘁𝗲 𝘀𝗽𝗼𝗻𝘀𝗼𝗿𝘀𝗵𝗶𝗽 𝗿𝗲𝗽𝗼𝗿𝘁 𝟳𝟴% 𝗥𝗢𝗜.
𝗪𝗶𝘁𝗵𝗼𝘂𝘁 𝗶𝘁? 𝗢𝗻𝗹𝘆 𝟳𝟭%.

That gap is the difference between isolated pilots and full-scale transformation.

𝗧𝗵𝗲 𝘁𝗿𝘂𝘁𝗵 𝗶𝘀 𝘀𝗶𝗺𝗽𝗹𝗲:

• AI ROI is no longer a theory.
• It’s measurable, repeatable, and needed for survival.

Your competitors are not debating value.
They’re scaling advantages-right now.

𝗛𝗼𝘄 𝗿𝗲𝗮𝗱𝘆 𝗶𝘀 𝘆𝗼𝘂𝗿 𝘁𝗲𝗮𝗺 𝘁𝗼 𝗰𝗮𝗽𝘁𝘂𝗿𝗲 𝘁𝗵𝗲 𝗻𝗲𝘅𝘁 𝘄𝗮𝘃𝗲 𝗼𝗳 𝗥𝗢𝗜?

- #AIROI
- #ArtificialIntelligence
- #AITransformation
- #DataGOL


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Everyone loves AI hype. I love fixing the data.

AI pilots get the spotlight.
But the real work?
It’s fixing what sits underneath.

Here is what I see everywhere:

→ Budgets race to new pilots
→ Dashboards get promised
→ AI “strategy” decks show up

But under the surface?
Teams are buried in
→ Cleaning duplicates
→ Reconciling definitions
→ Tracking lineage across Excel chaos

The irony:
Every shiny AI pilot just piles more tech on top of cracks nobody wants to fill.

No one claps for the plumbing.
But without it, nothing scales.
Zero business value.

I have lived this at DataGOL, Meta, everywhere:
Fundamentals always win.
Clean data, clear definitions, strong pipelines.
That’s where real impact starts.

Want AI to work at scale?
Flip the priorities.

→ Fund the data work first
→ Build governance into your DNA
→ THEN bring in the AI

Plumbing before party.
Every time.

That is the boring work that delivers.

What do you see in your org?
More budget for AI sizzle, or for fixing the foundation?

Hit Follow Vinod for daily, no-fluff insights on data, AI, and what actually works.
🔔 Tap the bell on my profile to get notified when I post.

𝗜 𝘄𝗮𝘁𝗰𝗵𝗲𝗱 𝘁𝗲𝗮𝗺𝘀 𝗶𝗻𝘃𝗲𝘀𝘁 𝗶𝗻 𝗔𝗜, 𝗯𝘂𝘁 𝗼𝗻𝗹𝘆 𝟰𝟮% 𝗽𝗿𝗶𝗼𝗿𝗶𝘁𝗶𝘇𝗲𝗱 𝗰𝗵𝗮𝗻𝗴𝗲 𝗺𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁. Most miss the real challenge.

Everyone is talking about AI budgets.
But smart money is moving in a new direction.

𝟳𝟳% 𝗼𝗳 𝗼𝗿𝗴𝗮𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻𝘀 𝗮𝗿𝗲 𝗶𝗻𝗰𝗿𝗲𝗮𝘀𝗶𝗻𝗴 𝗔𝗜 𝘀𝗽𝗲𝗻𝗱𝗶𝗻𝗴-𝗲𝘃𝗲𝗻 𝗮𝘀 𝘁𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆 𝗰𝗼𝘀𝘁𝘀 𝗱𝗿𝗼𝗽.
Why?
• Cheaper AI means wider deployment, not smaller budgets.
• Leaders are not cutting back.
• They are doubling down.

