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.

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?

Vinod SP

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

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“𝗔𝗜 𝗶𝘀 𝗰𝗼𝗺𝗶𝗻𝗴 𝗳𝗼𝗿 𝗲𝘃𝗲𝗿𝘆𝗼𝗻𝗲’𝘀 𝗷𝗼𝗯𝘀” 𝗶𝘀 𝘁𝗵𝗲 𝘄𝗿𝗼𝗻𝗴 𝗳𝗲𝗮𝗿. 𝗧𝗵𝗲 𝗿𝗲𝗮𝗹 𝘁𝗵𝗿𝗲𝗮𝘁?
AI exposing who’s been coasting.

If your daily work is copy-paste, checklists, or memorising facts…
AI will outrun you without breaking a sweat.

But if your edge is curiosity, courage, and creative fire…
AI just became your jetpack.

𝗟𝗲𝘁’𝘀 𝗯𝗲 𝗵𝗼𝗻𝗲𝘀𝘁:
Warren Buffet said "When the tide goes out, you discover who's been swimming naked" - and the tide analogy is a great one to understand the role of AI.

Here’s the simple truth:

𝗣𝗛𝗔𝗦𝗘 𝟭 — 𝗔𝗜 𝗗𝗢𝗘𝗦

- Blazing speed.
- Language generation
- Relentless pattern hunting.
- Code writing

𝗣𝗛𝗔𝗦𝗘 𝟮 — 𝗛𝗨𝗠𝗔𝗡𝗦 𝗗𝗘𝗖𝗜𝗗𝗘

- Judgment.
- Ethics.
- Context.
- Meaning.

𝗣𝗛𝗔𝗦𝗘 𝟯 — 𝗧𝗢𝗚𝗘𝗧𝗛𝗘𝗥 𝗧𝗛𝗘𝗬 𝗪𝗜𝗡

You bring the “why.”
AI brings the “how.”
That combo is unstoppable.

But most people still fall into the same traps:

𝗔𝘃𝗼𝗶𝗱 𝘁𝗵𝗲𝘀𝗲 𝘁𝗿𝗮𝗽𝘀:

- Thinking “busy” equals “valuable.”

- Hiding behind process instead of insight.

- Treating AI like a threat instead of a tool.

- Waiting for permission to level up.

The tide is going out fast.
Some will be caught exposed.
Others will rise, faster than ever.

The real question isn’t “Will AI take my job?”
It’s “Is my work worth amplifying?”

Because if you are - AI isn’t your replacement. It’s your force multiplier.

𝗖𝗼𝗻𝗳𝗶𝗱𝗲𝗻𝗰𝗲 𝗶𝗻 𝘆𝗼𝘂𝗿 𝗱𝗮𝘁𝗮 𝗺𝗲𝗮𝗻𝘀 𝗻𝗼𝘁𝗵𝗶𝗻𝗴 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗰𝗼𝗻𝘁𝗲𝘅𝘁. 𝗜 𝗹𝗲𝗮𝗿𝗻𝗲𝗱 𝘁𝗵𝗶𝘀 𝘁𝗵𝗲 𝗵𝗮𝗿𝗱 𝘄𝗮𝘆.

I once joined a project where the AI team told me, “𝗢𝘂𝗿 𝗱𝗮𝘁𝗮’𝘀 𝗿𝗲𝗮𝗱𝘆.”

Three weeks later, half the models were hallucinating.

The other half waited for “better data” that never came.

“Ready” only meant the data existed somewhere.

Not that it was structured, governed, or trusted.

That was the gap.

𝗔𝗜 𝗰𝗮𝗻’𝘁 𝗰𝗼𝗺𝗽𝗲𝗻𝘀𝗮𝘁𝗲 𝗳𝗼𝗿 𝗰𝗵𝗮𝗼𝘀.

No matter how advanced the model, if your data is not clear and reliable, the results will misfire or stall.

𝗜 𝘀𝗲𝗲 𝘁𝗵𝗶𝘀 𝗺𝗶𝘀𝘁𝗮𝗸𝗲 𝗶𝗻 𝗺𝗮𝗻𝘆 𝘁𝗲𝗮𝗺𝘀. They trust what they have-but miss what matters.

