10 Slides in Every GCC Pitch Deck

Adding some Hard truth complementing what it means, for some pages based on observations

10 Slides in Every hashtag#GCC Pitch Deck (and What They Actually Mean)
Slide 4: “We’re Not Just a Support Center”
What it means: But we’re still taking direction from HQ for every single product decision.
✅ Hard truth: Core work may not always be there; the trust factor may not be present.

Slide 5: “India Will Drive Innovation”
What it means: We haven’t defined ‘innovation,’ but it looks great on slide titles.
✅ Hard truth: Many times, it's hackathons that never turn into product features.

Slide 6: “We’ll Scale to 200 People in 12 Months”
What it means: We haven’t built a talent pipeline or local employer brand. But growth sounds impressive.
✅ Hard truth: Sometimes the focus is not on long-term strategy or learning, but on numbers and quantity.

Slide 8: “Hiring Top 1% Engineers”
What it means: We’ll post a JD with 10 must-have skills and wonder why no one’s applying.
✅ Hard truth: Most have every listed skill, follow standard patterns, and are masters of 50 tools, but lack focused depth.

Slide 10: “This Is Just the Beginning”
What it means: It definitely is, and depending on who we hire first, it’ll either scale fast or stall early.
✅ Hard truth: We will provide another narrative when the time changes.

hashtag#perspectives hashtag#observations

Ref - Link

Keep Thinking!!!

Underappreciated Things in Life

To all IT professionals working within rigid pyramid structures, where growth is linear and competition is often the currency of progress

Forgive the competition. Celebrate your life. There are countless underappreciated silent heroes in this sector.

• Saying yes to align with the team, even when your views differ

• Doing your work diligently without seeking recognition or visibility

• Choosing forgiveness when you could easily call out someone's mistakes

• Solving problems quietly, even when there's no direct reward or acknowledgment

• Staying away from visibility politics, yet remaining silent and dignified

• Taking time to learn deeply, rather than rushing to compete

• Balancing your own views while adapting to collective patterns

• Being patient enough to accommodate, even when others are agenda-driven rather than value-based

• Sensing narratives and intent clearly, yet stepping away gracefully once you’ve connected the dots

Keep Going. Your quiet strength matters. 🙏

AI-Friendly Before AI-First: GCC Perspectives

In a recent conversation with my ex-boss, we explored some of the unique dynamics of Global Capability Centers (GCCs). One key takeaway: a company must be AI-friendly before it can become AI-first.

Alignment Starts with Initiatives, Not Just ROI

Organizations often begin their AI journey through initiatives rather than immediate ROI-driven motives. There might be AI talent within the team, but project priorities shape what gets implemented. As a consultant, I’ve seen that internal teams possess strong capabilities but are often bandwidth-constrained. They will evaluate, challenge, and probe external perspectives, yet their ability to execute depends on time and focus.

The Role of GCCs: Delivery vs. Innovation

GCCs can function in multiple ways—either as pure delivery centers catering to regional project needs or as innovation hubs driving transformational change. The distinction is crucial because execution models differ significantly.

Innovation vs. Execution: The Long Game of AI

Project success, project innovation, and project vision are all distinct. A project doesn’t end when it goes live; it evolves. AI and data-driven products thrive on iteration—data quality improvements, model refinements, customer feedback loops, and continuous enhancement. The real differentiator isn’t just building and shipping—it’s about creating lasting impact.

Keep Going!!!

How My Days Go These Days – AI Advisory / Consulting / Strategy

• Code for a few hours, and develop a solution in mind.
• Attend meetings to align with the vision.
• Build a structured solution approach.
• Revisit and map your domain experiences.
• Balance Build fast vs. Fail fast identify red flags.
• Add a layer of insights.
• Test your solution, and integrate different perspectives.
• Some tasks run in parallel, others are sequential.
• Even when offline, the solution keeps running in your mind.
• Every paper, resource, or discussion sparks new ideas.
• Manage time between meetings, deep thinking, reading, and coding.
• Everything adds up incrementally—failures translate into new perspectives.
• Be selective - pick and choose the right opportunities.
• 10 hours or 15 hours - doesn’t matter. Quality over hours.
• Happy to Work Remotely/Freelance - No Time for Pollution, Traffic, or Office Gossip
• Learning thrives on diverse perspectives - what you learn, experiment with, research, and intuitively connect.

Keep Learning!!!

🚀 The Experimenter’s Mindset: Focus, Iterate, Persist! 🔑

• 🚀 Productivity = 🎯 Focus Time
• 💡 Ideation = 🤯 Aha! Moment + 🔄 Iteration
• ✅ Right Idea + Confidence = 🎉 Realization
• ❌ Don’t stop experimenting just because you haven’t learned everything
• 🔄 Some fundamentals & assumptions will keep evolving as you learn from mistakes
• 🌍 Tag along with the ecosystem + 📚 Courses = 🔥 Motivation
• 💪 Persistence > ⏳ Time / 📊 Data / 🎯 Needs / 🛠️ Tech Perspectives
• 🏆 Success = Sticking to your idea despite constraints! 🔑✨

 Keep Experimenting!!!

