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.

Vinod SP

“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.

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

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𝗪𝗵𝘆 𝟵𝟱% 𝗼𝗳 𝗚𝗲𝗻𝗔𝗜 𝗽𝗶𝗹𝗼𝘁𝘀 𝗮𝗿𝗲 𝗳𝗮𝗶𝗹𝗶𝗻𝗴 (𝗮𝗻𝗱 𝘁𝗵𝗲 𝗺𝗶𝘀𝘀𝗶𝗻𝗴 𝗽𝗶𝗲𝗰𝗲 𝗳𝗼𝗿 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗔𝗜)

Most enterprises don’t fail with AI because the models are bad.
They fail because they expect one model to magically fit every problem.

I recently met a CIO who proudly announced, “𝗪𝗲’𝗿𝗲 𝘀𝘁𝗮𝗻𝗱𝗮𝗿𝗱𝗶𝘀𝗶𝗻𝗴 𝗼𝗻 𝗼𝗻𝗲 𝗟𝗟𝗠 𝗮𝗰𝗿𝗼𝘀𝘀 𝘁𝗵𝗲 𝗰𝗼𝗺𝗽𝗮𝗻𝘆.”

𝗧𝘄𝗼 𝗺𝗼𝗻𝘁𝗵𝘀 𝗹𝗮𝘁𝗲𝗿, 𝘁𝗵𝗲 𝗰𝗼𝗺𝗽𝗹𝗮𝗶𝗻𝘁𝘀 𝘀𝘁𝗮𝗿𝘁𝗲𝗱 𝗿𝗼𝗹𝗹𝗶𝗻𝗴 𝗶𝗻:
• The legal team said it was too “creative.”
• The analytics team said it was too “dumb.”
• The product team said it was too “slow.”

𝗦𝗮𝗺𝗲 𝗺𝗼𝗱𝗲𝗹. 𝗧𝗵𝗿𝗲𝗲 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁 𝗿𝗲𝗮𝗹𝗶𝘁𝗶𝗲𝘀.
The problem wasn’t the LLM,  it was the assumption that one model could be everything, everywhere, all at once.


𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗔𝗜 ≠ 𝗰𝗵𝗼𝗼𝘀𝗶𝗻𝗴 𝗮𝗻 𝗟𝗟𝗠.
Enterprise AI = choosing the right model, right context for the right use case.

𝗧𝗵𝗶𝗻𝗸 𝗶𝗻 𝗹𝗮𝘆𝗲𝗿𝘀:
1️⃣ Foundation: broad LLMs for general reasoning.
2️⃣ Domain: specialized models tuned for your industry.
3️⃣ Task: lightweight models optimized for specific workflows.
4️⃣ Guardrails: policy, evaluation, and monitoring to keep everything aligned.

It’s a model ecosystem, not a monolith.

If your AI strategy starts with “𝗪𝗵𝗶𝗰𝗵 𝗟𝗟𝗠 𝘀𝗵𝗼𝘂𝗹𝗱 𝘄𝗲 𝘀𝘁𝗮𝗻𝗱𝗮𝗿𝗱𝗶𝘇𝗲 𝗼𝗻?”
You’ve already limited what your business can become.

Start with use cases, constraints, and context. Let the architecture follow.

𝗪𝗵𝗲𝗿𝗲 𝗶𝗻 𝘆𝗼𝘂𝗿 𝗼𝗿𝗴𝗮𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗮𝗿𝗲 𝘆𝗼𝘂 𝘀𝘁𝗶𝗹𝗹 𝗳𝗼𝗿𝗰𝗶𝗻𝗴 𝗮 𝗼𝗻𝗲-𝘀𝗶𝘇𝗲-𝗳𝗶𝘁𝘀-𝗮𝗹𝗹 𝗺𝗼𝗱𝗲𝗹?

