AI Hype to�Product Impact
Lessons from a Real AI Transformation
Part I
The Foundation�& Strategy
Background & Challenges
Platform Context
A 10-year-old Ruby on Rails monolith built during the company's early development phases.
Strategic AI Evolution
Requirement to accelerate AI transformation. AI-native features + leveraging AI to enhance engineering delivery.
The Speedboat Strategy
Legacy Product
Greenfield AI Product
2 Engineers
1.5 PM/Designer
The Speedboat Strategy
Greenfield product
Decoupled from the monolith and bypassing legacy friction to focus purely on rapid AI innovation.
AI-native tech stack
Utilizing a modern, serverless architecture to leverage AI capabilities without technical dependencies.
Autonomous execution
End-to-end ownership of the roadmap, infrastructure, and deployment, ensuring continuous delivery and high-speed iteration.
Part II
From Hype to�Impact
From Hype to Impact
6X
Velocity Gain
Reduced GTM to 1 month and stable& satisfying product within 3 months
AI-first coding & product flow
Fullstack autonomy
Low communication bottlenecks
→
→
→
Product Quality
Rapid iterations drive 30% higher customer satisfaction.
Dogfooding: Internal use from Week 1
Customer Testing: Active from Month 1
Continuous delivery and iterations
→
→
→
Strategic Path
Triggered the API strategy and paved the way for future architecture
Successful POC for integrating greenfield products with core
→
Paved road out of monolith
→
30%+
*measured with limited sample size, will continue monitoring
Part III
The AI-Native�Toolkit That Worked
Vibe Coding & Prototyping for Real-Time Validation
Validating at the Speed of Thought.
Focusing on experience and flow over document. Demonstrating ideas with tangible interactive prototypes.
Vibe coded prototype for reliable user testing
Prototype rather than PRD as the main deliverable for engineers
[ SCREENSHOT: VIBE CODING WORKFLOW ]
Prototype as the main way of communication
Product Management Directly from Codebase
Bridging Tech Constraints and Product Vision. Treating the codebase as the primary spec to slash the "meeting tax," ensures realistic planning, and provides transparency.
Chat with code base through Cursor
Product discovery based on actual code complexity
Dependency and estimation
Progress tracking through PRs
Product Strategy and
Business Modeling
Utilize AI to run competitor analysis, dig into customer feedback, brainstorm on product values and model long-term impact of product decisions on core business metrics.
AI for brainstorming product ideas
Research intelligence to compile feedback
Business modeling
Vibe coded simulator to demonstrate impact of the key metrics with time to facilitate business modeling (the data in the screenshot is random)
Automated Documentation from Single Source
By generating documentation from code and a central knowledge base, we create a single source of truth that automates all help guides and GTM assets.
Convert project data and meeting notes into a smart knowledge base to auto-generate custom documents
Turn source code into living documentation: generate product guides and technical explanations automatically.
Auto generate document from code and knowledge base
Part IV
The Process that Worked
Full-Stack Autonomy for Accountability and Speed
AI made it more than ever possible for every engineer to own the full lifecycle—from UI and backend logic to testing and deployment. This ensures deep accountability for code quality and rapid delivery.
Every engineer in Speedboat is fullstack, they code both frontend and backend, write automated test and deploy the change.
Reduced communication overhead
Increased code quality through total accountability
Continuous Delivery for Rapid Iteration
High automation and cross-functional alignment allow us to ship incrementally. This slashes time-to-market to a month and creates a continuous feedback loop to stably increase customer satisfaction.
The target is not a product predefined 6 months in advance blindly, the target is only customer satisfaction and we make the best decision towards the goal in every moment.
Leveraging high automation for seamless deployments.
Collaborative search for iterative solutions.
Blurred Boundary of Product, Design and Engineers
PM does not write PRD/detailed requirements from beginning any more, they focus on explaining the problem and target together with designers with visual assets like prototype. Engineers are trusted to build the MVP solution. PM/ designers then participate in polish the product further with tickets as well as code.
In this way, we reliably delivered against all our goals and increased final product quality continuously.
Reliable planning and estimation
Realistic MVP and continuous delivery
Dogfooding and Real Customer Involvement
We had a workable product for internal use from week 1 and customers using the product after 1 month.
Active usage of products led to higher empathy of internal team and concrete feedback from real usage, which in turn drives higher product quality.
Empathy comes from 1st hand experience
Lays foundation for active participation of engineers
Part V
The Hard Truths�& AI Pitfalls
The AI Pitfalls
Learnings from What Didn’t Work
The Expert Gap
AI augmented experts but failed to onboard non-experts.
The experiment we conducted to allow designers to "Vibe code" user interface in production brought more complaints in code review time than real benefits.