Beginning of AI Soldiers / Machines

Keep Thinking!!!

Interviews vs Perspectives

Happy to see the discussion encouraged the candidate to explore new perspectives.

The interview is not about pass or fail; it's an opportunity for meaningful discussion and the exchange of ideas.

Happy Learning

Creativity vs Copyright

 

Keep Thinking!!!

Proplens - GenAI Powered Real Estate Solution :)

 

We are in news !!!  Link

Happy Responsible, Accurate, Consistency and Low Latency Adoption :)

Balanced Perspective of AI Adoption - Human + AI (OR) Humans vs AI

 

Keep Exploring!!!

The Evolving Landscape of ML Hiring: A Veteran's Perspective

 

Job interviews often miss true talent. They reward rehearsed responses over candidates who can persistently build practical, context-aware solutions beyond just technical know-how

As someone in the trenches of data science hiring for over 7 years, I've watched our field transform dramatically. Recently, a job description for an ML role caught my eye - and not necessarily in a good way. It got me thinking about how our industry's hiring practices often need to catch up to the reality of our work. Let me share some observations:

The Commodity of Code

• LLM can generate working solutions / provide ideas / get started on any topic as long as you have good basic skills and coding knowledge. Now, I ask interns hiring assignment tasks to focus on accuracy and bugs. Code has become a commodity. The real value lies in understanding models, and limitations, bridging the gap between visions and technical realities, and architecting solutions that solve real-world problems.

The Kitchen Sink JD

• This particular job description reads like a wish list for a tech superhero. Data structures, algorithms, AI/ML, coding, system design - oh, and don't forget a dash of product sense! While it's great to aim high, this scattergun approach often misses the mark. We need specialists with deep expertise, not generalists who've dabbled in everything.

The Interview Gauntlet

• The hiring process outlined was a marathon: write-ups, HackerEarth assessments, coding tests, multiple rounds with the ML team, and then more conversations. In a market where top talent is scarce and in high demand, do we really need to put candidates through such a lengthy ordeal?

The Missing Pieces

• What struck me most was what the JD and process didn't emphasize. Where was the assessment of a candidate's ability to translate business problems into technical solutions? How about evaluating their capacity to stay ahead of rapidly evolving trends in ML?

A Call for Pragmatism

• To my fellow hiring managers and HR teams: let's get practical. The perfect candidate who ticks every box on your mile-long list probably doesn't exist - and if they do, they're likely happily employed or running their own startup.

Instead, focus on core competencies that drive real value:

• The ability to understand and translate business needs
• A knack for architecting scalable, efficient solutions
• Adaptability and a passion for continuous learning
• Strong communication skills to bridge technical and non-technical stakeholders

The ML landscape is changing faster than ever. Our hiring practices need to keep pace. Let's move beyond the "code on a whiteboard" era and design processes that identify true innovators who can propel our field forward.

Another Good Read - Why We Don't Interview Product Managers Anymore

Got Something worthy Today to post

This JD Rocks - Link

• Focus on practical software engineering, not algorithm challenges.
• Work through a system design problem relevant to your daily work.
• Talk about your perspectives on building a great product.
• Deep dive on engineering practices and culture 

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Ethical AI vs. Agentic Autonomous AI: Navigating the Complexities of Modern AI Systems