𝗚𝗼𝗼𝗱 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀 𝗻𝗲𝗲𝗱 𝗿𝗶𝗽𝗲 𝗱𝗮𝘁𝗮, 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝗺𝗼𝗿𝗲 𝗱𝗮𝘁𝗮. 𝗛𝗲𝗿𝗲’𝘀 𝗵𝗼𝘄 𝗜 𝗰𝗵𝗼𝗼𝘀𝗲 𝘄𝗵𝗮𝘁 𝘁𝗼 𝘁𝗿𝘂𝘀𝘁.

Most teams think all data is useful. That’s not true.

Acting on the wrong data at the wrong time can do more harm than having no data at all.

Wisdom is not about collecting more numbers.

It’s about knowing what to ignore.

𝗜 𝘀𝗲𝗲 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝗮𝗰𝘁 𝗼𝗻 𝗱𝗮𝘁𝗮 𝘁𝗵𝗮𝘁 𝗶𝘀 𝗿𝗮𝘄, 𝗶𝗻𝗰𝗼𝗺𝗽𝗹𝗲𝘁𝗲, 𝗼𝗿 𝘀𝘁𝗮𝗹𝗲.

• Their AI models underperform.
• Their dashboards mislead.
• The tool is not the problem.
• It’s the quality of the data.

𝗛𝗲𝗿𝗲’𝘀 𝘄𝗵𝗮𝘁 𝗜 𝗹𝗼𝗼𝗸 𝗳𝗼𝗿:

1. 𝗗𝗮𝘁𝗮 𝗶𝘀 𝗮 𝗽𝗿𝗼𝘅𝘆, 𝗻𝗼𝘁 𝗮 𝘁𝗿𝘂𝘁𝗵. Raw data is like a green banana. Not ready to eat. It needs context and refinement.

2. 𝗦𝘁𝗮𝗹𝗲 𝗱𝗮𝘁𝗮 𝗱𝗲𝗰𝗮𝘆𝘀. Insights have a shelf life. What worked last month can mislead you today.

3. 𝗥𝗶𝗽𝗲𝗻𝗲𝘀𝘀 𝗺𝗲𝗮𝗻𝘀 𝗿𝗲𝗮𝗱𝗶𝗻𝗲𝘀𝘀. Good decisions use data that is timely, complete, and well understood.

4. 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗯𝗲𝗮𝘁𝘀 𝗾𝘂𝗮𝗻𝘁𝗶𝘁𝘆. Many teams are drowning in data lakes but starving for insight.

If you use every data point you can find, you become data-bloated.

𝗜𝗻𝘀𝘁𝗲𝗮𝗱, 𝗜 𝘂𝘀𝗲 𝘁𝗵𝗿𝗲𝗲 𝘀𝘁𝗲𝗽𝘀:

1. Define what makes data ripe. Is it timely? Complete? Verified by a trusted source?

2. Build a discard culture. Teach teams to ignore data that is not ready.

3. Create ripening processes. Add governance, context, and checks so data is actionable.

𝗧𝗵𝗲 𝗴𝗼𝗮𝗹 𝗶𝘀 𝗻𝗼𝘁 𝘁𝗼 𝗲𝗮𝘁 𝗲𝘃𝗲𝗿𝘆 𝗯𝗮𝗻𝗮𝗻𝗮.

It’s to pick the right one at the right time.

Choose your data like you choose your banana.

I’d love to hear how you decide what data to trust in your business.

See how we make data business-ready at DataGOL.

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?

𝗪𝗵𝗲𝗻 𝗔𝗜 𝗳𝗮𝗶𝗹𝘀, 𝗶𝘁’𝘀 𝗻𝗼𝘁 𝘁𝗵𝗲 𝗺𝗼𝗱𝗲𝗹. 𝗜𝘁’𝘀 𝘁𝗵𝗲 𝗺𝗶𝘀𝘀𝗶𝗻𝗴 𝗰𝗼𝗻𝘁𝗲𝘅𝘁. 𝗛𝗲𝗿𝗲’𝘀 𝗺𝘆 𝘀𝘆𝘀𝘁𝗲𝗺 𝘁𝗼 𝗳𝗶𝘅 𝗶𝘁.

