Vin Vashishta

AI Strategist | Monetizing Data & AI For The Global 2K Since 2012 | 3X Founder | Best-Selling Author

I have helped over 9,000 senior++ ICs and executive leaders advance and stay relevant with high-end AI capabilities. Courses & Certifications: https://datascience.vin

Upskill your workforce to be AI-ready in 3 months: seminars@v2ds.com

Conference talks, brand partnerships, and thought leadership: partnerships@vinvashishta.com

Over 85% of AI projects fail to deliver a return on investment because they start with technology, not a business problem. I’m fixing that by empowering companies and training people to stop experimenting with AI and start monetizing it.

Define, align, and manage the business and technical transformations that turn C-level AI visions into products, productivity, and revenue. Since 2012, V Squared AI has worked with large enterprises to deliver over $4 billion in value.

My frameworks are built from working in the trenches. My practitioner-to-strategist journey allows me to bridge the critical gap between technical teams and the boardroom, ensuring that AI initiatives are not only technically sound but also commercially viable.

Partners & Disclosures: Dell Pro Precision Ambassador, NVIDIA Partner, & Gartner Ambassador.

Speaker’s Bio:

Vin Vashishta is an award-winning AI thought leader, best-selling author of ‘From Data to Profit,’ 3X founder with a successful exit, and CEO of V Squared AI, one of the first pure play data and AI consulting firms. The Wall Street Journal, Fast Company, and The Information have showcased Vin’s AI expertise.

He brings 30 years of experience in leadership, technical strategy, and hands-on technical product delivery. He has trained, productized, and monetized information and AI for 15 years and previously worked in software engineering and organizational leadership roles.

Founder & AI Advisor

Go with experience and a track record of success. V Squared is one of the oldest data and AI consulting companies in the world. Since 2012, that singular focus has built deep expertise in monetizing data, analytics, and AI.

V Squared's AI advisory services give clients clarity into the complete data and AI monetization journey. They trust us to implement frameworks that facilitate and streamline the journey. We provide expertise to remove roadblocks and avoid setbacks.

Our past and present clients include Airbus, Siemens, Walmart, JPMC, and over 20 SMEs.

Reach out for help with:
AI Product Strategy and Delivery
AI Strategy
Data & AI Organization Building and Scaling
Product Evangelization and Positioning

email: info@v2ds.com or DM me for more information.

Technology teams solve technical problems. Business teams solve business problems. If the technology being created doesn’t deliver value, that’s a business problem and the business’s fault. That mindset got me laid off twice.

It’s why Yan LeCunn got pushed aside at Meta and why thousands of technical leaders are being sidelined at other businesses. When the technology delivered does not create the value that business leaders expect, they rarely take the blame.

I’m business, product, and monetization-first today, but I wasn’t always. I used to think that value wasn’t my problem until the second time I was the one who dealt with the consequences of bad technical strategy choices.

Excluding firms that are building the AI industry, US companies spent an estimated $86 billion on AI in 2025, according to IDC. This year, IDC estimates that AI spending outside of tech will reach $131 billion.

In 2026, that spending comes with heightened expectations. Investors will be asking non-tech companies tough questions, and that pressure trickles down to every part of the business.

“To me it’s…the most important question that you should be asking a company because if they don’t have a strategy for this (AI), they’re probably going to get left behind.” Heath Terry, Global Head of Technology Research at Citigroup.

Accountability for value should start at the top, but as we’re seeing at Meta and many other businesses, it typically hits technical teams and their leadership first.

Technical leaders must be in the room where strategy is decided. They must become strategic partners, not just operational and tactical order takers.

Technical teams must ask tough questions about how every initiative creates value for the business. They must be more value-centric than ever and push back when prioritization strays from driving growth and cost savings.

Don’t rely on another team to take ownership for value creation. Most in the business don’t understand the AI and agentic monetization paradigms. They don’t see the right opportunities, so they can’t connect technology with value creation.

Most don’t know the difference between use cases where AI will generate significant returns and AI cost centers where AI is the wrong technology to implement.

