by
Anuj Gupta
In today’s rapidly evolving AI landscape, (Generative) AI promises to revolutionize businesses like never before. While it unlocks unparalleled opportunities, it also brings complex challenges in turning this disruptive technology into successful AI features, products & ventures. The key to unlock these opportunities is to elevate your organization's AI practices toward "Pragmatic AI"— AI efforts that deliver real-world impact.
We define Pragmatic AI as AI efforts that translate into tangible business successes, leading to increased revenues and market dominance, rather than focusing solely on model metrics.
"AI First by Design" is your guide to Pragmatic AI. It is a practical resource for Executives, Leaders, Startup Founders, Partners at VC/PE firms, Managers, Engineers, Scientist focused on being part of AI initiatives that deliver real business impact.
Artificial Intelligence is no longer a futuristic concept—it’s the defining force shaping the next era of business, technology, and innovation. Yet, despite the hype, many organizations struggle to build AI systems that drive real impact. AI projects often stall due to unclear objectives, poor alignment with business goals, talent gaps, or an underdeveloped AI strategy.
This playbook is designed to change that. It’s a no-nonsense, practical guide for leaders, Startup founders, and AI practitioners who want to build world-class AI systems, products, and teams—not just PoCs (Proof of Concepts) or prototypes that never make it to production. Whether you’re a startup founder, corporate executive, or VC partner, this playbook will help you navigate the complexities of AI adoption and execution.
We distill lessons from over two decades of hands-on AI experience—spanning early-stage startups (0-1, 1-n) to Fortune 50 giants. Having led numerous high-impact AI initiatives, we’ve curated the most valuable insights into a strategic, results-driven approach for building AI systems that truly move the needle.
This playbook serves as both a roadmap for AI adoption and a blueprint for success. It dives into the key parameters of AI readiness, addressing why AI development fundamentally differs from traditional IT/software systems and why the known playbooks for Software 1.0 fall short when building Software 2.0 (AI-driven systems).
Understanding these principles is crucial for AI founders, executives, CXOs, VC partners, and senior policymakers. Organizations with leaders who grasp these concepts will achieve higher AI maturity, readiness, and a distinct competitive edge.
Our hope is that this playbook becomes an invaluable resource for AI startup founders, MNC executives, AI scientists, developers, investors, and policymakers driving AI-led transformation worldwide. This isn’t just another AI book—it’s a playbook for action. By the end, you’ll have a clear roadmap to build and scale AI systems that deliver tangible business impact, innovation, and lasting competitive advantage.
Back in the late 90s when Internet had just come, 99% of the Organizations saw Internet as just another channel of distribution. 1% of the companies saw the internet as the business itself. It is these 1% - Google, Amazon, Facebook, Apple etc that went on to create massive wealth and made the most of once in a lifetime opportunity. AI is at a similar junction. It is not just another technology. 99% of the Organizations see it as a great way to optimize their manpower & operations (which will surely help them improve their bottomline). But that not the true power of this technology. The true power of it lies in reimagining one entire business in the light of this technology. Only 1% of the companies are doing so. But it is this 1% that is going make the most of AI potential - the next set of unicorns, decacorns will come from these 1% and not the 99%.
This is best summarised by Satya Nadella when he says "The core currency of any business will be its ability to harness AI and fundamentally reimagine what it does & how it operates in the light of AI."
Mark Cuban famously said "There are only two types of companies in this world - Those that are great at AI and everybody else. If you are not good at AI you are going to fail. Period. End of story. And if you are a CEO, you can’t just leave it to the tech guys". He puts the onus of AI transformation directly on the CEO & his entire leadership team and not just the CTO or CIO. Why so? because unlike IT, AI is not just a better way of doing things. Its an Organization wide initative and starts with CXO team taking bold bets and reimagining their entire business in the light of this technology.
It is owing to this promise, every team, organization, country is after mastering this technology. Head of States are conference chairs! Billions of dollars are being invested in this technology around the world and trillions more will be invested.
- Ch 1: Once in a Lifetime Opportunity
- Ch 2: The Fundamental Mistake
- Ch 3: Human Brain Analogy is Super Misleading
- Incremental Learning
- Explainability
- Ch 4: AI ≠ Replace Humans?
- AI = Augmented Intelligence rather than Artificial Intelligence
- Ch 5: The Right Methodology to Develop AI Systems
- Capability Curve for AI Systems vs IT Systems
- Make it Work, Make it Better
- Ch 6: AI Won’t Be Error-Free Anytime Soon
- Mistakes are Part and Parcel of AI Systems
- Ch 7: AI Problems Can Be Solved in Multiple Ways
- AI Teams Do Not Know the Right Path Upfront
- Finding the Path Involves Many Experiments
- Solutions Differ Based on the Problem and Dataset
- Ch 8: AI Solutions Are Not Incremental
- Going from 85% to 90% Accuracy May Require Starting Over
- Ch 9: Updating AI Models ≠ Old Learnings + New Learnings
- Can Lead to:
- Incorrect Predictions on Previously Correct Data Points
- Catastrophic Forgetting
- Can Lead to:
- Ch 10: AI Model Quality Mirrors Training Data Quality
- Unseen Data Examples
- Ch 11: Drift
- Data Drift vs Concept Drift
- Ch 12: Quantifying Data Needs is Hard
- Deep Learning is a Data Guzzler
- Transfer Learning Doesn’t Work in All Scenarios
- Ch 13: Data Quality Matters
- Can't Run Fighter Jets on Crude Oil!
