# Unlocking Growth — Full site content

> Turn AI Into a Company-Wide Growth System

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## Hero

Turn AI Into a Company-Wide Growth System

We design, build and run the AI function your organisation is missing — so pilots compound into enterprise capability and the board sees the numbers.

## Why now

### The AI Pressure Is Clear. The Operating Model Isn't.

The board is asking. Competitors are announcing. Employees are already using it — with or without permission. Every leadership team we talk to describes the same pattern: pilots in marketing, a chatbot in support, copilot licences nobody measures. It feels like progress. It rarely compounds.

The bottleneck isn't more AI tools. It's the absence of an operating function to turn AI activity into enterprise capability. That gap is what we fix.

- **88%** of organisations use AI in at least one function (McKinsey, Dec 2025)
- **7%** have scaled AI enterprise-wide (McKinsey, Dec 2025)

## Offerings

Inside the company, AI Enablement is the function that turns AI activity into enterprise capability. Outside the company, AI-Native Growth Operations rebuilds how you convert, onboard and retain customers on that same foundation. Start with one, add the other — they compound.

### AI Enablement — The AI function your organisation is missing.
We design, build and run AI Enablement alongside your team — portfolio, platform, governance, enablement and operational excellence on one cadence. The function that turns AI activity into enterprise capability, without the theatre.

- Portfolio & platform — live register of every AI initiative, scored for value and risk
- Approved tools & reference architecture — no relitigating the stack for every use case
- Permissive-first governance — the safe path is the easy path; audit trails by default
- Enablement — fluency beyond the early adopters, function by function
- Board-ready reporting — value captured, adoption, governance posture, quarterly

Owners: CEO, COO, Chief of Staff, CIO

### AI-Native Growth Ops — Growth ops, rebuilt on an AI-native foundation.
Once AI Enablement is the operating model, we point it at the customer. An intelligence layer on your existing stack becomes a Customer Brain — adaptive journeys, next-best-action, contextual lifecycle — measured against the metrics that move your P&L.

- Intelligence layer over your CDP, analytics and CRM — read-only on day one
- Adaptive onboarding and lifecycle — context-aware, not static
- Customer Brain v1 — next-best-action, churn and expansion scoring per account
- Experiment engine non-technical teams can actually run
- Tied to activation, trial→paid, NRR and time-to-value — agreed with the CFO up front

Owners: CRO, CMO, VP Growth, Head of Lifecycle

### Why they compound.
AI Enablement gives your organisation the muscle to run AI internally — portfolio, platform, governance, evaluation. AI-Native Growth Ops points that muscle at the customer. Do one and you plateau. Do both and every customer signal improves an agent; every agent run produces a customer signal. That learning loop is the whole point.

## Products

### Unlocking AI
The control plane that turns AI activity into enterprise capability.

Portfolio, platform, governance and evals on one cadence. Every initiative has an owner, every agent has a guardrail, every run is audited. The boardroom finally gets the number.

- Live portfolio register — every AI initiative scored for value & risk
- Approved tools & reference architecture — stop relitigating the stack
- Evaluation harness — golden sets, scoring, drift, full audit trail
- Permissive-first governance — PII, tone and compliance guardrails
- Board-ready reporting — value captured, adoption, posture

### Unlocking Signals
Surface growth insights on autopilot.

AI reads your product, billing and support data — and writes the insight. Confidence-scored, action-ready, delivered to the people who can act on it. No dashboard-archaeology required.

- AI-generated insights across funnels, lifecycle, ICP and behaviour
- Confidence scoring — high / medium / low on every signal
- ICP Assessment — behavioural fit, not just firmographics
- Connections over Segment, Amplitude, Stripe, Zendesk, HubSpot and many more…
- Pushes to Slack, not another dashboard nobody opens

## The seven traps of AI theatre

Recognise more than two? You're in good company — and the whitepaper shows you the way out.

