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Manufacturing Large · Industrial product manufacturer

Product Manufacturer: Modernizing Dealer & Customer Support at Scale

A large industrial product manufacturer with a complex two-tier support structure — dealer network and end customers. Legacy support systems, product knowledge scattered across engineering, sales, and service teams, and no unified channel strategy creating inconsistent resolution quality and high escalation rates.

46

Overall Readiness Score

NOT READY

Established manufacturer with domain-specific AI (predictive maintenance) and strong Salesforce ecosystem. Good foundation for expanding AI into customer experience, but talent gaps and inconsistent data quality limit near-term readiness.

48%

AI Readiness

44%

Data Readiness

200–430%

ROI on full remediation

Remediation Plan

Prioritized path to readiness

12

Minimum weeks

22

Recommended weeks

3

Remediation tracks

Implementation phases

Foundation & Talent · 6 weeks AI Expansion · 10 weeks Scale & Optimize · 6 weeks

Remediation tracks

AI Maturity
48% → 70%
HIGH
AI Talent & Capacity
35% → 55%
CRITICAL
Data Governance
38% → 60%
HIGH

Success criteria

  • Proactive service alerts reducing unplanned downtime by 20%
  • AI parts assistant live on dealer portal with 70%+ usage
  • CX-focused data scientist onboarded and productive
  • Data governance framework documented and adopted by all regional hubs
  • Case data quality consistency exceeds 85% across regions
  • Overall readiness score exceeds 60%
Playbook Output

IoT-Powered Predictive Service and Dealer Experience Transformation

Full playbook generated after CatalystOS™ assessment. Every output is grounded in the gaps and inputs identified above.

$350K–$750K

Investment range

$1.5M–$3.2M

Annual savings

5–10 months

Payback period

200–430%

ROI

⚠ Scenario built from structured inputs grounded in real-world operator experience. ROI projections are illustrative. Actual outputs depend on your organization's specific data and readiness scores.

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