The most successful AI teams do something different.

- They treat governance as the foundation, not a barrier.

- Good governance gives clarity, accountability, and speed.

𝗪𝗶𝘁𝗵𝗼𝘂𝘁 𝗶𝘁, 𝗔𝗜 𝗶𝘀 𝗷𝘂𝘀𝘁 𝗴𝘂𝗲𝘀𝘀𝗶𝗻𝗴.

With it, you get real value, not just numbers. I’ve seen companies waste months chasing “AI-ready” dreams.

But the teams that win start with the basics.

Build trust in your data first.

Everything else gets easier.

𝗛𝗼𝘄 𝗮𝗿𝗲 𝘆𝗼𝘂 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝘁𝗿𝘂𝘀𝘁 𝗶𝗻 𝘆𝗼𝘂𝗿 𝗱𝗮𝘁𝗮?

𝗧𝗵𝗲 𝗿𝗲𝗮𝗹 𝗔𝗜 𝘄𝗶𝗻𝗻𝗲𝗿𝘀 𝘀𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗼𝘂𝘁𝗰𝗼𝗺𝗲𝘀 - 𝗻𝗼𝘁 𝗮𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝘀 𝗼𝗿 𝗵𝘆𝗽𝗲.

I see it every day.

AI is not a science project anymore.

It is part of the real business engine now.

In CRMs, contact centers, and finance systems, AI is working quietly in the background.

𝗕𝘂𝘁 𝗵𝗲𝗿𝗲’𝘀 𝘄𝗵𝗮𝘁 𝘀𝘁𝗮𝗻𝗱𝘀 𝗼𝘂𝘁:

1. 𝗦𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗿𝗲𝘀𝘂𝗹𝘁𝘀.
A retailer does not want a chatbot. 𝗜𝘁 𝘄𝗮𝗻𝘁𝘀 𝗼𝗿𝗱𝗲𝗿 𝗶𝘀𝘀𝘂𝗲𝘀 𝘀𝗼𝗹𝘃𝗲𝗱 𝗳𝗮𝘀𝘁𝗲𝗿. 
A bank needs fraud stopped before customers notice. 
The teams moving forward set goals that matter to people, not only to the model.

2. 𝗗𝗮𝘁𝗮 𝗶𝘀 𝘁𝗵𝗲 𝗿𝘂𝗻𝘄𝗮𝘆.
I have watched strong models fail because the data was broken or stuck in silos. 
If sales, support, marketing and ops cannot share data, even the best AI will guess instead of act. 
Clean, connected, real-time data makes AI scale.

3. 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗯𝘂𝗶𝗹𝗱𝘀 𝘁𝗿𝘂𝘀𝘁.
More autonomy for AI means more structure is needed. 
I worked with a healthcare provider who built AI for scheduling, but only after adding human review at key points. 
Governance was not a blocker. It made adoption possible.

𝗧𝗵𝗲 𝘀𝗵𝗶𝗳𝘁 𝗶𝘀 𝗻𝗼𝘁 𝗮𝗯𝗼𝘂𝘁 𝘀𝗺𝗮𝗿𝘁𝗲𝗿 𝗺𝗼𝗱𝗲𝗹𝘀.

It is about changing how work gets done.

AI will win when it blends into the flow of business. Not as a headline.

As the new normal.

𝗪𝗵𝗮𝘁 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗼𝘂𝘁𝗰𝗼𝗺𝗲 𝗮𝗿𝗲 𝘆𝗼𝘂 𝗳𝗼𝗰𝘂𝘀𝗶𝗻𝗴 𝗼𝗻 𝗮𝘀 𝘆𝗼𝘂 𝗯𝘂𝗶𝗹𝗱 𝘆𝗼𝘂𝗿 𝗔𝗜 𝗿𝗼𝗮𝗱𝗺𝗮𝗽?

