Trusted by top-tier teams: generated $2B+ and multi-million dollar ARR through AI-native systems architecture.
$2B+ Generated via Systems
$2B+ Microsoft | $Multi-Million MRR SMEs
15+ Years in Growth Systems
Product, Growth, Marketing
B2B & DTC.
1,200+ Hours
AI-first strategy and execution with cross-funtional teams.
Built Across Scales
From global platforms to early-stage teams.
AI-Native Systems → Products → Scale
Your AI-Native System Blueprint
Generated architecture across product, AI agents, and growth systems.
One continuous learning and feedback loop
Unifying Product and Growth into one continuous system loop.
Architecting and building AI-native systems within the integrated learning and feedback loops of product, growth, and marketing.
AI-Native Systems
AI-native systems determine whether something can work repeatedly under real-world use.
The AI-Native System
AI Strategy League is designed with adoption, incentives, and outcomes in mind.
Design and ship AI-native systems that work in the real world across product, engineering, design, and growth.
✔︎
Built for Real-World Adoption
Systems start with real user intent and behavior, not abstract features.
✔︎
Behavioral Feedback Loops
Activation, retention, and conversion are designed into the system from day one.
✔︎
Outcome-Driven by Design
Every system is built to produce measurable outcomes within real constraints: time, trust, incentives, and operations.
Designed for
Who the AI-Native System Is For
Built for operators who ship under real constraints and care more about outcomes than ideas: You're already be comfortable building, willing to operate under constraints, and more interested in outcomes than ideas.
✔︎
Founders
You’re building AI-native products and are accountable not just for vision, but for shipping systems that people actually use, trust, and pay for in the real world.
✔︎
Senior Product, Growth & AI Leaders
You own execution across product, growth, and AI, measured by outcomes, adoption, and impact, not decks, demos, or disconnected roadmaps.
✔︎
Cross-Functional Teams
You work across product, engineering, design, and go-to-market to turn AI intent into reliable, scalable systems, not one-off experiments or technical novelty.
✔︎
Design & Engineering Leaders
You design systems, not just interfaces or models, optimizing for behavior, incentives, reliability, and scale, not isolated screens or proofs of concept.
Design, Build, Scale
Explore the AI Strategy League Ecosystem
Three ways to design, ship, and scale AI-native products that work, from first system to commercial growth.
Current Cohort: Enter the League
Join a selective cohort designing and shipping AI-native systems grounded in real user intent, incentives, and outcomes.
This is where builders turn AI strategy into production-ready products together.
Outcome Metric
→ Ship a working AI-native system in each cohort.
AI-Native Library & Resources
A living library of AI-native frameworks, system patterns, and playbooks shaped by real builds.
Designed to support activation, retention, and conversion from day one.
Outcome Metric
→ Reuse proven system patterns across products and teams.
Scale What You Built
Turn a working AI-native product into a scalable business.
Apply growth strategy, go-to-market systems, and AI-powered marketing workflows to drive adoption, revenue, and long-term scale within real commercial constraints.
Outcome Metric
→ Drive measurable adoption, revenue, and retention.
What is an AI-Native System
In AI-native systems, decisions are about:
-
When to automate vs augment
-
How much autonomy to give a model
-
Where to remove friction vs preserve human control
-
How transparent the system should be
-
How outputs should feel
What is an AI-Native System
AI-native systems are designed with intelligence at the core, shaping decisions, workflows, and outcomes from day one, not added on later.
An AI-native system:
✅ Is shipped in production, adopted by real users, and produce measurable outcomes
✅ Uses AI to make or augment decisions, not just generate content
✅ Is designed around real workflows, incentives, and constraints
✅ Improves with usage (feedback, signals, learning loops)
✅ Ships with adoption and accountability built in
✅ Continues working as usage, data, and complexity scale
What it is NOT
❌ A chatbot bolted onto an existing product
❌ Adding an LLM interface on top of a legacy workflow
❌ A collection of prompts or prompt libraries
❌ A demo, proof-of-concept, or innovation lab project
❌ Prompt engineering without integration into workflows
❌ A feature that depends on manual babysitting "Human-in-the-loop”
❌ AI as cost-cutting theater: Replace humans with AI” mandates
.png)
.png)
Design the AI-Native System
User intent, decision logic, feedback loops, and human–AI collaboration are defined first before any interface or feature is built.
Productize the System
We design the smallest product surface that allows real users to interact with the system and generate signal, trust, and adoption.
Execution → Feedback
Activation, retention, and learning loops are built directly into the system.
Every interaction produces signal, not noise.
Prepare for Scale
Only once the system works under real use do we layer growth, distribution, and operational scale.
Wall of Love
Trusted and supported by industry leaders
Design and ship AI-native systems that work in the real world across product, engineering, design, and growth.
"As I was exploring the mental health product area, Grace was gracious in sharing her time to be a sounding board to my product idea.
She provided nuanced approaches to creating value for target customers, and shared her excitement toward the future potential of the product.
If you have a product idea and looking for feedback on AI-enabled products, I recommend speaking to Grace."
Jae Kim
Head of Product | ex-Meta & ex-BlackBerry | Enterprise B2B SaaS |
"Grace is an inspiring leader with a real gift for making complex ideas feel clear and approachable.
Whether you're just starting out or looking to take things to the next level, her guidance and insights are incredibly valuable. She's thoughtful and truly committed to helping others grow.
I would highly recommend her to anyone looking to deepen their understanding of AI strategy or leadership."
Toey C.
Regional CRM & Data Manager at Michelin
"Grace, our session today was another productive one! You continue to provide invaluable guidance and support.
Your insights into the industry and your ability to connect the dots are truly impressive.
I left today's session feeling inspired to dig deep on some new topics. Thank you Grace."
Jeremy Edwards
Senior Product Leader, ex-SAP
Explore
Options to work with us
We are excited to share innovative ideas from our teams. Each project showcases our commitment to excellence.
The Story of AI Strategy League
Grace Man
Grace is a British product and marketing leader who architects AI-native systems designed to ship products and scale through high-velocity growth strategies.
AI Strategy League was founded by Grace Man after more than 15 years strategizing and operating at the intersection of product, growth, and technology inside both global enterprises and fast-scaling teams.
Before building the League, Grace led AI, analytics, and growth initiatives across organizations such as Microsoft, Canon, and global brands including Hyundai, Activision, Breitling, and more, driving nine-figure revenue impact and scaling multi-million-dollar ARR systems.
But the real insight didn’t come from success. It came from watching AI initiatives stall, not because the technology failed, but because systems were designed without adoption, incentives, and real-world constraints in mind. AI Strategy League exists to fix that, and and enhance that.