A global services company with operations across ten European countries gave each market full budget autonomy – and lost visibility into where tens of millions were going, what they were producing, or how to improve. We changed that.
| SECTOR Multinational Professional Services | REVENUE €1B+ | MARKETS 10 European countries | ENGAGEMENT 16-week project |
EXECUTIVE RESULTS AT A GLANCE
| –41% Reduction in cost per qualified lead | €50K Ad spend recovered & reallocated (Q1) | +5 pts Win rate improvement across European pipeline | 87% Attribution accuracy (up from 17%) | €15M+ Projected incremental revenue on open pipeline |
A machine with the engine running – but nobody steering.
The company was not underinvesting in marketing. With a multi-million euro paid media budget spread across ten autonomous country teams, Power BI dashboards covering sixty KPIs, and dedicated marketing resources in every market, they had the infrastructure. What they did not have was alignment – or any honest view of whether the collective investment was working.
“Eighty-three percent of leads were arriving with no known source. The dashboard showed a 2,100% return on marketing investment. Neither number could be trusted – and leadership was making resource decisions based on both.”
Each country operated independently – its own budget, its own agency relationships, its own definition of a lead, a conversion, a win. Global functions wanted to grow the organisation’s marketing maturity and begin adopting AI – but the foundation was not there, and there was no consensus on what ‘better’ looked like.
Key problems identified
| Advertising Inefficiency LinkedIn spend was generating leads at 15× the B2B industry benchmark. Google was performing well. No one had compared them. €12,614 cost per lead on LinkedIn vs. €800 benchmark | Broken Attribution 83% of all inbound leads carried a blank UTM source. The company could not trace spend to pipeline, let alone to revenue. 83% of leads = origin unknown |
| No Brand or Copy Governance Ten countries. Ten tones of voice. No agreed messaging framework, persona strategy, or content standards across the group. Zero centralised content governance | AI Readiness = Zero Leadership wanted to explore AI. But dirty data, fragmented processes, and no shared KPI definitions meant no AI programme could succeed. No structured AI adoption path |
| Activity Tracking, Not Revenue Ops Dashboards captured what teams did. No stage conversion, pipeline velocity, or forecast accuracy. No view from MQL to closed-won. 16 critical KPIs missing from reporting | €312M in Lost Pipeline CRM data showed €312M in opportunities marked Lost. With no win/loss analysis or stage conversion tracking, there was no way to distinguish structural losses from recoverable ones. 24.6% win rate – well below potential |
The numbers told the story leadership needed to hear.
We ran a full RevOps diagnostic across all eleven Power BI dashboards – roughly sixty KPIs – and benchmarked every metric against B2B industry standards. Then we mapped each gap to its commercial value.
“The advertising benchmark analysis was the turning point. When leadership could see – channel by channel, country by country – exactly how much was being spent above industry benchmarks, the conversation shifted from “should we centralise?” to “how quickly can we start?”
We connected the diagnostic to an AI adoption framework – not as a separate workstream, but as the logical next step once the data foundation was in place. Every recommended pilot was tied to a specific red flag in the data. The AI roadmap was not aspirational and it had a commercial number attached to each item.
AI Adoption Framework – Three Tiers
| TIER 1 – EMBEDDED AI | TIER 2 – AI-NATIVE TOOLS | TIER 3 – CUSTOM AI |
| Marketing Automation AI Salesforce Einstein scoring Microsoft Copilot 6sense predictive ABM LinkedIn Campaign AI | Clay (data enrichment) Apollo (outbound) Profound (AI search / GEO) Writer / Jasper / Typeface AI proposal generators | RevOps intelligence copilot Bid/no-bid AI system Content governance AI Customer health scoring Own MCP integration |
AI cannot scale content that has no agreed definition of good.
Fragmented budgets meant fragmented content. Each country was producing, translating, and adapting marketing materials independently – no shared brand foundation, no tone of voice standard, no agreed messaging hierarchy. The result was not just inconsistency. It was an AI adoption blocker.
Before any AI content tool could be deployed across markets, the company needed to resolve a more fundamental question: what does “correct” look like? Without that answer, any AI system would simply scale the inconsistency faster.
“Every country was translating into their local language – but the source material itself was inconsistent. AI doesn’t fix a broken foundation. It amplifies it.”
Local teams had developed their own workarounds: some worked from translated global templates that were months out of date; others commissioned local agencies with no brief beyond a brand logo. There was no single source of truth for what the company offered, who it served, or how it should sound – in any language.
What we built: a content governance foundation for AI
Before recommending any AI content tool, we designed the governance layer that makes AI adoption safe, scalable, and consistent across all ten markets.
