Oracle has been describing its EPM platform as AI-enabled for several years. For most of that time, the description was aspirational — AI features existed, but their practical impact on the finance team’s day-to-day work was marginal.
That changed in 2025 and 2026. Oracle’s quarterly release cadence has delivered a set of AI capabilities that are now production-ready, included in existing EPM Cloud licences, and producing measurable improvements in forecast accuracy, close efficiency, and planning cycle speed for finance teams that have activated them.
The CFOs and FP&A directors across Egypt, Saudi Arabia, the UAE, Qatar, and Kuwait who are asking “does Oracle EPM have AI?” in 2026 are asking the right question. The answer has changed materially, and the change is documented in Oracle’s own production data and in the Gartner assessments that named Oracle a Leader in both the Financial Planning Software and Financial Close and Consolidation Magic Quadrants in 2025 and 2026 respectively.
But the answer to “does it have AI” and the answer to “will our finance team experience a material improvement from these AI features” are different questions. The gap between the capability and the adoption — between what Oracle has built and what GCC finance teams are actually using — is the most important topic for CFOs evaluating their Oracle EPM investment.
This guide addresses that gap. It covers what the AI capabilities in Oracle Cloud EPM actually are in 2026, which ones are production-ready and delivering measurable value, which ones require specific configuration to activate, what the regional context adds to the adoption equation, and what the finance teams in the GCC and Egypt that are getting the most from these features did differently from those that are not.
Why 2026 Is the Year the AI Question Matters
For most of the period between 2019 and 2024, “AI in Oracle EPM” meant:
- Oracle Predictive Planning (Auto Predict): Available from 2019, it used time-series forecasting to generate a statistical baseline from historical data. Useful as a starting point, but limited to univariate forecasting that did not incorporate business drivers, and not embedded into the planning workflow in a way that made it easy for non-technical finance users to use.
- Oracle AI/ML for Transaction Matching in ARCS: Machine learning applied to the transaction matching process to improve auto-match rates over time. A background capability rather than a visible AI feature.
- Early IPM Insights: Anomaly detection in planning and consolidation data that flagged statistical outliers. Available but not widely adopted.
What changed in 2025 and 2026:
Oracle embedded a new generation of AI capabilities directly into the Oracle EPM Cloud planning and close workflows — not as separate modules requiring additional procurement but as native features in the existing platform, activated through configuration rather than additional licences. The capabilities that are now production-ready include:
- Planning Agent — an agentic AI embedded in the FP&A workflow that detects signals in financial and operational data, explains variance drivers, generates what-if scenarios, and surfaces recommendations that finance leaders can apply judgment to before committing to action
- Enhanced Predictive Planning with multivariate forecasting — expanding from univariate time-series to models that incorporate multiple business drivers (demand, pricing, workforce, macro indicators)
- IPM Insights (Anomaly, Bias, Prediction) — statistical anomaly detection, forecast bias detection, and predictive insights embedded into planning and close workflows
- GenAI Narrative Reporting — generative AI that produces first-draft management commentary from planning and close data, ready for finance team review and annotation
- AI-powered Transaction Matching in ARCS — enhanced ML that improves auto-match rates continuously from historical patterns
- Ledger Agent — embedded in the close process to monitor journal entries and flag anomalies in real time
The significance of this evolution is that AI is no longer a feature available alongside Oracle EPM — it is embedded in the planning, close, and reporting workflows that the finance team uses every day. The question has shifted from “does Oracle EPM have AI” to “are your finance teams using it.”
The Oracle Cloud EPM AI Architecture: How It Is Built
Understanding the architecture of AI in Oracle Cloud EPM matters because it determines what data the AI is working with and how trustworthy the outputs are.
Oracle’s AI in EPM operates on a foundational principle: AI embedded in the EPM platform has access to the same governed, validated financial and operational data as the finance team — not to raw, ungoverned data pulled from disparate sources. This distinction is significant and often missed in comparisons with standalone AI tools.
A standalone AI tool applied to financial data must first ingest the data from source systems, validate it, structure it, and then apply the AI model. The quality of the AI output depends on the quality of the data ingestion process, which is typically a separate, separately maintained integration. When the EPM data changes — when a new close cycle completes, when a budget version is updated, when actuals are reloaded — the standalone AI tool must be updated separately.
AI embedded in Oracle Cloud EPM operates on the data that is already in the EPM environment — the same close-validated actuals from FCCS, the same budget versions from Planning, the same reconciled trial balance from ARCS. The AI output is generated from data that has already been through the close and validation process. When the EPM data updates, the AI insight updates with it — automatically, without a separate integration step.
