I build AI products from zero to one, and I teach others how to do the same.Over 12+ years, I've taken AI/ML products from proof-of-concept to launch at Amazon, Nike, T-Mobile, and Disney. The work I love most is early-stage: validating ideas, getting executive greenlight, and shipping MVPs that prove value before scaling. At Nike, that approach drove a $100M annual reduction in product returns.I also teach what I build. I co-created and lead the AI Product Management Certificate at the University of Washington, where experienced PMs ship real GenAI products complete with evaluation plans and GTM strategies. Previously, I trained 4 cohorts at Product School.What I bring: the ability to translate messy AI capabilities into clear product value, align technical and business stakeholders, and move fast without breaking trust. I write about AI PM craft at aipmguru.substack.com.I'm currently exploring opportunities to lead 0→1 AI product work at companies solving meaningful problems. I'm especially energized by AI in healthcare and education, though I'm open to any domain where the product can genuinely improve lives. 

Shaili Guru

AI Product Builder @ NanoKernel | Generative AI, AI Agents, AIoT, Responsible AI, AI Product Management | Ex-Apple, Accenture, Cognizant, Verizon, AT&T | I help companies build standout Next-Gen AI Solutions

I help visionary companies build standout AI Products. 

I am seeking a Senior IC AI Product Manager role at established AI-focused companies. 

I am a transformative Product and IT Executive, blending innovation with leadership across every role I've embraced. My journey from the technical trenches to the strategic echelons of Product Leadership, Solutions Architecture, and Enterprise Data Strategy has been fueled by an insatiable drive for growth, an unwavering focus on customer success, and a passion for adapting and thriving in diverse organizational landscapes.

From Fortune 50 giants to agile startups, my path has been marked by over 8 groundbreaking product launches and more than 20 transformative project deliveries. At the heart of these achievements lies a deep-seated belief in the power of innovative thinking, strategic execution, and the magic of collaboration across domains and disciplines. 

My mantra? Value creation, operational excellence, and nurturing talent are the cornerstones of my transformative leadership. 

My commitment to thought leadership has led me to author several books on AI and Data, secure several patents, and contribute to the knowledge pool with articles and papers featured in renowned journals, blogs, and media channels. Dive into my insights on LinkedIn and Medium at https://harsha-srivatsa.medium.com

Education is an ongoing journey for me, enriched by formal degrees in Engineering, Math, Computer Science, and Management, alongside continuous learning from prestigious institutions like MIT, Stanford, Kellogg, and IDEOU. This foundation empowers me to lead at the intersection of technology and business, navigating the complexities of AI, IoT, Cloud Platforms, and beyond with ease and insight.

My commitment extends beyond the professional realm. As a High School Educator with Microsoft's TEALS Program, a Master Gardener certified by University of California, a mentor for immigrants through Upwardly Global, and a volunteer with the Coast Guard Auxiliary, I believe in giving back, sharing knowledge, and supporting communities. I am a believer in the transformative power of technology, education, and community service. Let’s connect, collaborate, and create impact together.

Email:  harsha.srivatsa at connectwithharsha.info | Website: https://www.connectwithharsha.me

Harsha Srivatsa

“When everyone is Super, no one will be.” — Syndrome, The Incredibles

Syndrome's plan was simple: give everyone superpowers so that having superpowers means nothing. That's exactly what's happening in AI right now in 2026.

AI tools have given everyone the superpower of building: vibe coding, one-click deployment, and AI-assisted everything. A weekend hackathon can now produce what took a funded team six months not long ago. One-person teams ship what used to require twenty engineers.

The barrier between "I have an idea" and "I have a product" has nearly disappeared. 

So here's the uncomfortable question: if everyone can build, what's actually worth building?

When everyone can build at speed, speed stops being an advantage.
Execution becomes a commodity and a moat.  The same prototype you ship on Monday has three clones by Friday. The AI feature you launched last sprint? An incumbent ships it as a checkbox this sprint. Speed is no longer strategy. It's table stakes.

I've been studying this dynamic closely — analyzing AI startups that raised millions and flatlined despite shipping constantly, AI companies that dominated categories and then watched their moats evaporate in weeks, and the rare few that built durable positions in markets that seemed impossible to defend.

The pattern was clear. The winners weren't the fastest builders. They were the ones who understood what kind of company they were and what market they were actually playing in. They made deliberate choices about where to compete and how to grow — before they wrote a single line of code.

That realization, combined with learning from Reforge, Ravi Mehta, Brian Balfour convinced me that what's missing in the AI ecosystem isn't better tools. It's a better strategic framework for the AI era. 

So I wrote one. Adapted and built on the classic Blue Ocean Strategy. 

AI Ocean Strategy introduces a new model for navigating AI markets. The core idea: In AI markets, you need to know what kind of fish you are before you start swimming.