| Document | What it contains | How it feeds the AI |
| Brand Foundation Guide | Purpose, positioning, values, brand personality, approved boilerplate | Forms the master brand context for every AI output |
| Tone of Voice & Editorial Style | Voice principles, language rules, banned phrases, preferred terminology, channel-specific guidance | Prevents generic AI copy and protects the brand voice |
| Core Messaging House | Master value proposition, pillars, proof points, objection-handling language | Guides campaign claims, landing pages, ads, and sales copy |
| Audience & Persona Guide | Decision-makers, pain points, buying triggers, regional adaptation rules | Lets AI adapt copy by persona, sector, funnel stage, and region |
| Proof Point & Evidence Bank | Approved case studies, stats, analyst references, claims | Reduces hallucination risk – AI draws only from verified sources |
| Legal & Compliance Guardrails | Claims rules, data privacy restrictions, restricted wording, approval triggers | AI uses this as a “do not cross” layer before every draft |
| Knowledge Base & Content Examples | Previous approved content for structure, layout, tone reference | Critical for understanding what cohesive output looks like |
| Competitive Differentiation Guide | Competitor categories, advantages, objection handling language | Stops AI from producing bland generic copy |
The AI content stack: four layers, one consistent voice
With the governance foundation in place, we designed a four-layer AI content architecture that each market can activate independently – while producing content that shares the same brand, intelligence, and quality standards globally.
| LAYER | PURPOSE | TOOLS |
| Layer 01 Content Intelligence | Brand, tone, messaging, proof points, and content governance – governs every AI output | Custom Claude Project · Writer · Jasper · Typeface |
| Layer 02 AI Visibility | Track how the company appears in LLMs and AI search. Identify gaps before competitors fill them | Profound (GEO/AEO) |
| Layer 03 Content Production | Create blogs, emails, landing pages, ads, social, and sales assets at scale across all markets and languages | Writer / Jasper / Typeface · Adobe GenStudio · OpenAI / Claude LLMs |
| Layer 04 GTM Activation | Find, enrich, segment, and activate target accounts. Push content to the right buyer at the right moment | Clay · Apollo · Marketing Automation · CRM / Analytics |
The human-AI content workflow
The goal was not to remove humans from content production – it was to refocus them on judgment, insight, and quality. Every piece of content follows this governed workflow:
1. Human brief [AI step]
2. AI-assisted first draft [AI step]
3. AI self-check vs tone, claims & audience [AI step]
4. Human content review [Human decision]
5. Subject-matter expert review [Human decision]
6. Legal/compliance review (when required) [Human decision]
7. Performance tagging & learning loop back into the system [Measurement]
Sixteen weeks. Measurable results at every milestone.
The engagement ran in three phases, each designed to produce visible outcomes before the next began. We did not hand over a strategy document at the end. We stayed in the room until results were measurable.
Phase 1 – Diagnostic & Benchmark (Weeks 1–4)
Full audit of all sixty KPIs across eleven dashboards. Advertising spend benchmarked by channel and by country against B2B industry standards. Commercial value attached to every gap. Delivered to leadership as a board-ready business case for change – not a consulting report.
Phase 2 – Foundations & Quick Wins (Weeks 5–10)
UTM tagging architecture rebuilt across all paid and organic channels. LinkedIn campaigns paused and budget reallocated to Google and ABM. Attribution accuracy moved from 17% known to 87% known. Global content governance framework and brand standards published. RevOps 1.0 dashboard deployed with stage conversion, pipeline velocity, and forecast accuracy.
Phase 3 – AI Pilots & GTM Redesign (Weeks 11–16)
First AI pilots launched in parallel: 6sense predictive ABM for paid spend reallocation, Clay for ICP data enrichment and lead routing, AI lead scoring integrated into CRM. Content Intelligence System built on Claude and Writer. New Global GTM operating model adopted by all ten markets. Centre of Excellence established with dedicated functions for RevOps, AI, Brand, and Campaign Execution.
AI pilots launched during the engagement
▸ 6sense Predictive ABM Live & measuring
▸ Attribution Automation (Clay) Live & measuring
▸ AI Lead Scoring & Routing Live & measuring
▸ AI Conversation Intelligence In pilot
▸ AI Search Visibility – Profound (GEO/AEO) In pilot
▸ Content Intelligence System (Claude / Writer) Next phase
▸ AI NRR & Churn Pilot Next phase
▸ Executive Results
The numbers that matter to leadership
Every result below is tied directly to a KPI gap identified in the original diagnostic.
87%
Attribution Accuracy
From 17% known source to 87% in 8 weeks. Leadership can now trace every euro of spend to pipeline stage.
From 17% → 87% known
€50K
Ad Spend Recovered & Reallocated
LinkedIn CPL was running at 15× the B2B benchmark. Immediate reallocation of one country’s LinkedIn budget to higher-performing channels generated €50K in recovered spend in Q1, with a roadmap to optimise the remaining countries in subsequent phases.
Q1 recovery – full roadmap in motion
41%
Lower Cost Per Qualified Lead
Blended CPL across all channels reduced through channel mix optimisation and AI-powered targeting.
Across European markets
+5 pts
Win Rate Improvement
Stage conversion visibility and faster speed-to-lead enabled by AI routing contributed to consistent win rate improvement.
24.6% → ~30% win rate
€15M+
Projected Incremental Revenue
CRM data showed €312M in opportunities marked Lost over the review period. We applied a conservative 5-point win rate improvement to the active open pipeline using stage conversion data. The €15M figure represents improvement on live opportunities only – it excludes pipeline expansion from AI pilots and improved lead quality.
Conservative model on open pipeline only
10/10
Markets Aligned
All ten European country teams adopted the new GTM framework and Centre of Excellence model within the engagement period.
Recognise any of this in your organisation?
Most engagements start with a diagnostic sprint. We review your current state, benchmark what we find, and give you a prioritised action plan – with commercial value attached – before you commit to anything further.