Japan's shrinking workforce capped headcount. Instead of relying on IT, non-coding staff built AI workflows for daily budget approvals.
A technology and engineering consultancy deploying 6,500 consultants and engineers across Japan.
Japan's aging demographic and shrinking workforce severely limited available talent pools. The organization needed to maintain productivity and...
“With Japan’s population declining, companies can no longer rely on traditional workforce growth to stay competitive. To maximize their potential and compete globally, they must raise labor productivity and empower more people to innovate. That requires efficiency, agility, and a new way of working—and the same is true for us.”
Digital engineering and IT services consultancy for global industrial sectors.
Enterprise software, cloud infrastructure, and consumer electronics platform.
Akkodis's Workflow automation is part of this use case:
Related implementations across industries and use cases
Manual coordination across systems bottlenecked teams. Now, employees build custom AI agents to automate tasks like purchase analysis.
Infrastructure compliance checks took months. Now, engineers use AI to parse massive datasets, cutting final review cycles by up to 90%.
Rapid growth overloaded HR with manual drafting. Generative AI now writes job descriptions and answers policy queries instantly.
Experts spent 15 minutes pulling data from scattered systems. Natural language prompts now generate detailed reports instantly.
Evaluating one investment took 2 hours. Custom GPTs cut reviews to 5 minutes, reducing the division's workload by 70%.
Infrastructure compliance checks took months. Now, engineers use AI to parse massive datasets, cutting final review cycles by up to 90%.
Technical debt in large codebases hindered velocity. Now, AI handles execution-heavy updates while engineers retain merge approval.
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.
Japan's shrinking workforce capped headcount. Instead of relying on IT, non-coding staff built AI workflows for daily budget approvals.
A technology and engineering consultancy deploying 6,500 consultants and engineers across Japan.
Japan's aging demographic and shrinking workforce severely limited available talent pools. The organization needed to maintain productivity and...
“With Japan’s population declining, companies can no longer rely on traditional workforce growth to stay competitive. To maximize their potential and compete globally, they must raise labor productivity and empower more people to innovate. That requires efficiency, agility, and a new way of working—and the same is true for us.”
Digital engineering and IT services consultancy for global industrial sectors.
Enterprise software, cloud infrastructure, and consumer electronics platform.
Akkodis's Workflow automation is part of this use case:
Related implementations across industries and use cases
Manual coordination across systems bottlenecked teams. Now, employees build custom AI agents to automate tasks like purchase analysis.
Infrastructure compliance checks took months. Now, engineers use AI to parse massive datasets, cutting final review cycles by up to 90%.
Rapid growth overloaded HR with manual drafting. Generative AI now writes job descriptions and answers policy queries instantly.
Experts spent 15 minutes pulling data from scattered systems. Natural language prompts now generate detailed reports instantly.
Evaluating one investment took 2 hours. Custom GPTs cut reviews to 5 minutes, reducing the division's workload by 70%.
Infrastructure compliance checks took months. Now, engineers use AI to parse massive datasets, cutting final review cycles by up to 90%.
Technical debt in large codebases hindered velocity. Now, AI handles execution-heavy updates while engineers retain merge approval.
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.