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.
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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.
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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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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.
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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.
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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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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.
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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.
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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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