𝗕𝘂𝘁 𝗵𝗲𝗿𝗲’𝘀 𝘄𝗵𝗲𝗿𝗲 𝘁𝗵𝗲 𝗺𝗼𝗻𝗲𝘆 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗴𝗼𝗲𝘀:
→ 42% on change management (𝘵𝘩𝘦 𝘩𝘢𝘳𝘥𝘦𝘴𝘵 𝘱𝘢𝘳𝘵)
→ 41% for data quality and knowledge systems
→ 40% for talent development and partnerships
→ 37% for new tools and compute resources
→ 33% to govern AI and manage risk

𝗧𝗵𝗲 𝗮𝗴𝗲𝗻𝘁𝗶𝗰 𝘀𝗵𝗶𝗳𝘁 𝗶𝘀 𝗵𝗮𝗽𝗽𝗲𝗻𝗶𝗻𝗴 𝗳𝗮𝘀𝘁.
Early adopters now put 50%+ of their AI budgets into agents.
• Not chatbots.
• Not simple automation.
Agents that act, decide, and manage complex workflows-without needing a human in the loop.

𝗜 𝘀𝗲𝗲 𝗶𝘁 𝗲𝘃𝗲𝗿𝘆 𝗱𝗮𝘆:
→ Customer service agents solve problems solo
→ Marketing agents track competitors and write analyses
→ Security agents spot threats and coordinate responses
→ Finance agents predict demand and optimize inventory

𝗧𝗵𝗲 𝗿𝗲𝗮𝗹 𝗽𝗹𝗮𝘆?
𝗟𝗲𝗮𝗱𝗲𝗿𝘀 𝗮𝗿𝗲 𝘁𝗿𝗲𝗮𝘁𝗶𝗻𝗴 𝗔𝗜 𝗮𝘀 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲, 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝘀𝗼𝗳𝘁𝘄𝗮𝗿𝗲.
They are rebuilding their business around AI capabilities, not adding another tool to the stack.

𝗧𝗵𝗲 𝗶𝗺𝗽𝗮𝗰𝘁 𝗶𝘀 𝗲𝘃𝗲𝗿𝘆𝘄𝗵𝗲𝗿𝗲:
→ Manufacturing: predictive maintenance, smarter quality checks
→ Healthcare: faster diagnostics, smoother patient flow
→ Finance: real-time risk and fraud detection
→ Retail: personalization engines, supply chain intelligence

This is not about chasing the next shiny AI feature.
The next wave is about changing how business works at the core.

Smart money is building AI-first business models.
Not for today’s edge.
For tomorrow’s advantage.

Are you preparing for the agentic revolution-or still optimizing yesterday’s playbook?

#DataGOL @AITransformation #BusinessInnovation

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𝗧𝗲𝗮𝗺𝘀 𝗮𝗿𝗲 𝗻𝗼𝘁 𝘂𝘀𝗶𝗻𝗴 𝗔𝗜 𝗳𝗼𝗿 𝗲𝗺𝗮𝗶𝗹 𝗮𝗻𝘆𝗺𝗼𝗿𝗲.

Big change is here. I see two groups now.
• One learns fast with AI agents.
• One still does old work by hand.

𝗕𝗶𝗴 𝗴𝗮𝗽. 𝗡𝗼𝘁 𝘀𝗺𝗮𝗹𝗹.

I talk to leaders often.
𝗧𝗵𝗲𝘆 𝘁𝗲𝗹𝗹 𝗺𝗲: 
• “We build reports in minutes now.”
• “We help more customers without feeling tired.”
• “Our IT teams do big things. AI fixes small problems.”

𝗜 𝘀𝗲𝗲 𝗻𝘂𝗺𝗯𝗲𝗿𝘀 𝘁𝗼𝗼.
• 39% say their work doubled with AI.
• 52% already use AI agents in real work, not just for fun.

𝗕𝘂𝘁 𝘁𝗵𝗲𝗿𝗲 𝗶𝘀 𝘀𝗼𝗺𝗲𝘁𝗵𝗶𝗻𝗴 𝗺𝗼𝗿𝗲.
• AI agents and people work together.
• AI sees patterns. People decide what matters.
Together, teams grow faster.

If you wait, you fall behind.

If you start now, you lead.

𝗛𝗼𝘄 𝗶𝘀 𝘆𝗼𝘂𝗿 𝘁𝗲𝗮𝗺 𝗰𝗵𝗮𝗻𝗴𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗔𝗜?
Do you build with AI, or still watch from the side?
(That is your edge-or your risk)

#AI #Teams #Productivity #FutureOfWork #DataGOL

𝗦𝘁𝗮𝘆𝗶𝗻𝗴 𝗔𝗵𝗲𝗮𝗱 𝗶𝗻 𝘁𝗵𝗲 𝗔𝗴𝗲 𝗼𝗳 𝗔𝗜. 𝗢𝗽𝗲𝗻𝗔𝗜'𝘀 𝟱 𝗘𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹𝘀 𝗳𝗼𝗿 𝗟𝗲𝗮𝗱𝗲𝗿𝘀. AI adoption is scaling faster than any prior technology - 5.6× more frontier model releases since 2022, 280× cost reduction in 18 months, and adoption 4× faster than the internet. 

