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 Leader, I am driven by the mission to revolutionize how businesses harness data and AI to drive innovation and accelerate growth. I lead with a strategic vision to empower business with tools that make data collaboration seamless, AI adoption intuitive, and business centric innovation scalable.

At @DataGOL, I lead initiatives that position our platform as the go-to solution for business-centric innovation. By creating compelling value propositions and delivering intelligent experience engines like BusinessOS, AI Agents, and Self-Serve Analytics, I focus on making data collaboration seamless, AI adoption intuitive, and decision-making effortless.

With over 20+ years of experience in enterprise SaaS, data platforms, and AI/ML products. My passion lies in driving innovation and helping businesses unlock new opportunities through data intelligence. Whether it's simplifying complex systems or enabling scalable growth, I thrive at the intersection of strategy, technology, and customer-centricity.

🤝 Connect with me: Let’s connect if you're interested in exploring how AI and data can redefine what's possible for your business. 🚀

Everyone’s chasing GenAI wins. Almost everyone’s failing.

Here’s the stat nobody in the hype parade wants to talk about:
95% of enterprise GenAI pilots aren’t delivering P&L impact.

That’s not a typo. That’s MIT’s latest research.
Let’s break it down:
- Only 5% of initiatives are driving real revenue lift.
- The rest? Stalled. Overhyped. Zero business value.
- 95% of execs admit to at least one AI mishap already.
- And just 2% of companies meet even the bare minimum for responsible AI use.

Even Sam Altman’s calling this a bubble "dot-com vibes" and all.

So what separates the 5% that succeed from the 95% that burn cash and credibility?
Let’s talk facts:
✅ Buy > Build
 Vendor tools are 2x more likely to succeed than internal builds. Chasing custom models before proving value is a rookie move.
✅ Adoption is Local
 AI doesn’t scale from an ivory tower. It scales from frontline teams solving specific problems. Your “AI Center of Excellence” isn’t delivering value unless the field is using it.
✅ Narrow Focus Wins
 The winners aren’t deploying 15 pilots at once. They’re going deep on one high-value use case and proving ROI fast. No boil-the-ocean approach.

Here’s the truth:
 AI works. But most companies don’t know how to implement it. Yet.
They’re underestimating the prep work, overestimating data maturity, and skipping straight to shiny demos.

You want GenAI to work? Start with a business problem, not a tech stack.
Prove value in 90 days. Then scale.

Otherwise, you’re just another stat.

DataGOL

Vinod SP

🚀 Building the Future of Data Intelligence @ DataGOL | Ex-Meta | CDAIO | Harvard Business School💡

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