Most teams chase bigger models and longer prompts.

𝗕𝘂𝘁 𝘁𝗵𝗲 𝗿𝗲𝗮𝗹 𝗯𝗿𝗲𝗮𝗸𝘁𝗵𝗿𝗼𝘂𝗴𝗵? 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴.

Context is not a feature. It’s the product.

𝗛𝗲𝗿𝗲’𝘀 𝗵𝗼𝘄 𝗜 𝗱𝗼 𝗶𝘁:

1️⃣ 𝗗𝗮𝘁𝗮 𝘀𝗲𝗹𝗲𝗰𝘁𝗶𝗼𝗻. I pick what matters, chunk it down, and format it for clarity.

2️⃣ 𝗜𝗻𝘁𝗲𝗻𝘁 𝗳𝗿𝗮𝗺𝗶𝗻𝗴. I shape the prompt, give the agent memory, and set clear instructions.

3️⃣ 𝗗𝘆𝗻𝗮𝗺𝗶𝗰 𝗮𝗱𝗮𝗽𝘁𝗮𝘁𝗶𝗼𝗻. I let the AI adjust in real time using tools, policies, and grounding.

𝗜 𝘀𝗲𝗲 𝘁𝗵𝗶𝘀 𝗹𝗶𝗸𝗲 𝘁𝗮𝗸𝗶𝗻𝗴 𝗻𝗼𝘁𝗲𝘀 𝗶𝗻 𝗮 𝗹𝗼𝗻𝗴 𝗺𝗲𝗲𝘁𝗶𝗻𝗴.

- You write down the key decisions. You clear space in your head.

- You focus on what’s next, but you never lose track of what matters.

𝗧𝗼𝗼 𝗺𝘂𝗰𝗵 𝗱𝗮𝘁𝗮 𝗰𝗮𝗻 𝗱𝗿𝗼𝘄𝗻 𝘁𝗵𝗲 𝘀𝗶𝗴𝗻𝗮𝗹.

- More tokens do not mean better answers.

- Retrieval is not lookup. It is reasoning.

- When the signal gets lost, output collapses.

- The future is not prompt engineering alone.

𝗜𝘁 𝗶𝘀 𝗰𝗼𝗻𝘁𝗲𝘅𝘁 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗮𝘁 𝘀𝗰𝗮𝗹𝗲.

- AI is only as good as the ecosystem of inputs you build around it.

Ready to rethink how you feed your models?

Let me know how you’re building context into your AI stack.

𝗧𝗵𝗲 𝗵𝗮𝗿𝗱𝗲𝘀𝘁 𝗽𝗮𝗿𝘁 𝗮𝗯𝗼𝘂𝘁 𝗹𝗲𝗮𝗱𝗶𝗻𝗴 𝗔𝗜 𝘁𝗲𝗮𝗺𝘀? 𝗬𝗼𝘂𝗿 𝗯𝗼𝘀𝘀 𝗵𝗮𝘀 𝗻𝗼 𝗶𝗱𝗲𝗮 𝘄𝗵𝗮𝘁 𝘆𝗼𝘂 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗱𝗼.

I learned this the hard way.

Early in my career, I worked for leaders who knew more than me.

They taught. They pushed. They gave direction.

You grow fast in that space.

Then you get promoted. You become the Head of Data or AI.

𝗦𝘂𝗱𝗱𝗲𝗻𝗹𝘆, 𝘆𝗼𝘂 𝗿𝗲𝗽𝗼𝗿𝘁 𝘁𝗼 𝗹𝗲𝗮𝗱𝗲𝗿𝘀 𝘄𝗵𝗼 𝗮𝗿𝗲 𝗲𝘅𝗽𝗲𝗿𝘁𝘀 𝗶𝗻 𝘀𝗮𝗹𝗲𝘀, 𝗳𝗶𝗻𝗮𝗻𝗰𝗲, 𝗼𝗿 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀-𝗯𝘂𝘁 𝗻𝗼𝘁 𝗱𝗮𝘁𝗮.

