Services / Advise
AI Consulting & Implementation
Turn AI ambition into an operating plan your teams actually execute.
We assess where AI can move your revenue metrics, design the operating model—workflows, governance, human approval points—and implement it alongside your teams. No slideware: every engagement ends with systems running in your environment.
The problem
Most AI initiatives stall between the strategy deck and the day-to-day work. Pilots multiply, tools pile up, and nothing changes the numbers leadership actually watches.
The outcome
A working AI operating model—not a strategy deck.
The AI Operating Model Framework
Four stages that take you from ambition to an operating model running in your environment. Each stage ends with a concrete artifact your team keeps.
1. Assess — Where AI moves your numbers
We map your revenue motion, data, tools and team against the metrics you need to move, and rank the opportunities by impact and readiness—not by hype.
2. Design — The operating model on paper
Workflows, governance rules, human approval points, tool and model selection—designed around how your teams actually work, with clear boundaries for what AI may and may not do.
3. Implement — Running in your environment
We build and configure the workflows in your stack, connect the data, and run the first cycles side by side with your team until the loop holds without us.
4. Enable — Your team owns it
Playbooks, training and role definitions so your people direct the system confidently—plus a review cadence that keeps the operating model improving.
The workflow
Like every GigaRev system, this service runs the operating loop: Understand → Act → Learn, under human direction.
- 1. Map the revenue motion and current stack (Understand) — Interviews, data audit and metric baselines across the teams the work touches.
- 2. Rank AI opportunities by impact and readiness (Understand) — A scored opportunity map tied to the metrics leadership already tracks.
- 3. Leadership aligns on scope and boundaries (Human direction) — You approve the priorities, the governance rules and the human approval points.
- 4. Design workflows, governance and tooling (Act) — The operating model documented: who approves what, which tools, which models.
- 5. Implement in your environment (Act) — Workflows configured in your stack, data connected, first cycles run together.
- 6. Enable the team with playbooks and training (Act) — Roles, escalation paths and playbooks so the model runs without us in the room.
- 7. Review cycles refine the operating model (Learn) — What worked is kept, what did not is redesigned—on a fixed review cadence.
What's included
- AI opportunity & readiness assessment tied to revenue metrics
- Operating-model design: workflows, governance, approval boundaries
- Tool and model selection across your existing stack
- Hands-on implementation and team enablement
- Change management with clear human-in-the-loop roles
What you keep
- Scored AI opportunity map
- Operating-model blueprint (workflows, governance, approvals)
- Implemented workflows running in your stack
- Team playbooks and training sessions
- Review cadence and improvement backlog
Questions leaders ask
What does GigaRev AI consulting include?
GigaRev AI consulting covers four stages: assessing where AI can move your revenue metrics, designing the operating model (workflows, governance and human approval points), implementing it in your environment alongside your teams, and enabling your people with playbooks and training. Every engagement ends with systems running—not a strategy deck.
How is GigaRev consulting different from a typical AI strategy engagement?
Typical AI strategy engagements end at recommendations. GigaRev engagements end at implementation: workflows configured in your stack, data connected, first cycles run side by side with your team, and a review cadence that keeps improving the model. The framework is Assess → Design → Implement → Enable.
How long does an AI consulting engagement take?
Assessment and design typically complete within the first weeks; implementation and enablement depend on scope. Because each stage ends with a concrete artifact—an opportunity map, an operating-model blueprint, implemented workflows, team playbooks—you see value at every stage rather than waiting for a final report.
Do we need a data team or AI experience to start?
No. The assessment stage maps what you have—data, tools, team, process—and ranks opportunities by readiness as well as impact. The operating model is designed around your current capability, with human approval points where judgment matters, and grows as your team gains confidence.