‘It takes more time to review the code than to write it’ - by angry engineers
The AI Pitfalls
Learnings from What Didn’t Work
AI-Generated Specs
Tickets/PRD created purely by AI drifted quickly. They were often unfriendly to human readers and not actively maintained with project evolving.
Our AI-generated PRD is estimated as 67 minutes of reading time :)
The AI Pitfalls
Learnings from What Didn’t Work
AI Generated False Alarm for Code Review
The AI code review attempt by external members proved to be full of hallucination, and due to the lack of contexts, many issues pointed by AI were controlled on API or UI layer.
AI can only serve as a starting point for code investigation, not the conclusion.
The Hard Truth
Transformation is 20% Technology and 80% People
Transformation in Theory
Transformation in Reality
Fragile Team Structure
New tech stack, new skillsets, and a small team led to a lack of backup talent. The team had to be carefully protected against any organizational changes.
1 change in the small team led to another change, quickly led to the collapse of the expertise.
Operational Friction
Using 20+ new tools/services required an efficient operational model to balance security, budget, and innovation, speed without creating new bottlenecks.
An experiment team with new tech stack means a lot of new software and services to implement. An effective operational model to empower the team with fast adoption balancing security and budget is essential to even kick it off.
X20
Information Overload
The "learning by doing" process required high autonomy but created pressure to learn new things fast while delivering immediate results.
The new reality changes every day. To be an experiment team means staying ahead of the trend and challenging the status quo on a daily basis while trying to deliver the business outcome.
Knowledge Sharing Gap
A constant challenge to effectively bridge the experiment team with the core team for knowledge transfer and to cover integration needs.
Knowledge transfer and organisational transformation is an intentional efforts that require dedicated time. Without proper organisational level support and intentional planning, the knowledge stay with the few pioneers and cannot be successfully spread.
Part VI
Reflection &�The Path Forward
To what extent do our experiences align with current industry best practices?
Small Autonomous Team: Small, autonomous teams are the operational standard for leading AI-first organizations. The values include increased agility, autonomy and effective communication, driven by productivity increase by AI. Confirmed by Google, OpenAI, Anthropic, Cursor, Pilot AI, OAK’s LAB. In some cases, 2-people teams are recommended, regular recommendations are 5-9 members per team.
The Full-Stack Expansion: AI tools enable a "new breed of versatile developers" who can operate across backend, frontend, and design silos. At Anthropic, security researchers now capably analyze front-end visualizations, and safety teams build transactional databases.
Fluid roles/Campfire model: cross-functional teams of researchers, engineers, and product specialists gather around a living prototype to sculpt its behavior in real-time, rather than handing off requirements through rigid silos.
MVP first: Start with MVP that solves 80% of the abstract problem without much human control and involve experts to polish the last 20%
AI for code understanding: the most common use case from Antropic engineers and researchers is for fixing code errors and learning about the codebase.
What else could we try or improve? (things we can do immediately without prerequisites)
Shared context: OpenAI encourages the use of unified context systems, such as committing AGENTS.md files to repositories to guide agent behavior via explicit conventions and guardrails. This institutionalizes the "intent" of the team, allowing agents to function with higher autonomy while remaining aligned with organizational standards.
Let AI builds MVP rather than engineers: Engineers use Claude Code for rapid prototyping by enabling “auto-accept mode” (shift+tab) and setting up autonomous loops where Claude writes code, runs tests, and iterates continuously. They give Claude abstract problems they’re unfamiliar with, let it work autonomously, then review the 80% complete solution before taking over for final refinements
Onboard non-PDE people to use codebase for product understanding: legal, support and many other people can use code base as the source of truth too for product questions
Empower fluid roles: proved as a key value in top companies that AI can expand the scope of people and help them get into the domain they couldn’t do before. We need to speed up filling in the gap for designers to work on production code as well as engineers to have enough contexts to fix small UI issues.
What Could Have Been Done Differently
01
Thorough Cost & Business Planning
Conduct a thorough cost planning and business plan simulation to avoid financial and strategic surprises.
02
Think Again if We Really What a Speedboat
Sign clear agreements for budget and measurement. Involve 'investors' correctly to protect team autonomy against fragility.
03
Early Core Team Counterpart
Embed a counterpart from the core team from the very beginning to drive knowledge transfer and manage dependencies proactively.
04
Strategic Project Selection
Pick projects like a startup. Define success clearly (e.g., customer satisfaction, new segment served) and align project selection to those metrics.
How is product operational model involving with AI in industry?
References:
How AI is transforming work at Anthropic 12.2025
How Anthropic teams use Claude Code
Building an AI-native engineering team
Unlocking the value of AI in software development
The AI Product Management Workflows Every PM Needs in 2026
Google DeepMind's AI playbook for engineering at hyperspeed
Product management in the era of AI
Part VI
Appendix