• They ask for value.
• They want speed.
• They ask for a roadmap.
• But they don’t see the late-night pipeline failures.
• They don’t know why a model takes weeks, not days.
• They can’t tell if your work is solid or fragile.
• You are held accountable for outcomes.
• But you have less support and fewer levers.

𝗧𝗵𝗮𝘁’𝘀 𝘄𝗵𝗲𝗻 𝘆𝗼𝘂 𝗹𝗲𝗮𝗿𝗻: 𝘁𝗵𝗲 𝗷𝗼𝗯 𝗶𝘀 𝗻𝗼𝘁 𝗼𝗻𝗹𝘆 𝗮𝗯𝗼𝘂𝘁 𝗱𝗮𝘁𝗮.

• It’s about building trust.
• You connect every project to business goals.
• You explain the complex in simple words.
• You show progress with stories, not just numbers.
• You lead people who don’t speak your language.
• You help them believe in a future they can’t quite see.

𝗧𝗵𝗲 𝗿𝗲𝗮𝗹 𝗰𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲 𝗶𝘀 𝗻𝗼𝘁 𝘁𝗵𝗲 𝗱𝗮𝘁𝗮.

It’s moving the organisation forward, even when things are unclear.

If you lead AI teams, focus on trust, clarity, and action.

𝗪𝗵𝗮𝘁’𝘀 𝘆𝗼𝘂𝗿 𝗯𝗶𝗴𝗴𝗲𝘀𝘁 𝗹𝗲𝘀𝘀𝗼𝗻 𝗳𝗿𝗼𝗺 𝗹𝗲𝗮𝗱𝗶𝗻𝗴 𝘄𝗵𝗲𝗻 𝗻𝗼 𝗼𝗻𝗲 𝗲𝗹𝘀𝗲 “𝗴𝗲𝘁𝘀 𝗶𝘁”?

𝗔𝗜 𝘄𝗼𝗻’𝘁 𝗳𝗶𝘅 𝘆𝗼𝘂𝗿 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗶𝗳 𝘆𝗼𝘂𝗿 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗰𝗮𝗻’𝘁 𝗱𝗲𝗰𝗶𝗱𝗲 𝘄𝗵𝗮𝘁 𝗶𝘁 𝘄𝗮𝗻𝘁𝘀.

That’s the part nobody wants to admit.

Last quarter, a CEO told me:
“𝗪𝗲 𝘄𝗮𝗻𝘁 𝗔𝗜, 𝗯𝘂𝘁 𝘄𝗲 𝗱𝗼𝗻’𝘁 𝗸𝗻𝗼𝘄 𝘄𝗵𝗲𝗿𝗲 𝘁𝗼 𝘀𝘁𝗮𝗿𝘁.”

- That’s not an AI problem.

- That’s a clarity problem.

𝗛𝗲𝗿𝗲’𝘀 𝘁𝗵𝗲 𝗽𝗮𝘁𝘁𝗲𝗿𝗻 𝗜 𝘀𝗲𝗲 𝗶𝗻 𝗻𝗲𝗮𝗿𝗹𝘆 𝗲𝘃𝗲𝗿𝘆 𝗳𝗮𝗶𝗹𝗲𝗱 𝗔𝗜 𝗽𝗿𝗼𝗷𝗲𝗰𝘁:

1. 𝗖𝗹𝗮𝗿𝗶𝘁𝘆 𝗯𝗲𝗳𝗼𝗿𝗲 𝗰𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝘆.

- Write down the 10 real decisions your business makes each month.

- Pick the 3 that matter most.

- If you can’t name the decision, AI can’t help you make it.

2. 𝗖𝗼𝗻𝘀𝘁𝗿𝗮𝗶𝗻𝘁𝘀 𝗯𝗲𝗳𝗼𝗿𝗲 𝗰𝗿𝗲𝗮𝘁𝗶𝘃𝗶𝘁𝘆.

- Set your budget, risk limits, and compliance rules before you start.

