AI-GrowthLab OS
For teams who want to turn AI ideas into repeatable growth
Most organisations are full of AI ideas, demos and pilots – but very few turn them into a system that compounds. With AI-GrowthLab OS, Vault Mark helps Thai and APAC brands build an AI-driven experimentation and growth operating system that takes ideas from “we should try this” to “this is how we grow across channels and markets”.
What AI-GrowthLab OS Is: Turning AI Ideas into a Growth Engine
In most organisations, “experimentation” means:
- someone has an idea
- a small test runs in a corner
- a few slides are made
- then everyone moves on.
There’s no system for:
- deciding which ideas deserve attention
- designing tests properly
- capturing learnings
- and scaling what works across channels, products and markets.
An AI-GrowthLab OS changes that.
It is how you turn AI and experimentation from “side projects” into a core growth habit.
Why “Trying Things” Isn’t the Same as Experimenting
The old experimentation pattern looks like this:
- hackathons, brainstorming sessions and innovation days
- one-off A/B tests in ads or landing pages
- vendor- or tool-led AI pilots around chatbots, content or bidding
- local teams doing their own tests without a shared framework.
It feels busy.
But after a year, leaders still ask:
- “What did all these experiments and AI pilots actually change?”
- “What did we learn that we use every week?”
Here’s the problem:
- No clear focus
Tests are spread thinly across too many topics, channels and markets, diluting impact. - Weak design and measurement
Many experiments aren’t set up to produce clear, trusted results – so people don’t act on them. - No path from test to standard
Even when something looks promising, there is no playbook for rolling it out and maintaining it. - AI treated as a toy, not a capability
AI pilots happen in isolation – not as part of a coherent OS that links Strategy, Data, Channels and Ops.
Without an AI-GrowthLab OS, organisations end up with:
- innovation theatre instead of real progress
- AI fatigue among teams and leaders
- experiments that never scale
- and a sense that “we’re testing a lot, but not really growing”.
Who AI-GrowthLab OS Is Built For in Your Organisation
Best fit if you…
AI-GrowthLab OS is designed for organisations that:
- already run multiple campaigns, channels or markets – and want to improve systematically
- have many ideas for AI, automation and optimisation, but no clear way to manage them
- want a shared experimentation system across marketing, digital, product and data
- need to show leadership that AI and experiments are delivering real, compounding value.
Typical roles involved:
- CMO / Head of Digital / Head of Growth
- Heads of Performance, Ecom, CX, Product and CRM
- Data, Analytics and Marketing Ops leads
- Transformation / Innovation / Strategy teams.
Real questions we hear:
- “How do we stop doing random tests and start doing real experimentation?”
- “How do we use AI in growth without creating chaos?”
- “Where should we focus our experiments first?”
- “How do we make sure wins are actually rolled out, not forgotten?”
Probably not a fit if you…
AI-GrowthLab OS may not be the right starting point if:
- you rarely run campaigns or changes, and have little appetite for testing
- you only want a list of “growth hacks” or AI tools, not a system
- you are not ready to involve cross-functional stakeholders
- you see experimentation as a one-time project, not a continuous practice.
Growth Problems You Can’t Solve with One More Big Bet
Across Thai and APAC brands, we see similar issues:
- Random, unconnected tests
Experiments happen in isolation – by channel, agency or market – with no shared view of what’s being tried and why. - Low trust in experiment results
Tests are underpowered, poorly designed or interpreted inconsistently, so teams argue instead of decide. - No learning memory
Insights live in slides, inboxes and people’s heads. When people move or agencies change, knowledge disappears. - Difficulty scaling wins
Even when something works, it’s hard to roll it out across markets, segments or channels without breaking something. - AI initiatives with unclear value
AI pilots are launched to “try things out”, but they are not tied to specific growth questions or OS modules.
AI-GrowthLab OS is built to address these by giving you:
- a single system for experiments and AI initiatives
- clear rules for design, measurement and decisions
- a shared “learning memory”
- and a path from pilot to standard practice.
Before & After: From Random Tests to Disciplined Experimentation
- Ideas come from everywhere, with no clear filter
- Tests are designed and measured in different ways
- AI pilots live with vendors, tools or isolated teams
- Learnings are hard to find or trust
- Wins don’t scale, and teams feel busy but stuck
- Ideas are prioritised through clear growth questions
- Experiments follow shared design and measurement standards
- AI is used where it supports OS modules and outcomes
- Learnings are captured in a shared library
- Wins are rolled out through playbooks and Ops support
How AI-GrowthLab OS Powers Every Other OS
AI-GrowthLab OS lives in the Ops & Innovation layer of the Vault Mark AI Marketing OS. It provides the experimentation and learning system that feeds improvements into other OS modules – Strategy, Brand & GEO, Search, Social, Paid, Influencer, Lead, Ecom, CX, Data and Ops. Instead of each team running its own tests, you get a shared GrowthLab that compounds learning across channels and markets.
Within the AI Marketing OS:
- AI-Strategy OS defines where growth is needed most
- AI-Brand & GEO OS sets the brand and footprint foundation
- AI-Search, AI-Social, AI-Paid and AI-Influencer OS run demand experiments
- AI-Lead and AI-Ecom OS test improvements in conversion and journeys
- AI-CX & Retention OS explores ways to reduce churn and grow LTV
- AI-Data & Measurement OS provides the signals and dashboards experiments rely on
- AI-Ops OS helps embed successful experiments into daily operations.
We design AI-GrowthLab OS so experiments are not a separate hobby – they are how your AI Marketing OS learns and improves.
