Enterprise AI

Enterprise AI Blueprint: Pilot to Production

McKinsey & Company

Anton Nakaliuzhnyi

10 yrs QuantumBlack AI/ML @ McKinsey; MBA Harvard

Achuta Kadambi

Co-founder of two exited AI startups; PhD in AI, MIT

How to get enterprise stakeholders excited about your pilot idea.

How to Pitch your Enterprise Pilot

A technical and business framework of how to execute your pilot and report ongoing success metrics.

How to Execute your Pilot

We discuss root causes of why enterprise pilots die and how to avoid them.

How to Scale your Pilot

Learn to turn scattered AI experimentation into org-wide transformation that ships at scale.

3x CPTO | CEO, ChatPRD | Host, How I AI

LaunchDarkly

Color

Optimizely

Claire Vo

3x CPTO, Founder ChatPRD, and Host of How I AI - expert on AI transformation.

Principal Engineer | Prev. Dir of Eng

Atlassian

Zach Davis

Principal Engineer who implemented AI across a 100-person org.

Cohort 1

Cohort 2

Your team has AI tools, but you're not close to AI-native.Most EPD orgs buy Claude Code, help their PMs vibe code a bit, and call it progress. But the gap between "some are using AI a lot" and "AI is changing how our entire EPD org operates" is huge, and almost nobody is closing it.The problem isn't access to tools, it's that nobody invested in the culture change, technical readiness, or operations redesign required to turn individual adoption into organizational leverage. 🤖 Your codebase isn't ready for agents. 🎁 Your PMs create prototypes and expect them to get built. 🤷‍♀️ Your designers don't know how to get their fixes to production. And you're supposed to have the plan. This 2-day intensive will help you build it.Taught by Claire Vo and Zach Davis — operators who've done this firsthand, not consultants who've read about it. Claire has interviewed 50+ EPD leaders on How I AI and has more first-person data on AI adoption than anyone in the industry. Zach led AI transformation across 100+ engineers and built the systems that got an entire org actively using AI tools weekly.You'll leave with specific tactics, real examples, and a peer group for accountability + inspiration.

Learn how to go from AI tool adoption to team impact and product velocity.

Know where AI creates real leverage, and where it doesn't 

Build a culture that actually adopts

Make your codebase work with agents

Redesign your operating model for the full EPD org

Lead the hard conversations

VPs of Engineering who need a real answer when their board asks "what's our AI strategy?" — not just "we bought licenses."

CTOs and CPOs who need to lead AI adoption across engineering, product, and design — not just sponsor an engineering tool rollout.

Staff and Principal Engineers tapped to "figure out AI" for the org, who need both the playbook and the leverage to make it work.

Shift from ad hoc AI efforts to disciplined, structured, workflow-driven collaboration before significant investment.

Workshop Lead: Netflix, Google, LinkedIn

Google

Netflix

Microsoft

LinkedIn

Paul Bryan

I help organizations design and develop AI products that deliver business value.

July 2026 Cohort

<blockquote>AI applied to the wrong workflows leads to chaos. This course helps Enterprise teams launch successful AI pilots by aligning on workflows, roles &amp; responsibilities, risks, guardrails, and AI functional requirements before major investments.Industry reports suggest that over half of enterprise AI pilots never scale to production. Many pilots stall because the workflows were not ready for AI.This course addresses the real bottleneck: cross-functional alignment on key details before committing to technical debt.You will leave with:<ul><li>An AI Workflow Canvas: A structured way to identify where AI can move the needle in your organization</li><li>An AI Workflow Scorecard: A shared evaluation framework your cross-functional team can use to assess value, feasibility, data readiness, and risk</li><li>A Thin-Slice Prototype: Storyboards, scene cards, and an action flow diagram that aligns stakeholders across PM, DS, UX, Eng, and Biz on exactly what the AI system will do, feasibility and risk, and where humans are needed in the loop.</li></ul></blockquote>

Identify High-ROI Workflows Ready for AI

Identify high-friction workflows suitable for Enterprise AI

Prioritize AI initiatives using structured evaluation criteria

Clarify roles and decision rights before pilot launch

Surface feasibility and governance risks early

Make AI behavior visible to cross-functional stakeholders

Define readiness criteria before committing to a pilot

AI product team members (PM, UX, DS, ENG) who need a shared framework to align on workflows, feasibility, and responsibility before building

Product and business leaders accountable for Enterprise AI outcomes who must prioritize workflows and reduce risk before committing budget

AI governance, legal, and risk stakeholders who need structured ways to surface constraints, guardrails, &amp; ownership early in AI initiatives

You leave with deployable systems and documented architecture — not slide decks.