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Siddharth Swain

𝗔𝗻𝗼𝘁𝗵𝗲𝗿 𝗱𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱 𝘄𝗼𝗻’𝘁 𝗳𝗶𝘅 𝘆𝗼𝘂𝗿 𝘁𝗿𝘂𝘀𝘁 𝗶𝘀𝘀𝘂𝗲𝘀. 

Every company says the same thing.

We need one more dashboard.

But I see the real problem every day.

Sales and Finance pull different “revenue” numbers.

Marketing calls a “customer” something else.

Operations trusts none of it.

So teams build another dashboard.

𝗔 𝗻𝗲𝘄 𝗕𝗜 𝘁𝗼𝗼𝗹. 𝗔 𝗽𝗿𝗲𝘁𝘁𝗶𝗲𝗿 𝗰𝗵𝗮𝗿𝘁.

𝗧𝗵𝗲𝗻 𝘀𝗼𝗺𝗲𝗼𝗻𝗲 𝗮𝘀𝗸𝘀, “𝗪𝗵𝗶𝗰𝗵 𝗻𝘂𝗺𝗯𝗲𝗿 𝗶𝘀 𝗿𝗶𝗴𝗵𝘁?”

Silence.

Dashboards don’t create trust. Clear definitions do.

Before the next big visualization, start here:

→ 𝗗𝗲𝗳𝗶𝗻𝗲 𝘄𝗵𝗼 𝗼𝘄𝗻𝘀 𝗲𝗮𝗰𝗵 𝗺𝗲𝘁𝗿𝗶𝗰.

→ 𝗦𝘁𝗮𝗻𝗱𝗮𝗿𝗱𝗶𝘇𝗲 𝗲𝘃𝗲𝗿𝘆 𝗸𝗲𝘆 𝗻𝘂𝗺𝗯𝗲𝗿.

→ 𝗔𝗹𝗶𝗴𝗻 𝗼𝗻 𝗼𝗻𝗲 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗳𝗼𝗿 𝘁𝗵𝗲 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀.

Fifty charts won’t help if nobody agrees what they show.

You don’t need more dashboards.

𝗬𝗼𝘂 𝗻𝗲𝗲𝗱 𝗼𝗻𝗲 𝘀𝗼𝘂𝗿𝗰𝗲 𝗼𝗳 𝘁𝗿𝘂𝘁𝗵.

How are you building trust in your data?

𝗠𝗼𝘀𝘁 𝗔𝗜 𝗽𝗶𝗹𝗼𝘁𝘀 𝗻𝗲𝘃𝗲𝗿 𝗽𝗮𝘆 𝗼𝗳𝗳. 𝗠𝘆 𝟯-𝘀𝘁𝗲𝗽 𝗽𝗹𝗮𝗻 𝘁𝘂𝗿𝗻𝘀 𝗽𝗶𝗹𝗼𝘁𝘀 𝗶𝗻𝘁𝗼 𝗽𝗿𝗼𝗳𝗶𝘁 𝘁𝗵𝗶𝘀 𝗾𝘂𝗮𝗿𝘁𝗲𝗿.

I see many companies launch AI pilots with high hopes. They want quick wins, but end up with stalled projects and little impact. The difference between those stuck in “pilot mode” and those driving real ROI? It’s not more tools or bigger budgets. It’s a simple, focused approach that 𝗰𝗼𝗻𝗻𝗲𝗰𝘁𝘀 𝗔𝗜 𝘁𝗼 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝘃𝗮𝗹𝘂𝗲-𝗿𝗶𝗴𝗵𝘁 𝗳𝗿𝗼𝗺 𝘁𝗵𝗲 𝘀𝘁𝗮𝗿𝘁.

𝗛𝗲𝗿𝗲’𝘀 𝘄𝗵𝗮𝘁 𝘄𝗼𝗿𝗸𝘀 𝗳𝗼𝗿 𝗺𝗲, 𝗽𝗿𝗼𝗷𝗲𝗰𝘁 𝗮𝗳𝘁𝗲𝗿 𝗽𝗿𝗼𝗷𝗲𝗰𝘁:

𝗦𝘁𝗲𝗽 𝟭: Start with a clear AI vision that ties to your 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗴𝗼𝗮𝗹𝘀. Don’t chase tech for tech’s sake. Anchor every initiative to what matters for your company. I listen to both leadership and front-line teams. The best ideas often come from the people closest to the problems. Organize your efforts by strategic domains-like customer journey or claims-so you can build on each win, not just chase random use cases.