The real question: 𝗵𝗼𝘄 𝗱𝗼 𝗹𝗲𝗮𝗱𝗲𝗿𝘀 𝗸𝗲𝗲𝗽 𝗽𝗮𝗰𝗲?

Early adopters moved beyond efficiency and are growing revenues 1.5x faster than peers. Building on learnings from firms like Estée Lauder, Notion and BBVA OpenAI’s crafted a new leadership guide that outlines the essentials for thriving in the AI era:


𝟭 𝗔𝗹𝗶𝗴𝗻 – 𝗕𝘂𝗶𝗹𝗱 𝗰𝗹𝗮𝗿𝗶𝘁𝘆 𝗮𝗻𝗱 𝘁𝗿𝘂𝘀𝘁
● Executive storytelling – Communicate why AI is central to the future.
● Company-wide AI adoption goals – Set measurable targets and embed them into KPIs
● Leaders role-modeling AI use – Show employees how executives use AI
● Functional leader sessions – Connect AI to team-specific realities


𝟮 𝗔𝗰𝘁𝗶𝘃𝗮𝘁𝗲 – 𝗘𝗺𝗽𝗼𝘄𝗲𝗿 𝗲𝗺𝗽𝗹𝗼𝘆𝗲𝗲𝘀 𝘁𝗼 𝗮𝗱𝗼𝗽𝘁 𝗔𝗜
● Launch structured AI skills programs – Role-specific training embedded in daily work
● Establish AI champions networks – Internal mentors to spread adoption
● Make experimentation routine – Hackathons, dedicated time for testing ideas
● Make it count – Tie AI adoption to performance and career growth


𝟯 𝗔𝗺𝗽𝗹𝗶𝗳𝘆 – 𝗦𝗰𝗮𝗹𝗲 𝘄𝗶𝗻𝘀 𝗯𝗲𝘆𝗼𝗻𝗱 𝘀𝗶𝗹𝗼𝘀
● Centralized AI knowledge hub – One place for resources, policies, and best practices
● Consistently share success stories – Highlight big breakthroughs and small wins
● Build active internal communities – Peer-to-peer learning in Teams/Slack groups.
● Reinforce wins at team level – Regular recognition in meetings and updates


 𝟰 𝗔𝗰𝗰𝗲𝗹𝗲𝗿𝗮𝘁𝗲 – 𝗠𝗼𝘃𝗲 𝗳𝗮𝘀𝘁 𝗳𝗿𝗼𝗺 𝗽𝗶𝗹𝗼𝘁 𝘁𝗼 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻
● Unblock access to AI tools and data – Ensure speed in approvals and availability
● Clear AI intake and prioritization process – Transparent idea submission and review
● Cross-functional AI council – Executive-backed group to unblock and fast-track
● Reward success to speed innovation – Invest back into high-impact teams


𝟱 𝗚𝗼𝘃𝗲𝗿𝗻 – 𝗕𝗮𝗹𝗮𝗻𝗰𝗲 𝘀𝗽𝗲𝗲𝗱 𝘄𝗶𝘁𝗵 𝗿𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝘆
● Simple responsible AI playbook – Clear “safe-to-try” rules to reduce bottlenecks
● Regular reviews of AI practices – Quarterly checks to adapt to new risks and regulations


🚀 Companies that embrace these principles move beyond experimentation into business impact and competitive advantage. AI isn’t just a tool - it’s a new way of working

https://lnkd.in/gJk_njEd

I use 8 proven steps to make AI agents smarter and faster. Ready to see how?

Here’s the truth:

AI agents are only as good as the context you give them.
Context = everything the agent “knows” as it works: instructions, memory, history, data.
But more isn’t always better.
Too much, or the wrong kind, and your agent loses focus.

I’ve seen this problem in the wild:
→ Agents that forget what matters  
→ Tools that dump piles of data  
→ Long tasks that confuse the system

After 20+ years in data and AI, I’ve learned you need a playbook.
Here are my 8 steps for smarter, faster agents:

1️⃣ Curate high-signal tokens  
Pick only what moves the needle. Cut out noise.