### Trap 01 — The Theatre Trap
Lots of AI activity. Little compounding value.

### Trap 02 — The Shadow AI Iceberg
What IT sanctioned is the tip. The rest is happening anyway.

### Trap 03 — The Tool Sprawl Tax
Every team buying its own stack. No one owning the whole.

### Trap 04 — The Prioritisation Paradox
The loudest use cases get funded. The most valuable ones don't.

### Trap 05 — The Capability Cliff
A few fluent users. Everyone else guessing.

### Trap 06 — The Accountability Void
AI belongs to everybody — which means it belongs to nobody.

### Trap 07 — The Boardroom Credibility Gap
Directors want ROI in numbers. You have stories.

## Our approach

AI Enablement is the operating function your enterprise is missing — portfolio, platform, governance, enablement and operational excellence on one cadence. We design, build and run it alongside your team, starting with an intelligence layer above your current stack.

### Portfolio & Platform First
A live register of every AI initiative, scored for value and risk, reviewed on a fixed executive cadence. An approved tools register and reference architecture so a new use case moves from idea to production without relitigating the stack.

### Interoperable by Design
Our agents and workflows are portable across AI platforms. A stable interface layer lets components be swapped as the ecosystem evolves — no lock-in to any single AI vendor, and your data and logic come with you if you switch.

### Governance That Enables
Permissive-first policy that makes the safe path the easy path. Every agent has a named owner, guardrails, baseline metrics and a full audit trail. Measurement is agreed with the CFO before deployment, not retrofitted after.

## The 5-stage AI Enablement maturity model

Fifteen statements. Rate each from 1 (strongly disagree) to 5 (strongly agree). We'll place you on the 5-stage model and send a personalised playbook to your inbox.

### Stage 1 — Ad Hoc (score 15–27)
Tagline: You're where most organisations are. The first move is ownership.

Recommended next move: **Book a 30-minute executive briefing**

At Ad Hoc, the bottleneck isn't tools — it's ownership. Pilots happen in pockets. Shadow AI is everywhere. The fastest unlock is a named AI Enablement lead with a real mandate — and three to five use cases identified and on paper within 30 days.

What to do now:
- Appoint an interim AI Enablement lead this quarter — executive sponsor with a real mandate, not a task force
- Run a 30-day inventory — every AI tool in actual use (sanctioned or not), who owns it, and where the three to five highest-value workflows sit
- Measure your adoption baseline now, not later. Shadow AI is already in your organisation — inventory it, don't suppress it. Most organisations at this stage find 20–40% of employees are already using unsanctioned AI tools
- Pick one workflow and commit to deployment — crawl-phase scope, CFO-agreed baseline, in production within 6–8 weeks
- Set a responsible AI baseline — even at this early stage, a one-page policy on data handling and prohibited use cases protects you as you move fast

What good looks like: One workflow live in production. Three to five more identified and sequenced. A named owner who shows up to a fortnightly review. An adoption baseline number you can actually track against.

Adoption pulse: 0–20% active sanctioned use — shadow AI likely higher. Measure it.

### Stage 2 — Coordinated (score 28–40)
Tagline: You've got motion. Now you need a function.

Recommended next move: **Apply for the 90-Day Establishment Sprint**

Coordinated organisations have policy on paper and practice catching up. Governance lags usage. The 90-Day Sprint turns that patchwork into a running AI Enablement function — with a charter, a live portfolio of five or more active initiatives, and board-ready reporting from day one.

What to do now:
- Stand up the AI Enablement function — executive mandate, quarterly prioritisation, fortnightly delivery cadence, portfolio of at least 5 executive-owned initiatives in parallel
- Stand up the first “AI for All” infrastructure — a weekly or fortnightly open drop-in, a shared prompt library, and a role-specific playbook for your two highest-use functions. Target: 30% of the organisation with structured access and training within 60 days
- Move from policy-on-paper to permissive-first governance — clear enough to manage risk, simple enough that employees actually follow it
- Baseline the portfolio — value captured, adoption rate, governance posture — first wins visible within 6 weeks
- Name responsible AI owners — a named person accountable for ethical use, bias monitoring, and regulatory alignment

What good looks like: Five or more active AI initiatives running under one operating model. Early wins in 6 weeks. 30% of the org with structured AI access and foundational training. A 12-month strategic blueprint in place.

Adoption pulse: Target 20–40% active regular sanctioned use. BCG data shows 5+ hours of structured training is the threshold that separates orgs that sustain adoption from those that plateau.