𝗦𝘁𝗲𝗽 𝟮: Ruthlessly prioritize. I use a simple matrix: Value × Actionability × Feasibility. 𝗔𝘀𝗸: Will this move the needle for customers or employees? Can we deliver value fast? Is the data ready? I see contact center AI-like virtual agents-score high here. They solve real pain, are quick to launch, and show results you can measure.

𝗦𝘁𝗲𝗽 𝟯: Measure what matters, from day one. Don’t wait until after launch. Track five areas: model quality, system health, user adoption, operational impact, and business impact. Set baselines before you build. Make these metrics visible to both execs and product teams. This keeps everyone honest and focused on results that count.

Most AI pilots fail because they chase hype, pick the wrong problems, or measure only the tech-not the business. The companies winning with AI treat it as a business strategy, not a side project. Vision, prioritization, and disciplined measurement make all the difference.

𝗪𝗵𝗮𝘁’𝘀 𝘆𝗼𝘂𝗿 𝗯𝗶𝗴𝗴𝗲𝘀𝘁 𝗹𝗲𝘀𝘀𝗼𝗻 𝗳𝗿𝗼𝗺 𝘁𝘂𝗿𝗻𝗶𝗻𝗴 𝗔𝗜 𝗽𝗶𝗹𝗼𝘁𝘀 𝗶𝗻𝘁𝗼 𝗿𝗲𝗮𝗹 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲?

𝗧𝗵𝗲 𝗯𝗶𝗴𝗴𝗲𝘀𝘁 𝗔𝗜 𝗺𝗶𝘀𝘁𝗮𝗸𝗲? 𝗟𝗲𝗮𝗱𝗲𝗿𝘀 𝗮𝗻𝗱 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀 𝘄𝗼𝗿𝗸𝗶𝗻𝗴 𝗶𝗻 𝘀𝗶𝗹𝗼𝘀. 𝗪𝗲 𝗳𝗶𝘅 𝘁𝗵𝗮𝘁 𝗼𝗻 𝗱𝗮𝘆 𝗼𝗻𝗲.

I’ve learned this lesson the hard way.

Progress stalls when data and AI leaders are separated from the engineers building the systems.

- Projects burn budget, but impact stays fuzzy.

- Teams train on LLMs, but real intuition comes from shipping at scale.

𝗪𝗵𝗲𝗻 𝗹𝗲𝗮𝗱𝗲𝗿𝘀 𝗮𝗻𝗱 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀 𝘄𝗼𝗿𝗸 𝘁𝗼𝗴𝗲𝘁𝗵𝗲𝗿, 𝘁𝗵𝗶𝗻𝗴𝘀 𝗰𝗵𝗮𝗻𝗴𝗲.

- Small teams. One clear KPI. Direct Slack or Teams channel.

- Blockers get removed in hours, not quarters.

- This isn’t a nice-to-have-it’s the operating system for enterprise AI.

𝗣𝗶𝗹𝗼𝘁𝘀 𝗹𝗼𝗼𝗸 𝗴𝗼𝗼𝗱, 𝗯𝘂𝘁 𝘁𝗵𝗲 𝘁𝗿𝘂𝗲 𝘁𝗲𝘀𝘁 𝗶𝘀 𝗮𝗰𝗰𝘂𝗿𝗮𝗰𝘆 𝗮𝘁 𝘀𝗰𝗮𝗹𝗲.

- Confidently wrong responses kill adoption fast.

- You need technology that knows when it’s unsure.

- Systems that let users steer in real time.

- Corrections that feed back and make the AI smarter for your business.

- Generic models don’t know your workflows, edge cases, or risk posture.