2️⃣ Modular, efficient tools  
Each tool should give lean, clear info. No wasted space.

3️⃣ Just-in-time retrieval  
Fetch what you need, when you need it. Don’t overload memory.

4️⃣ Compaction  
Summarize-keep the essentials, toss the rest.

5️⃣ Structured note-taking  
Let agents save notes outside the main context. Pull them when needed.

6️⃣ Multi-agent teamwork  
Split hard jobs. Let sub-agents dive deep, and report back.

7️⃣ Hybrid strategies  
Mix preload and retrieval. Adjust for short or long tasks.

8️⃣ Iterative tuning  
Start small. Add as you go. Test, then trim.

Every step saves attention and keeps your agent sharp.

Here’s what I’ve seen at DataGOL:
→ More reliable results
→ Less confusion
→ Agents that scale with your business

Context engineering is the next frontier.
Not hype. Not magic.
A real system for smarter decisions.

What’s your biggest challenge managing agent context right now?

Enjoyed this post? Like 👍, comment 💭, or re-post ♻️ to share with others.

#DataGOL #AIagents #ContextEngineering #DataProducts #AI #BusinessGrowth

𝟯,𝟱𝟬𝟬 𝗲𝘅𝗲𝗰𝘀 𝘁𝗼𝗹𝗱 𝗚𝗼𝗼𝗴𝗹𝗲 𝘄𝗵𝗲𝗿𝗲 𝗔𝗜 𝗶𝘀 𝗺𝗮𝗸𝗶𝗻𝗴 𝗺𝗼𝗻𝗲𝘆. 𝗧𝗵𝗲 𝗮𝗻𝘀𝘄𝗲𝗿𝘀 𝗺𝗶𝗴𝗵𝘁 𝘀𝘂𝗿𝗽𝗿𝗶𝘀𝗲 𝘆𝗼𝘂.

Google asked leaders across the world one question: 𝘄𝗵𝗲𝗿𝗲 𝗶𝘀 𝗔𝗜 𝗱𝗿𝗶𝘃𝗶𝗻𝗴 𝗿𝗲𝗮𝗹 𝗿𝗲𝘀𝘂𝗹𝘁𝘀?

I spent my morning with this report, looking for what matters.

Here are the pages you want to check out first:

→ Page 16: 𝗦𝗲𝗲 𝘄𝗵𝗶𝗰𝗵 𝗶𝗻𝗱𝘂𝘀𝘁𝗿𝗶𝗲𝘀 𝗮𝗿𝗲 𝘂𝘀𝗶𝗻𝗴 𝗔𝗜, 𝗮𝗻𝗱 𝗲𝘅𝗮𝗰𝘁𝗹𝘆 𝗵𝗼𝘄.
Manufacturing, retail, finance, healthcare-each has a different playbook.
Some use AI for supply chain, some for customer support, some for product design.

→ Page 26: 𝗙𝗶𝗻𝗱 𝗼𝘂𝘁 𝗵𝗼𝘄 𝗽𝗲𝗼𝗽𝗹𝗲 𝗮𝗿𝗲 𝘄𝗼𝗿𝗸𝗶𝗻𝗴 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁𝗹𝘆 𝘄𝗶𝘁𝗵 𝗔𝗜 𝗶𝗻 𝘁𝗵𝗲 𝗺𝗶𝘅.
Teams are shifting from manual tasks to higher-value work.
AI is not just a tool; it is shaping how companies run.

→ Page 31: 𝗚𝗲𝘁 𝘁𝗵𝗲 𝗵𝗮𝗿𝗱 𝗻𝘂𝗺𝗯𝗲𝗿𝘀 𝗼𝗻 𝗥𝗢𝗜.
Who is seeing the biggest wins?
Which use cases turn investment into profit?

→ Page 41: 𝗪𝗵𝗲𝗿𝗲 𝘁𝗵𝗲 𝗻𝗲𝘅𝘁 𝘄𝗮𝘃𝗲 𝗼𝗳 𝗔𝗜 𝗶𝗻𝘃𝗲𝘀𝘁𝗺𝗲𝗻𝘁 𝗶𝘀 𝗵𝗲𝗮𝗱𝗶𝗻𝗴.
This is where the smart money is moving-think automation, agentic AI, and data-driven products.

