Solutions
Two tracks. Twelve solutions. Each one is a place where forty years of Oracle EPM experience meets a generation of AI tools that genuinely change the work.
Track A · 9 solutions
Replace month-long planning cycles, manual close work, and slow reporting with AI-assisted models that learn from your actuals and adapt every period.
What it is: P&L, Balance Sheet, and Cashflow planning across the enterprise.
How AI changes it: Driver models that re-baseline as actuals land, scenario generation in minutes instead of weeks, and forecast accuracy that improves every cycle.
What it is: Headcount, salary, and benefit planning by department, role, and geography.
How AI changes it: Attrition prediction by cohort, AI-flagged hiring lag against plan, and bottoms-up rollups that reconcile to top-down targets without manual fudging.
What it is: Long-range capability and skills planning aligned to corporate strategy.
How AI changes it: Skills-gap analysis against future-state operating models, AI-suggested build-vs-buy tradeoffs, and scenario planning for market shifts.
What it is: Capital expenditure budgeting, project tracking, and depreciation forecasting.
How AI changes it: AI-assisted ROI scoring on competing project requests, automated depreciation re-calc when projects slip, and red-flagging of capital exposure before quarter end.
What it is: Project-level revenue, cost, and resource forecasting integrated with EPM.
How AI changes it: Margin erosion prediction at the project level, AI-suggested resource re-allocation, and burn-rate alerts before the milestone, not after.
What it is: Allocation modeling that traces cost to product, customer, and channel profitability.
How AI changes it: AI-driven allocation tuning that surfaces unprofitable segments in hours instead of quarters, and what-if reallocation modeling on demand.
What it is: Multi-entity, multi-currency financial close and group reporting.
How AI changes it: Anomaly detection on intercompany and journal entries that flags issues before close week starts, and AI-suggested elimination matches across entities.
What it is: Balance sheet reconciliation, sub-ledger tie-outs, and supporting documentation workflow.
How AI changes it: Auto-matching across thousands of low-risk accounts, AI-prioritised review queue for the high-risk ones, and prep work that's 80% done before the analyst opens it.
What it is: Management commentary, board reporting, and statutory disclosure packages.
How AI changes it: Draft commentary and variance explanations generated from your data, reviewed by humans, ready in minutes instead of days.
Track B · 3 solutions
The plumbing your EPM stack is only as good as. Stop hand-mapping hierarchies and chasing source-system breaks. AI-powered integration keeps the foundation clean as the business evolves.
What it is: Centralised management of dimensions, hierarchies, and metadata across Oracle EPM applications.
How AI changes it: AI-suggested mappings between source and target hierarchies, rationalisation of duplicated members, and metadata maintenance cut from weeks to hours.
What it is: Source-to-cube data pipelines from ERP, CRM, HRIS, and operational systems into Oracle EPM.
How AI changes it: Self-healing pipelines that detect source-system schema breaks before they hit your cube, and AI-flagged data quality issues at the load step.
What it is: Governance of reference data — accounts, cost centres, products, customers — across master systems.
How AI changes it: Continuous reconciliation across source and target systems with AI-flagged drift, plus suggested merges for duplicate references introduced upstream.
Pick the solution that hurts most. Tell us where Oracle EPM is the bottleneck. We'll tell you what AI can actually fix — and how fast.
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