- Most AI chaos comes from fuzzy boundaries.

- No clear rules, no clear results.

3. 𝗖𝗼𝗻𝘁𝗿𝗼𝗹𝗹𝗲𝗱 𝗲𝘅𝗽𝗲𝗿𝗶𝗺𝗲𝗻𝘁𝘀, 𝗻𝗼𝘁 𝘄𝗶𝗹𝗱 𝗽𝗿𝗼𝗺𝗶𝘀𝗲𝘀.

- Pick one use case.

- Prove a measurable lift in 30–45 days.

- Show the value, then scale.

𝗛𝗲𝗿𝗲’𝘀 𝘁𝗵𝗲 𝘁𝗿𝘂𝘁𝗵 𝗻𝗼𝗯𝗼𝗱𝘆 𝗹𝗶𝗸𝗲𝘀 𝘁𝗼 𝘀𝗮𝘆:

AI fails when leaders can’t agree on what “good” looks like.

𝗠𝗼𝗱𝗲𝗹𝘀 𝗱𝗼𝗻’𝘁 𝗵𝗮𝗹𝗹𝘂𝗰𝗶𝗻𝗮𝘁𝗲 𝗮𝘀 𝗺𝘂𝗰𝗵 𝗮𝘀 𝗼𝗿𝗴𝗮𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻𝘀 𝗱𝗼.

- Alignment comes first.

- AI comes next.

𝗪𝗵𝗲𝗿𝗲 𝗱𝗼 𝘆𝗼𝘂 𝘀𝗲𝗲 𝘁𝗵𝗲 𝗯𝗶𝗴𝗴𝗲𝘀𝘁 𝗮𝗹𝗶𝗴𝗻𝗺𝗲𝗻𝘁 𝗴𝗮𝗽 𝗶𝗻 𝘆𝗼𝘂𝗿 𝘁𝗲𝗮𝗺 𝗿𝗶𝗴𝗵𝘁 𝗻𝗼𝘄?

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?

𝗠𝗼𝘀𝘁 𝗱𝗮𝘁𝗮 𝘁𝗲𝗮𝗺𝘀 𝗱𝗼𝗻’𝘁 𝗳𝗮𝗶𝗹 𝗾𝘂𝗶𝗲𝘁𝗹𝘆 — 𝘁𝗵𝗲𝘆 𝗳𝗮𝗶𝗹 𝗹𝗼𝘂𝗱𝗹𝘆. 𝗟𝗲𝗮𝗱𝗲𝗿𝘀 𝗷𝘂𝘀𝘁 𝗮𝗿𝗲𝗻’𝘁 𝗹𝗶𝘀𝘁𝗲𝗻𝗶𝗻𝗴.

Everyone keeps hunting for the “mystery reason” their data team can’t deliver.

There is no mystery. The signals are screaming.

The problem is knowing *which* signals matter.

𝗛𝗲𝗿𝗲’𝘀 𝘁𝗵𝗲 𝘁𝗿𝘂𝘁𝗵:
If a data team can’t 𝗯𝘂𝗶𝗹𝗱 𝗳𝗮𝘀𝘁, 𝗱𝗲𝗹𝗶𝘃𝗲𝗿 𝘃𝗮𝗹𝘂𝗲, or 𝘀𝗵𝗮𝗽𝗲 𝘁𝗵𝗲 𝗳𝘂𝘁𝘂𝗿𝗲, one of three things is broken.

𝗣𝗛𝗔𝗦𝗘 𝟭: 𝗙𝗢𝗨𝗡𝗗𝗔𝗧𝗜𝗢𝗡 𝗖𝗛𝗘𝗖𝗞

If everything takes forever, you’re not looking at a talent gap you’re staring at 𝘁𝗲𝗰𝗵 𝗱𝗲𝗯𝘁 𝗽𝗶𝗹𝗲𝗱 𝘀𝗸𝘆-𝗵𝗶𝗴𝗵.