### Stage 3 — Operationalised (score 41–55)
Tagline: The muscle exists. Point it at the customer.

Recommended next move: **Explore AI-Native Growth Operations**

You've built the function. Now the leverage is external — turning AI Enablement maturity into a Customer Brain that sharpens activation, conversion and retention. Organisations at this stage typically have 8–15 live AI workflows and are ready to deploy agents into customer-facing journeys.

What to do now:
- Convert AI Enablement into an engine for broad deployment — an AI Build Club (functional leads + AI Enablement + front-line champions) meets fortnightly to identify, prioritise, and build automations from the bottom up. This is where adoption breaks through the 50% ceiling
- Invest in data infrastructure — at 8–15 live workflows, the constraint shifts to data quality and measurement. Establish clean data pipelines and workflow-level ROI tracking
- Identify the growth metric with the most compounding upside — activation, trial-to-paid, or NRR — and build an AI-native journey around it
- Run the first AI-native customer journey in production inside 90 days — layer intelligence over your existing stack, with human oversight built in from day one
- Embed responsible AI into workflow design — bias checks, explainability, and audit trails standard for any customer-facing deployment

What good looks like: 10+ live AI workflows. At least one AI-native customer journey in production. An AI Build Club running fortnightly. Adoption at 50–70% of target functions.

Adoption pulse: Target 50–70% active use across target functions, measured quarterly. BCG: organisations with active C-suite sponsorship move positive employee sentiment from 15% to 55%.

### Stage 4 — Embedded (score 56–67)
Tagline: AI is how the business runs. Now, compounding.

Recommended next move: **Executive peer session**

Embedded organisations don't need a sprint. They need a peer — someone who has stood up both sides of the operating model across 20+ deployed use cases and can benchmark you against Stage 5.

What to do now:
- Make agentic deployment your next architecture — move from discrete workflow automation to multi-agent systems that operate continuously across functions. BCG's top 5% of orgs are already allocating 15% of their AI budget to agentic programs. This is where ROI compounds
- Invest in MLOps infrastructure — model monitoring, drift detection, feedback loops, retraining pipelines
- Make AI adoption a managed KPI — track weekly active use by function, time-saved per workflow, and adoption depth alongside commercial outcomes. BCG: frontline adoption stalls at 51% without sustained leadership support and 5+ hours of structured training
- AI literacy is now an onboarding standard — every new hire arrives with baseline AI fluency expectations; adoption is a KPI in team scorecards
- Make AI reporting a standing board item — portfolio health, adoption depth, value attributable, next bets

What good looks like: AI embedded across the majority of functional workflows. Agentic programs running autonomously. 70–85% of workforce with AI in daily workflow. Board-level reporting with commercial attribution.

Adoption pulse: Target 70–85%+ of workforce with AI embedded in daily workflow. At this stage, “AI for All” is no longer a program — it's a standard. Adoption gaps are managed the same way performance gaps are.

### Stage 5 — AI-Native (score 68–75)
Tagline: You're the case study. Let's talk partnership.

Recommended next move: **Partner conversation**

AI-Native organisations are rare — and the biggest risk at this stage is complacency. You're likely operating 30+ AI initiatives across the enterprise, with agentic programs running continuously in production.

What to do now:
- Develop your model strategy — not just your model vendor list. Make deliberate decisions about which tasks run on frontier APIs (GPT-4o, Claude), which run on fine-tuned domain models trained on your proprietary data, and which are candidates for self-hosted open-source deployment (Llama 3, Mistral, Qwen). At high inference volumes: 60–80% per-token cost reduction through self-hosted models vs cloud APIs, and up to 55% TCO reduction over 18 months. Fine-tuning a domain-specific model typically costs $4,000–$20,000 — and produces a model that outperforms a general frontier model on your specific tasks at a fraction of the per-query cost at scale
- Your proprietary data is a strategic asset that compounds. Every AI interaction, every feedback loop, every corrected output is training signal. The moat is not the model — it is the data and the orchestration layer around it
- Institutionalise a reinvention rhythm — your current AI advantage has a shelf life of 24–36 months; design the next operating model before the current one plateaus
- Responsible AI at this scale is a governance architecture — model cards, automated bias auditing, explainability layers on high-stakes decisions, regulatory horizon-scanning
- Consider co-authoring the research that shapes how the rest of the market thinks about this — you have the operational proof; we have the research infrastructure

What good looks like: 30+ active AI initiatives. 90%+ active employee AI use — proficiency is a hiring criterion. A deliberate model portfolio strategy balancing frontier APIs, fine-tuned domain models, and self-hosted open-source. Active reinvention cadence.