What You Get When Experiments Become a Core Capability
Group 1: Growth questions, focus and backlog
- Growth question framework
A clear set of strategic growth questions (e.g. “How do we improve qualified leads from channel X?”, “How do we increase repeat purchase in segment Y?”) that focus experimentation. - Experiment domains and themes
A map of priority experimentation areas across channels and OS modules – demand, conversion, CX, operations, AI usage and more. - Prioritised experiment backlog
A structured backlog of potential experiments, scored by impact, confidence, effort and risk.
Group 2: Experiment design, AI usage and operating model
- Experiment design standards
Guidelines for how to frame hypotheses, define success metrics, choose samples, run tests and avoid common pitfalls. - AI usage and guardrails in experiments
Principles for when and how to use AI in testing – from creative generation and targeting to decision support – with clear human oversight. - GrowthLab operating model
Definition of roles, decision rights and cadences: who proposes, reviews, approves, runs and evaluates experiments.
Group 3: Measurement, learning system and scale-up playbooks
- Experiment measurement framework
Standard KPIs, reporting formats and thresholds for deciding whether to scale, iterate or stop experiments. - Learning library and knowledge system
A central repository for experiments, results and insights – structured so teams can search and reuse learnings. - Scale-up & rollout playbooks
Playbooks for turning successful experiments into standard practices, including integration with AI-Ops OS and local market adaptation.
90 Days to Build a Real Experiment Habit
In the first 90 days, we move from random tests and AI pilots to a working AI-GrowthLab OS. We review your current experiments, AI initiatives and constraints, then design a focused growth question set, operating model and measurement framework. By the end of the first 90 days, you’ll have a clear system for what to test, how to test it and how to scale what works.
How We Work with Product, Marketing, Data, and Ops on Experiments
AI-GrowthLab OS is not a lab in a corner – it’s a system that connects to how your teams already work.
In practice, that means:
- Co-creating with your internal teams
We involve people from marketing, digital, product, data and operations to ensure the GrowthLab reflects real work, not theory. - Integrating agencies and vendors
Agencies and technology partners run experiments within the same OS – with shared standards and dashboards. - Right-sizing complexity
We design the GrowthLab to match your scale, traffic and resources – not to copy big tech or global templates blindly. - Building capability, not dependency
We aim to leave your teams with the skills and habits to keep the GrowthLab running, not to own every test forever.
Why Teams Who Want Repeatable Wins from AI Choose Vault Mark’s GrowthLab
Vault Mark treats experimentation and AI as part of your operating system, not as side projects. We combine growth strategy, AI capabilities and Thai/APAC realities to build an AI-GrowthLab OS that sits on top of your channels and data. Instead of random tests and pilots, you get a clear system for where to experiment, how to measure and how to scale wins across markets.
Typical “experimentation” vs Vault Mark AI-GrowthLab OS
Typical experimentation & AI pilots
- Tests chosen ad hoc, often tool-driven
- Inconsistent design and measurement standards
- AI pilots isolated from core KPIs and OS modules
- Learnings scattered in decks and chats
- Hard to answer “What changed because of this?”
Vault Mark AI-GrowthLab OS
- Experiments driven by clear growth questions
- Shared standards for design, metrics and decisions
- AI used deliberately, with guardrails and real outcomes
- Learnings captured in a reusable system
- Clear path from test → playbook → rollout via AI-Ops OS
FAQ: AI-GrowthLab OS, Testing, and Risk
How is AI-GrowthLab OS different from normal A/B testing?
A/B testing is a tool. AI-GrowthLab OS is the operating system around experimentation: which questions matter, how ideas are prioritised, how tests are designed and measured, who decides what to do next, and how wins are scaled. It includes A/B testing, but goes far beyond it.
Do we need huge volumes of data and traffic to benefit?
Not necessarily. High-traffic organisations can run more granular tests, but even medium-scale brands can benefit from a clearer experimentation system. In AI-GrowthLab OS, we design experiment types and expectations to match your traffic, data and risk profile – sometimes focusing more on quasi-experiments or phased rollouts.
Can we use our existing tools and platforms?
Yes. AI-GrowthLab OS is tool-agnostic. We work with the analytics, testing, marketing and AI tools you already have – and recommend adjustments only where they unblock critical capabilities. The OS defines how you experiment; tools are there to support that, not the other way around.
How do we avoid burning out teams with constant testing?
A good GrowthLab OS protects teams from chaos, it doesn’t create it. We define clear rhythms, limits on concurrent tests, and rules for when to say no. Experiments are aligned with strategy and capacity, so teams work on fewer, more meaningful tests rather than trying everything.
How does AI-GrowthLab OS relate to AI-Strategy, AI-Data and AI-Ops OS?
AI-Strategy OS sets the big questions and priorities. AI-Data & Measurement OS provides the signals and dashboards experiments rely on. AI-Ops OS helps turn successful experiments into standard ways of working. AI-GrowthLab OS sits between them, turning strategy into tests, and tests into change.
How long does it take to see impact?
You’ll often feel internal impact within 1–2 cycles of running experiments under the new OS – greater clarity, less randomness, better discussions. Tangible external impact (e.g. improved conversion rates, reduced acquisition cost or higher retention) typically emerges over 3–6 months, depending on your cycles and experiment mix.
Ideas aren’t your bottleneck. Disciplined experimentation is.
AI-GrowthLab OS is for teams who want a repeatable way to turn AI ideas into measured wins.
👉 Ask for an “Experiment Portfolio MRI”.
We’ll scan what you’re currently testing (and not testing), how you read results, and how AI-GrowthLab OS can turn experimentation into a core capability instead of a side hobby.