Use Cases

How agentic systems create value in practice.

These use cases show how Prescriptive Insights applies agentic systems in real enterprise situations — connecting signals, context, reasoning, governance, and action to solve operational and commercial problems.

The examples on this page are representative use cases — selected from a wider range of enterprise scenarios and operating environments.

From capability to scenario.

The value of agentic systems becomes clearest when viewed in context. Each use case on this page is structured around a specific enterprise scenario: what is happening, what the system sees, what it does, what it produces, and why it matters.

§ 03 · 01 · Use case — Agentic BI when topline is quiet but the business is shifting

Agentic BI — Emerging Commercial Risk Detection

Early, cross-signal detection of commercial risk that is still forming — before it becomes a visible gap in topline reporting.

Scenario

§ 01
  • topline reporting still looks stable
  • early signals begin to diverge across a product-channel combination
  • demand weakens, promotional response softens, and service friction rises
  • no single signal is large enough on its own, but together they indicate developing risk

What the system sees

§ 02
  • demand shifts by product, region, and channel
  • promotional response rates
  • margin compression
  • service and fulfillment friction
  • forecast confidence deterioration
  • regional divergence

What the system does

§ 03
  • detects the emerging pattern early
  • assembles relevant business context
  • evaluates likely commercial drivers
  • flags where the issue is concentrated
  • shows what may happen next if no action is taken
  • recommends management actions before the issue becomes visible in standard reporting

What it produces

§ 04
  • early risk alert
  • cross-signal pattern summary
  • likely commercial drivers
  • impacted segments and channels
  • forward-risk view
  • recommended management actions

Business value

§ 05
  • earlier visibility into emerging risk
  • more time to respond
  • clearer management direction
  • better ability to intervene before the issue becomes a material performance gap
§ 03 · 02 · Use case — Agentic Commerce when intent is present but the decision path is too complex

Agentic Commerce — Guided Product Discovery and Conversion Support

A commerce journey where real intent exists, but the path to decision is too complex — and the system guides the customer toward the right product, bundle, or next step.

Scenario

§ 01
  • a customer enters with clear intent but not enough confidence to convert
  • they understand the need, but not which product, regimen, or bundle is right
  • as they browse and compare options, the journey begins to stall
  • demand exists, but the path to decision is too complex

What the system sees

§ 02
  • browsing behavior and product exploration
  • prior purchase or interaction history
  • product relationships and compatibility
  • regimen or bundle logic
  • cart additions, removals, and hesitation points
  • journey drop-off and checkout friction

What the system does

§ 03
  • assembles the relevant customer and product context
  • narrows the decision path
  • surfaces the most relevant product set
  • recommends a regimen or bundle
  • adapts the journey when hesitation appears
  • supports conversion with context-aware next-best actions

What it produces

§ 04
  • guided product recommendations
  • regimen or bundle suggestions
  • ranked offers
  • adaptive commerce journeys
  • next-best-action prompts
  • conversion-supporting responses

Business value

§ 05
  • higher probability of conversion
  • higher average order value through better regimen and bundle selection
  • reduced decision friction
  • fewer abandoned journeys before purchase
§ 03 · 03 · Use case — Agentic Operations routine moves, exceptions get reviewed

Agentic Operations — End-to-End Claims Processing

A high-volume workflow where routine claims should move quickly, exceptions should receive scrutiny, and the system handles a large share of cases from intake through closure.

Scenario

§ 01
  • a claims operation handles large volumes of incoming cases across intake, validation, exception handling, and resolution
  • some claims are straightforward and should move quickly
  • others require more scrutiny and should be escalated
  • too many cases are still pushed through the same manual process, slowing cycle time and increasing cost

What the system sees

§ 02
  • claim intake data
  • supporting documents and attachments
  • policy or coverage rules
  • prior claim history
  • exception and fraud indicators
  • workflow status and SLA thresholds

What the system does

§ 03
  • classifies the claim at intake
  • extracts and validates relevant information
  • applies policy and eligibility logic
  • identifies exceptions and higher-risk cases
  • advances straightforward claims through the correct path to closure
  • escalates only the cases that genuinely require additional review

What it produces

§ 04
  • classified claims
  • extracted and validated claim data
  • eligibility and rule-check outcomes
  • adjudication-ready decisions
  • closed routine claims
  • escalated exception cases

Business value

§ 05
  • higher straight-through processing of routine claims
  • lower manual workload
  • shorter cycle times
  • more consistent decisioning
  • better use of human review capacity for true exceptions
§ 03 · 04 · Use case — Agentic Governance controlled flexibility, consistent approvals

Agentic Governance — High-Value Discount Approval and Escalation

A commercial workflow where some discount requests should move quickly, others should be escalated, and the system applies policy, threshold, and approval logic with consistency.

Scenario

§ 01
  • a sales organization is handling discount requests across customers, products, and channels
  • many requests are routine and fall within policy
  • others exceed threshold limits, create margin risk, or require special approval
  • too many requests are still reviewed manually or escalated inconsistently, slowing response times and weakening control

What the system sees

§ 02
  • requested discount level
  • account, channel, and product context
  • pricing and margin thresholds
  • prior approval history
  • policy exceptions and override conditions
  • workflow status and approval requirements

What the system does

§ 03
  • evaluates the request against pricing policy and approval rules
  • determines whether it can be approved, escalated, or declined
  • identifies where the request crosses margin or threshold boundaries
  • routes non-standard cases to the correct approval path
  • preserves a traceable decision record across the workflow
  • ensures commercial flexibility stays within enterprise control limits

What it produces

§ 04
  • policy-aware approval decisions
  • escalated exception cases
  • approval-ready summaries
  • traceable decision paths
  • governed overrides
  • audit-supporting workflow records

Business value

§ 05
  • faster handling of in-policy requests
  • fewer unnecessary escalations
  • stronger pricing and margin discipline
  • more consistent approvals across teams
  • better control without slowing the business
A working session

Let's identify the right use cases for your enterprise.

The value of agentic systems depends on where they are applied, how they are governed, and how they fit the operating environment. We work with organizations to identify the use cases that create the most practical and strategic value.