You will be provided with a reference and some statements. Please determine whether each statement is 'supported', 'unsupported', or 'unknown' with respect to the reference. Please note:
First, assess whether the reference contains any valid content. If the reference contains no valid information, such as a 'page not found' message, then all statements should be considered 'unknown'.
If the reference is valid, for a given statement: if the facts or data it contains can be found entirely or partially within the reference, it is considered 'supported' (data accepts rounding); if all facts and data in the statement cannot be found in the reference, it is considered 'unsupported'.

You should return the result in a JSON list format, where each item in the list contains the statement's index and the judgment result, for example:
[
    {
        "idx": 1,
        "result": "supported"
    },
    {
        "idx": 2,
        "result": "unsupported"
    }
]

Below are the reference and statements:
<reference>
Vodafone - TOBi | IBM

See how Vodafone is enhancing its digital assistant TOBi with generative AI.

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Case Studies

Vodafone - TOBi

Enhancing digital assistant TOBi with generative AI

Vodafone + IBM

As a global leader in telecommunications, Vodafone is always looking for new opportunities to improve the customer experience. One great example of its digital innovation is TOBi, a virtual assistant that is available 24x7 to answer customer questions on a wide range of topics in approximately 14 different languages. It is powered by
IBM® watsonx® Assistant
, a product that helps build better virtual agents to drive enterprise productivity, overcome the friction of traditional support and deliver exceptional experiences for their customers.

5 minutes

to conduct gap analysis

99%

improvement in turnaround time of journey testing

Vodafone began exploring where generative AI could make its conversational journey building processes faster and improve the quality of the content created for each journey. Through a series of workshops led by IBM, the Vodafone team identified five key use cases that would reduce the time conversational designers and testers spent creating journeys for TOBi. They prioritized two use cases that offered the most value and were the most feasible: testing journeys and gap analysis, which is the process of identifying discrepancies between the journeys in Vodafone’s Central Library and various markets.

IBM Client Engineering
and
IBM Consulting®
then led an accelerated five-week build phase with Vodafone. The project spanned several interconnected workstreams—data, design, architecture, development. An orchestration service was built and deployed using
IBM Cloud® Code Engine
, which was responsible for executing multiple test scenarios in
Voice Flow
(link resides outside of ibm.com). The service relied on
IBM watsonx.ai™
, a next-generation enterprise studio for AI builders, to simulate customer interaction with TOBi. The transcripts generated by these interactions were then analyzed using watsonx.ai. The solution provided sentiment and emotional analysis to assess customer satisfaction and identify areas to improve journey content. It was also used to deeply analyze journeys from Central Library and Markets, providing meaningful analysis on differences to help conversational designers improve the journeys.

The results of the pilot showed great potential for the impact generative AI could have on TOBi. For example, the team observed a 99% improvement in the turnaround time of journey testing. Before the pilot, it took the team around 6.5 hours to test each new journey before it was ready to be deployed into production. The pilot demonstrated it could take less than one minute per journey per persona. Before the pilot, there was no capacity to conduct gap analysis between the Central Library and Markets. The pilot demonstrated that a comprehensive gap analysis could be conducted in less than five minutes per journey with the help of watsonx.ai. The Vodafone team also expects to achieve meaningful time savings by using AI to conduct conversation sentiment analysis. This should also improve overall customer satisfaction given the additional testing and knowledge of the journeys prior to open them to actual live traffic.

About Vodafone

Vodafone
(link resides outside of ibm.com) is a European and African telecommunications company.

Solution components

IBM® watsonx™ Assistant

IBM watsonx.ai™

IBM Cloud® Code Engine

IBM Consulting®

IBM Client Engineering

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Legal

© Copyright IBM Corporation 2024. IBM Corporation, 75 Binney St, Cambridge, MA 02142

Produced in United States, March 2024.

IBM, the IBM logo, ibm.com, IBM Cloud, IBM Consulting, watsonx, and watsonx.ai are trademarks or registered trademarks of International Business Machines Corporation, in the United States and/or other countries. Other product and service names might be trademarks of IBM or other companies. A current list of IBM trademarks is available on
ibm.com/legal/copyright-trademark
.

This document is current as of the initial date of publication and may be changed by IBM at any time. Not all offerings are available in every country in which IBM operates.

All client examples cited or described are presented as illustrations of the manner in which some clients have used IBM products and the results they may have achieved. Actual environmental costs and performance characteristics will vary depending on individual client configurations and conditions. Generally expected results cannot be provided as each client’s results will depend entirely on the client’s systems and services ordered. THE INFORMATION IN THIS DOCUMENT IS PROVIDED "AS IS" WITHOUT ANY WARRANTY, EXPRESS OR IMPLIED, INCLUDING WITHOUT ANY WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND ANY WARRANTY OR CONDITION OF NON-INFRINGEMENT. IBM products are warranted according to the terms and conditions of the agreements under which they are provided.
</reference>

<statements>
1. IBM reports numerous client case studies demonstrating watsonx‑driven transformation: NatWest’s AI‑powered mortgage support platform "Marge" using watsonx Assistant, Bouygues Telecom’s call‑center modernization handling over 800,000 calls per month with watsonx Assistant and reducing pre‑ and post‑call workloads by 30%, and Water Corporation’s SAP migration using watsonx Code Assistant and Red Hat Ansible Lightspeed to save roughly 1,500 hours annually and cut development costs by 30%. Additional cases include Artefact’s generative‑AI personas for a French bank, Nelen & Schuurmans’ gen‑AI assistant for water‑management software, Vodafone’s TOBi assistant enhanced with watsonx.ai, and EY’s AI‑driven tax compliance solution EY.aifor tax built on watsonx.ai and watsonx.data.
2. Common AI application themes include customer‑service transformation (virtual assistants, contact‑center optimization), marketing and personalization, operational optimization in supply chains and procurement, finance and tax automation, and software‑engineering acceleration. Industry‑specific cases span banking and insurance (mortgage support platforms, risk and pricing tools), telecom (call‑center AI, digital assistants), retail and CPG (pricing, inventory, and customer experience), and public‑sector and healthcare use cases.
</statements>

Begin the assessment now. Output only the JSON list, without any conversational text or explanations.