AI in Product Strategy: Adapting to Global Shifts and Emerging Roles

AI is not just another technology trend — it is rewriting the rules of product strategy and management. The actual job is no longer only about deciding what to build, but about understanding how AI capabilities intersect with user needs, business goals, and societal impact. If you fail to adapt, your products will fall behind, and your role will be marginalized.

India’s tech ecosystem stands at a unique inflection point. The exponential growth of generative AI, the advent of new data privacy laws, and the rising expectations for ethical AI create both opportunity and complexity. You must learn to build AI product strategies that are not only technologically advanced but also grounded in Indian market realities and global best practices.

The third wave of product management demands an AI mindset

Product management has evolved in waves. The first wave was about project coordination; the second wave brought user-centric product thinking. Now, the third wave is AI-driven product strategy.

Talvinder Singh framed it like this in a recent session: “2018 to 2024 — the changes and highlights show that the greatest wave hitting product management is AI. Bill Gates calls it the Age of AI. Andrew Ng talks about exponential growth. This is the biggest shift since the internet itself.”

This new era requires you to:

• Understand AI as a general purpose technology that can be applied across industries and product types.

• Shift from traditional feature prioritization to AI capability evaluation — what AI can do, what it cannot, and what users actually need.

• Embrace responsible AI — managing ethical risks, data privacy, and inclusivity as core product features, not afterthoughts.

• Lead cross-functional teams that include ML engineers, data scientists, and ethicists, while translating complex AI concepts into user value.

The PM role is becoming more strategic and interdisciplinary than ever.

AI’s impact on product strategy: three core dimensions

AI reshapes product strategy along three broad dimensions:

1. Product innovation and user experience

AI enables new product capabilities — personalization, predictive analytics, natural language interaction — that were impossible before.

Consider GitHub Copilot, which blends AI and developer creativity to redefine coding workflows. Or AI-powered customer service that anticipates user needs and offers personalized solutions instantly.

For Indian companies, this means products must deliver context-aware AI features that respect local languages, data quality issues, and cost constraints.

2. Business operations and decision-making

AI automates routine tasks, optimizes workflows, and provides predictive insights that improve operational efficiency.

For example, Indian SaaS companies are using AI to forecast churn, automate billing, and tailor marketing campaigns.

PMs must integrate AI into business processes while carefully managing costs, latency, and data governance.

3. Ethical, regulatory, and societal considerations

AI introduces new risks: bias, privacy violations, algorithmic opacity.

India’s Personal Data Protection Bill and global regulations like GDPR impose strict requirements on data handling.

Talvinder Singh emphasized: “Privacy is not a checkbox — it is a product feature. Ethical AI and inclusivity must be baked in from day one.”

This means PMs must build products that earn user trust and comply with evolving laws — or face backlash and legal penalties.

The AI product strategy framework: six essential questions

Building a coherent AI product strategy requires answering six fundamental questions:

1. What user problem does AI solve better than non-AI alternatives? Be specific. Quantify how AI improves speed, accuracy, or cost for particular user segments.

2. Where does AI sit in the user workflow? Is AI the main interaction (e.g., chatbot) or a background enhancer (e.g., recommendation engine)? This affects UX design and performance requirements.

3. What proprietary data advantage do you have? Foundation models are commodities. Your moat is the unique, high-quality data you feed them — customer behavior, domain-specific knowledge, or regional nuances.

4. What happens when AI is wrong? Define failure modes: Is a wrong recommendation ignorable or critical (medical, financial)? This guides investment in accuracy versus speed.

5. What is the cost model? AI inference costs can balloon quickly. Does your pricing reflect this? Many Indian startups have suffered margin erosion by offering AI features for free.

6. What is your 18-month defensibility story? Foundation models improve fast. Your strategy must articulate what remains valuable as models get cheaper and better — data, workflows, integrations, or distribution.

Talvinder Singh calls this the foundation of responsible AI product strategy: “If you cannot answer these six questions clearly, you do not have a strategy — you have a hunch.”

The PM’s evolving responsibilities in AI product development

Your job as a PM in the AI era is not to become an ML engineer, but to translate AI capabilities into customer value and business impact.