Pipelines breaking. Migrations dragging. Models “𝗻𝗲𝗲𝗱𝗶𝗻𝗴 𝗿𝗲-𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲.”

That’s a foundation begging for repair.

𝗣𝗛𝗔𝗦𝗘 𝟮: 𝗘𝗫𝗘𝗖𝗨𝗧𝗜𝗢𝗡 𝗥𝗘𝗔𝗟𝗜𝗧𝗬

If your team “𝗱𝗲𝗹𝗶𝘃𝗲𝗿𝘀” but nobody uses the output, you’re not shipping value — you’re shipping artifacts.

No engagement. No feedback. No impact.

That’s an execution discipline problem.

𝗣𝗛𝗔𝗦𝗘 𝟯: 𝗦𝗧𝗥𝗔𝗧𝗘𝗚𝗜𝗖 𝗜𝗡𝗧𝗘𝗡𝗧

If your team only works the Jira queue, you don’t have a data function — you have a service desk.

No challenge. No vision. No strategic spine.

That’s the dangerous one.

𝗪𝗔𝗧𝗖𝗛 𝗢𝗨𝗧 𝗙𝗢𝗥 𝗧𝗛𝗘𝗦𝗘 𝗧𝗥𝗔𝗣𝗦

* Believing more dashboards = more value
* Treating tech debt like an inconvenience instead of a threat
* Assuming silence from users means satisfaction
* Mistaking “busy” for “directionally aligned”
* Accepting polite status reports as truth

The fix starts with three conversations:
𝗨𝘀𝗲𝗿𝘀 → 𝗧𝗲𝗮𝗺 → 𝗧𝗲𝗰𝗵 𝗹𝗲𝗮𝗱𝘀.
Listen long enough and the real story reveals itself.

Because the biggest risk isn’t a broken data team.
It’s a leadership team that stopped asking uncomfortable questions.

𝗪𝗵𝗮𝘁’𝘀 𝘁𝗵𝗲 𝗼𝗻𝗲 𝘀𝗶𝗴𝗻𝗮𝗹 𝘆𝗼𝘂𝗿 𝗱𝗮𝘁𝗮 𝗳𝘂𝗻𝗰𝘁𝗶𝗼𝗻 𝗵𝗮𝘀 𝗯𝗲𝗲𝗻 𝗴𝗶𝘃𝗶𝗻𝗴 𝘆𝗼𝘂, 𝘁𝗵𝗮𝘁 𝘆𝗼𝘂’𝘃𝗲 𝗯𝗲𝗲𝗻 𝗶𝗴𝗻𝗼𝗿𝗶𝗻𝗴?

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?

𝗜𝗳 𝘆𝗼𝘂 𝘄𝗮𝗻𝘁 𝗿𝗲𝘀𝘂𝗹𝘁𝘀, 𝗵𝗶𝗿𝗲 𝗯𝘂𝗶𝗹𝗱𝗲𝗿𝘀. 𝗜𝗳 𝘆𝗼𝘂 𝘄𝗮𝗻𝘁 𝗯𝗲𝗹𝗶𝗲𝗳, 𝗵𝗶𝗿𝗲 𝗠𝗰𝗞𝗶𝗻𝘀𝗲𝘆.

McKinsey is not shaping the AI world by building models or launching agents.

They are shaping it by selling belief to the people who control the budgets.

𝗛𝗲𝗿𝗲 𝗶𝘀 𝗵𝗼𝘄 𝘁𝗵𝗲 𝗰𝘆𝗰𝗹𝗲 𝘄𝗼𝗿𝗸𝘀.

- A glossy report lands in your inbox. It promises billions in AI opportunity.

- Your board forwards it at 2am with a note: “Are we behind?”

- You scramble.

- McKinsey steps in. Strategy meetings. Slide decks. High fees.

- Little changes.

- The next quarter, a new report drops.

-Repeat.