AI & DeepTech Leader & Entrepreneur

Hike

Yatra

Apparel Group

Oracle

Bytelearn

Dr Ankur Narang

Three decades building HPC and AI systems processing billions of transactions

Enterprise AI Agent Architect

Adda247

Mobileum

Kush Khurana

AI product and deep-tech builder, Visiting Professor at Ashoka University

AI Systems Architect & Leader

Stryker

Samsung

Dr Aveek Brahmachari

Stryker Senior Staff Engineer; IIT-ISM, USF PhD; ex-DRDO, Samsung

Founding Cohort

A 4-week intensive for engineers mastering autonomous cognitive workflows — from cost compression and self-correcting loops to revenue-scaling agents that turn AI from a cost center into a profit driver.<a target="_blank" rel="noopener noreferrer nofollow" href="https://deepcorexlabs.com/courses/production-ai-agents">Course Webpage</a>Build The Economic Engine of Agents: This goes beyond the technical deep-dive. We focus exclusively on production-grade impact—engineering the measurable business outcomes that deliver exponential ROI.20x ROI via Cost CompressionAchieve 20x return on investment by reducing operational overhead — replacing manual human-in-the-loop processes with self-correcting agentic loops.100x ROI via Revenue ScalingUnlock up to 100x return on investment by deploying agents that solve high-intent, real-time use cases — turning AI from a cost center into a primary profit driver.Achieve 5–10x lower AI infrastructure costsToken routing, caching, and cost modeling that saves six figures at scale.Systematic evaluation capabilityInternal eval infrastructure — catch regressions before users do.De-risked architecture decisionsBuild-vs-buy calls backed by production data, not vendor pitch decks.Reusable patterns across teamsOne cohort creates blueprints your entire engineering org can adopt.

Deploy Agentic systems that deliver 20x–100x return on investment.

Ship a functioning agent with structured tool use and state management.

Ship a retrieval pipeline that's grounded, verified, and citation-backed.

Ship cost controls and failure recovery into your agent.

Ship your capstone with full eval harness and deployment pipeline.

<h3>Engineering &amp; Product Leaders</h3>Responsible for shipping AI features. Need production patterns beyond prompt engineering and API wrappers.

<h3>Technical Founders &amp; CTOs</h3>Built the prototype yourself. Need it reliable enough for paying customers and investor due diligence.

<h3>Lead Developers</h3>You're the person who makes the AI system actually work — and keeps it working when inputs get messy.

Your AI agent works in demos. But Production isn't one.

Principal Consultant. MD @ Datawhistl

Tata Consultancy Services

Wipro

Serco Group

Dheeraj Saxena

Ex-IBM, TCS, Wipro Consultant. | 25+ Years Scaling Data, AI & MarTech Solutions

Cohort 4

📉 Most production deployments underperform or fail — <a target="_blank" rel="noopener noreferrer nofollow" href="https://www.gartner.com/en/newsroom/press-releases/2025-06-25-gartner-predicts-over-40-percent-of-agentic-ai-projects-will-be-canceled-by-end-of-2027">Gartner expects over 40% of agentic AI projects to be cancelled by 2027</a>. The velocity that Claude Code and Cursor give developers in the sandbox quietly hides architectural debt that production exposes the moment the agent meets real customers, real data, and real regulators. The cause is rarely the LLM call — it's the workflow architecture around it.🏛️ Frameworks like <a target="_blank" rel="noopener noreferrer nofollow" href="https://docs.aws.amazon.com/wellarchitected/latest/generative-ai-lens/generative-ai-lens.html">AWS's Well-Architected Generative AI Lens</a> stop at defining best practices but completely lack the specificity required to prevent production failures.⚖️ Governance standards like <a target="_blank" rel="noopener noreferrer nofollow" href="https://iapp.org/certify/aigp">AIGP</a> focus on control rather than engineering patterns and lack practical actionability for the engineering team.This workshop closes that gap.Using real case studies, you'll learn how to identify and apply AI architecture principles to design agents that survive contact with reality — real customers, real data, and real regulators. And do so in a repeatable, auditable manner across your entire Agentic AI project portfolio.

From flaky sandbox demos to consistent business value. Proven, structured techniques to architect agentic AI systems in production.

What AI architecture principles are

What happens if you do not use architecture principles

Alignment with AWS Well-architected-framework/GenAI Lens

How to enforce principles through governance and CI/CD pipelines

Step-by-step framework to help you decide which principles you need

what they automate, what they decide, what's being deployed where. You don't need to code them; you do need to understand them.

Business understanding of AI agents and what they do in enterprises

Senior Developers who are responsible for building production-grade Agentic AI workflows.

AI Governance &amp; Risk Professionals translating governance policy into architecture decisions that engineers can actually build against.

Business Heads/Project Managers accountable for AI initiatives and who need to understand architecture challenges without getting into code.

Agentic AI

Coding with AI

AI Workflows

Claude Code

OpenClaw

Vibe Coding

AI Evals

AI Transformation

RAG & Search

AI for PMs

AI for Engineers

AI for Designers

AI for Marketers

AI for Founders

Product Sense

Product Discovery

User Research

Prototyping

Growth

Analytics

Tech Foundations

Strategy

Influence

Leadership

Career Growth

Machine Learning

LLM Ops

Context Eng

Security

System Design

Figma

Design Systems

Visual Design

Design Strategy

Automation

Content Marketing

Demand Gen

Go-to-Market

Product Marketing

Positioning

Social Media

Brand

B2B Marketing

SEO & AEO

AI for Leaders

AI Governance

Communication

Management

People Operations

Exec Presence

Storytelling

Goal-setting

Personal Brand

Fundraising

Everyone

Operators

Data Scientists

Business Analysts

User Researchers

Customer Success

Project Managers

HR Professionals

Sales People

Lawyers

Finance

Investors

Real Estate

Educators

Creators

Product managers who drive strategy, development, and launch of products.

Executives and senior leaders who set strategy, lead teams, and drive organizational outcomes.

Applying AI tools and techniques to professional workflows — assign alongside more specific AI topics.

Leading organizational AI adoption — change management, strategy, and building AI-ready teams.

Enterprise AI Blueprint: Pilot to Production

What you'll learn

Why this topic matters

You'll learn from

Anton Nakaliuzhnyi

Achuta Kadambi