Context Windows brings together credible AI case studies from the open web,
so you can pick and prioritise the use cases that are already working.
Real-world implementations from
Ideas from your team, no outside signal → Best guess prioritization
Demos that impress, outcomes that don't
Ideas from your team, no outside signal
Best guess prioritization
Demos that impress, outcomes that don't
Your shortlisted ideas, validated against 2,400+ real implementations → Prioritise by what's already paying off
Measurable ROI and competitive edge
Your shortlisted ideas, validated against 2,400+ real implementations
Prioritise by what's already paying off
Measurable ROI and competitive edge
Use case intelligence lets you see the winners,
so you can be in the 5%
Context Windows brings together credible AI case studies from the open web,
so you can pick and prioritise the use cases that are already working.
Real-world implementations from
Ideas from your team, no outside signal → Best guess prioritization
Demos that impress, outcomes that don't
Ideas from your team, no outside signal
Best guess prioritization
Demos that impress, outcomes that don't
Your shortlisted ideas, validated against 2,400+ real implementations → Prioritise by what's already paying off
Measurable ROI and competitive edge
Your shortlisted ideas, validated against 2,400+ real implementations
Prioritise by what's already paying off
Measurable ROI and competitive edge
Use case intelligence lets you see the winners,
so you can be in the 5%
Phone tag and manual scheduling buried store managers; candidates dropped before interviews. An AI agent now screens and schedules by text.
Emails and PDFs, each decoded and entered manually. AI agents now propose materials and ship-to details; employees review, then approve.
Manually pulling flight risk data took half a day — so it never got done. Three words later, Rippling built a scoring rubric from scratch.
Renewal quotes built by hand, scattered across regional systems—quarter-end always a scramble. Watsonx now generates them six months early.
Data siloed across acquisitions; building a conversational AI meant stitching six systems. A governed platform collapsed that to one.
30-minute report-digging kept fleet audits chronically deferred. Now one prompt delivers every device, flag, and follow-up emails at once.
AI agents that engage website visitors and inbound prospects 24/7 — qualifying interest, scoring intent, and booking meetings with sales reps automatically.
AI agents that autonomously handle customer requests — processing refunds, modifying accounts, making bookings, and resolving issues without human intervention.
LLMs that analyze customer calls, chats, and meetings — generating coaching summaries, deal insights, quality scores, and sentiment trends.
Foundation models that read sensor streams alongside maintenance logs, manuals, and technician notes to predict equipment failures.
Forecasting demand, credit risk, churn, and sales pipelines — foundation models extending traditional forecasting with reasoning over unstructured signals like emails, calls, and reports.
AI-assisted creation of written, visual, and multimedia content across marketing, communications, and publishing workflows.
Phone tag and manual scheduling buried store managers; candidates dropped before interviews. An AI agent now screens and schedules by text.
Emails and PDFs, each decoded and entered manually. AI agents now propose materials and ship-to details; employees review, then approve.
Manually pulling flight risk data took half a day — so it never got done. Three words later, Rippling built a scoring rubric from scratch.
Renewal quotes built by hand, scattered across regional systems—quarter-end always a scramble. Watsonx now generates them six months early.
Data siloed across acquisitions; building a conversational AI meant stitching six systems. A governed platform collapsed that to one.
30-minute report-digging kept fleet audits chronically deferred. Now one prompt delivers every device, flag, and follow-up emails at once.
AI agents that engage website visitors and inbound prospects 24/7 — qualifying interest, scoring intent, and booking meetings with sales reps automatically.
AI agents that autonomously handle customer requests — processing refunds, modifying accounts, making bookings, and resolving issues without human intervention.
LLMs that analyze customer calls, chats, and meetings — generating coaching summaries, deal insights, quality scores, and sentiment trends.
Foundation models that read sensor streams alongside maintenance logs, manuals, and technician notes to predict equipment failures.
Forecasting demand, credit risk, churn, and sales pipelines — foundation models extending traditional forecasting with reasoning over unstructured signals like emails, calls, and reports.
AI-assisted creation of written, visual, and multimedia content across marketing, communications, and publishing workflows.