Build AI-Ready Marketing Operations with the firm that operates underneath the platforms
Logarithmic operates at the privacy architecture, RevOps plumbing, and platform integration layer underneath Oracle Eloqua, Adobe Marketo Engage, Salesforce Marketing Cloud, and HubSpot, where our team builds the foundations that decide whether enterprise marketing AI scales in production.
9+ years
We have built the substrate underneath enterprise marketing automation for over nine years across the four major platforms.
4 platforms
Genuine certified depth across Oracle Eloqua, Adobe Marketo Engage, Salesforce Marketing Cloud, and HubSpot.
Privacy practice
A dedicated privacy services team that operates next to your DPO.
Two-week diagnostic, no platform sales
01 · The Gap
Enterprise marketing AI succeeds in pilot and stalls in production rollout for two structural reasons
By year two the same pattern recurs across enterprise rollouts: the pilot that impressed the steering committee runs on a fraction of the data it was supposed to unlock, the team has rolled back the personalization rules, the attribution model has sat in validation for months, and the program owner has shifted budget away from initiatives that did not deliver on the original promise.
The AI itself is rarely the issue, because the foundation underneath it was never built to carry what enterprise teams are now asking it to do, and two structural gaps explain why so many of these rollouts stall in transition without showing up in the vendor evaluation that preceded them.
Gap 01 · Privacy
Privacy architecture written for email marketing does not survive AI personalization, because most enterprise teams designed their consent frameworks, data residency rules, and first-party data plumbing for a world running 40 campaigns a quarter, not for the 40,000 real-time decisions per second that AI activation now generates, each one an implied promise about how customer data moves.
Gap 02 · RevOps
RevOps foundations built for a pre-AI organisation do not govern AI agents, and the handoffs, attribution logic, scoring models, territory rules, and ops playbooks that got your marketing to where it is now never anticipated a stack where AI models make real-time decisions that nobody on your team fully audits.
02 · Framework
Enterprise marketing AI runs on top of three foundations that determine whether it scales beyond pilot, and building those foundations is what we have done for nine years
∞ · Application Layer
AI models, agents, generative tools
III · Activation Foundation
Eloqua · Marketo · SFMC · HubSpot
II · RevOps Foundation
Attribution · Scoring · Handoffs
I · Data & Privacy Foundation
Consent · Residency · First-party data
Foundation I · Data & Privacy
Consent architecture, first-party data plumbing, and governance designed so the organisation can legally and ethically feed data to AI at enterprise scale, with the DPO able to sign off on the AI activation plan without triggering a 60-day legal review cycle.
Foundation II · RevOps
Attribution that survives multi-agent activation, scoring and routing logic that is governed instead of black-boxed, and handoff design from human to AI that the CRO can explain to a board without a disclaimer when AI signal is being separated from AI noise.
Foundation III · Activation
Platform architecture, integration surface, and governance layer with audit trails and rollback that an AI agent can act through end-to-end while the marketing ops lead retains full visibility into what the agent did, why, and how to stop it.
03 · Practice
What AI-Ready Marketing Operations covers in delivery, mapped against the services we have run for nine years.
04 · Discussion
The five questions your AI vendor will not answer
Your RevOps plumbing was not built for AI.
Do you know where it breaks first?
Foundation II: attribution, handoffs, scoring under load.
Privacy law was not written for AI personalization.
Is your consent architecture ready for what is coming?
Foundation I: consent, residency, AI-specific data policy.
Your marketing ops team spent a decade getting fluent in campaigns.
Are they ready to govern AI agents?
Foundation II plus organizational design.
AI vendors promise to work with your stack.
Who is fixing your stack?
Foundation III: platform readiness, integration surface.
Pilots succeed easily, but enterprise marketing AI rollouts are where most programs die.
What separates the 20% who scale from the 80% who stall?
The synthesis across all three foundations.
Bring these five questions to your next CMO and CIO meeting, and we will help your team answer each of them through the structural lens of the three foundations.
Request a Foundation Audit05 · Positioning
The practical reasons enterprise teams route their AI-readiness work through Logarithmic
9+ years
We have been building the substrate underneath enterprise marketing automation since long before “AI-ready” was a category, working at the layer where AI activation has to function.
4 platforms
Our team holds genuine certified depth across Oracle Eloqua, Adobe Marketo Engage, Salesforce Marketing Cloud, and HubSpot, the four platforms enterprise marketing AI tends to land on top of.
Privacy practice
We run a dedicated privacy services team at enterprise depth, working alongside DPOs to design consent architecture and first-party data governance built for AI activation.
We have spent a decade naming, diagnosing, and remediating the failure patterns that kill enterprise marketing operations, and we recognise what breaks first under AI load because we have already rebuilt those foundations across multiple Fortune-class engagements.
Frankenstein Stack
Seven or more point tools stitched together by tribal knowledge, where AI trained on the resulting incoherent data ends up confidently wrong about the same entity in five different places.
Attribution Theater
A dashboard everyone cites in meetings and nobody trusts in private, which gets pushed into farce once multi-agent AI activation goes live and the model decisions stop being traceable.
Automation Graveyard
Hundreds of half-finished programs and orphan campaigns sit in the platform, and once AI starts dynamically selecting from that inventory it picks from graves alongside live programs.
Handoff Black Holes
Records disappear at the MQL-to-SQL, agent-to-rep, and human-to-AI transitions, creating pipeline leaks that nobody on the team can fully see or measure.
The CDP Trap
A platform bought to solve what was a governance problem in disguise, now eighteen months past go-live with partially unified data and an audit incident waiting to happen the moment AI starts activating against it.
06 · The Foundation Audit
A two-week diagnostic that maps where the foundations break under AI load
A structured two-week diagnostic that scores your current data, privacy, RevOps, and platform layers against our AI-readiness framework, then delivers a prioritized 12-month remediation roadmap that names what will break first under AI load, what is worth fixing this quarter, and what can safely wait.
Consent and privacy architecture, AI activation readiness
First-party data plumbing and governance
Attribution, scoring and handoff logic under AI load
Platform configuration: integration surface and governability
Organization and skills readiness for governing AI agents
Prioritized 12-month remediation roadmap
What clients see after remediation
Teams that run the remediation typically reach production AI activation between 9 and 12 months faster than teams that attempt the rollout without a foundation audit, and avoid the average €400K to €1.2M of rework that comes from discovering structural gaps mid-deployment.