The practical implication for GCC finance teams: the AI features in Oracle Cloud EPM are only as useful as the Oracle EPM environment they are embedded in. A finance team running Oracle EPM on a well-configured Planning model with reliable actuals from FCCS will extract significantly more value from the AI features than a team running Oracle EPM with data quality issues in the underlying model. Activating the AI features is straightforward; getting value from them depends on the EPM foundation they operate on.
The Six AI Capabilities in Oracle Cloud EPM: What They Are and What They Actually Do
1. Oracle Predictive Planning (Auto Predict) — Enhanced in 2025
What it does: Oracle Predictive Planning uses machine learning to generate statistical forecasts from historical data in the planning model. The enhanced 2025–2026 version adds multivariate forecasting — the ability to incorporate external drivers (macroeconomic indicators, commodity prices, FX rates) and internal operational drivers (headcount, capacity utilisation, demand signals) alongside the historical financial time series.
What it actually delivers: Oracle’s production data reports forecasting accuracy improvements of up to 25 percent compared to manual trend-based forecasting for finance teams that have activated and calibrated Predictive Planning. The improvement is most pronounced for revenue line items with strong cyclical patterns and for cost lines that are driven by measurable operational variables. For line items that are driven by management decisions or one-off events, the statistical model adds less value.
What GCC finance teams need to know: Predictive Planning works on historical data in the Oracle EPM Planning model. A planning model with two or three years of clean, consistent historical actuals will produce better predictions than one with gaps, restatements, or entity structure changes that make the historical series discontinuous. GCC enterprises that implemented Oracle EPM in the past two to three years may not have the historical depth for Predictive Planning to produce reliable forecasts immediately — the model improves as more clean periods accumulate. This is an argument for starting now rather than waiting until “the data is ready.”
Regional specificity: Multivariate Predictive Planning for Saudi enterprises can incorporate FX rate assumptions (SAR is pegged but intercompany transactions may involve multiple currencies), energy price indicators for manufacturing and industrial enterprises, and Saudization compliance rate assumptions that affect workforce cost planning. Configuring these external drivers requires deliberate setup — they are not automatically included in the model.
2. Oracle Planning Agent
What it does: The Planning Agent is an agentic AI embedded directly in the FP&A workflow. It monitors financial and operational data continuously, detects signals that are material to the planning outcome — revenue run rates that diverge from the forecast, cost categories that are accelerating beyond budget tolerance, driver relationships that have changed — and surfaces these as actionable insights within the planning interface. The FP&A team member can interrogate the agent’s finding, explore the scenario it implies, and apply their own judgment before the planning model is updated.
What it actually delivers: The Planning Agent addresses the single most time-consuming FP&A activity in most GCC finance functions — the identification of variances that require attention before the next formal review cycle. A finance team that currently discovers mid-quarter variances at the monthly management review can, with the Planning Agent configured, receive alerts when a variance exceeds a defined threshold, with the driver analysis and scenario implication already prepared. The FP&A team’s role shifts from variance discovery to variance assessment and response.
What GCC finance teams need to know: The Planning Agent requires a planning model with sufficient driver structure to generate meaningful insights. A planning model built around simple revenue and cost line items with no underlying driver connections will generate fewer useful insights than one with explicit driver-based forecasting — headcount drivers for workforce cost, volume drivers for variable costs, rate assumptions for currency-sensitive items. GCC enterprises with well-structured driver-based planning models get more from the Planning Agent; those with budget-entry models get less.
3. IPM Insights (Anomaly, Bias, Prediction)
What it does: IPM Insights is Oracle’s embedded analytics intelligence layer, providing three distinct capabilities across planning and close:
- Anomaly Detection: Statistical identification of values in the planning or close data that deviate significantly from expected ranges, based on historical patterns. Flags journal entries, plan submissions, or actuals loads that contain potential errors before they propagate through the model.
- Forecast Bias Detection: Identifies systematic patterns in how the planning team forecasts — consistently over-forecasting revenue, consistently under-forecasting a specific cost category — that the human reviewers cannot easily see because the bias is distributed across many forecasting cycles. The bias detection surfaces these patterns so that the FP&A manager can address them in the planning process.
- Predictive Insights: Surfaces leading indicators within the EPM data that have historically predicted specific outcomes — revenue performance in the following quarter, close cycle duration, cash position at period end — allowing the FP&A team to act on forward-looking signals before they become trailing variances.