𝗦𝗽𝗲𝗰𝗶𝗮𝗹𝗶𝘇𝗲 𝘆𝗼𝘂𝗿 𝗔𝗜, 𝗼𝗿 𝘀𝘁𝗮𝘆 𝘀𝘁𝘂𝗰𝗸 𝗮𝘁 𝘁𝗵𝗲 𝘀𝘂𝗿𝗳𝗮𝗰𝗲.

At DataGOL OL, we build this way from day one.

- Leaders and engineers, same loop.

- Accuracy and trust, engineered in.

- AI that learns your business as you use it.

That’s how we drive real value and why this model will disrupt the $200B enterprise AI industry.

𝗛𝗼𝘄 𝗮𝗿𝗲 𝘆𝗼𝘂 𝗯𝗿𝗶𝗱𝗴𝗶𝗻𝗴 𝘁𝗵𝗲 𝗴𝗮𝗽 𝗯𝗲𝘁𝘄𝗲𝗲𝗻 𝗹𝗲𝗮𝗱𝗲𝗿𝘀𝗵𝗶𝗽 𝗮𝗻𝗱 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗶𝗻 𝘆𝗼𝘂𝗿 𝗔𝗜 𝗷𝗼𝘂𝗿𝗻𝗲𝘆?

𝗪𝗵𝗮𝘁’𝘀 𝘁𝗵𝗲 𝗼𝗻𝗲 𝘁𝗵𝗶𝗻𝗴 𝗲𝘃𝗲𝗿𝘆 𝘄𝗶𝗻𝗻𝗶𝗻𝗴 𝗔𝗜 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝘆 𝗻𝗲𝗲𝗱𝘀? 𝗠𝘆 𝗮𝗻𝘀𝘄𝗲𝗿 𝗺𝗮𝘆 𝘀𝘂𝗿𝗽𝗿𝗶𝘀𝗲 𝘆𝗼𝘂.

I see the same mistake repeat across companies.

- They chase the latest tools.

- They build proofs of concept.

- They stack up licenses and dashboards.

But the winners do something else.

They start with a clear strategy-and stick to it.

𝗛𝗲𝗿𝗲’𝘀 𝗺𝘆 𝟱-𝘀𝘁𝗲𝗽 𝗯𝗹𝘂𝗲𝗽𝗿𝗶𝗻𝘁 𝗳𝗼𝗿 𝗔𝗜 𝘁𝗵𝗮𝘁 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗱𝗿𝗶𝘃𝗲𝘀 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝘃𝗮𝗹𝘂𝗲:

1. 𝗔𝗹𝗶𝗴𝗻 𝗔𝗜 𝘄𝗶𝘁𝗵 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗴𝗼𝗮𝗹𝘀. No side projects. Every use case must move the needle.

2. 𝗕𝘂𝗶𝗹𝗱 𝗮 𝘀𝘁𝗿𝗼𝗻𝗴 𝗱𝗮𝘁𝗮 𝗳𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻. Good AI runs on good data. Fix your data quality before chasing models.

3. 𝗖𝗵𝗼𝗼𝘀𝗲 𝘁𝗵𝗲 𝗿𝗶𝗴𝗵𝘁 𝘁𝗲𝗰𝗵 𝘀𝘁𝗮𝗰𝗸. Don’t buy what’s shiny. Pick what fits your team and your goals.

4. 𝗗𝗲𝘃𝗲𝗹𝗼𝗽 𝗰𝗿𝗼𝘀𝘀-𝗳𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝗮𝗹 𝘁𝗲𝗮𝗺𝘀. AI is not just for data scientists. Train business and tech teams together.

5. 𝗘𝗺𝗯𝗲𝗱 𝗿𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗹𝗲 𝗔𝗜 𝗳𝗿𝗼𝗺 𝗱𝗮𝘆 𝗼𝗻𝗲. Set the guardrails early. Make transparency and ethics part of the process.

That’s the playbook I use at @DataGOL, and what I’ve seen work at scale.

The companies winning at AI aren’t moving fastest.

They’re being most thoughtful about where to start.

Want to see faster ROI? Start with strategy, not tools.

What’s the one thing you think every AI strategy needs?

Absolutely electric atmosphere at SFF! 

