The reality is that product strategy is rarely about predicting the future. It is about making choices in uncertainty and being ready to course-correct fast.
Product strategy today is defined by rapid shifts—economic volatility, new technology like generative AI, and evolving user expectations. The trap is to react impulsively or chase buzzwords without a clear north star. Your actual job is to make deliberate choices that balance opportunity, risk, and ethics while keeping the user’s core problem front and center.
This lesson walks through the forces at play, the pitfalls product teams fall into, and the frameworks that help you build strategy that lasts.
Macroeconomic and generational shifts demand new strategy thinking
The world your product competes in is not the same as five years ago. Inflation, supply chain disruptions, and geopolitical tensions have reshaped customer priorities and budgets. At the same time, younger generations—Gen Z and Millennials—bring different values and behaviors. They expect products to be socially responsible, inclusive, and privacy-conscious.
Ignoring these forces leads to building products that miss the mark. For example, a luxury e-commerce platform that ignores Gen Z’s sustainability concerns will lose relevance fast. Or a fintech app that overlooks rising data privacy awareness faces regulatory and trust risks.
Quarterly strategy offsite at a fast-growing SaaS startup in Mumbai
CEO: “We need to double down on growth, but our churn is creeping up. Any ideas?”
Product Lead: “Our younger users are dropping off because they don’t trust how we handle their data. We could build clearer privacy controls and transparency.”
Marketing Head: “But that might slow down onboarding and add friction.”
You (PM): “We need to weigh short-term growth against long-term trust. The data privacy expectations are non-negotiable for Gen Z. If we delay, competitors will capitalize.”
This tension between growth and responsibility is the new normal.
Balancing growth ambitions with evolving user trust needs
India-specific context matters here: diverse regulations, varying digital literacy, and cost sensitivity shape how these macro forces play out. For instance, Indian users may tolerate less friction if the value proposition is strong and immediate.
Generative AI is the new strategic frontier — but it is not a silver bullet
AI is everywhere in product conversations today. The pressure to "add AI" is intense, but the pattern I see is familiar: teams rush to build AI features without a clear strategy, resulting in wasted time and disappointed users.
The first step is to clarify what role AI plays in your product:
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AI as feature: AI enhances an existing product. For example, Freshworks adding AI-suggested responses to customer tickets. The core product works without AI, but AI can make it faster or smarter.
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AI as product: AI is the core value proposition. Without it, there is no product. Grammarly or an Indian startup like Karya that uses AI for data labeling fit this.
Most Indian SaaS companies are in the AI-as-feature camp. The trap is using AI-as-product thinking here — hiring big ML teams or building custom models when an API call would suffice.
Three strategic traps in AI product strategy
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AI as a press release. If you remove the AI feature, does any customer complain? If not, it’s marketing fluff, not strategy.
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Building what the model provider will build. Many startups built thin wrappers around GPT-3.5 only to be overtaken by ChatGPT’s native capabilities. Your moat is your proprietary data, workflows, or domain expertise — not the model architecture.
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Optimizing for model performance instead of user outcomes. Improving accuracy from 89% to 94% means nothing if users don’t see the output or the UX is poor.
You are PM at a mid-stage Indian HRtech startup (Series B, 500 B2B customers). Your engineering lead proposes building a custom LLM fine-tuned on Indian job descriptions for a 'compensation benchmarking' feature. A competitor just launched a similar feature using OpenAI API.
The call: Do you approve the fine-tuning project? What is your recommendation to the CEO?
Your reasoning:
You are PM at a mid-stage Indian HRtech startup (Series B, 500 B2B customers). Your engineering lead proposes building a custom LLM fine-tuned on Indian job descriptions for a 'compensation benchmarking' feature. A competitor just launched a similar feature using OpenAI API.
Your task: Do you approve the fine-tuning project? What is your recommendation to the CEO?
your reasoning:
Building responsible, privacy-conscious, and inclusive products is not optional
Responsibility and ethics have moved from buzzwords to business imperatives. McKinsey’s principles of responsible innovation emphasize transparency, fairness, and accountability.
Indian regulations like the PDP Bill and global standards like GDPR have raised the stakes. Privacy and data security must be baked in from day one, not bolted on later.
Inclusivity means designing for India’s diversity: languages, literacy, accessibility, and cultural norms. A one-size-fits-all approach fails.
Design review at a fintech startup in Bangalore
Design Lead: “Our onboarding screens are in English only. Should we add Hindi and Tamil versions?”
You (PM): “Yes. Our analytics show 40% of users drop off in onboarding. Feedback from tier-2 cities says language is a barrier.”
Engineering Lead: “Localization will add 3 sprints of work.”
You (PM): “It’s a trade-off. More inclusive onboarding may slow initial delivery but will increase activation and retention. We must prioritize long-term value.”
This is the new product leadership: balancing speed with equity.
Trade-offs between speed and inclusivity
Sustainability is becoming a key product dimension
Sustainability now influences product design and lifecycle decisions. From energy-efficient infrastructure to reducing e-waste, products must consider their environmental impact.
Indian startups are beginning to integrate sustainability into their roadmaps, recognizing it as a competitive advantage and a user expectation.
The PM’s actual job in this era of rapid change
Your job is to:
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Make strategic choices grounded in evidence and user reality. Don’t chase every trend. Choose what fits your product’s mission and market.
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Balance competing priorities: growth vs responsibility, speed vs inclusivity, innovation vs cost.
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Translate technology potential (like AI) into real user value, not hype.
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Own the product’s ethical footprint and data stewardship.
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Communicate clearly with leadership and teams about trade-offs and rationale.
Field exercise: Assess your current product strategy against today’s forces (20 minutes)
Pick your current product or one you know well. For each of the following, write a short answer:
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How do current macroeconomic trends affect your users? How have you adapted your strategy?
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What generational shifts (values, behaviors) are relevant to your users? How do you reflect these in your product?
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Is AI part of your product? If yes, is it AI-as-feature or AI-as-product? How does that shape your roadmap?
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What responsibility and privacy principles have you embedded in your strategy and design?
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How inclusive and sustainable is your product? What are the biggest gaps?
Review your answers with peers or mentors to identify blind spots and opportunities.
Test yourself: The AI strategy decision at an Indian EdTech startup
You are PM at a mid-stage Indian EdTech company serving 50,000 monthly active students preparing for competitive exams (JEE, NEET). The CEO wants an AI tutor that answers student questions in real time. CTO says it needs 6 months and 4 ML engineers. A board meeting is in two weeks.
You must present an AI strategy recommendation to the board in two weeks.
PL alumni now work at Flipkart, Razorpay, Swiggy, PhonePe, Amazon, and 30+ other companies.
Where to go next
- Ground your strategy in user evidence: User Research Methods
- Translate strategy into a clear product vision: Product Vision and Strategy
- Understand the ethical dimensions of product: Ethical PM
- Measure what matters to users and business: Metrics and KPIs
- Build AI products with real user impact: AI Product Strategy