Businesses need technical teams and leaders who can turn AI, information, and agents into top and bottom-line impacts. Don’t be afraid to step forward because we’re the ones who deal with the consequences when it doesn’t.

At CES 2026, I saw the tech world’s vision for the future of work. It’s bleak for traditional jobs, both physical and knowledge work. By 2028, Hyundai will produce 30,000 humanoid robots annually.

Robots will outnumber people in most of its factories by 2032, and that’s where nearly all major manufacturers are headed. Warehouses at Amazon and Walmart have already started a similar transformation.

A head chef will oversee more robots than people in restaurant kitchens that are being redesigned for this transformation. A head waiter will manage a fleet of food delivery robots to serve even the busiest restaurant.

AI kiosks of every shape and function will replace attendants, with a single person supervising high-traffic entryways, like those at Disneyland. Autonomous checkout will replace most cashiers.

AI will generate synthetic data for new AI to be trained with. More AI will orchestrate the agentic development lifecycle. Simulations will test and improve the models that automate all those robots, cars, and kiosks.

The data centers that people are building today will be put up much faster by construction robots overseen by a handful of construction workers.

Everyone I spoke with said the same thing: “It’s not that the robots don’t work today. We can’t produce them fast enough or at the right price point, yet.” Robotics will see a similar boom in the next two years as data centers are seeing now.

That’s why metals and other resources are being stockpiled, and their prices are skyrocketing. Most companies are preparing to provide the next gold rush with robotics picks and shovels.

We should be reskilling people now. The future of work requires more people who can repair and maintain all this AI and the new AI hardware platforms. We need people who can manage AI workers.

It’s important to be proactive here because reacting after job losses take hold isn’t a version of reality we want to experience.

Dell Technologies

• Led award-winning LLM initiatives, winning APJC Region First Prize for architecting a RAG-based RFP analysis system with cross-encoder reranking, accelerating compliance verification by 80% while enhancing knowledge sharing across teams
• Designed and implemented multi-agent LLM systems using LangGraph that intelligently route complex queries to specialised experts, reducing pre-sales response time by 70% while maintaining 95% accuracy on hardware inquiries 
• Engineered inference optimisation through speculative decoding and tensor parallelism, slashing LLM inference time by 90% while preserving response quality
• Spearheaded centralised vLLM/SGLang API infrastructure delivering 80% improved resource utilisation and 200% faster deployment
• Built end-to-end MLOps/LLMOps pipeline (vLLM/SGLang, MLflow, Langfuse, FastAPI, Prometheus) for AI application evaluation, deployment and monitoring with comprehensive observability
• Conducted 15+ GenAI workshops using relatable metaphors and interactive demos for audiences ranging from C-suite executives to technical teams, directly contributing to hardware sales through high-impact demonstrations

Gen AI Data Scientist / Forward Deployed Engineer

• Implemented multi-modal RAG solutions processing documents, images, and tables that enhanced content creation efficiency by 70% through advanced contextual understanding
• Accelerated Great Barrier Reef research by optimizing vision models for coral identification, enabling analysis of 51,000 images/week (vs. 13,000/2 months), achieving 97% time savings
• Drove 50% team productivity increase by architecting automated feature engineering pipelines and standardized, actionable dashboards with clear KPIs

Data Scientist

• Designed and implemented Tableau dashboards, providing clear insights for internal teams on strategic focus areas, enhancing decision-making processes.
• Developed a comprehensive NLP solution incorporating various models and methodologies, guiding clients on investment strategies within Singapore's market

Associate Consultant | Data & Analytics (DnA)

Rolls-Royce

• Automated data mining and processing procedures, resulting in up to 70% time savings
• Conducted data regression analyses of the relationship between battery health (SoH) and other extracted features
• Built multiple machine learning algorithms to balance accuracy and performance for deployment, including support vector regression (SVR), decision tree, random forest, K-nearest neighbour (KNN), and extreme learning machine, achieving up to an RMSE of 0.09 with KNN
• Utilised PCA for comparison and achieved up to RMSE of 0.13 with KNN
• Proposed methods to deploy models, including one-time, batch and real-time training using Apache Spark and Kafka

