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Commercial Model

Resources

A simpler way to plan adoption, consolidation and scale.

Resources provides a commercial view of the Advanze platform. Pricing is designed to support consolidation, predictable adoption and a clearer relationship between platform investment and operating outcomes.

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Platform and governance team reviewing AI execution, controls and architecture.
Resources starts with people trying to make the right call. Teams want to use AI more widely.

Commercial Model / Resources

Commercial clarity for platform adoption

Advanze is positioned around the idea that enterprise software must evolve from passive systems of record into active systems of execution. In that model, this page is not just a feature description. It explains how Resources contributes to an operating environment where people define intent, agents execute governed work, and leadership can see progress through unified data.

The value is strongest when the capability is connected to adjacent processes. Records, workflows, controls, communications and analytics should not live in separate tools. They should participate in a shared execution fabric that can coordinate work across departments while preserving human accountability.

Platform and governance team reviewing AI execution, controls and architecture.
Where Resources becomes real work people can trust. Platform, architecture and governance teams controlling agentic execution, cost and operational risk.

Core capabilities

What Resources enables

Each capability is designed to work as part of the broader execution platform rather than as a disconnected module.

Centralized Resource Pool

Centralized Resource Pool enables seamless coordination across the platform, with AI agents executing governed workflows while humans maintain accountability through unified data and audit trails.

Resource Booking and Scheduling

Resource Booking and Scheduling leverages the agentic operating model where agents assist with execution while people retain control over strategic decisions and business outcomes.

Cost Tracking

Cost Tracking connects to unified platform data, enabling real-time insights and intelligent automation that adapts to your business context.

Resource Performance Analytics

Resource Performance Analytics ensures compliance and security through governed processes, with full audit trails and role-based access controls across all operations.

Capacity vs Demand Analysis

Capacity vs Demand Analysis accelerates execution by automating routine tasks while escalating exceptions to human decision-makers when needed.

Resource Onboarding

Resource Onboarding provides visibility into platform performance, resource utilization, and business outcomes through comprehensive analytics.

Platform and governance team reviewing AI execution, controls and architecture.
The outcome is not just automation. It is confidence in what happens next. When Resources runs inside a governed execution model, teams can move faster without losing judgement, accountability or trust.

Implementation path

How to move from concept to production

Advanze can be introduced progressively. The recommended path is to start with a visible workflow, prove the operating model, then expand into adjacent capabilities as the platform foundation matures.

  1. 1
    Scope the suite

    Identify which applications, services and integrations should be included in the first adoption wave.

  2. 2
    Model value

    Estimate consolidation savings, productivity lift and execution capacity using conservative assumptions.

  3. 3
    Choose the path

    Start with a focused pilot or move directly into a platform foundation programme.

  4. 4
    Scale deliberately

    Expand suites, agents and integrations with clear governance and measurement.

Why this pricing path matters

Resources pricing should be understood through the work it helps govern.

Teams want to use AI more widely.

What makes it harder in the real world: The cost of enterprise AI is not only token price. It includes context packaging, orchestration, approvals, safety layers, retries, evaluations, auditability and operations support.

What Advanze changes: Control AI execution by routing tasks to the right model, setting run budgets, applying permissions, stopping loops and reporting usage by use case.

Router AgentSelects the smallest capable model or workflow path for the task.
Budget AgentTracks per-run and monthly use-case budgets.
Evaluation AgentChecks output quality, policy fit and repeated failure patterns.
Runtime Control AgentStops loops, retries safely and escalates when budget or quality thresholds are breached.
OwnerReviews value, adoption, cost and exceptions.
Challenge

The cost of enterprise AI is not only token price. It includes context packaging, orchestration, approvals, safety layers, retries, evaluations, auditability and operations support.

Orchestration

Control AI execution by routing tasks to the right model, setting run budgets, applying permissions, stopping loops and reporting usage by use case.

Success

Control AI execution by routing tasks to the right model, setting run budgets, applying permissions, stopping loops and reporting usage by use case.

Related execution patterns

Complex products usually touch more than one governed workflow.

DataServices, Data Governance, Operations

Data Quality Exception and Point-in-Time Recovery
Detect bad data, classify impact, identify the source, route approval and restore or correct records with auditable point-in-time recovery.

Explore pattern
GRC, Security, Operations

Incident Response with Governed Escalation
Coordinate incident intake, severity classification, evidence collection, stakeholder notification, escalation and remediation tasks through governed agentic workflow.

Explore pattern
Finance, Accounting, Control

Finance Reconciliation and Exception Resolution
Use agents to gather statements, ledger extracts, rules and prior exceptions, then propose safe matches and route unresolved items for review without posting journals automatically.

Explore pattern
Platform and governance team reviewing AI execution, controls and architecture.
AI runtime control 26Platform, architecture and governance teams controlling agentic execution, cost and operational risk.
Platform and governance team reviewing AI execution, controls and architecture.
AI runtime control 27Platform, architecture and governance teams controlling agentic execution, cost and operational risk.
Platform and governance team reviewing AI execution, controls and architecture.
AI runtime control 28Platform, architecture and governance teams controlling agentic execution, cost and operational risk.

Control model

Useful AI execution needs pricing, permissions and governance to move together.

That is why pricing has to connect to the operating model: the number of users is only one part of the cost; the real value comes from governed execution, reusable controls and agent work that can safely scale.

  • The cost of enterprise AI is not only token price. It includes context packaging, orchestration, approvals, safety layers, retries, evaluations, auditability and operations support.
  • The right agent must receive the right context, tools, permissions and approval path before work moves forward.
  • Audit evidence, exception handling and human judgement need to be part of the workflow, not notes added after the fact.
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Next step

Build this into your execution platform roadmap

Explore how Resources can be implemented as part of a broader Advanze platform adoption programme, from first pilot to enterprise scale.

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