What GCC finance teams need to know: IPM Insights is available within the existing Oracle EPM Cloud licence and requires activation rather than additional procurement. Most GCC organisations running Oracle EPM have IPM Insights available and are not using it. Activation is a configuration task, not an implementation project.
4. GenAI Narrative Reporting
What it does: Oracle’s generative AI narrative reporting capability produces first-draft management commentary from planning and close data — variance explanations, performance summaries, forward-looking assessments — in natural language, structured for the specific report format and audience, ready for finance team review, annotation, and approval before publication.
What it actually delivers: Management commentary — the written narrative that accompanies financial performance reporting — is one of the most consistently time-consuming activities in the finance close cycle. It requires the finance team to interpret the numbers, draft an explanation, circulate for review, and revise before the board pack or management report is finalised. GenAI Narrative Reporting produces a first draft of this commentary from the EPM data automatically, which the finance team reviews and annotates rather than drafts from scratch. The time saving depends on the quality of the first draft; the quality of the first draft depends on the quality of the EPM data and the narrative template configuration.
What GCC finance teams need to know: Arabic-language GenAI Narrative Reporting is an important consideration for GCC finance teams producing bilingual management packs. Oracle’s GenAI narrative capability supports Arabic through the underlying language models — but the quality of Arabic-language output from financial data requires testing against the specific Arabic vocabulary used in the organisation’s management reporting convention. Finance teams that produce board packs in Arabic should test the Arabic narrative output against their own reporting standards before deploying it in a live close cycle.
5. AI-Powered Transaction Matching in ARCS
What it does: As described in the Oracle ARCS pillar, the machine learning in Transaction Matching learns from historical matching decisions made by the finance team and improves the auto-match rate for bank, intercompany, and credit card reconciliations continuously across successive close cycles.
What it actually delivers: An ARCS Transaction Matching configuration that starts at 65 to 70 percent auto-match in month one can reach 85 to 90 percent by month four or five as the ML model learns the organisation’s specific transaction patterns. This improvement happens automatically — no additional configuration is required after the initial setup — provided the finance team is using the Transaction Matching tool and the exception decisions are being recorded in the system for the model to learn from.
What GCC finance teams need to know: The ML improvement requires that exceptions are handled within the ARCS Transaction Matching interface rather than being extracted to Excel for manual resolution. A finance team that uses ARCS for matched items and Excel for exceptions is not giving the ML model the learning data it needs. The exception handling workflow must stay within ARCS for the auto-match rate improvement to materialise.
6. Ledger Agent (Close Process)
What it does: The Ledger Agent monitors journal entry activity across the close cycle in real time, flagging entries that deviate from expected patterns — unusual amounts, unusual accounts, unusual posting times, entries from accounts that do not normally generate journals at this point in the close cycle — and surfacing these as alerts for the controller or close manager to review.
What it actually delivers: The Ledger Agent addresses a specific and consequential gap in most large GCC close processes — the detection of journal entry anomalies that human reviewers cannot catch at scale. A close process with thousands of journal entries across multiple entities cannot be manually reviewed for anomalies at the individual entry level; it is reviewed at the account summary level, which means individual entry errors may not be detected until the close output is reviewed by auditors or management. The Ledger Agent detects these at the entry level, in real time.
The GCC and Egypt Context: What AI in Oracle EPM Means Specifically
Vision 2030 and the Real-Time Planning Requirement
Saudi enterprises participating in Vision 2030 programmes are managing reporting relationships with government counterparties and sovereign fund principals that require financial performance data more frequently and at a higher level of granularity than traditional quarterly planning cycles can support. The Planning Agent’s continuous monitoring and scenario generation capability is directly relevant to this context: instead of waiting for the monthly plan review to discover that a project’s revenue run rate has diverged from the programme commitment, the Planning Agent surfaces this as an alert — with the scenario implication and the driver analysis — before the divergence becomes a programme reporting problem.
Saudization Workforce Planning and AI
Saudi enterprises managing Saudization compliance under the 2026 Nitaqat updates — which apply profession-specific quotas across 269 roles, with new thresholds effective April 2026 — need to plan workforce composition at a level of granularity that most planning models were not built for. Oracle EPBCS Workforce Planning, combined with Predictive Planning for headcount forecasting and the Planning Agent for monitoring Saudization compliance rates against Nitaqat thresholds, provides the planning environment that Saudization compliance management requires. The AI features — particularly Predictive Planning for attrition and hiring scenario modelling, and IPM Insights for Saudization rate anomaly detection — add measurable value in this specific context.
