Use cases span software development acceleration and DevOps optimization. For product and systems engineering use cases include simulation and digital twins, predictive maintenance, computer vision–based quality inspection, and design optimization. Increasingly, teams also embed AI into data engineering and platform operations.

Forecasting and planning, anomaly and fraud detection, and transaction matching and close automation are most frequent. A second major set of use cases centers on risk and compliance, including credit risk modeling, counterparty exposure analysis, controls testing, audit support, and regulatory monitoring. Increasingly, AI is also used for decision support and productivity.

Use cases include talent acquisition (screening, matching, scheduling, and skills inference) and workforce analytics (attrition prediction, capacity planning, and comp benchmarking). Also, employee experience and productivity, including HR chatbots, policy Q&A, learning recommendations, and internal mobility matching. Additionally, AI is used for risk, compliance, and fairness.

Most frequent AI deployments include IT operations and reliability (AIOps for incident prediction, root-cause analysis, alert triage, and capacity optimization). Another leading area is cybersecurity, including threat detection, user behavior analytics, vulnerability prioritization, and automated response. Increasingly, AI is deployed for service management and cost control.

Frequently deployed use cases for document-intensive work, including contract review, clause extraction, e-discovery, and legal research are observed. A second major area focuses on risk, compliance, and governance, such as regulatory change monitoring, policy management, litigation risk assessment, and matter prioritization. AI is also used for legal operations efficiency.

AI for customer segmentation & targeting, content generation & personalization, and campaign optimization across digital channels are increasingly common. Customer insights, including predictive analytics, sentiment analysis, and attribution modeling are also seen. Increasingly, AI is used for media & spend optimization, such as dynamic pricing, promotions, and budgeting.

Forecasting and optimization use cases are frequent, including demand planning, inventory optimization, scheduling, and logistics routing. Also, use cases for asset and process reliability, such as predictive maintenance, computer vision–based quality inspection, and throughput optimization in manufacturing and fulfillment. AI use in operational visibility and resilience is on the rise.

Deployments for lead scoring and prioritization, pipeline forecasting, and next-best-action recommendations for reps are visible. Additionally, productivity and enablement, including sales copilots for call summaries, CRM automation, proposal generation, and account insights are seen. AI is also used for pricing and revenue optimization.