Industrial Sponsored Project (ISP/FYP)

SEDS-NTU

• Part of an 8-men team to set up a Space Committee for Space enthusiasts to come together in Singapore in collaboration with SEDS-NUS 
• Utilised Trello and Agile methodology, update core team on weekly updates and push for tasks' completion
• In charge of crafting emails to notify the entire school and club of upcoming events and activities
• Arranged meetings and prepare agendas and necessary papers for the executive committee
• Guided the executive committee concerning their roles and responsibilities

Honorary General Secretary and Co-Founder

Dell

• Automated hot-plug tests for USB-A and USB-C, saving up to 6 hours of manual testing
• Automated testing allowed for testing of multiple peripherals simultaneously, leading to an intangible amount of savings
• Time savings of up to 8 hours by automating power management tests (modern standby) for Windows system
• Increased test coverage with automated testing, making products even more reliable and improving engineering rigour

Test Engineering Intern

Durapower Holdings Pte Ltd 苏州新中能源科技有限公司

•  Collaborated with an engineering team of 5 to secure a milestone contract to provide battery storage solutions for Automated Guided Vehicles (AGV) fleet
•  Coordinated with different departments simultaneously (Engineering, business, aftersales), handling multiple tenders
•  Accelerated ongoing negotiations with clients with technical designs
- Revamped company brochures and marketing collaterals for international customers
•  Refined existing design of Energy Storage System (ESS) for increased airflow, cable management, improved fire safety and aesthetic purposes
•  Conceived an alternate battery solution with liquid cooling to boost battery performance and prolong its lifespan

Internship

NTU Hall of Residence 10

•  Led a committee of 32 people to organise an overnight cycling event for an entire hall
•  Did multiple fundraisers
•  Established online drive to streamline workflow and facilitate manpower
•  Organised overnight cycling event for 83 people whilst slashing costs by 15% compared to the previous year

Vice President (BikeX)

Nanyang Technological University

Development of VELOX-Lyon-1 Student CubeSat
Aug 2018 – Dec 2018
•  Led a sub-team of 3 focusing on Electronic Power System, fabricated space hardware and software to deliver payloads of 2 infrared radiation and visible spectral sensors (Arduino)
• Designed a system replying on solar energy factoring complications such as heat dissipation and battery for areas without direct exposure to sunlight

EEE Design & Development Project (DIP)

Borrelli Walsh

- Analysed, tallied and assessed the credibility of accounting statements with overseas branches.
- Coordinated administrative tasks for monthly seminars and corporate events
- Printed, scanned and photocopied documents
- Archived candidate expired contracts/secrecy documents
- Resorted documents from 2012

Part Timer

Sushi-Tei Pte Ltd (Singapore)

- Scheduled dining reservations for guests in advance
- Cheerfully greet guests, careful and quick planning of guests seating to maximise seating and turnover while also helping my colleagues with serving guests while the restaurant is full
- Strategically maximised restaurant capacity to increase revenue

Part Time Host

Award-winning LLM whisperer who makes AI solutions that actually work (and won't take over the world... yet). At Dell Technologies, I transform complex AI concepts into business results that make executives say "I finally get it!" and teams say "this saved us 97% of our time!"

When I'm not slashing inference time by 90% with speculative decoding or building multi-agent systems that are smarter than most meetings I've attended, you might find me explaining RAG architectures to C-suite execs using metaphors they actually remember, or helping coral researchers identify 51,000 images/week instead of 13,000/2 months.

Currently pursuing my M.Sc. in Analytics at Georgia Tech while building AI systems that amplify human capabilities rather than replace them. Because let's face it - even the best LLMs still hallucinate their way through a conversation sometimes (unlike me, who only occasionally hallucinate after my third espresso).

Expertise: Advanced RAG architectures, multi-agent orchestration, inference optimization, vector search, LLMOps

Fluent in Python, English, Mandarin, and translating "tech speak" into human language. Also secretly building a perfume recommendation engine because even AI deserves to smell nice.

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