Welcome back to Inside Partnering, where we dig deep with the leaders shaping the tech ecosystem. In this episode, I’m thrilled to host Jagjit Dhaliwal -- known by colleagues and friends simply as “JD” -- who now leads GenAI ISV partnerships for the Americas at AWS.

From a career spanning consulting at TCS and Cognizant to a strategic turn as Deputy CIO of LA County, and later driving Intelligent Automation at UiPath, JD brings a rare blend of public-sector grit and enterprise-scale execution into the GenAI arena.

Today, he’s at the heart of AWS’s convergence of AI infrastructure and partner-led innovation.

The Dual Pillars of GenAI Partnership

With over 25 years of experience, JD explains how his team’s mission at AWS has evolved into two core pillars:

* Partnering with GenAI ISVs

Covering the full stack - LLMs, vector databases, MLOps, agents, infrastructure - his team manages partner engagement across every layer of the GenAI ecosystem, ensuring startups and innovators scale effectively on AWS.

* Empowering Other ISVs with GenAI

Beyond native GenAI developers, JD evangelizes the integration of AWS GenAI solutions into traditional software stacks across business apps, data engineering, and security - helping established ISVs evolve their offerings and deliver enriched value.

The Velocity of Innovation & New Growth Norms

JD underscores how GenAI ISVs operate at an unprecedented pace and scale. Gone are the multi-year ramp-ups—today’s startups are setting new benchmarks with hypergrowth:

* From “t2 d3” (triple growth in 2 years, then double in 3) to “q2 d3”, meaning quadruple growth in year one, then triple in the next three—a whole new bar‍—underscores the explosiveness of this market.

Implication? Traditional partner models don’t cut it. AWS must be agile, hyper-responsive, and automate onboarding, self-service, and scalable GTM motions to keep pace with ISV growth and market demand.

Framing Use Cases—Horizontal and Vertical

Early GenAI enterprise value often comes from horizontal use cases—customer support AI assistants, coding copilots, chat-based marketing content. But the real step-up lies in domain-focused scenarios:

* Financial services: fraud detection, loan process automation

* Healthcare: personalized patient interactions, authorization workflows

* Manufacturing: predictive maintenance, supply chain insights

* Life Sciences: synthetic data generation for drug discovery and clinical trials

* Media/Entertainment: creative content generation, animations, next-gen storytelling

JD sees this vertical deepening as the next frontier for GenAI adoption.

From POCs to Production: The Reality Check

JD candidly notes the market dichotomy: soaring expectations versus stark results. As referenced in the recent MIT report, “State of AI in Business 2025” claiming that 95% of GenAI pilots fail. But that doesn’t mean doom - it reflects the gap between ad-hoc experimentation and strategic execution.

He frames success around three readiness pillars:

* Enterprise Architecture & Data Maturity

GenAI is only as good as the data foundation. Legacy data silos, poor accessibility, and quality issues hinder real value.

* Governance, Security & Compliance

Especially in regulated industries, model transparency, bias control, explainability, and legal accountability are mandatory for production.

* Organizational Readiness

It’s not enough to use GenAI - it must align with business outcomes and have CXO-level alignment to drive ROI. That muscle comes from intentions grounded in enterprise needs, not curiosity.

Metrics of Success in GenAI Partnerships

When asked how his team’s success is measured, JD highlights an ecosystem-first mindset including:

* Joint co-sourced revenue with partners

* Marketplace-driven sales

* Number of co‑built solutions

* Usage of AWS AI services alongside GenAI features

* Marketing impact, leads, account pursuits and ICP identification

* Partner success stories for validation and expansion

Underlying all of this is the belief that “partner success equals AWS success.”

Final Thoughts

It’s rare to meet someone who understands generative AI not just from the future-forward technology lens, but also from enterprise architecture, public-sector governance, and scaled partnerships. Jagjit “JD” Dhaliwal embodies that rare intersection.

If you’re a partner trying to navigate the whirlwind of GenAI innovation, or an enterprise leader seeking to cut through the noise and invest in real, production-scale AI transformation, this episode will give you practical frameworks, market reality checks, and strategic clarity.

Chapters & Timecodes

00:00 – Introduction to JD’s Journey

02:30 – Role Overview: AWS GenAI ISV Partnerships

05:00 – The Converging Worlds of Automation & GenAI

07:20 – The Surge of Growth and Innovation Norms

10:00 – Agile Back-end Motions for ISVs

13:00 – Managing Influx: Agentic AI Marketplace Overview

16:00 – Work-Backward Approach: Customer-First GTM

18:00 – Horizontal vs. Vertical GenAI Use Cases

22:00 – POCs to Production: Readiness Criteria

25:00 – Metrics of Success & Joint GTM

28:00 – The Value of Customer Stories

30:00 – Closing Thoughts & Final Takeaways

Key Takeaways

GenAI ISVs grow and move at unmatched speed - AWS continues to adapt partner models accordingly.

Success today requires agility: automated onboarding, self-service, reusable assets, and PLG-aligned GTM.

Horizontal use cases (help desks, copilots) yield early value, but true impact comes from verticalized applications.

Enterprise deployment hinges on data readiness, governance, and business alignment - not just shiny tech.

AWS measures success through co-sourced revenue, joint solution development, marketplace traction, and customer storytelling.

Key Quotes

“The lines between automation and AI have blurred - now generative AI is redefining intelligent automation.”

“There’s no five‑year ramp anymore - it’s quadruple growth year one, then triple year two onwards.”

“Enterprise readiness, governance, and strategic alignment - that’s what separates pilot from production.”

Hashtags: #GenAI #AIpartnerships #AWSGenAI #IntelligentAutomation #InsidePartnering #PartnerEcosystem #EnterpriseAI

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