My story
I'm Shardul Nayak, an AI strategy consultant who works at the intersection of business, product, and emerging technology. I help companies move beyond the hype and into deliberate, high-leverage AI adoption.
Most organizations I speak to are fascinated by AI but paralyzed by the options. They're unsure where to start, what to prioritize, and how to evaluate the flood of vendors and tools competing for their attention. I help them slow down long enough to ask the right questions, and then move fast with conviction.
My consulting work spans AI opportunity assessments, product strategy for AI-powered features, executive workshops, and ongoing advisory for leadership teams navigating AI transformation. I'm not tied to any vendor or platform, which means the advice I give is always in the client's best interest.
Alongside consulting, I'm building a library of online courses, starting with the AI PM Blueprint Masterclass, designed to give product managers and business leaders a genuine, working understanding of AI without requiring an engineering background.
What I believe
Four principles that shape every engagement and every piece of content I create.
Strategy before technology
AI is a strategic decision, not a technology decision. Most companies bolt AI onto existing processes and wonder why it doesn't work. I start with the business outcome and work backwards to the AI architecture.
Clarity is competitive advantage
In a world full of AI jargon and hype, the ability to communicate clearly about AI (what it can and can't do, what to prioritize, and why) is genuinely rare and valuable.
Most companies don't need custom AI yet
The majority of AI value comes from using off-the-shelf tools intelligently, not building from scratch. Custom models make sense for a narrow set of use cases with the right data and scale.
Education is the real moat
A company where every product manager, strategist, and leader has genuine AI fluency will outcompete one where AI knowledge sits with one person or one team. I build toward that.
Ready to put these principles into practice?
How I think about AI
Strategy before stack
Most AI failures are strategy failures dressed up as tech failures. I start with business outcomes, then work backwards to the AI architecture.
Ship, then optimize
A good AI feature that ships beats a perfect one that doesn't. Speed to learning beats theoretical perfection every time.
Build vs. buy is a trap
The real question is: what gives you durable differentiation? Fine-tuning for commodity tasks is a waste. Building on top of foundation models is almost always smarter.
Measure everything
AI without clear success metrics is just experimentation with a marketing budget. Define what good looks like before you write a single prompt.
Humans in the loop
The best AI products amplify human judgment, they don't try to replace it. Design for trust, not just accuracy.
AI is a product feature
Not a product category. 'AI-powered' as a differentiator is disappearing fast. The moat is workflow, data, and UX, not the model.
Ready to work together?
I take on a limited number of consulting engagements each quarter. Book a discovery call to see if we're a fit.