HubSpot

- Partner with mid-market businesses to optimize growth strategies using HubSpot’s CRM and marketing solutions.
- Work with executives to align technology adoption with revenue and customer experience goals.
- Deliver consultative, value-focused sales engagements across industries.

Growth Specialist

Gartner

Helped tech CEOs drive aggressive revenue growth through Gartner’s research and advisory services.

Enterprise Sales Director - High Tech

leap.club

As one of the founding team members at leap.club, I played a pivotal role in driving net new business, and leading the sales team to onboard 2000+ paying members within the first 8 months since launch. I significantly contributed to the company's success of 25,000 paying members across 13 countries and $2.2 million in funding.

Founding Team, Associate Vice President of Sales

United Nations

• Led strategic partnerships with Southeast Asian conglomerates and UN agencies, achieving media coverage across 43 channels.  
• Orchestrated workshops and events, culminating in a large-scale competition with over 650 participants focused on climate action.  
• Spearheaded an international climate tech initiative, securing $11.5M in sponsorship through targeted, data-driven proposals.

Strategic Partnerships

Zomato

• Drove digital transformation for major restaurant chains through SaaS sales.
• Developed and executed data-driven sales strategies for CXOs of large enterprise accounts.
• Facilitated measurable ROI for advertising spends, leading to increased revenue for clients.

Key Account Executive

Founder @ 108x.ai | AI & Quantum Transformation Partner

📩 Speaking & Collaboration Inquiries: please DM


I share insights on:
- Enterprise AI transformation strategies
- Quantum and Robotics
- Generative AI adoption for business growth
- Future of work & AI-driven leadership
- AI talent, skills, and career trends
- Building a personal brand in the AI era

As an AI leader and speaker my mission is to make AI adoption understandable and actionable turning the hype into real business value for enterprises.

All opinions and posts are my own and not those of my employer.

The AI leaderboard of 2025 is here.

From $500B giants like OpenAI to fast-rising startups like Perplexity, Mistral, and Hugging Face - here's a that chart shows just how massive the AI ecosystem has become.

Top 3 by valuation
• Sam Altman – OpenAI: $500B
• Dario Amodei – Anthropic: $183B
• Ali Ghodsi – Databricks: $100B+

The challengers
• Aravind Srinivas (Perplexity) → $20B
• Alexandr Wang (Scale AI) → $14B
• Arthur Mensch (Mistral AI) → $12.6B

The innovators
From Brett Adcock at Figure ($39B) to Aidan Gomez at Cohere ($7B), and even Hugging Face, Writer, and Character.AI - these companies are shaping how enterprises, developers, and consumers interact with AI every day.

The spread of valuations proves one thing:
AI is no longer just about building models. It’s about building companies that can scale, monetize, and create real-world impact.

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Avani Rajput

Helping businesses scale with AI | Enterprise Sales @ HubSpot | Ex-Gartner

Everyone’s talking about how LLMs.

But very few understand how they actually work behind the scenes.
Here’s the full lifecycle of a Large Language Model (LLM) - simplified 👇

1. Data Collection → Massive datasets (books, code, research papers, websites) are gathered.

2. Data Cleaning → Irrelevant or duplicate text is removed and structured for training.

3. Model Architecture → Neural networks (like Transformers) define how patterns are learned.

4. Pretraining → The model learns to predict the next word across huge datasets.

5. Fine-Tuning → Specialized retraining (often with human feedback) for domain expertise.

6. Scaling & Optimization → Distributed training across thousands of GPUs/TPUs.

7. Alignment & Safety → Adjustments made to reduce bias, follow human instructions, and ensure safe use.

8. Evaluation & Benchmarking → Tested against real-world tasks and benchmarks.

9. Deployment & APIs → Integrated into products and apps for real-world usage.

10. Continuous Monitoring → Outputs are tracked and improved with new data.

What fascinates me most: It’s not just about training a model once - it’s a living cycle of data, safety, and iteration.

Which step do you think is the most critical for building trustworthy AI - pretraining, fine-tuning, or alignment?

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Agentic AI is quietly becoming the operating system for enterprises.

Here are 31 leading vendors (plus 3 open-source options) shaping the space, and the landscape is fascinating:

• Enterprise-Level AI Providers – From SAP to NVIDIA, these players are embedding agents deep into workflows.

• Business Ops & Automation – Think UiPath, Microsoft Copilot, and Amazon Bedrock, making process automation agent-first.

• Customer Experience & Support – AI-powered agents like Ada, Interface.ai, and ServiceNow are redefining CX.

• Specialized Domains – From Hippocratic AI (healthcare) to Cognition Labs’ Devin (software development), niche use cases are exploding.