𝗢𝗻𝗲 𝘁𝗵𝗶𝗻𝗴 𝗶𝘀 𝗰𝗹𝗲𝗮𝗿:
• AI is no longer just hype.
• It is changing how businesses make money right now.

And sometimes, the biggest insights come wrapped in the strangest visuals.

If you want to cut through the noise and see what actually works, check out the report.

#google #ai #data #leadership #businessgrowth #googlecloud #research

Enjoyed this breakdown? Like 👍, re-post ♻️, or share with someone who wants the real story on AI ROI.

𝗘𝘃𝗲𝗿 𝗻𝗼𝘁𝗶𝗰𝗲 𝗵𝗼𝘄 𝗰𝗮𝗹𝗹𝗶𝗻𝗴 𝗰𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝘀𝗲𝗿𝘃𝗶𝗰𝗲 𝗳𝗲𝗲𝗹𝘀 𝗺𝗼𝗿𝗲 𝗽𝗮𝗶𝗻𝗳𝘂𝗹 𝗻𝗼𝘄, 𝗲𝘃𝗲𝗻 𝘄𝗶𝘁𝗵 𝗮𝗹𝗹 𝘁𝗵𝗶𝘀 𝗻𝗲𝘄 𝗔𝗜 𝗶𝗻 𝘁𝗵𝗲 𝗺𝗶𝘅?

𝗟𝗲𝘁’𝘀 𝗯𝗲 𝗵𝗼𝗻𝗲𝘀𝘁: Most companies use AI to slap a quick fix on bad processes and hope nobody notices the duct tape.
A shiny chatbot here, a useless email automation there.
And what’s the 𝗿𝗲𝘀𝘂𝗹𝘁?
More customers stuck in endless loops, repeating themselves to bots that just don't get it.

𝗧𝗵𝗲 𝗿𝗲𝗮𝗹 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 𝗶𝘀𝗻’𝘁 𝘁𝗼𝗼 𝗹𝗶𝘁𝘁𝗹𝗲 𝗔𝗜.
𝗜𝘁’𝘀 𝘂𝘀𝗶𝗻𝗴 𝗔𝗜 𝗳𝗼𝗿 𝘁𝗵𝗲 𝘄𝗿𝗼𝗻𝗴 𝗿𝗲𝗮𝘀𝗼𝗻𝘀.

While everyone else is bragging about “cutting support costs,” the most successful companies are quietly doing the opposite.
They’re asking: 𝗛𝗼𝘄 𝗱𝗼 𝘄𝗲 𝘂𝘀𝗲 𝗔𝗜 𝘁𝗼 𝗯𝘂𝗶𝗹𝗱 𝗿𝗲𝗹𝗮𝘁𝗶𝗼𝗻𝘀𝗵𝗶𝗽𝘀 𝘁𝗵𝗮𝘁 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗹𝗮𝘀𝘁?

Here’s where things get interesting:

→ 𝗗𝗼𝗻’𝘁 𝘁𝗿𝗲𝗮𝘁 𝗔𝗜 𝗹𝗶𝗸𝗲 𝗮 𝗽𝗹𝘂𝗴𝗶𝗻.
The winners treat it as a company-wide playbook.
Not just a shiny add-on, but a new way to coordinate every single touchpoint between customer and company.

→ 𝗗𝗼𝗻’𝘁 𝗼𝗯𝘀𝗲𝘀𝘀 𝗼𝘃𝗲𝗿 𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆 𝗮𝘁 𝘁𝗵𝗲 𝗲𝘅𝗽𝗲𝗻𝘀𝗲 𝗼𝗳 𝗹𝗼𝘆𝗮𝗹𝘁𝘆.
Real value?
It’s not just about faster tickets.
It’s about experiences so good that customers stick around for years.
Measure loyalty.
Measure lifetime value.
That’s the real scoreboard.

→ 𝗦𝘁𝗼𝗽 𝗽𝗮𝘁𝗰𝗵𝗶𝗻𝗴.
Start transforming.
Instead of layering AI onto broken systems, the leaders use it to completely rethink how they serve customers.
They break down silos so every part of the business actually talks to each other.
The goal isn’t one less support ticket—it’s a customer who leaves thinking, “Wow, that was easy.”

