AI for finance and HR
AI that works on the systems you already run
Start with the AI already in your Workday and Oracle subscriptions, connect the data those systems hold, and build only what’s missing on Google Cloud. Delivered by consultants who implement the systems your numbers come from.
When teams call us
Usually one of these is on the table.
The board wants an AI plan
Leaders are asked what AI will do for the business, and need an answer grounded in their own systems and data.
A pilot never reached production
A proof of concept looked promising, then stalled on data access, security or ownership.
AI features nobody uses
Workday and Oracle ship AI with every release, but it sits switched off or untested on your data.
The data is in five places
The numbers an AI use case needs are spread across ERP, EPM and HR systems that don’t share definitions.
Three layers, one plan
Use what you have, connect it, then build what’s missing.
Most AI value for finance and HR comes from the first two layers. We build custom models only where they pay for themselves.
1. Use the AI you already pay for
Workday’s AI features and agents, and the AI built into Oracle Fusion Cloud and Oracle EPM, such as predictive forecasting and anomaly insights: configured, tested on your data and adopted by the people who should use them.
2. Connect your data
Finance and HR data from Workday and Oracle brought together in BigQuery, with hierarchies and definitions intact, so every model and assistant works from numbers the controller signs off.
3. Build what’s missing
Forecasting models, document processing and Gemini assistants on Google Cloud, for the needs the vendors’ AI doesn’t cover.
Where AI pays off first
Use cases by team
Finance
- ForecastingDriver-based revenue, cost and cash forecasts that learn from history.
- CloseAnomalies and unusual entries flagged before review, not after.
- ReconciliationTransactions matched automatically, exceptions sent to people.
- DocumentsInvoices and contracts read and structured for posting.
- CommentaryFirst drafts of variance explanations for analysts to edit.
HR
- Workforce planningHeadcount and cost scenarios built from live Workday data.
- Policy answersEmployees and managers get answers from your own policies.
- Attrition signalsEarly warning on teams at risk, for HR partners to act on.
IT and operations
- IntegrationsFailed or unusual integration runs caught and explained.
- Enterprise answersOne place to ask questions across ERP, EPM and HR data.
- Release readinessVendor release changes checked against your configuration.
How we deliver
Value first, then scale.
- ChooseScore use cases by business value and data readiness, and pick the first one.
- PrepareGet the data it needs into shape, with finance and HR definitions intact.
- PilotRun it against today’s process with a measured baseline.
- ScaleMove to production with monitoring, then add the next use case.
We use the vendor’s AI when it does the job, a product when one exists, and build only where it gives you an advantage.
Responsible by design
AI your auditors and employees can trust.
Your data stays yours
It stays in your Workday, Oracle and Google Cloud environments, under your contracts.
People stay in the loop
AI drafts and flags. People approve anything that reaches the ledger or an employee record.
Access follows your roles
Assistants and models respect the security you already have in place.
Results are measured
Every use case has a baseline, and models are monitored after go-live.
How we work
We use AI on our own projects, too.
Our consultants use AI in release testing, application design and validation, data conversion, and the design and validation of role, group and entity-level security. We also use it to run our projects: planning resources, assigning the right people and managing project financials. Checks are more thorough, and your team gets results sooner.
Release testing
Vendor releases tested against your configuration and business processes.
Design and validation
Application designs reviewed and configuration validated against requirements.
Data conversion
Source data mapped, cleansed and reconciled with less manual effort.
Security
Role, security group and entity-level security designed and validated, with gaps and conflicts found before testing.
Resource planning and assignments
Consultants matched to each project by skills, certifications and availability, and staffing gaps spotted early.
Project financials
Budgets, burn and forecasts tracked as the work happens, so cost and schedule questions surface before they become surprises.
Start here
AI Readiness Assessment
A fixed-fee assessment of your systems and data. You get a shortlist of the use cases with the best value and readiness, and a roadmap with a business case for the first one.
- ReviewYour ERP and other systems, the AI already available in them, and the data each use case would need.
- ShortlistThe three use cases with the clearest value and readiness, scored with your team.
- PlanA roadmap and business case for the first use case, with what it takes to reach production.
Eligible clients and projects may be co-funded by Cogleus and Google. We check eligibility with you before work starts.
Why Cogleus for AI
We implement the systems your data comes from.
We know the source systems
We implement Workday, Oracle EPM and Oracle ERP, so we know where the data lives and what it means.
Certified across the stack
Workday- and Oracle-certified consultants, and consultants certified in Google Cloud AI.
Google Cloud Partner
A partner in the Google Cloud Partner Network for data and AI on Google Cloud.
Questions leaders ask us about AI
Where should finance start with AI?
With a use case that has clear value and data you already trust, often forecasting, close anomaly detection, reconciliation matching or invoice processing. The AI Readiness Assessment picks the first one and builds the business case.
Do we need Google Cloud to use AI with Workday or Oracle?
No. Workday and Oracle include AI features and agents you can use today. Google Cloud comes in when a use case needs data from several systems together, or a model the vendors don’t offer.
Is our data ready for AI?
Often partly. AI needs consistent definitions across systems. The assessment checks the data each use case needs and what it takes to get it ready.
How do you keep AI outputs accurate and secure?
Outputs are grounded in your own data, people review them before they reach the ledger or an employee record, access follows your existing security roles, and results are monitored after go-live.
How soon will we see results?
The first use case is piloted against today’s process with a measured baseline, so you see whether it works before you scale it. The assessment gives you a plan with dates for your use case and data.
Should we build or buy?
Use the vendor’s AI when it does the job, buy where a product exists, and build only where it gives you an advantage or nothing else fits.