• Open-Source Momentum – Frameworks like AgentGPT and SuperAGI are fueling a new wave of builders.

What stands out to me:
The shift from static AI assistants to autonomous, adaptive AI agents.
Open-source players are leveling the playing field for startups and enterprises alike.

We’re at the early innings of the agentic AI era, but the ecosystem is already crowded and growing.

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Agentic AI vs AI Agents vs RPA vs AI Tools - what’s the real difference?

Most people throw these terms around interchangeably.
But they’re not the same thing.

Here’s the breakdown :

• Agentic AI → Think of it as the most autonomous. It defines goals, plans tasks, makes decisions, executes, learns, and self-optimizes. Almost like a co-worker who manages projects end-to-end.

• AI Agents → They’re goal-driven but narrower. You set the objective, they reason, take actions, and return results. More flexible than tools, less independent than Agentic AI.

• RPA (Robotic Process Automation) → Rule-based automation. Great for repetitive tasks like keystrokes, clicks, and data transfers. Zero intelligence, just execution.

• AI Tools → The most common today. You give input, they process and return output (ChatGPT, Midjourney, etc.). Powerful, but you’re always in the driver’s seat.

Leaders need to understand these layers to deploy the right AI system for the right business problem.
- Use AI tools for productivity boosts.
- Use RPA for repetitive workflows.
- Use AI agents to automate processes with reasoning.
- Use Agentic AI when you want systems that plan, adapt, and self-optimize.

The future of enterprise AI won’t be about “which is best” - it’ll be about orchestrating all four together.

Which of these are you experimenting with in your team today? Let me know in comments

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12 AI Tools Every Coder Should Know in 2025

If you’re a developer, these tools will massively speed up your workflow 👇
I’ve tested most of these personally - and here’s the truth:

AI isn’t replacing developers; it’s amplifying them.
Whether you write backend systems, design frontends, or prototype products - AI is now your fastest teammate.

Here are the top 12 tools worth learning this year:
1. GitHub Copilot – still the gold standard for AI pair programming
2. Cursor – the AI-powered VS Code fork that’s changing how we code
3. Tabnine – privacy-first completion assistant for secure environments
4. Amazon Q Developer – AWS’s answer to Copilot, optimized for the cloud
5. Claude – surprisingly good at generating clean, readable code
6. ChatGPT – unbeatable for debugging, documentation, and learning new frameworks
7. Replit Ghostwriter – perfect for quick builds and cloud projects
8. Gemini CLI – brings AI to your terminal, for the old-school devs 😉
9. JetBrains AI Assistant – ideal for enterprise-level workflows
10. Windsurf (formerly Codeium) – smooth AI-native IDE
11. Devin by Cognition AI – the “autonomous engineer” everyone’s talking about
12. Codespell – helps you go beyond code completion — testing, debugging, docs

Developers who learn to collaborate with AI - not compete with it, will 10x faster than those who resist it.

Your edge isn’t in writing code.
It’s in knowing which AI tools to trust and how to combine them.

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ChatGPT = Agentic AI.

At least… that’s what most people think.

When people hear “agent,” they picture a chatbot answering questions.
But that’s just the surface.

Agentic AI is much more than conversation.

It’s a full system with 3 powerful layers:
- Cognitive Layer : planning, memory, reasoning
- Action Layer : tools, workflows, API calls, execution
- Learning Layer : feedback loops, error handling, optimization

In other words, it’s not just a Q&A machine.

It’s a system that can decide, do, and improve - almost like running a mini-organization inside one AI.

Curious to hear from you:
Where do you see Agentic AI creating the biggest disruption first - in decision-making, execution, or learning? Let me know in comments !

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The 2025 AI Chatbot Leaderboard is here and it says a lot about how we work today.

Monthly visits (in billions):
- ChatGPT : 46.6B (crossed 5B+ monthly visits for the first time)
- DeepSeek : 2.7B (China’s breakout player scaling globally fast)
- Gemini : 1.7B (Google’s edge in productivity + search)
- Perplexity : 1.5B (AI-powered search reimagined)
- Claude : 1.2B (thoughtful, long-form reasoning at scale)
- Microsoft Copilot : 957.2M (deep inside Office + Windows)
- Grok : 686.9M (steady growth inside X)
- Poe : 378.1M (multi-model choice for users)
- Meta AI : 130.4M (social + messaging focus)
- Mistral : 101.3M (lightweight open models at scale)

What stands out?
- ChatGPT’s dominance is unmatched but the pack behind is innovating fast.
- DeepSeek proves regional players can go global almost overnight.
- Tools merging search + productivity (Perplexity, Copilot, Gemini) are becoming daily companions, not side tools.