Arabic-Language AI Outputs
The most important regional consideration for AI in Oracle Cloud EPM is Arabic language. Oracle’s GenAI Narrative Reporting generates Arabic-language commentary from financial data, but the quality requires validation against the specific vocabulary and conventions of the organisation’s Arabic management reporting. Finance teams should test Arabic narrative output against at least three months of historical close data before deploying it in live reporting — not because the technology is unreliable but because Arabic financial terminology has specific conventions that may differ between organisations and that the GenAI model may interpret differently from the finance team’s convention.
IPM Insights anomaly detection and Planning Agent insights are generated in the language of the EPM interface — for GCC enterprises with Arabic-language Oracle EPM configurations, these insights surface in Arabic. For enterprises running Oracle EPM in English, the AI insights are in English even when the underlying data relates to Arabic-language entities. Bilingual EPM configuration produces the most useful AI output for finance teams working across both languages.
Data Foundation: The Prerequisite That Determines AI Value
The research is consistent on one point: AI in Oracle EPM is only as useful as the data it operates on. For GCC enterprises with well-configured Oracle EPM Planning models, reliable actuals feeds from Oracle FCCS or ERP, and two or more years of clean historical data, the AI features in Oracle Cloud EPM produce measurable improvements in forecast accuracy, close efficiency, and management reporting speed. For enterprises with EPM environments that have data quality issues, incomplete driver structures, or interrupted historical data series, the AI features produce less reliable outputs.
This is not a reason to defer AI feature activation — it is a reason to assess the EPM environment’s readiness for AI activation before deploying AI-generated outputs in high-stakes management reporting contexts. The assessment is straightforward: review the historical actuals in the Planning model for completeness and consistency, review the driver structure for the key planning lines, and assess whether the FCCS-to-Planning actuals feed produces consistent, reliable data. If the EPM foundation is solid, AI activation is a configuration task. If it is not, the investment in the EPM foundation produces AI readiness as a by-product.
What GCC and Egypt Finance Teams Are Getting Wrong About AI in Oracle EPM
Mistake 1: Waiting for a Separate AI Procurement
Most GCC finance teams running Oracle Cloud EPM believe that accessing AI features requires a separate procurement — an AI add-on, an additional licence, or a new product module. In most cases, this is incorrect. Oracle Predictive Planning, IPM Insights, the Planning Agent, and GenAI Narrative Reporting are included in the existing Oracle EPM Cloud licence for organisations on current subscription terms. The AI features require activation and configuration — but not additional procurement. Finance teams that have been deferring AI adoption because of a procurement assumption should check with their Oracle account manager and their implementation partner about what is already available in their current licence.
Mistake 2: Activating AI Features Without Preparing the Data Foundation
The temptation when AI features become available is to activate them immediately and assess the output. For Oracle EPM AI features, this approach consistently produces disappointing results — not because the AI is poor but because the EPM data it is operating on is not sufficiently clean, consistent, or historically complete to generate reliable predictions and insights. The finance teams that get the most value from Oracle EPM AI activate it after reviewing their EPM data foundation and addressing the gaps that a Predictive Planning model would expose.
Mistake 3: Expecting AI to Replace Finance Team Judgment
The Oracle Planning Agent, Predictive Planning, and IPM Insights generate insights, alerts, and forecasts that the finance team applies judgment to — they are not autonomous decision-making systems. A finance team that treats Oracle EPM AI outputs as decisions rather than inputs to decisions will make errors that a finance team treating AI as a decision-support layer will catch. The value of Oracle EPM AI is in the speed and breadth of signal detection, not in the replacement of financial judgment.
Mistake 4: Deploying Arabic GenAI Narrative Without Testing
Finance teams that deploy Oracle GenAI Narrative Reporting for Arabic-language board packs without first validating the Arabic output against their own reporting standards risk producing commentary that is linguistically correct but terminologically inconsistent with the organisation’s established reporting conventions. A two-to-three-month parallel period — running GenAI narrative generation alongside the manual commentary process and comparing the outputs — identifies and resolves terminology gaps before the AI-generated narrative is used in live board reporting.
Mistake 5: Remaining on Hyperion and Expecting AI
Finance teams still running Hyperion Planning on-premises cannot access any of the AI features described in this guide. Predictive Planning, the Planning Agent, IPM Insights, GenAI Narrative Reporting, and the Ledger Agent are all Oracle Cloud EPM capabilities. They are not available on Hyperion. For CFOs whose AI ambition is constrained by an on-premises Hyperion environment, the Hyperion to Cloud EPM migration is not only a platform upgrade — it is the prerequisite for accessing AI capabilities that Oracle’s on-premises product will never receive.