The TGB AI Village wasn't just a startup showcase. It was a front row seat to the future fintech innovations. The excitement around AI guardrails, data governance, and combating threats like data poisoning, personal data protection, hallucinations, and compliance risks was palpable.

Everyone I spoke with, from founders to enterprise leaders wanted real answers. Not hype. They were hungry to understand how to govern AI responsibly, secure their data pipelines, and build trust in their AI outcomes. 

The conversations at the booth went deep. People get it , without solid data governance and proactive AI risk controls, your GenAI roadmap is a house of cards.

SFF proved that we're not just talking about AI anymore. We're governing it, securing it, and scaling it for real business value. The urgency is real. The curiosity is high. 

The potential? Massive.

Walled AI,  DataGOL, The Generative Beings Rishabh Bhardwaj
https://lnkd.in/giCR4_A8

TGB AI Village at SFF - wrapped!!

3 days, 6 AI startups, hundreds of 1:1 conversations, thousands of visitors on booth, unlimited business potential - that summarises our experience showcasing the AI startups from Singapore at Singapore FinTech Festival.

See the highlights on our Youtube playlist: https://lnkd.in/gu8JZyCB
🧠 x2 panel discussions on 'GTM expansion strategies for AI startups in SEA'
💡 x6 AI startup showcases at our booth
📸 Selfies with friends, peers, and potential new business partners

Thanks to the 6 amazing startups and teams for participating with us: Walled AI Pints.ai TheLibrarian.io Pro5.ai VOICERAcx HaskyOS Rishabh Vinod Partha Calvin Tiago David Wouter Nuno Sunil Jibi John Lionel 🙌 

Thanks to all those who came to our booth and met with the teams (apologies for missing out on any): Manoj Bhanu Ned Dario Shashank Saurav Idris Rahul David Prahlad Matthias Vidushan Chris Nishant Charu Ashok 

Thanks to the fantastic organising team for supporting the showcase: Singapore FinTech Festival Global Finance & Technology Network (GFTN) Tiffany Princeton Vanessa Aparna Madhu

#TGBAIVillage #TheGenerativeBeings #SingaporeFinTechFestival #sgfintechfest #SFF2025 #AIinFinance

𝗔𝗜 𝗶𝘀 𝗼𝗻𝗹𝘆 𝗮𝘀 𝘀𝗺𝗮𝗿𝘁 𝗮𝘀 𝘁𝗵𝗲 𝗰𝗼𝗻𝘁𝗲𝘅𝘁 𝘆𝗼𝘂 𝗱𝗲𝘀𝗶𝗴𝗻 𝗳𝗼𝗿 𝗶𝘁. 𝗧𝗵𝗮𝘁’𝘀 𝘁𝗵𝗲 𝗻𝗲𝘄 𝗴𝗮𝗺𝗲.

Everyone is chasing the next big LLM. But context is where the real value happens.

Context engineering means building the “environment” that AI thinks inside.

• Not just prompts.
• Not just model choice.

𝗧𝗵𝗿𝗲𝗲 𝗽𝗮𝗿𝘁𝘀 𝗺𝗮𝘁𝘁𝗲𝗿 𝗺𝗼𝘀𝘁:

- 𝗗𝗮𝘁𝗮 𝘀𝗲𝗹𝗲𝗰𝘁𝗶𝗼𝗻: What do you surface? How do you chunk and format it for the AI?

- 𝗜𝗻𝘁𝗲𝗻𝘁 𝗳𝗿𝗮𝗺𝗶𝗻𝗴: How do you capture what the user wants? How do you give AI the right memory and rules?

- 𝗗𝘆𝗻𝗮𝗺𝗶𝗰 𝗮𝗱𝗮𝗽𝘁𝗮𝘁𝗶𝗼𝗻: How do you let the AI adjust to each interaction, each policy, each tool in real time?

This is not theory. It’s the new architecture for business AI.

You can have the best model in the world.

But if you feed it noise, you get noise back.

If you design context with purpose, you unlock real business impact.

Context engineering is not a buzzword.

It’s how RAG, agents, copilots, and AI apps actually create value.