𝗡𝗼 𝗺𝗼𝗱𝗲𝗹𝘀 𝘁𝗼 𝘁𝗿𝗮𝗶𝗻. 𝗡𝗼 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝘁𝗼 𝗺𝗮𝗶𝗻𝘁𝗮𝗶𝗻. 𝗡𝗼 𝗿𝗶𝘀𝗸 𝗼𝗳 𝗯𝗲𝗶𝗻𝗴 𝘄𝗿𝗼𝗻𝗴 𝗯𝗲𝗰𝗮𝘂𝘀𝗲 𝗻𝗼𝘁𝗵𝗶𝗻𝗴 𝗶𝘀 𝘀𝗽𝗲𝗰𝗶𝗳𝗶𝗰.

- Their real product is not AI. It is a narrative that calms the boardroom.

- Executives do not crave functional models.

- They want vision stories that pass the boardroom test.

- They want language that sounds deep, even when it is vague.

- They want human agents who make them feel ready for the future.

𝗠𝗰𝗞𝗶𝗻𝘀𝗲𝘆 𝗱𝗲𝗹𝗶𝘃𝗲𝗿𝘀 𝗮𝗹𝗹 𝗼𝗳 𝘁𝗵𝗶𝘀.

- Change in most companies does not start with a prototype.

- It starts with a whitepaper and an offsite.

- McKinsey knows how to light that spark.

𝗕𝘂𝘁 𝗵𝗲𝗿𝗲 𝗶𝘀 𝘁𝗵𝗲 𝗰𝗵𝗼𝗶𝗰𝗲.

- If you want to build real products, hire engineers.

- If you want to impress the board, hire McKinsey.

- If you want both, find people who can build and sell the vision @DataGOL

Because in enterprise AI, the most valuable skill is not prompt engineering.

It is narrative engineering.

𝗛𝗼𝘄 𝗮𝗿𝗲 𝘆𝗼𝘂 𝗯𝗮𝗹𝗮𝗻𝗰𝗶𝗻𝗴 𝗯𝗲𝗹𝗶𝗲𝗳 𝗮𝗻𝗱 𝗿𝗲𝘀𝘂𝗹𝘁𝘀 𝗶𝗻 𝘆𝗼𝘂𝗿 𝗔𝗜 𝗷𝗼𝘂𝗿𝗻𝗲𝘆?

𝗔𝗜 𝘄𝗼𝗻’𝘁 𝗰𝗹𝗲𝗮𝗻 𝘂𝗽 𝘆𝗼𝘂𝗿 𝗺𝗲𝘀𝘀 𝗼𝗳 𝟯𝟳 𝗘𝘅𝗰𝗲𝗹𝘀 𝗮𝗻𝗱 𝟳𝟮 𝗱𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱𝘀.

You already know the feeling.

𝗟𝗲𝗴𝗮𝗰𝘆 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 𝗲𝘃𝗲𝗿𝘆𝘄𝗵𝗲𝗿𝗲. 𝗣𝗿𝗼𝗰𝗲𝘀𝘀 𝗼𝘃𝗲𝗿𝗵𝗲𝗮𝗱. 𝗗𝗮𝘁𝗮 𝘀𝗶𝗹𝗼𝘀 𝘀𝘁𝗮𝗰𝗸𝗲𝗱 𝗼𝗻 𝘁𝗼𝗽 𝗼𝗳 𝗼𝗿𝗴 𝘀𝗶𝗹𝗼𝘀.

Meetings to talk about meetings. Politics that slow the real work.

I’ve seen this in Enterprises. Dependencies everywhere. Docs and tools that promise change but only add noise.

𝗗𝗲𝗮𝗱𝗹𝗶𝗻𝗲𝘀 𝗮𝗹𝘄𝗮𝘆𝘀 𝗰𝗵𝗮𝘀𝗶𝗻𝗴 𝘁𝗵𝗲 𝗻𝗲𝘅𝘁 𝗿𝗲𝗹𝗲𝗮𝘀𝗲.

- “But now we have AI,” you’ll say.

- Truth? We had AI before. It didn’t solve the core problem then, either.

- Technology is powerful. But it only multiplies what’s already there.

