Strategic AI North Star

Establish a clear, measurable AI ambition tied to quote cycle time and win rate that the executive team owns and sponsors.

For a configure-to-order precision manufacturer, the North Star should anchor AI to commercial velocity: faster, more profitable quotations. Frame it as a business outcome — not a technology programme — and secure executive alignment on the primary KPI before any tool is selected.

• Executive workshop to agree the primary commercial KPI and AI ambition
• Current-state baseline measurement of quotation cycle time
• Stakeholder mapping: who must sponsor, who must adopt
• Document the North Star in one page — shared with all pilot participants

Output: One-page AI North Star aligned with executive team and commercial leadership

Opportunity discovery

Identify and rank AI use cases by strategic fit, data availability and implementation risk — confirming that quotation support is the right first pilot.

The most promising opportunity areas for this company are quotation support, engineering knowledge access and production planning intelligence — ranked by proximity to revenue and data availability. Discovery should validate which data is accessible before committing to a pilot.

• Map current RFQ and quotation workflow in detail — identify handoffs and bottlenecks
• Assess availability and quality of historical RFQ, documentation and project data
• Interview sales engineers and technical leads on pain points and workarounds
• Score and rank the three opportunity areas against agreed criteria

Output: Prioritised opportunity portfolio with strategic-fit scores and data-readiness assessment

Use-case prioritisation

Produce a signed-off pilot brief that defines scope, metrics, governance and the go / no-go decision gate.

Select the RFQ and quotation support pilot as the first use case. Define the exact scope — one product line, one customer segment — and set the baseline metrics before any tool is deployed. Do not proceed to tooling until the business case is written and approved.

• Draft and agree the pilot brief with sales, engineering and management
• Define and measure the cycle-time baseline for the selected product line
• Agree governance rules: human review requirements, data access, audit log
• Select and configure the AI tooling — scoped to pilot users only

Output: One pilot brief with KPIs, success thresholds, governance rules and a clear decision gate

Pilot design and validation

Validate that AI-assisted quotation drafting reduces cycle time and is adopted by the sales team — and surface any workflow, data or governance issues before scaling.

Run the RFQ support pilot with a small group of sales engineers on the selected product line. Measure against the agreed baseline at week 4 and week 8. Human review of every customer-facing output is non-negotiable throughout. Use the pilot to learn, not just to validate a hypothesis.

• Onboard pilot users with structured training on the workflow change (not just the tool)
• Run weekly check-ins to catch adoption blockers early
• Measure cycle time, adoption rate and quality score at weeks 4 and 8
• Document lessons learned and prepare the scaling recommendation

Output: Pilot results report vs. agreed KPI baseline, with a scaling recommendation

Scaling and adoption

Embed AI-assisted quotation and knowledge retrieval across the commercial and engineering functions, with clear ownership, governance and a roadmap to the production planning intelligence use case.

If the pilot validates the hypothesis, extend to the remaining product lines and integrate the engineering knowledge assistant as the second use case. Establish a centre of excellence with a named AI owner, a governance committee and a quarterly KPI review — this is when AI shifts from a project to an operating capability.

• Extend quotation support to all product lines with a structured change management programme
• Launch the engineering knowledge assistant, reusing the document retrieval infrastructure
• Establish quarterly AI KPI review with the executive team
• Define the data and integration requirements for the production planning use case

Output: Scaling plan with governance structure, training programme and quarterly KPI rhythm