2025 is not the year of “AI hype” anymore. It’s the year chatbots became digital co-pilots - shaping how we sell, learn, and create.

Which of these chatbots has actually changed how you work this year? Let me know in the comments 

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Source : https://lnkd.in/gTnFsZMW

Google recently launched a new Startup Technical Guide for AI Agents and it’s the most practical roadmap I’ve seen yet for startups building in AI.

Most teams are still figuring out how to go from a GPT-style prototype → to a real, production-grade agent that can reason, act, and scale.

This guide shows exactly how to do that - end-to-end :

Here are some core components :

1. Agent Development Kit (ADK)
Build, test, and deploy production-ready AI agents - fast.

2. Multi-agent collaboration (A2A protocol)
Agents can now talk, delegate, and collaborate across workflows.

3. Model Context Protocol (MCP)
A universal standard to make tools, models, and agents interoperable.

4. Agentic RAG (Grounded reasoning)
Agents that reason while retrieving - delivering verified, context-aware outputs.

5. Vertex AI Agent Engine
Deploy, monitor, and scale agents without managing infra.

6. Google Agentspace
No-code builder to design and orchestrate AI agents visually.

7. Memory architecture
Integrates long-term memory so agents learn across sessions.

8. AgentOps
Framework for testing, auditing, and securing agents before launch.

AI agents aren’t a feature anymore. They’re becoming the operating system for how startups build, automate, and scale.

If you’re building in AI, this guide is worth bookmarking.

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Startup Technical Guide To Build AI Agents

The harsh reality about deploying agentic AI

Everyone talks about what AI can do.

But few talk about the roadblocks companies actually face when trying to make it work inside their business.

After working with leaders across industries, I see the same 7 challenges come up again and again:

1. Integration complexity – Legacy IT + outdated APIs = slow, painful rollouts.
2. Messy data – Biased or inconsistent data = unreliable AI.
3. Security & compliance – Regulations (GDPR, HIPAA) can’t be an afterthought.
4. High costs – Infrastructure + licensing + talent can crush SMBs.
5. Talent gap – Few people know how to build, deploy, and maintain agentic AI.
6. Reliability & control – Agents can act in ways you didn’t intend.
7. Change resistance – Teams fear disruption, slowing adoption.

The way forward is not to avoid these challenges but to anticipate and design around them.

Start small. Scale responsibly. Build trust inside the org, not just in the tech.
Companies that nail this balance will see the fastest ROI.

Which of these 7 challenges do you think is the biggest blocker for enterprises right now? Let me know in the comments !

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Top 7 Challenges Businesses Face When Deploying Agentic AI

Google just launched something that could redefine how we pay in the age of AI: the Agent Payment Protocol (AP2).

For the first time, we have a global standard that lets AI agents securely transact - whether with credit cards, bank transfers, or even stablecoins.

Why this matters :
Right now, every payment system assumes a human is clicking “buy.” But what happens when your AI agent books flights, negotiates deals, or buys your concert tickets?

Without a trusted protocol, agent-led commerce would be chaos.
AP2 solves this with:
- Authorization : proof you gave your agent specific authority
- Authenticity : actions must reflect your true intent
- Accountability : an audit trail to catch mistakes or fraud

The magic lies in Mandates: cryptographically signed digital contracts that capture user intent. Think of them as receipts for your AI’s decisions.

What this unlocks:
- Agents booking travel within budget, without back-and-forth approvals
- Buying products the moment they’re in stock
- Seamless support for crypto and stablecoins

This is not just payments - it’s infrastructure for the next phase of AI-driven commerce.

My take: Agentic commerce will absolutely go mainstream within 5 years. The question is - who will build the trust layer consumers and enterprises adopt at scale?

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Read the full release here : https://lnkd.in/gQCctSc8

OpenAI just launched Agent Builder 
 
A visual, no-code tool to build and deploy AI agents.

This might be one of the most practical product updates from OpenAI yet.
Agent Builder lets you design agentic workflows visually - connecting logic, tools, and models like a flowchart.

You can start from templates or create from scratch, test your agents with built-in evals, and then deploy them instantly.

Here’s what stands out:
- No-code interface: Build agents by connecting nodes visually instead of writing code.
- Built-in evaluation: Test how your agents perform before deployment.
- Easy integration: Export as code, or plug directly into your app using OpenAI’s SDK or ChatKit.
- Custom outputs: Add widgets and tailor the experience for richer, interactive responses.

The result - a single workspace where you can design, test, and launch intelligent agents faster than ever.

This marks a shift in AI development from prompt engineering → to system design.