The future is not about “prompt engineering.”

𝗜𝘁’𝘀 𝗮𝗯𝗼𝘂𝘁 𝘀𝗰𝗮𝗹𝗶𝗻𝗴 𝗰𝗼𝗻𝘁𝗲𝘅𝘁-𝗺𝗮𝗸𝗶𝗻𝗴 𝘀𝘂𝗿𝗲 𝗲𝘃𝗲𝗿𝘆 𝗔𝗜 𝗶𝗻 𝘆𝗼𝘂𝗿 𝘀𝘁𝗮𝗰𝗸 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝘀 𝘁𝗵𝗲 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀, 𝘁𝗵𝗲 𝘂𝘀𝗲𝗿, 𝗮𝗻𝗱 𝘁𝗵𝗲 𝘁𝗮𝘀𝗸.

That is how you win with AI.

Enjoyed this post? Like, share, or follow for more on building business-ready AI.

𝗗𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱𝘀 𝗱𝗼𝗻’𝘁 𝗺𝗮𝗸𝗲 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀. 𝗟𝗲𝗮𝗱𝗲𝗿𝘀 𝗱𝗼.

A few years back, I sat in a crowded conference room. A senior executive stood up and said, “We’re now a data driven organisation.”

Heads nodded. People looked inspired. My colleague leaned over and said, “Brace yourself-the dashboards are coming.”

He was right.

Soon, the dashboards arrived. Charts, KPIs, heatmaps. All bright and polished. None of it changed how decisions were made.

𝗟𝗲𝗮𝗱𝗲𝗿𝘀 𝗳𝗲𝗹𝘁 𝗺𝗼𝗿𝗲 𝗶𝗻𝗳𝗼𝗿𝗺𝗲𝗱. 𝗕𝘂𝘁 𝗻𝗼𝘁 𝗮𝗻𝘆 𝘄𝗶𝘀𝗲𝗿.

This is what I call the dashboard delusion.

Data looks powerful. But it is always partial. Always biased. Always looking backward.

When leaders let dashboards call the shots, they don’t lower risk. They multiply it.

It’s like driving with eyes fixed on the rearview mirror. You see the road behind, but miss what’s ahead.

There is a better way.

𝗗𝗮𝘁𝗮 𝘀𝗵𝗼𝘂𝗹𝗱 𝗴𝘂𝗶𝗱𝗲. 𝗝𝘂𝗱𝗴𝗺𝗲𝗻𝘁 𝘀𝗵𝗼𝘂𝗹𝗱 𝗴𝗼𝘃𝗲𝗿𝗻.

Data driven means “the numbers told us to.” Data informed means “the numbers helped us see, and we made the call.”

Aristotle called it the golden mean. Not reckless guessing. Not blind trust in numbers.

𝗚𝗼𝗼𝗱 𝗹𝗲𝗮𝗱𝗲𝗿𝘀 𝘂𝘀𝗲 𝗱𝗮𝘁𝗮 𝗮𝘀 𝗮 𝗹𝗶𝗴𝗵𝘁-𝗻𝗼𝘁 𝗮𝘀 𝗮 𝗰𝗿𝘂𝘁𝗰𝗵.

Dashboards can show you where you’ve been. Only judgment helps you choose where to go next.

If you want real intelligence in your company, ask better questions. What does this metric mean? What could it be hiding? Where does human judgment matter most?

The moment you stop letting numbers drive and start letting judgment steer, “data driven” stops sounding smart-and starts sounding dangerous.

Let’s build cultures where data guides, but leaders decide.

𝗪𝗵𝗮𝘁 𝗱𝗼𝗲𝘀 𝗿𝗲𝗮𝗹 𝗱𝗮𝘁𝗮 𝗶𝗻𝗳𝗼𝗿𝗺𝗲𝗱 𝗹𝗲𝗮𝗱𝗲𝗿𝘀𝗵𝗶𝗽 𝗹𝗼𝗼𝗸 𝗹𝗶𝗸𝗲 𝗳𝗼𝗿 𝘆𝗼𝘂?

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