Skip to content
0727
InsightsAcademyCasesEventsSign in
中文/EN
Cases/Vodafone
← Back to cases
← Previous21 / 127Next →

Vodafone

Business-level AI combination of passenger services, web carriers, employee assistants and software development
First-party enterprise source Telecommunications Enterprise deployment Deep case | Key delivery chain is substantially documented Evidence level A Cross-market scale
A Evidence level
Vodafone H1 FY26 Results Presentation – AI at scale
Publisher: Vodafone · Enterprise investor material · Direct sources at the enterprise level
Claim origin: Disclosure of enterprise investor material · Independent verification: No · Accessed: 2026-09-19
Evidence level measures whether a source can be located and reviewed; it does not mean vendor-reported claims were independently audited.
Deep case | Key delivery chain is substantially documented 12 / 12
Business context2 / 2
Transformation workflow2 / 2
Technical workflow2 / 2
Human roles & governance2 / 2
Measured outcomes2 / 2
Source traceability2 / 2
All six dimensions meet the current completeness threshold.
Vodafone expands AI from a single passenger robot to a combination of passenger service, network wiring, employee productivity and software engineering, and sets resolution, NPS, MTTR and adoption rates for each link.
Business problem

Large transnational telecommunications operators face high-frequency customer service dialogues, complex network failures, on-site services and software delivery efficiency problems at the same time, and it is difficult to develop cross-market reuse, harmonization of metrics and continuous adoption if teams are tested separately.

Solution

Vodafone embedds AI into four categories of real workflows: SuperTOBi handles client dialogue and closed-ring problem solving, sit-in assistants aggregate historical communication, Zero Touch Operation serves employees with internal generative AI assistants based on a real-time diagnostic recommendation network and on-site action, while also providing code-assisted access to the development of software life cycle.

Technical architecture & production workflow
Step 01
Client history, network telemetry, workbooks and code context
→
Step 02
Harmonization of data access and real-time diagnostic layers
→
Step 03
Services/networks/staff/research and development-specific AI capacity
→
Step 04
Tobi, Sitting Assistant, Driver Reference and Code Assistant
→
Step 05
Manual upgrade, disposal confirmation and code review
→
Step 06
Ongoing monitoring of resolution, NPS, MTTR and adoption rates
Key technology & infrastructure components
Dialogue AIGenerate AI SummaryReal-time network diagnosisRecommendations for on-site servicesCorporate staff assistantCode generation and review
Human roles & accountability

The virtual assistant handles standard issues and diagnostic recommendations and upgrades complex client issues; network personnel identify high-risk disposals, engineers review and accept AI generation codes, and staff are responsible for final business communication.

FDE delivery actions
  • We'll take AI into the customer's service, the network, the staff and develop four measurable links.
  • Definition of different operational indicators for each link
  • Promotion of the same virtual assistant capacity across the European market
  • Maintenance of manual upgrades and engineering to review boundaries
  • Use adoption rates, resolution rates and MTTR to manage back-up effects
Reusable delivery patterns
  • Enterprise-level re-engineering should be based on job flow indicators rather than on total users only
  • The same base needs to be designed to interact with different jobs.
  • Large scale roll-out must be accompanied by tracking of adoption and operational results
  • High-risk network actions should retain manual identification and traceable diagnosis
Business outcomes & delivery results

Vodafone H1 FY26 investor material disclosed: Tobi processed approximately 60 million dialogues per month, SuperTOBi end-to-end resolution rate 70 per cent and NPS 8 percentage points higher; network repair time averaged 43 per cent; over 50,000 employees had a monthly implementation rate of more than 90 per cent; and 2,800 engineers developed a 12 per cent increase in productivity over their life cycle.

Evidence boundaries & verification notes
  • The full figure is from page 22 of the Vodafone H1 FY26 investor demonstration.
  • The material aggregates multiple markets and workflows and does not attribute all indicators to the same model.
Primary source: Vodafone H1 FY26 Results Presentation – AI at scale ↗
Traceable does not mean independently audited
Open primary public source
0727.ai · Trusted agents, built together.Case research: FDE-case-library ↗ · MIT