Adoption pulse: 90%+ active use. AI proficiency is a hiring criterion. There is no “AI for All” program at this stage — AI fluency is simply how the organisation operates. The metric that matters now is reinvention velocity, not adoption rate.

## Outcomes

Each offering is measured on the metrics its audience actually defends at the board. Measurement is agreed with the CFO up front, not retrofitted after.

### Operating Outcomes
How the organisation runs AI
- **Value Captured per Agent** (Higher): Every workflow in the portfolio has a CFO-agreed baseline — and is measured against it on a quarterly cadence.
- **% Work AI-Assisted** (Higher): Measured adoption by business unit — not licence count. Fluency beyond the early adopters, function by function.
- **Idea → Production Lead Time** (Faster): A reference architecture and approved-tools register means a new use case ships without relitigating the stack.
- **Governance Posture** (Higher): Named owners, guardrails, audit trails and a permissive-first policy employees actually follow. Shadow AI, surfaced.

### Customer Outcomes
How the customer experiences you
- **Activation & Time-to-Value** (Faster): Adaptive, context-aware onboarding — reach the first value moment faster, on the path the customer actually needs.
- **Trial-to-Paid Conversion** (Higher): Convert more trial users by delivering the right experience at the right time — scored per account, not per segment.
- **Net Revenue Retention** (Higher): Expand accounts through timely, contextual upsell and engagement — driven by Customer Brain scoring.
- **Customer Lifetime Value** (Higher): Compound gains across activation, retention and expansion — because every customer signal sharpens the next decision.

## Pilot — The 90-Day Establishment Sprint.

Stop running pilots that don't compound. In one quarter, stand up the function that turns AI activity into measurable enterprise capability.

### A named owner and a mandate
The AI Enablement function with an executive sponsor, a charter and a cadence your leadership team respects.

### Shadow AI, made visible
Full inventory of the AI tools in actual use — and a permissive-first policy employees will actually follow.

### One workflow in production
Live, measured, with human oversight and a baseline the CFO signed off before you deployed a thing.

### Board-ready reporting
Value captured, adoption by business unit, governance posture — in the format your directors will accept.

## Webinar

A 45-minute executive briefing on the operating moves that separate organisations compounding AI leverage from those still running pilots. What to stand up in the next 90 days, what to say no to, and how to make the advantage stick while your competitors are still arguing about tools.

Sessions:
- APAC: Mon · May 25, 4:00 PM AEST
- EMEA: Mon · Jun 8, 4:00 PM BST
- Americas: Tue · Jun 9, 2 PM ET · 11 AM PT

## FAQ

### How are AI Enablement and AI-Native Growth Ops different?
AI Enablement is inside-out — we rebuild how your organisation operates with AI (portfolio, platform, governance, enablement, cadence). AI-Native Growth Operations is outside-in — we rebuild how customers experience you on that same foundation (intelligence layer, adaptive journeys, Customer Brain). Most clients start with one. Teams that eventually do both get a learning loop the single-track teams don't.

### Do you replace our existing tools?
Not on day one. We start by connecting to your existing infrastructure. Over time, some tools may be replaced with better-fit alternatives — but only when the data supports it and you're ready.

### How long until we see value?
First signal within two weeks of kickoff. The 90-day sprint is designed so you see clear, measurable value well before you decide to expand.

### How is this different from hiring a consultant?
Consultants hand you a strategy deck. We hand you a running function — designed, built and operated alongside your team, accountable to measurable outcomes.

### Will we be locked into your platform?
No. Our workflows are portable across AI platforms, and we use a stable interface layer so components can be swapped. If you switch or bring capabilities in-house, your data and logic come with you.

### Do we need a data team to work with you?
It helps, but it's not required. We handle the integration and intelligence layer. What we need is access to your data, an executive sponsor, and a team willing to act on insights.

## Contact

Start with a 30-minute executive briefing. We'll assess where you sit on the AI Enablement maturity model, identify the two or three workflows worth crawl-phase deployment, and map out what the establishment sprint looks like for your organisation.

Email: info@unlockinggrowth.co
Founder: peter@unlockinggrowth.co