Foundations
Southwest Airlines uses
GenAI to turn legacy code
into modernisation-ready requirements
1/4
The prompt
Southwest Airlines’ crew attendance and leave application ran on a legacy tech stack with limited documentation and heavy reliance on tacit knowledge. Executives resolved to find ways to make the system easier to maintain and upgrade—while managing the time, cost, and risk of modernisation.
01
The move
Southwest worked with PwC to apply GenAI and advanced software engineering to reverse-engineer the application’s source code into clear functional requirements for the updated system and a prioritised modernisation backlog. Southwest knowledge specialists then validated and refined the outputs through workshops, producing a detailed delivery plan with greater confidence and a repeatable approach for future modernisation efforts.
02
The outcome
GenAI cut the time needed to create backlogs by 50%—from ten weeks to five—and saved more than 200 hours across engineering, technology, and business teams during planning and design. The work also produced upwards of 600 requirements, 90% of which were accepted as high-quality, reducing the risk of the modernisation effort before development began.
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prompt
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The prompt
A major technology company with millions of customers faced rising expectations for seamless, personalised service. But its largely manual customer engagement model couldn’t keep up. Company leaders wanted to improve customer experience while keeping costs under control.
01
The move
PwC designed and deployed an AI-driven, omnichannel contact centre that combined predictive intent modelling, adaptive dialogue, and real-time analytics to support humans and AI agents. A centralised AI agent management hub enabled orchestration across channels, scaled deployment, and governance and oversight. To help employees use the new software effectively, the company also stood up enterprise-wide Responsible AI, workforce upskilling, and new ways of working for human–AI teams.
02
The outcome
The results were immediate and measurable: customers spent 25% less time on the phone to get requests resolved, and call transfers fell by as much as 60%, meaning more issues were handled on the first contact. Customer experience improved as well; the company’s Net Promoter Score (NPS) rose 7%, and customer satisfaction rose 10%.
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move
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prompt
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outcome
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prompt
01
A large technology provider improves customer experience
2/4
Wyndham scales trusted AI agents
3/4
The prompt
For Wyndham, a global hotel franchise, delivering a distinctive travel experience involves giving hotel owners the trustworthy, timely support they need to apply the company’s brand standards accurately yet have room for regional customisations. However, the process for changing brand standards averaged about 30 days of manual effort. Company leaders sought to improve this process. They put Responsible AI at the heart of their strategy to ensure a sound solution that employees felt confident adopting.
01
The move
PwC helped Wyndham put trusted AI to work by designing agentic workflows with human oversight built in—using automated prompts, co-authoring, and real-time monitoring so teams could guide and oversee the agents. Wyndham also positioned the programme to scale with a Responsible AI framework and ongoing upskilling to build trust and adoption.
02
The outcome
The agents consolidated standards, simplified workflows for change requests, and created centralised, user-friendly access for franchisees. Wyndham achieved brand consistency at speed without sacrificing rigour and reliability: review time for changes to brand standards dropped 94% (AI reviews were 20x as fast), saving 40–80 hours per review and positioning Wyndham to confidently apply trusted AI solutions across its operations.
03
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move
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prompt
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outcome
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The prompt
An industry-leading healthcare organisation knew its oncology data could help it deliver better care and accelerated research. But much of that information was trapped in siloed systems and unstructured notes. Even after the company modernised some of its platforms, key information like pathology, biomarkers, treatment history, and social determinants remained scattered. Executives resolved to unify this data so they could facilitate timely analysis and enable doctors to personalise care or match patients to trials.
01
The move
With PwC and Google Cloud, the organisation built a scalable, AI-ready oncology data foundation that streamlined how data was ingested, cleaned, organised, and made searchable—across records, claims, third-party sources, and clinical notes. AI helped convert unstructured information into usable formats, while Google Cloud tooling delivered real-time insights designed around frontline clinical and research workflows, with embedded monitoring of data quality to build trust.
02
The outcome
The programme organised about 2,000 data tables into reusable assets built for real-world decisions, such as recognising when a patient could benefit from more affordable—but equally effective—treatment options. Care teams now access analytics 50% faster, enabling quicker matching of patients to trials, point-of-care treatment comparisons, and earlier identification of risks. The privacy-protected insights also created more than US$50 million in new value potential through research acceleration and life sciences partnerships.
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move
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outcome
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move
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prompt
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A leading healthcare company turned data into actionable insight—and revenue potential
4/4
Use
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outcome
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outcome
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outcome
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move
02
Foundations
Lucid goes finance first, then AI everywhere
1/3
Use
The prompt
As automaker Lucid prepared for its next phase of growth, executives wanted the finance department to evolve from reporting results to shaping them—improving the speed and quality of forecasting, planning, and decision support so finance could serve as a foundation for enterprise intelligence.
01
The move
Working with PwC, Lucid rapidly prototyped AI-enabled forecasting and reporting capabilities using operational data, applied AI models, and agent-based tools. Cross-functional pods combined Lucid and PwC specialists to embed AI into finance workflows—automating forecasting, reconciliation, analytics, and monitoring, and creating a repeatable blueprint for scaling AI decision support across the business.
02
The outcome
Lucid reduced end-to-end forecasting cycle time from weeks to less than a minute, and in ten weeks, designed and began scaling 14 AI-driven use cases. The work is now expanding beyond finance into such areas as procurement and operations, including an AI-enabled executive concierge that supports faster leadership decision-making with visibility into more than US$1 billion in capital investments.
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The prompt
Faced with growing pressure from nimble AI-native competitors, executives at a global retail leader knew they would need AI to drive productivity and business reinvention at enterprise scale—along with new ways of working, new processes, and an operating model that could manage risk while moving fast.
01
The move
The company collaborated with PwC to build a centralised AI hub: a universal platform to prototype, deploy, and govern AI agents. The first wave of agents supported software development from end to end. Subsequent waves supported functions such as customer service and people management. In parallel, the company began reorganising for human–agent collaboration by upskilling talent, defining new roles, building trust through validation and ethics oversight, and establishing agent life-cycle management.
02
The outcome
Within months, software development cycle times were as much as 60% shorter, and production errors had fallen 50%, which helped teams attack a large IT backlog. As the company introduced agents in more functions, customer response times dropped by as much as 40%; attrition fell 10% through improved workforce planning; fraud declined 25% via real-time transaction monitoring; and marketing performance improved, with 15% higher conversions and 20% higher ROI.
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prompt
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prompt
01
A retail giant scales AI agents across the enterprise
2/3
The prompt
For farmers, rising input costs and sustainability pressures place greater importance on outcomes like reduced chemical use, higher yields, and better stewardship. For John Deere, these shifts mean opportunities to create value with innovative offerings that bring AI into more sophisticated machines.
In response, John Deere has made it a priority to create a solutions-and-services business model that lowers upfront barriers and supports recurring, outcomes-linked revenue.
01
The move
John Deere deployed See & Spray, an AI-powered ‘sense-and-act’ precision spraying system that uses boom-mounted cameras and onboard computing to identify weeds and trigger nozzles to squirt herbicides only where they’re needed. John Deere packaged the capability in a service-like commercial model that allowed customers to pay for verified outcomes.
02
The outcome
In the 2024 growing season, John Deere reported that See & Spray was used on more than 1 million acres, saving farmers an estimated 8 million gallons of herbicide mix, with 59% average herbicide savings across corn, soybean, and cotton fields. Beyond offering these cost and sustainability gains for farmers, the model positions John Deere to capture more value from a scalable services revenue stream rather than a one-time hardware differentiator.
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John Deere reinvents itself by adding AI
3/3
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outcome
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move
02