This means:

• Setting acceptance criteria in user terms, not just technical metrics. For example, measure task completion, error rates experienced by users, or trust in AI suggestions.

• Designing feedback loops that capture user corrections and feed into model improvements.

• Communicating AI’s probabilistic nature to leadership, engineering, and customers. AI will be wrong sometimes — set realistic expectations.

• Owning the AI cost model and unit economics. Cloud bills grow with usage. This is your responsibility, not just the ML team’s.

• Navigating data privacy and ethics, ensuring compliance with regulations and building inclusive products that serve diverse Indian users.

The three strategic traps in AI product strategy

Talvinder Singh warned about three common traps:

Trap 1: AI as a press release

Adding “AI-powered” to marketing without meaningful AI integration is common but dangerous.

Ask: “If you remove the AI feature, does any customer notice or complain?” If not, it’s a marketing gimmick, not strategy.

Trap 2: Building what model providers will build

Many startups build thin wrappers around GPT or other foundation models. Within 18 months, providers ship those features natively.

Your moat is not the model architecture but your unique data, domain expertise, workflow integration, or distribution.

Trap 3: Optimizing for model metrics instead of user outcomes

Technical founders often chase incremental improvements in accuracy (e.g., 89% to 94%) without considering user impact.

If a wrong AI suggestion causes users to lose trust and abandon the feature, the metric that matters is user trust, not accuracy.

Sometimes a simpler, faster rules-based system is better for users than a complex model with high latency.

AI in the Indian context: three realities you must face

Cost sensitivity is real

Indian B2B customers will not pay a 3x premium for AI features. Your AI must deliver measurable ROI and be cost-efficient.

This often means choosing smaller, cheaper models or intelligent caching over flagship models.

Data quality is a challenge

Indian enterprises have messy, multilingual, inconsistent data.

Your AI strategy must treat data cleaning as a first-class concern.

The team that can make AI work on messy Indian data has a genuine moat.

The talent arbitrage is shrinking

Top ML talent in Bangalore commands salaries comparable to mid-tier US cities.

Your strategy should not depend on hiring a large ML team but on a small, sharp team that leverages foundation models smartly.

Case study: AI strategy at a mid-stage Indian HRtech startup

Imagine you are PM at a Series B Indian HRtech company with 500 B2B customers.

Your engineering lead proposes fine-tuning a custom LLM on Indian job descriptions and salary data — estimated 4 months, 2 ML engineers.

A competitor just launched a similar feature using the OpenAI API.

What do you do?

Talvinder Singh’s advice: “Do not approve yet. Build a quick API-based MVP and test with 10 customers. If 80% of use cases are covered, ship it and save 4 months of engineering time.

Fine-tuning only makes sense if you find a specific failure mode the base model cannot handle — like Indian job title taxonomy or language code-switching — and customers will pay more for that.

The real risk is spending months building a custom model when an API-based solution suffices.”

The common mistake is approving fine-tuning because it sounds defensible. “Custom model equals moat” is a narrative, not a strategy.

Your moat is your dataset — collect it first with an API-based feature.

Embedding ethics, privacy, and inclusivity in AI products

Responsible AI is non-negotiable.

Talvinder Singh emphasizes three tenets of being a responsible PM:

• Ethical AI: Avoid bias, ensure fairness, and prevent harm.

• Privacy by design: Treat user data as a product feature, not a compliance checkbox.

• Inclusive design: Build for India’s linguistic, cultural, and accessibility diversity.

Ignoring these leads to product failure, user distrust, and regulatory penalties.

Agile methodologies for AI product teams

The fast-evolving AI landscape demands agility.

Spotify’s “Squad” model and Zoom’s pivot to AI-powered meetings are examples of rapid adaptation.

PMs must foster cross-functional collaboration, rapid experimentation, and continuous learning.

Test yourself: The AI strategy decision

PL alumni now work at Flipkart, Google, Razorpay, PhonePe, Swiggy, Amazon, Microsoft, and 30+ other companies.

Where to go next

• Understand user research methods to ground AI strategy: User Research Methods

• Build product visions that incorporate AI: Product Vision and Strategy

• Master ethical AI practices: Ethical PM

• Measure AI product impact effectively: Metrics and KPIs