- Quantifying the Impact of Bad Data is Tough
- Ch 14: AI Ops is Not DevOps
- Why Deploying and Maintaining Models is Much More Nuanced
- Ch 15: AI is Not a Sledgehammer
- Ch 16: Defining the Right Metrics to Measure Success
- AI Metrics
- Business Metrics
- Synchrony Between AI & Business Metrics
- Ch 17: Data Labeling
- AI is Only as Good as Its Data Labelers
- Ch 18: Testing AI Systems
- “Incorrect” Output
- Ch 19: Agile is Unfit for Managing AI Development
- AI Development is Highly Unpredictable
- Ch 20: Data Collection
- Building Nuclear Systems Requires Rigorous Data Collection, Cleaning, and Enrichment
- Ch 21: The Safety Net of AI Systems
- Control Loop
- Feedback Loop
- Human in the Loop
- UX for AI
- Handling Errors and Failures Gracefully
- Introducing AI to Users Gradually
- With AI, Always Better to Under-Promise than Over-Promise
- Ch 22: Explainability and Trust
- Ch 23: The Business of AI is Far from SaaS
- Why Profit Margins in AI (~40%) are Much Lower than SaaS (~85%)
- Ch 24: Why AI Endeavors Are Still Expensive
- Ch 25: Crucial to Quickly Find the Market's Price for AI
- Customers Pay for Value, Not AI
- Ch 26: Accuracy Obsession Early On is Counterproductive
- When is Improving AI Systems Incrementally (1-2%) Worth It?
- Ch 27: How is GPU Cost Likely to Play Out in the Future?
- Ch 28: Stakeholder Management
- Do You Even Need AI?
- Ill-Formed Problems
- Capability Curve for AI Systems
- Make it Work, Make it Better
- Who is the Best Person to Manage Stakeholders?
- Ch 29: The Right Hiring Strategy for AI
- Generalist vs Specialist
- Does Hiring PhDs Give You an Edge?
- When is the Right Time to Hire a Head of AI?
- What Should the Head of AI Be—PhD or MBA?
- Bilingual AI Leaders
- Ch 30: Managing Individual Contributors (ICs)
- Occam's Razor
- Falling in Love with the Solution, Not the Problem
- AI System >> AI Model
- Clues Lie in Mistakes
- Ch 31: AI Product Manager: The Underrated Ace
- Ch 32: Publishing
What I do:
- On-demand Head of (Gen) AI to multiple Startups & MNCs across US, Europe & India.
- AI Advisor to multiple Boards, mentoring them on various facets of (Gen) AI.
- Helping MNCs put together a strong (Gen) AI strategy & roadmap in place
- Conduct Highly bespoke workshops on (Gen) AI for CXOs, Executives, Board Members, VC/PE partners.
- Mentor Global Capability Centers (GCCs) in setting up AI Center of Excellence (CoE)
My background:
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Seasoned AI executive with 20+ years of extensive experience in spearheading core AI efforts, driving AI KPIs as Chief AI Officer.
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Recently mentored a YC company build critical AI systems, demoed to Sam Altman (Open AI) & Vinod Khosla (Khosla Ventures); helping YC company secure series B funding by Khosla Ventures.
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Led AI efforts for one of the earliest startups to be funded for its AI-first approach, back in 2013; leading to its acquisition by FreshWorks (Nasdaq listed) in 2016.
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Authored a landmark book in AI. The book has been endorsed by Globally Top AI Leaders by from CMU, UCSD, DeepMind, Google AI, Microsoft Research, Amazon AI Research, Meta, Spotify, YC startups including Airbnb & Sharpest Minds.
The book has already been translated in 5 languages, 270+ citations, and used by 50+ universities globally for their AI curriculum. Ranks consistently in the top 1% of AI books.
Presented our book to Prof Raj Reddy (Turing award winner, doyen of AI & Robotics at CMU) and Dr Srinivas Bangalore (SVP of AI at Interactions Corporation. Visiting professor at Columbia University, Princeton University, and Copenhagen Business School)
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Incubated & spearheaded AI efforts at both startups (0-1, 1-n) as well as Fortune 50 companies. Developed commercially successful AI products and features leading to multi-million $$ in revenue & contributing significantly to Org KPIs.
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Incubated & led AI teams of size 5-100 people spanning multiple geographies, managing the entire team & lifecycle of AI projects.
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Incubated & led AI efforts & built multiple AI systems in NLP, Vision, Speech and Data Science at both startups (0-1, 1-n) & Fortune 50.
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Worked very closely with Founders and my C-suite peers across Business, Product, Engineering, Sales, and HR to drive KPIs as Chief AI Officer at the organizational level.
You can connect with me here:
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}
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