The Honest Assessment: What AI in Oracle EPM Delivers in 2026 vs What It Does Not
| AI Capability | Current Production Maturity | Measurable Value in GCC Context | What It Does Not Do |
|---|---|---|---|
| Predictive Planning (Auto Predict) | High — production-ready, documented 25% accuracy improvement | Forecast baseline generation; scenario modelling; statistical variance flagging | Does not replace management judgment on strategic assumptions; requires 2+ years clean history |
| Planning Agent | High — generally available in 2026 releases | Continuous variance monitoring; driver explanation; scenario generation | Does not make planning decisions; requires well-structured driver model to generate useful insights |
| IPM Insights — Anomaly Detection | High — production-ready | Journal entry anomaly detection; balance anomaly flagging in close | Does not replace audit; requires activation; works best on volumes where manual review is impractical |
| IPM Insights — Forecast Bias Detection | High — production-ready | Identifies systematic FP&A forecasting bias over time | Requires multiple forecast cycles to detect patterns; not useful for organisations with less than 12 months EPM history |
| GenAI Narrative Reporting | Medium-High — production-ready in English; Arabic requires validation | First-draft management commentary; variance narrative; board pack text | Does not replace finance judgment on narrative tone and strategic framing; Arabic quality requires validation |
| AI Transaction Matching (ARCS) | High — production-ready, improving continuously | Auto-match rate improvement 65% → 85–90% over 4–6 cycles | Requires exceptions to be handled within ARCS, not exported to Excel |
| Ledger Agent | Medium — newer capability, production deployments increasing | Real-time journal anomaly detection in close | Requires FCCS integration; generates alerts that still require human review |
| Multivariate Predictive Planning (external drivers) | Medium — available, requires configuration | Incorporates macro indicators, commodity prices, FX into forecasts | External driver data feeds must be configured; not automatic |
Implementation: What It Takes to Activate Oracle EPM AI Features
Activating Oracle Cloud EPM AI features is not an implementation project in the same sense as implementing a new Oracle EPM module. Most features are activated through configuration within the existing Oracle EPM environment rather than through a new implementation engagement. The investment is in configuration, data preparation, and adoption support — not in a multi-month build.
| AI Feature | Activation Effort | Data Prerequisite | Adoption Investment |
|---|---|---|---|
| Predictive Planning (Auto Predict) | Low — enable in Planning configuration | 2+ years clean monthly actuals in Planning | Train FP&A team on interpreting and using the statistical baseline |
| Planning Agent | Medium — configure signal detection rules and thresholds | Well-structured driver model; reliable actuals feed | Train FP&A on alert review, scenario generation, and judgment application |
| IPM Insights — Anomaly | Low — enable in Planning/FCCS configuration | Historical data for anomaly baseline | Brief controller training on reviewing and acting on alerts |
| IPM Insights — Bias | Low — enable in Planning configuration | Multiple forecast cycles for pattern detection | Brief FP&A training on bias report interpretation |
| GenAI Narrative | Medium — configure narrative templates, validate Arabic output | Reliable EPM close data | Establish review and annotation workflow; validate Arabic output before live deployment |
| AI Transaction Matching (ARCS) | Low — already active if ARCS Transaction Matching is deployed | Exception handling within ARCS (not Excel) | No additional training — improvement is automatic as usage generates ML training data |
| Ledger Agent | Medium — configure monitoring rules and alert routing | FCCS integration active | Train close manager on alert interpretation and exception follow-up |
Frequently Asked Questions
Q: Does Oracle Cloud EPM have AI features in 2026, and are they included in our existing licence? Yes — Oracle Cloud EPM includes a significant set of AI capabilities that are generally available in 2026 and included in the existing Oracle EPM Cloud subscription for most customers: Predictive Planning (Auto Predict) for statistical forecast generation, the Planning Agent for continuous FP&A signal detection, IPM Insights for anomaly and bias detection, GenAI Narrative Reporting for first-draft management commentary, and AI-powered Transaction Matching in ARCS. These features are activated through configuration, not through additional procurement. Organisations that have been deferring AI adoption because they assumed it required a separate purchase should verify their licence terms with Oracle directly — in most cases, the AI features are already available.