𝗛𝗲𝗿𝗲’𝘀 𝘁𝗵𝗲 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻 𝘁𝗵𝗮𝘁 𝗺𝗮𝘁𝘁𝗲𝗿𝘀:
Are you rolling out another AI tool, or are you designing the playbook that will make customers love you for the next ten years?

𝗢𝗻𝗲 𝗰𝗿𝗲𝗮𝘁𝗲𝘀 𝘀𝗵𝗼𝗿𝘁-𝘁𝗲𝗿𝗺 𝘀𝗮𝘃𝗶𝗻𝗴𝘀.
𝗧𝗵𝗲 𝗼𝘁𝗵𝗲𝗿 𝗿𝗲𝘄𝗿𝗶𝘁𝗲𝘀 𝘆𝗼𝘂𝗿 𝘄𝗵𝗼𝗹𝗲 𝗺𝗮𝗿𝗸𝗲𝘁.

So, which game are you playing?

What trends or frustrations are you seeing with AI in customer service lately?

𝗘𝘃𝗲𝗿𝘆 𝗶𝗻𝗱𝘂𝘀𝘁𝗿𝘆 𝗽𝗹𝗮𝘆𝘀 𝗮 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁 𝗔𝗜 𝗴𝗮𝗺𝗲.

𝗥𝗲𝘁𝗮𝗶𝗹? I see brands using AI to shape shopping journeys.
𝗠𝗮𝗻𝘂𝗳𝗮𝗰𝘁𝘂𝗿𝗶𝗻𝗴? AI keeps supply chains smooth and goods moving.
𝗙𝗶𝗻𝗮𝗻𝗰𝗲? Risk checks now run in the background, fast.
𝗛𝗲𝗮𝗹𝘁𝗵𝗰𝗮𝗿𝗲? AI speeds up patient care and protects data.

𝗪𝗶𝗹𝗱, 𝗿𝗶𝗴𝗵𝘁?

But here’s where leaders win:
• They do not bolt AI on top.
• They rethink how all the work gets done, team by team.

For example:
- 𝗥𝗲𝘁𝗮𝗶𝗹 runs AI for better customer service (47% say it leads)
- 𝗠𝗮𝗻𝘂𝗳𝗮𝗰𝘁𝘂𝗿𝗶𝗻𝗴 uses AI to check quality (54% now automate checks)
- 𝗛𝗲𝗮𝗹𝘁𝗵𝗰𝗮𝗿𝗲 gets AI in tech support (56% now use it) plus data safety (46%)
- 𝗙𝗶𝗻𝗮𝗻𝗰𝗲 runs AI for customer help (57%)
- 𝗧𝗲𝗹𝗲𝗰𝗼𝗺 Support teams use AI (56%)
- 𝗠𝗲𝗱𝗶𝗮 𝗮𝗻𝗱 𝗘𝗻𝘁𝗲𝗿𝘁𝗮𝗶𝗻𝗺𝗲𝗻𝘁 use AI to protect ideas and speed up content

𝗧𝗵𝗲 𝗹𝗲𝘀𝘀𝗼𝗻?
𝗔𝗜 𝗮𝗱𝗼𝗽𝘁𝗶𝗼𝗻 𝗶𝘀 𝗻𝗼𝘁 𝘁𝗶𝗰𝗸𝗶𝗻𝗴 𝗮 𝗯𝗼𝘅.𝗜𝘁 𝗶𝘀 𝗮𝗯𝗼𝘂𝘁 𝗻𝗲𝘄 𝘄𝗮𝘆𝘀 𝘁𝗼 𝘄𝗼𝗿𝗸.

𝗧𝗵𝗲 𝗿𝗲𝗮𝗹 𝗰𝗵𝗮𝗻𝗴𝗲 𝗶𝘀 𝗶𝗻 𝘁𝗵𝗲 𝗽𝗿𝗼𝗰𝗲𝘀𝘀.
• Who adapts wins.
• Who copies falls behind.

Are you building your own AI-first path?

