Innowaring Labs

I help enterprise startups and organizations transform their operating models for AI-native innovations and unlock growth.

For enterprise tech startups, I can help you set up your product organization as an Interim CPO in an embedded role. 

For enterprise technology teams, I can help you implement and refine the product operating model to unlock AI-native innovation within your IT teams through workshops, coaching, and consulting engagements.

Founder and Owner

Axonal .AI

I lead the product strategy, GTM solutions and operations for domain-specific AI products tailored for Life Sciences commercial workflows.

Chief Product Officer

ConvergeHEALTH by Deloitte

Managing Director

Vice President Of Product Development

Senior Product Manager

Deloitte

Healthcare & Life Sciences/Deloitte Consulting Innovation Practice
- Healthcare Technology Products
- Health Information Management
- Health Informatics
- Strategy and Planning

Senior Manager

Beckman Coulter, Inc.

Corporate Strategy Planning
- Competive Strategy 
- Business Planning
- New Market Entry

MBA Intern - Corporate Strategy Planning

Tata Consultancy Services Ltd.

IT Consultant
- Analysis\Design\Development of IBM ISeries software

IT Consultant

Hi, I’m Amarinder.

I help organizations move beyond AI hype and into execution—by building AI-native systems that deliver real business value.

As Chief Product Officer at Axonal AI, I lead the design of LLM-powered products and agent workflows that are transforming life sciences marketing. My focus is on integrating foundational models into enterprise environments—not as a feature, but as a shift in how work gets done and how jobs-to-be-done are reimagined.

I’m also the founder of Innowaring Labs, a consultancy that helps enterprise tech organizations and startups evolve their operating models for innovation—by aligning strategy, product, and systems thinking.

Earlier in my career, I helped build and scale ConvergeHEALTH by Deloitte into a $250M+ ARR software products and services business. That journey—from IT delivery to product-centric teams—taught me what it really takes to lead innovation inside a large enterprise environment.

I captured many of those lessons in my book, Becoming a Software Company—a guide for enterprise leaders navigating the shift to product thinking.

Today, my work lives at the intersection of AI, health tech, systems innovation, and digital transformation. I thrive when working with teams serious about building software systems that transform how industry-focused workflows.

For as long as we’ve built software, the hardest part has been concept translations: converting user goals into specs, specs into code, and messy real-world concepts into data and rules.

Software engineering, at its core, is teams of smart people working through these translations. But these translations are lossy. Rich, context-heavy human experience must be reduced into screens, logic, queries, and databases.

Fred Brooks, the legendary computer scientist, referred to this as the “essential difficulty” of building software: avoiding conceptual errors in translating user goals into functional systems. In his 1986 essay, No Silver Bullet, he argued that the very nature of software made a true silver bullet unlikely.

For nearly four decades, Brooks’ assertion held true until Generative AI burst on the scene. Suddenly, a universal translator seemed within reach: a system that could turn human intent expressed in natural language into the “languages” software systems understand.

It looked like a silver bullet. So much of knowledge work (incl. writing code) is just processing some form of linguistic content. If I could format inputs, provide detailed instructions, and specify outputs, there didn't seem to be any limit to what AI could help me process.

Sounds great…until we discovered a new layer of conceptual translation problems:
1️⃣ Translating user intent into detailed prompts (aka Prompt Engineering)
2️⃣ Providing the right context—examples, policies, prior chats, external data (aka Context Engineering)
3️⃣ Translating user workflows into agentic systems (aka Agentic Patterns)
4️⃣ Designing ways to measure and benchmark AI outputs (aka AI Evals)

Today, I see a lot of smart people tackling these new translation problems. The core problem hasn't gone away, just shape shifting. This isn't a knock on Gen-AI; it’s just that I don’t see the silver sheen on the bullet anymore.

Maybe there never was one. It was just the essential difficulty of engineering systems shifting yet again, like it has been for the last 4 decades.

#ai #strategy #technology #innowaring #transformation

Amarinder Sidhu

Chief Product Officer | Author of “Becoming a Software Company” | Previously @Deloitte

When embedding AI into workflows, we often tend to reduce the "job-to-be-done" into a set of outputs.

Then we build quantitative metrics—number of summaries created, % cycle-time reduction—and use them as proxies for AI output quality.

But AI models can produce multiple valid outputs from the same input. What makes one good, acceptable, or bad depends on nuance that can’t be captured by metrics alone.

That’s why a qualitative human assessment remains the peak measure of AI output quality. It should at least assess:

1. Accuracy – Is the information correct?
2. Relevance – Does it address the intent of the user query?
3. Coverage – Does it capture the full scope of the query?
4. Clarity – Is it structured and understandable?
5. Compliance – Does it follow regulatory or brand guardrails? (Optional in some cases, but critical in regulated domains)

Each dimension needs scoring criteria and benchmark examples—very hard to define when implementing AI for the first time for a particular workflow, but can be refined over time as the system improves.

AI output quality can't be about how fast outputs are produced, but how useful they are in helping humans in their jobs-to-be-done.

A well-defined qualitative rubric may be hard to create, but it’s essential for fine-tuning AI systems and helping teams understand what “good output” really means.

#ai #innovation #strategy

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