𝗛𝗲𝗿𝗲’𝘀 𝘄𝗵𝗮𝘁 𝗜 𝗹𝗲𝗮𝗿𝗻𝗲𝗱 𝗹𝗲𝗮𝗱𝗶𝗻𝗴 𝘁𝗵𝗼𝘀𝗲 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀:

1️⃣ 𝗙𝗶𝘅 𝘁𝗵𝗲 𝗽𝗿𝗼𝗰𝗲𝘀𝘀.

→ Sit with the owners.
→ Simplify before you automate.
→ Make compliance and efficiency work together.

2️⃣ 𝗜𝗻𝘃𝗼𝗹𝘃𝗲 𝘁𝗵𝗲 𝗿𝗶𝗴𝗵𝘁 𝗽𝗲𝗼𝗽𝗹𝗲.

→ Cut the meetings you don’t need.
→ Keep agendas sharp and decisions fast.
→ Use tech to speed up, not add noise.

3️⃣ 𝗚𝗲𝘁 𝘆𝗼𝘂𝗿 𝗱𝗮𝘁𝗮 𝗶𝗻 𝗼𝗿𝗱𝗲𝗿.

→ Most orgs don’t have usable data.
→ It’s scattered-SharePoints, laptops, hard copies.
→ Start cleaning now. AI is useless without it.

4️⃣ 𝗠𝗮𝗽 𝘆𝗼𝘂𝗿 𝘁𝗼𝗼𝗹𝘀.

→ Excel hides more than it solves.
→ Who uses it? How? Why?
→ Replace only after you understand the flow.

𝗗𝗼 𝘁𝗵𝗶𝘀 𝗳𝗶𝗿𝘀𝘁. 𝗔𝗜 𝘄𝗶𝗹𝗹 𝗮𝗺𝗽𝗹𝗶𝗳𝘆 𝘆𝗼𝘂𝗿 𝘄𝗶𝗻𝘀.

Skip it, and complexity wins again.

If the pile feels too high, reach out DataGOL.

Are you clearing the mess before adding AI?

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?

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.

Bema Beauty SPA

As both a beauty spa business owner and a business strategy analyst, my strength lies in leveraging data-driven insights to streamline operations, enhance client satisfaction, and implement growth strategies that ensure long-term business sustainability.

Business Owner

GCE JAPAN 

Driven and customer-focused, I match every client with the perfect car through expert product knowledge and persuasive sales skills.

Assistant

Atlas College of Professional studies

I am a dedicated service provider at ATLAS College of Professional Studies, committed to delivering efficient support and ensuring a positive experience for students, staff, and visitors. I take pride in providing clear communication, timely assistance, and solutions that contribute to the smooth running of daily operations while upholding the institution’s values and standards.

Service Provider

I am a Business Strategy Analyst with a strong focus on leveraging data analytics, financial modeling, and market intelligence to inform high-stakes decision-making and strategic planning. My work involves synthesizing complex datasets, identifying key performance drivers, and developing actionable insights that support both short-term objectives and long-term business growth.

With hands-on experience in building financial models, conducting sensitivity analyses, and evaluating industry trends, I support executive teams in aligning operational execution with strategic goals. I am proficient in translating quantitative findings into clear recommendations, using tools such as SQL, Excel, Power BI, and Python for advanced data analysis and visualization.

My expertise also extends to cost-benefit analysis, competitive benchmarking, and scenario planning, enabling data-driven recommendations in fast-paced, dynamic environments. I work cross-functionally with finance, operations, and product teams to identify inefficiencies, design performance metrics, and monitor strategic initiatives from conception to implementation.

Above all, I bring a rigorous, analytical approach to solving business problems, combining technical acumen with strategic insight to drive measurable impact.

Benard Odhiambo

Finance & Accounting Specialist | Audit & Compliance | Budget Forecasting | Inventory Control | Remote-Ready Financial Operations

In Algeria, three individuals of unknown nationality were arrested as they attempted to cross into Spain disguised as sheep. 🤔

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