#AI #Leadership #BusinessGrowth #DigitalTransformation #AIPlaybook

Your AI initiative launches with fanfare. Executives champion it. Budgets get approved. Tools get purchased. Six months later? Scattered pilot project

Open article: The AI Transformation Paradox: Why Most Organizations Fail Where Others Thrive by Vinod SP • 3 min read

The AI Transformation Paradox: Why Most Organizations Fail Where Others Thrive

𝟭𝟴% 𝗼𝗳 𝗖𝗵𝗮𝘁𝗚𝗣𝗧 𝘂𝘀𝗲𝗿𝘀 𝘄𝗮𝗻𝘁 𝗔𝗜 𝘁𝗼 𝘄𝗿𝗶𝘁𝗲 𝗮𝗻𝗱 𝗲𝗱𝗶𝘁 𝗳𝗼𝗿 𝘁𝗵𝗲𝗺. 𝗧𝗵𝗲 𝗻𝗲𝘅𝘁 𝗯𝗶𝗹𝗹𝗶𝗼𝗻-𝗱𝗼𝗹𝗹𝗮𝗿 𝗦𝗮𝗮𝗦 𝗶𝘀 𝗵𝗶𝗱𝗶𝗻𝗴 𝗵𝗲𝗿𝗲.

OpenAI’s latest data is a goldmine for builders.

Every bar in their new usage chart?
A real-world demand signal.

𝗧𝗵𝗲 𝗯𝗶𝗴𝗴𝗲𝘀𝘁 𝘄𝗲𝗱𝗴𝗲:
Personal writing + editing (18% combined).
• 8% want AI to write emails.
• 10.6% want editing or critique.

𝗨𝘀𝗲𝗿𝘀 𝗮𝗿𝗲 𝗻𝗼𝘁 𝗮𝘀𝗸𝗶𝗻𝗴 𝗳𝗼𝗿 𝗿𝗮𝗻𝗱𝗼𝗺 𝗮𝗻𝘀𝘄𝗲𝗿𝘀.
• They want copilots.
• AI that sits inside sales, legal, HR, PR workflows.
• Right where the work happens.
• Not just a chatbot-an embedded teammate.

But that’s not all.
Look what else people crave:

→ 𝗧𝘂𝘁𝗼𝗿𝗶𝗻𝗴 + 𝘁𝗲𝗮𝗰𝗵𝗶𝗻𝗴 (𝟭𝟬.𝟮%)
On-demand personal AI tutors.
Explain things your way.
Remember your progress.
Nudge you when you need it.
Education, personalized to the max.

→ 𝗛𝗼𝘄-𝘁𝗼 𝗮𝗱𝘃𝗶𝗰𝗲 (𝟴.𝟱%)
Micro-niche SaaS for every “how do I…?”
Resume fixes.
Meal prep.
Shopify setup.
Every “how-to” is a wedge for a vertical AI agent.

→ 𝗛𝗲𝗮𝗹𝘁𝗵, 𝗳𝗶𝘁𝗻𝗲𝘀𝘀, 𝗯𝗲𝗮𝘂𝘁𝘆, 𝘀𝗲𝗹𝗳-𝗰𝗮𝗿𝗲 (𝟱.𝟳%)
People trust AI with their bodies.
AI trainers, nutritionists, skin coaches.
Add a human layer for accountability.
Suddenly, you have a trusted companion.

→ 𝗣𝘂𝗿𝗰𝗵𝗮𝘀𝗮𝗯𝗹𝗲 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝘀 (𝟮.𝟭%)
Tiny %-big opportunity.
People ask AI what to buy.
The Amazon affiliate model, reborn.
Whoever builds the “AI-powered shopping layer” owns the new SEO.

→ 𝗧𝗿𝗮𝗻𝘀𝗹𝗮𝘁𝗶𝗼𝗻 (𝟰.𝟱%)
Not just words-context, tone, culture, slang.
AI that localizes your intent.
Still unsolved, still massive.

→ 𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗽𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 (𝟰.𝟮%)
Devs want copilots.
But the real play?
Vertical code copilots.
Shopify dev copilot, Unreal Engine copilot, you name it.

𝗛𝗲𝗿𝗲’𝘀 𝘁𝗵𝗲 𝘁𝗿𝘂𝘁𝗵:
Every bar is a behavior people already pay for elsewhere.
Turn those habits into vertical AI agents.
Build trust, own the wedge, and you unlock billion-dollar outcomes.

The data doesn’t lie.
𝗪𝗵𝗮𝘁 𝘃𝗲𝗿𝘁𝗶𝗰𝗮𝗹 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁 𝘄𝗼𝘂𝗹𝗱 𝘆𝗼𝘂 𝗯𝘂𝗶𝗹𝗱-𝗼𝗿 𝘂𝘀𝗲-𝗳𝗶𝗿𝘀𝘁?

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