Month 0 - Setup

We establish the operating environment: access, stakeholder map, decision rights, communication channels, work board, commercial strategy context, desired operating state, and initial backlog.

Month 1 - Assess

We assess the current state of HubSpot and the operating model around it: CRM architecture, data, reporting, lifecycle, pipeline, automation, adoption, governance, AI readiness, and priority gaps.

Month 2 - Foundation

We stabilize the highest-impact gaps: data quality, governance, reporting, quick-win automations, process documentation, prompt or rubric foundations, and initial AI workflow opportunities where the foundation is ready.

Month 3 - Optimize

We move from stabilization into a continuous improvement cadence: roadmap execution, reporting reviews, adoption support, AI workflow review, and executive-level operating recommendations.

After Day 90

The engagement becomes a steady operating rhythm:

• quarterly RevOps & AI planning
• monthly revenue operations reviews
• continuous backlog management
• HubSpot governance and improvement
• AI use-case review and implementation
• revenue maturity rescoring

AI is useful when it improves a real workflow.

AI is not treated as a separate gimmick. It is introduced where the data, process, and governance foundation can support it.

Typical use cases include:

• account research briefs and signal-based prioritisation
• buying-committee and stakeholder gap analysis
• lead routing, SLA alerts, enrichment, and reactivation triggers
• meeting, call, and CRM update workflows
• discovery quality scoring and coaching prompts
• champion, business case, and multithreading checks
• deal health scoring and stage-exit evidence review
• forecast category checks and risk alerts
• manager deal-review and 1:1 preparation
• rep call review, role-play, and development support
• marketing campaign, audience, and handoff workflows
• customer health, renewal-risk, and expansion-signal reviews
• executive pipeline, methodology, ICP, and signal-effectiveness digests
• prompt, rubric, and governance libraries for repeatable work

Some of this is simple automation. Some of it needs AI reasoning, summarisation, drafting, scoring, or pattern recognition. We separate the two so AI is used where judgement is useful, not where ordinary workflow logic is enough.

Humans still decide and act. AI helps the operating system move faster with better context.

This is for companies with operating complexity and not enough RevOps ownership.

Best-Fit Signals

• HubSpot is already important to the revenue team, or is becoming the source of truth.
• Sales, Marketing, Customer Success, or leadership teams rely on HubSpot data.
• Reporting is important, but not fully trusted.
• There is no dedicated RevOps owner, or the internal owner is overloaded.
• Processes are changing faster than the system can keep up.
• Leadership wants AI, but needs practical workflow design and governance.
• The business needs ongoing ownership, not ad hoc HubSpot support.

Not A Fit When

• The company wants only occasional troubleshooting.
• There is no active revenue motion to improve.
• HubSpot is not a meaningful operating platform.
• The expectation is unlimited embedded labor.
• The goal is AI novelty rather than operational improvement.