• Human Oversight vs. AI Independence: Ethical AI frameworks typically advocate for human-in-the-loop systems, ensuring human oversight. Agentic Autonomous AI aims to minimize human intervention, raising questions about responsibility and control.
• Short-term Gains vs. Long-term Consequences: The push for rapid AI advancement (often seen in Agentic Autonomous AI) may overlook long-term ethical implications. Ethical AI approaches tend to prioritize careful consideration of potential future impacts.
• The Reasoning Conundrum: While Large Language Models (LLMs) demonstrate language understanding and generation capabilities, they still lack true reasoning abilities. This limitation is crucial when considering the ethical implications of deploying AI systems in decision-making roles.
• Ethical Constraints vs. Autonomous Agency: The core tension between Ethical AI and Agentic Autonomous AI lies in balancing moral safeguards with the desire for increasingly independent AI systems. Ethical AI prioritizes human values and safety, while Agentic Autonomous AI pushes for greater AI self-direction.
• Transparency Trade-offs: Ethical AI often demands explainability and interpretability, potentially limiting model complexity. Conversely, highly autonomous AI systems may sacrifice transparency for increased capabilities, raising ethical concerns about accountability and trust.
• Data Ethics in AI Development: Ethical AI emphasizes the importance of unbiased, representative datasets. Agentic Autonomous AI, however, may prioritize data quantity over quality to enhance its learning capabilities, potentially perpetuating or amplifying societal biases.
• Continuous Learning and Ethical Drift: Agentic Autonomous AI systems that engage in continuous learning pose risks of ethical drift over time. Ethical AI frameworks must grapple with how to maintain moral constraints in evolving systems.
• Global Ethics vs. Local Autonomy: As AI systems become more autonomous, they may encounter scenarios where global ethical standards conflict with optimal local decisions. This tension between universal ethics and situational autonomy remains a critical challenge.
• Responsible AI Adoption in Practice: Implementing either Ethical AI or Agentic Autonomous AI requires a deep understanding of models, data, and their limitations. Superficial adoptions of either approach can lead to irresponsible and potentially harmful AI deployments.
• The Role of Human Values: Ethical AI explicitly encodes human values into AI systems, while Agentic Autonomous AI may develop its own set of values through learning. The alignment (or potential misalignment) of these values with human ethics is a crucial area of ongoing research and debate.

Technology will continue to change the world. A thoughtful approach is needed to prioritize use cases that offer broader positive impacts over those that primarily lead to monetization. This way of thinking can help align AI adoption with human values and ensure a more substantial positive impact on humanity.

Keep Going!!!

GenAI Two Use Cases - Two Lessons

Creative and Learning Use Case

Instead of playing sports or watching cartoons, kids nowadays are coding. 👀 pic.twitter.com/POX50bt4MH

— Vivek Naskar (@vivek_naskar) September 8, 2024

Wrong Guardrails Applied, Content for opinions

What other options

• Provide Factual data
• Do not provide recommendations for entities
• Reason for bias
• Do not rely on Guardrails

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Painful Moments - Potential People vs. Wrong Mindset

While evaluating answers: Some candidates document well, attempt, and submit answers but miss the basics. This reflects both intent and missed guidance in learning. High potential is evident, but basics are either overlooked or dot-connecting skills are lacking.

While teaching: Some PhD/lateral folks tend to generalize everything or focus on proving theories break. Learning is not about proving your knowledge but about gaining a balanced perspective. One class is not sufficient to judge anything. Observing these types of learners makes me feel sad as they are so short-sighted.

Education is not mindset; experience does not mean competency!!!

Keep Exploring!!!

Memorization vs Generalization

Memorization vs Generalization

When you develop #GenAI apps, After a certain stage, When things work fine, The immediate next question is

• Model is memorizing or Learning patterns
• Test with variations / Analyze on patterns of responses

I don't want my life to be memorization - Company1 - Company2 ..., Exploring out of comfort zones provides diverse perspectives.

Earlier I had time to regret, Now I don't have time to think about anything. A long day of managing and solving different problems and different lenses of execution. Sometimes some experiences don't fill your pocket but fill your soul. In the end, I want to smile at death, I have tried all my wishlists.

Keep Exploring!!!

LLM perspectives

• LLM - Fast, Simple, and Dumb Sometimes
• Prompts - Looks simple but when structured relevant to context can give magical results
• With extended context length, embedding it looks more magical with the abstraction of representations of useful knowledge

LLM generation kids / learning using LLM products will have a different perspective of thinking / before and after ChatGPT :)

Why we don't see good AI / ML work

Why we don't see good AI / ML work. Good AI / ML work depends

• Choosing a use case with a strategic and AI-focused approach. Selecting the use case that has a balance of vision/strategy / applying AI lens
• Ensuring access to adequate data for training and deployment
• Garnering robust business support
• Acquiring or developing tools to deliver meaningful AI/ML contributions

Struggling with your AI strategy? Let's connect and navigate it together.

Keep Exploring!!!

2024 - Year of Opportunities / Lessons / Learning's

My memorable moments/projects/achievements.  

• Build vision capability
• 2 Granted Patents
• Won Vision solutions for Retail, and FMCG customers
• Built products in a consulting role (Some failed / some worked)
• Rewrote warranty for 220million consoles in Microsoft

Next Steps

• Teaching + Deep Dive + Part-time is my goal for sustainable health and learning
• Pick and select a few things and deep dive and build a point of view

Keep Exploring!!!

AI skills at work

• Selling AI is a skill
• Building (Billing) with AI is a skill
• Keeping the end goal a moving target is a skill
• Build vs Buy vs Manage cost is a skill
• Hiring someone who can Build (Bill) effectively is a skill
• Differentiating AI demos vs AI reality is a skill

Choose wisely!!!!

AI Skills <> AI Experience

• How to build it right = Skill
• What it takes to build it right in the first iteration = Experience

Keep Exploring!!!