Use cases: what works and what failed

Use cases: what works and what failed

Four value patterns

PatternTRAXIA examples
Createbids, case studies, job ads, translations
Find and summarisetender documents, manuals, contracts, site meetings
Analyse and decideproject variances, tender win/loss, ticket triage
ActCowork assembles the monthly report, an agent creates a ticket

Departments – success and failure

DepartmentUse casesSuccessFailure
Marketing and salestailored bids, meeting prep, win/lossLumen: ~4 h/week per seller (Microsoft figure)Chevrolet 2023: chatbot “sold” an SUV for $1
HRjob ads, onboarding agent, trainingUnilever: AI in early hiring roundsAmazon: CV model penalised women
Financevariance commentary, payment forecasts, invoice checksJPMorgan COiN: 360,000 h/yearZillow 2021: algorithm overpaid for homes
Servicetriage, technician assistant, complaint replies+14 % / novices +34 % (study of 5,179 agents)Air Canada 2024: the company is liable for its chatbot
IT and developmentcode, documentation, helpdesk, securityGitHub Copilot: task 55 % fasterPocketOS 2026: agent deleted the production DB

AI in ITS – processes vs products

  • AI in how we work – the company is a deployer: bids, service planning, reporting, technician know-how.
  • AI in what we sell – the company can be a provider: WIM anomalies and calibration, video analytics, predictive maintenance of VMS and level crossings, adaptive junction control.
AI Act

AI systems intended as safety components in road-traffic management are high-risk (Annex III). Obligations from 2 December 2027.

Anatomy of failure – 5 root causes

  1. No human check – Avianca, Deloitte
  2. Too much autonomy and permissions – PocketOS, OpenClaw
  3. Bad data or no grounding – Air Canada, Amazon
  4. Wrong use case or process – McDonald’s, Zillow
  5. People and communication – Klarna, Commonwealth Bank

Almost no failure was caused by a “bad model”. It was caused by a management decision.

Exercise

➡ Workshop 1 – Use-case hunt