AI-Enabled Operating Model for a 40-Year-Old B2B Distribution Business
Client Archetype
A 40-year-old industrial-distribution business operating across India and Southeast Asia (~USD 60M revenue, 18% gross margin). Family-owned with second-generation leadership. Mid-2020s margin compression and customer-experience benchmarks set by digital-native competitors had made the existing model structurally vulnerable.
The Situation
Margin compression, talent-attrition risk, and a slow shift in buyer behaviour had compounded over three years. Leadership recognised the need for an AI-enabled operating-model reset but had no internal benchmark for what "good" looked like. Specifically:
- ●Order-to-cash cycle averaged 38 days against an industry-leading benchmark of 18.
- ●Sales force structurally focused on relationship maintenance, not on margin or share-of-wallet.
- ●Procurement and inventory decisions made with manual ERP exports — no demand-sensing automation.
- ●Customer service was staffed reactively; first-contact resolution was unmeasured.
- ●Three previous "digital transformation" attempts had stalled at the pilot stage with no enterprise rollout.
The Approach
A 20-week programme in four phases, designed to ship enterprise-scale change rather than another stalled pilot:
Weeks 1–4: Diagnostic & Value-at-Stake
- ●Process diagnostic across order-to-cash, procure-to-pay, and customer-service value chains.
- ●Value-at-stake quantification per intervention.
- ●AI / automation feasibility scan against current data, system, and people maturity.
- ●Three-horizon transformation roadmap with explicit business-case prioritisation.
Weeks 4–10: Operating-Model Redesign
- ●Target operating model designed for AI-enabled service, sales, and supply chain.
- ●Activity-by-activity automation vs. augmentation vs. eliminate decisions.
- ●RACI redesign across order-to-cash and procure-to-pay.
- ●Capability map against the new model with explicit hire / build / partner decisions.
Weeks 8–16: Pilot to Scale
- ●Two priority interventions piloted with explicit go/no-go gates.
- ●Demand-sensing engine deployed against three highest-value SKU categories.
- ●AI-assisted customer-service workflow with first-contact-resolution KPI tracking.
- ●Enterprise rollout plan informed by pilot performance against gates.
Weeks 16–20: Governance & Capability Embed
- ●Transformation governance committee chartered.
- ●Quarterly rhythm with explicit owner per workstream.
- ●Capability uplift plan: hires, training, partner engagements.
- ●Year-1 milestones and value-tracking framework handed over.
The Outcome
Engagements of this type typically deliver:
- ●Order-to-cash cycle compression of 25–45% over 12 months.
- ●Demand-sensing accuracy lift translating to 8–15% inventory reduction.
- ●AI-augmented service workflow lifting first-contact resolution by 20–35%.
- ●Sales-force redesign from relationship-only to share-of-wallet and margin orientation.
- ●Enterprise-rollout governance that survives the engagement — not another stalled pilot.