Q: What is Oracle Predictive Planning and how much does it improve forecast accuracy? Oracle Predictive Planning uses machine learning to generate statistical forecasts from historical data in the Oracle EPM Planning model, with an enhanced multivariate version that can incorporate external drivers — macro indicators, FX rates, commodity prices, operational metrics — alongside the historical financial series. Oracle’s production data reports forecasting accuracy improvements of up to 25 percent compared to manual trend-based forecasting for organisations that have activated and calibrated Predictive Planning against their specific planning model. The improvement is most pronounced for revenue and cost lines with strong historical patterns and identifiable drivers. The enhancement requires at least two years of clean, consistent historical actuals in the Planning model to generate reliable predictions.
Q: What is the Oracle Planning Agent and how does it work in an FP&A context? The Oracle Planning Agent is an agentic AI embedded in the Oracle Cloud EPM FP&A workflow that continuously monitors financial and operational data in the Planning environment, detects signals — revenue run rates diverging from forecast, cost lines accelerating beyond budget, driver relationships that have changed — and surfaces these as actionable insights that finance leaders can interrogate, apply judgment to, and convert into planning actions. It does not make decisions autonomously; it makes the finance team’s decision-making faster and more informed by surfacing material signals before they become visible in the monthly management review cycle. For GCC Vision 2030 programme participants managing performance against programme commitments, the Planning Agent’s continuous monitoring capability addresses a specific and practical need.
Q: Does Oracle EPM’s GenAI Narrative Reporting work in Arabic for GCC finance teams? Oracle’s GenAI Narrative Reporting capability supports Arabic through the underlying language models included in Oracle Cloud. However, the quality of Arabic-language financial commentary output requires validation against the specific Arabic vocabulary and reporting conventions used by the organisation before it is deployed in live management reporting. Finance teams should run a two-to-three-month parallel period — generating AI narrative in Arabic and comparing it against the finance team’s manually produced Arabic commentary — to identify and resolve terminology gaps before the AI-generated narrative is used in board packs or regulatory submissions. Arabic-language GenAI narrative from Oracle EPM is a practical capability in 2026; it is not a capability that can be deployed without validation.
Q: Our Oracle EPM environment is not performing well. Will activating AI features improve it? No — Oracle EPM AI features operate on the data in the EPM environment. If the Planning model has data quality issues, incomplete driver structures, or gaps in the historical actuals series, the AI output will reflect those problems rather than compensate for them. Predictive Planning applied to an unreliable historical series will produce unreliable forecasts. The Planning Agent monitoring a model without a meaningful driver structure will produce fewer useful insights. The correct sequence is to address the EPM foundation — data quality, driver structure, actuals feed reliability — and then activate AI features. Organisations that have stalled Oracle EPM environments should use the AI feature opportunity as the business case for remediation rather than as an alternative to it.
Q: Can GCC finance teams on Hyperion Planning access Oracle’s AI features? No — all of Oracle’s current AI features for EPM (Predictive Planning, Planning Agent, IPM Insights, GenAI Narrative, Ledger Agent) are Oracle Cloud EPM capabilities. They are not available on Hyperion Planning on-premises. For CFOs whose finance teams are still running Hyperion, the migration to Oracle Cloud EPM is the prerequisite for accessing these capabilities. Oracle’s quarterly release cadence for Cloud EPM means the gap between Hyperion functionality and Cloud EPM functionality will continue to widen — the AI features available in 2026 represent a material step ahead of what Hyperion can offer, and Oracle’s AI roadmap for Cloud EPM continues to expand.
About Loop Wise Solutions
Loop Wise Solutions is an enterprise performance consultancy based in Cairo, serving medium and large enterprises across Egypt, Saudi Arabia, the UAE, Qatar, and the broader Arab world. We implement and optimise Oracle Cloud EPM — including Planning, FCCS, ARCS, PCMCS, and TRCS — and we work with clients specifically on AI feature activation within existing Oracle EPM environments.
Our AI activation engagements start with an EPM readiness assessment — reviewing the data quality, driver structure, historical completeness, and actuals feed reliability that determine whether Oracle EPM AI features will produce reliable, useful outputs for the finance team. We then configure, validate, and support adoption of the specific AI features that are most relevant to the organisation’s planning, close, and reporting priorities — including Arabic-language GenAI narrative validation for GCC clients producing bilingual management reporting.
If you are evaluating Oracle Cloud EPM AI features for the first time, trying to understand which AI capabilities are already in your existing licence, or working through why your Oracle EPM environment is not delivering the AI value you expected, we are happy to have a direct conversation.
Contact: contact@loop-wise.com | Website: www.loop-wise.com
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