🔴 Red Fish — execution-first competitors thriving in fierce, crowded markets through operational excellence
🔵 Blue Fish — category creators building demand where none existed
⚪ Neutral Fish — ambidextrous adapters balancing exploitation of what works with exploration of what's next

AI Ocean Strategy sits at the layer above both: the strategic positioning layer where you decide what game you're playing before you build anything.  

Because in a world where everyone is Super — where everyone can build — the question that matters isn't "can you ship?"

It's: are you the right fish, in the right ocean, with the right strategy?

If you are thinking of building an AI product / AI company this become an existential and driving consideration. 

Dive more into the video explainer below and the audio explainer / book overview links in the comments.

Senior AI Product Manager | Generative AI, AI Agents, AIoT, Responsible AI, AI Product Strategy | Ex-Apple, Accenture, Cognizant | I help companies build, debug and launch standout AI products

While I was discussing my recent book publication "The Illustrated Guide to Multi-Agent Systems", an interesting topic of discussion came up. 

How will you do a bake-off, decide and choose which AI agent platform to implement a given MAS? 

This article is an outcome of intense discussions and thought exercises for an AI agent platform framework which I call the AAA framework. 

The AI agent platform landscape is exploding — but most evaluations still rely on feature checklists.

That’s a problem.

A platform with great multi-agent orchestration but weak deployment maturity may never get past prototype.
 A platform with strong governance but low adaptability may become an expensive chatbot.
 And a platform with broad integrations but weak auditability can become a serious risk.

 I applied the AAA Framework — Autonomy, Automatability, Accountability — as a practical way to evaluate AI agent platforms and products based on what actually matters in production:
Can it operate with the level of independence you need? (Autonomy)
Can it be deployed, integrated, and scaled reliably? (Automatability)
Can it be governed, audited, and trusted at your risk level? (Accountability)

I also compare leading platforms across open-source, enterprise, and hybrid categories using a structured AAA scorecard (with sub-factors, trade-offs, and real-world decision implications).

A few key takeaways:
* There is no universal winner across all 3 A’s
* “Build vs buy” often maps directly to AAA trade-offs
* The biggest gap in many agent initiatives is not demos — it’s the A2 + A3 gap (production readiness + governance)

If you’re evaluating AI agent platforms for enterprise use, this article gives you a more defensible way to think beyond feature marketing and toward use-case fit.

Would love your perspective: What’s the #1 factor you prioritize when evaluating an AI agent platform today?

#AIAgents #AgenticAI #AIArchitecture #AIAgentPlatforms

Introduction The AI agent platform landscape has exploded. Dozens of frameworks and products now claim to help organizations build, deploy, and manage

Open article: Applying the AAA Framework: A Comparative Evaluation of Top AI Agent Platforms by Harsha Srivatsa • 8 min read

Applying the AAA Framework: A Comparative Evaluation of Top AI Agent Platforms

When I read the Mar 5 2026 report from Anthropic titled 
"Labor Market Impacts of AI" [https://lnkd.in/gWF2M6Kf], it set off trepidation and future anxiety in me.  

As a white collar AI professional, I cannot brush away the impacts of AI on my and others future work prospects. After all, I am Human (still). 

In my earlier article, https://lnkd.in/gFJ_K5fN I had a rather spicy take on what I called AI anticipation paradox, where I highlighted that companies are making permanent workforce decisions based on temporary assumptions about a technology (AI) that hasn't proven itself. 

The Anthropic report had a more sobering effect that one must prepare for what seems to be inevitable. 
* Companies may start thinking about "Resources" and the Human factor maybe sidelined
* The current clarity of company strategy and business management which has stood the test of time, may devolve into chaos as decisions are made more on potential and promises rather than performance
* Entry level jobs evaporation is more or less certain

Bottom Line: “The first labor shock of AI will not be from automation; it is company management acting as if uncertain capability were already realized value.”

AI may not eliminate work as fast as the hype suggests—but it may distort hiring, judgment, and organizational design much sooner than most leaders realize. 

I have said before that "Hope is not a Strategy" but I do hope that companies adopt a new model that I came up with —the “Nowcasted Organization”—as a more disciplined path for redesigning work, talent pipelines, and governance in an AI-augmented future. 

Then perhaps we can regain some calm and clarity (hopefully).

The core problem is no longer AI capability, it is decision irreversibility The Spreadsheet That Fired Them First The board pack arrives on a Thursday

Open article: The AI Anticipation Paradox 2.0: Nowcasting AI, Redesigning HR, and the Next Architecture of Work by Harsha Srivatsa • 15 min read

The AI Anticipation Paradox 2.0: Nowcasting AI, Redesigning HR, and the Next Architecture of Work

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