Routine admin bottlenecked teams navigating rapid FinTech shifts. Now, employees use Copilot to draft minutes and surface internal data.
Japan's only international payment brand navigates a rapidly transforming financial technology landscape that requires continuous operational evolution to maintain competitiveness.
To grow amid rapid industry change, the organization needed to streamline daily administrative tasks like taking meeting minutes, searching for...
“We view generative AI for work transformation in three levels: 1) streamline daily work, 2) enhance internal tasks, 3) support customer service. Level 1 is like strength training—it builds foundational skills needed for more advanced use. We needed AI that every employee could use in daily work.”
International payment network and credit card issuer based in Japan.
Enterprise software, cloud infrastructure, and consumer electronics platform.
JCB's Workforce productivity is part of this use case:
Related implementations across industries and use cases
Sales reps lost hours manually hunting for proposal data. Now, teams consult AI first to surface best practices and draft pitches.
Rapid growth buried staff in admin; employees had already turned to ungoverned AI. Weeks of controls, then Copilot replaced shadow use.
Reps were buried in manual research. Now, reasoning models draft strategic proposals and answer technical questions live.
Sales reps lost hours manually hunting for proposal data. Now, teams consult AI first to surface best practices and draft pitches.
Rapid growth buried staff in admin; employees had already turned to ungoverned AI. Weeks of controls, then Copilot replaced shadow use.
Analyzing 200-page reports took senior reps hours. Now, AI extracts key insights, empowering any sales rep to build tailored client decks.
Escalations from 2.5M daily transactions bottlenecked teams. Multi-agent AI now dynamically routes and resolves complex payment issues.
With 99% of records missing metadata, teams sifted unlabeled databases. Now, AI generates column descriptions for data experts to verify.
Manual document summaries bottlenecked experienced staff. Now, AI drafts preliminary risk assessments, freeing teams for complex advisory.
Routine admin bottlenecked teams navigating rapid FinTech shifts. Now, employees use Copilot to draft minutes and surface internal data.
Japan's only international payment brand navigates a rapidly transforming financial technology landscape that requires continuous operational evolution to maintain competitiveness.
To grow amid rapid industry change, the organization needed to streamline daily administrative tasks like taking meeting minutes, searching for...
“We view generative AI for work transformation in three levels: 1) streamline daily work, 2) enhance internal tasks, 3) support customer service. Level 1 is like strength training—it builds foundational skills needed for more advanced use. We needed AI that every employee could use in daily work.”
International payment network and credit card issuer based in Japan.
Enterprise software, cloud infrastructure, and consumer electronics platform.
JCB's Workforce productivity is part of this use case:
Related implementations across industries and use cases
Sales reps lost hours manually hunting for proposal data. Now, teams consult AI first to surface best practices and draft pitches.
Rapid growth buried staff in admin; employees had already turned to ungoverned AI. Weeks of controls, then Copilot replaced shadow use.
Reps were buried in manual research. Now, reasoning models draft strategic proposals and answer technical questions live.
Sales reps lost hours manually hunting for proposal data. Now, teams consult AI first to surface best practices and draft pitches.
Rapid growth buried staff in admin; employees had already turned to ungoverned AI. Weeks of controls, then Copilot replaced shadow use.
Analyzing 200-page reports took senior reps hours. Now, AI extracts key insights, empowering any sales rep to build tailored client decks.
Escalations from 2.5M daily transactions bottlenecked teams. Multi-agent AI now dynamically routes and resolves complex payment issues.
With 99% of records missing metadata, teams sifted unlabeled databases. Now, AI generates column descriptions for data experts to verify.
Manual document summaries bottlenecked experienced staff. Now, AI drafts preliminary risk assessments, freeing teams for complex advisory.