Developers, product teams, and even non-technical users can now orchestrate real agent workflows without needing deep ML or backend experience.

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Chrome just got its biggest AI upgrade yet!

Google is bringing Gemini directly into Chrome, making your browser smarter, safer, and more helpful than ever. From summarizing complex webpages to managing your tabs, Here are the highlights:

1. Gemini is now built into Chrome (desktop + mobile) to help you understand complex info on any page.

2.  Soon, it’ll act as your agentic browsing assistant - booking appointments, ordering groceries, handling repetitive tasks.

3. Multi-tab intelligence: Summarize and compare across tabs (goodbye travel planning chaos).

4. Recall past websites without digging through history.

5.  Deep integration with Calendar, YouTube, Maps - all inside Chrome.

6. AI Mode in the omnibox for smarter, conversational search.

7. AI Overviews + contextual Q&A right on any page.

8. Scam detection powered by Gemini Nano.

9. Smarter handling of spammy notifications & intrusive permissions.

10. 1-click AI-powered password changes on supported sites.

Chrome is no longer “just a browser.” It’s becoming your AI-powered digital workspace.

Do you see Chrome as a browser reimagined or as the foundation of a personal AI assistant?

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Curious to learn about AI agents ?

Here I’ve curated 9 free courses that take you from absolute beginner to building advanced AI agent systems. 

Whether you’re non-technical and just want to understand the basics, or a developer ready to build multi-agent workflows, these resources cover it all:

1. AI For Everyone
Perfect for non-technical professionals - learn AI terminology, applications, strategy, and ethics.
🔗 https://lnkd.in/g2ZKmi5K

2.  Multi-AI Agent Systems with crewAI
Master prompting multiple AI agents, automating tasks, defining roles, and streamlining workflows.
🔗 https://lnkd.in/gEVUPdFX

3. AI Agents for Beginners
Beginner-friendly course with lessons, code samples, Azure tools, and community support.
🔗 https://lnkd.in/gejZesNY

4. Learn AI Agents
Covers ReAct prompting - design dynamic, context-aware AI agents that respond intelligently.
🔗 https://lnkd.in/gU4Uy74Y

5. Fundamentals of AI Agents using RAG & LangChain
Learn RAG, LangChain, prompt engineering, and hands-on projects for building intelligent agents.
🔗 https://lnkd.in/gzk9uv68

6. AI Agent Developer Specialization
Deep dive into Python, OpenAI, memory, custom GPTs, and responsible AI with real-world projects.
🔗 https://lnkd.in/g8b7RDsc

7. Fundamentals of Building AI Agents
Covers architecture, principles, productivity, and integrating AI agents into real-world systems.
🔗 https://lnkd.in/g4Mwa2RR

8. AI Agents Specialization
Step-by-step from basics to advanced design-multi-agent systems, autonomy, and practical apps.
🔗 https://lnkd.in/g2Uhv3en

9. AI Agents with Python
Hands-on Python training to design, build, and deploy AI agents for practical use cases.
🔗 https://lnkd.in/gR3bfuqX

AI agents are not just the future of AI - they’re quickly becoming the skill every professional needs in 2025.

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Most people think ChatGPT is just for writing.

But the data shows something different :

Here’s what people actually use it for:
- 18.3%: Seeking specific information
- 10.6%: Editing or critiquing text
- 10.2%: Tutoring & teaching
- 8.5%: How-to advice
- 8.0%: Communication & personal writing
- 4.2%: Programming help & image creation
- and even 5.7%: Health, fitness & self-care

AI is becoming less of a single-use tool and more of an everyday companion. People are leaning on it for learning, guidance, creativity, and even wellness.

This shift changes how businesses should think about AI adoption:
It’s not just about efficiency → it’s about versatility.
It’s not just for teams → it’s for individuals, every day.

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ChatGPT just hit 5.8 billion visits last month.

And the data tells a fascinating story about where AI adoption is really happening.

🇺🇸 The U.S. leads with 883M visits.
🇮🇳 But India isn’t far behind, contributing 544M visits, making ChatGPT the 4th most-visited website in the country.

This isn’t just a stat. It’s a signal.

AI is no longer a niche tool for techies, it’s becoming a daily habit across industries, roles, and geographies.

From students and founders to enterprise teams, people aren’t just talking about AI anymore, they’re using it.

For business leaders, this raises two big questions:
- How are you enabling your team to use AI daily?
- Are you treating AI like a side project or a core skill?

Because the biggest competitive edge in 2025 won’t come from hiring more people, It’ll come from teams who know how to work with AI.

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Source : https://lnkd.in/gAs4SWvM

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