Financial systems strategy
Set transformation priorities across core finance, planning, reporting, and operating teams.
Work
FINANCE SYSTEMS.
REAL CHANGE.
Enterprise financial transformation, governed AI implementation, and independent products built around how work actually moves.
Enterprise transformation
CONTROLLED CHANGE.
AT ENTERPRISE SCALE.
At Omnicom Group, I lead financial applications work across systems, planning, reporting, delivery, controls, and enterprise AI.
This is the leadership surface: how a finance transformation moves from requirements to adoption without exposing internal systems, data, or client work.
Interactive capability map
Enterprise finance capability map
An interactive, public-safe view of the platforms, operating capabilities, and outcomes represented across the published case studies.
PlatformCapabilityOutcome
Platforms
Governed finance layer
Operating outcomes
Leadership scope
Built for the work a financial transformation leader actually owns end to end.
Enterprise-scale leadership is not a title treatment. It is the ability to make systems, controls, teams, and decisions work together.
Integration and delivery
Translate requirements into architecture, governance, UAT, cutover, and post-launch operating plans.
Data and enterprise AI
Create governed ways for authorized users to analyze finance data in plain language.
Controls and assurance
Build control evidence, access discipline, and release readiness into the delivery model.
Enterprise AI implementation
AI that fits finance operations.
Not a public chatbot and not a claim that AI replaces finance judgment. The implementation is about governed context, useful questions, reviewable outputs, and a controlled rollout.
Governed finance context
Define approved data domains, ownership, access boundaries, and the questions each audience can answer.
Natural-language analysis
Give authorized finance users a constrained way to ask questions, inspect supporting context, and move into analysis.
Adoption with controls
Test scenarios, capture feedback, validate access, and release in measured phases with accountable owners.
Public summary
The portfolio shows the operating model, not internal prompts, models, financial data, source systems, or employee access patterns.
Enterprise implementation work
Projects behind the operating model.
Each project shows the business problem, how the operating model was implemented, controls and delivery constraints, and a public-safe outcome. The first seven go deeper: why the work matters to AI-native finance teams, the capabilities it demonstrates, and the architecture. No client, employee, financial, or configuration detail is published.
01
Platform & data
DatabricksUnity CatalogAI/BI
Governed analytics and natural-language access
Finance teams want faster answers, but an unrestricted natural-language route into sensitive data can return unverifiable results or bypass access controls. The layer has to enforce what each user can ask and see, tie every answer to an approved view, and let the user inspect and challenge the result.
- Natural-language access over governed finance data
- Row and column level access applied before the model
- Answer lineage to an approved view
02
Controls & assurance
General ledgerTrial balanceInternal auditExternal assurance
Financial controls and reconciliation
Control owners have to identify reconciling differences between the general ledger and supporting records, resolve the exceptions, and then show that reporting controls operated as designed. When that evidence lives in spreadsheets and email threads, every internal audit and external assurance request turns into a manual reconstruction.
- GL and trial-balance reconciliation design
- Exception ownership and break management
- Audit-ready evidence capture
03
Platform & data
IBM Planning AnalyticsTM1Cubes and dimensions
Planning model and cube engineering
Planning models decay when the cube, dimension, and rule structure stops matching how finance actually plans, leaving fragile views, unclear ownership, and slow change cycles. Small edits then carry outsized risk because there is no clear validation step before a change reaches users.
- IBM Planning Analytics and TM1 administration
- Cube, dimension and rule design
- Driver-based planning structures
04
Platform & data
Dynamics 365SAP HANADatabricks
Finance platform connectivity
Finance and operations lose time when teams work from disconnected extracts, inconsistent mappings, and unclear ownership across ERP and analytical platforms. Without an authoritative source and reconciled mappings, every report becomes a negotiation about whose numbers are right.
- ERP to analytics integration (Dynamics 365, SAP HANA)
- Curated data products with named owners
- Reconciled mappings across platforms
05
Finance operations
FP&APlanning AnalyticsData integration
Acquisition planning integration
Acquired entities arrive with their own planning structures, ownership, fiscal calendars, and local records, and finance has to fold them into the target operating model without breaking reconciliation or losing cutover control. Done informally, entity onboarding drags across reporting periods and the combined forecast inherits gaps that are hard to trace later.
- Planning integration for acquired entities
- Mapping acquired structures to a target operating model
- Controlled cutover with reconciliation gates
06
Finance operations
Workday HCMFP&ARole-based access
Workforce planning connection
Finance scenarios drift from reality when workforce inputs, role and cost definitions, and access rules sit outside the planning model and are refreshed by hand. Headcount changes then reach the forecast late, and sensitive people data is either overshared or missing.
- Workday HCM to FP&A integration
- Role-based access to sensitive workforce data
- Aligned definitions between HR and Finance
07
Decision intelligence
Databricks AppsDatabricks GenieReactNext.js
Real estate decision intelligence
Real estate teams work from asset detail spread across leasing, valuation, and finance systems, so building a portfolio view means manual reconciliation and every conclusion is hard to trace back to a source. Leaders need one governed place to see which assets need attention and to ask follow-up questions without losing that source context.
- Databricks Apps (React, Next.js) in production
- Genie natural-language analysis over governed data
- Portfolio, asset and exception view design
08
Decision intelligence
HTML dashboardsData viewsAI orchestrationExecutive reporting
AI finance dashboard studio
Finance audiences need different views of the same data, but rebuilding a dashboard for each request is slow and tends to separate the commentary from the view it describes. Teams need a way to go from approved data to a decision-focused executive view, and to take plain-language edit requests, without treating every request as a fresh build.
Read case study09
Finance operations
FP&AERP dataPlanning AnalyticsDatabricks
Working capital decisioning
Working-capital decisions usually arrive after receivables, payables, cash, and inventory data has fragmented across ERP and operational systems, so operators cannot quickly tell a real cash problem from a timing or data-quality artifact. Finance needs a reviewable view that names the decision owner and the evidence behind each action.
Read case study10
Controls & assurance
AzureDynamics 365WorkdaySAPHyperionAribaDatabricks
ESG reporting and assurance pipeline
ESG disclosures pull from operational, finance, workforce, and vendor systems, and each reported figure has to trace back to a source and a method for internal review and limited assurance. Without that lineage, assurance teams cannot answer where a number came from or how it was derived.
Read case study11
Controls & assurance
UATInternal controlsExternal compliance
UAT, compliance, and release assurance
Enterprise finance changes become risky when test evidence, issue ownership, and compliance checks sit outside the release process and get reconstructed after go-live. Regulators and assurance teams increasingly expect the same discipline applied to AI-assisted work as to any other change in an audited reporting environment.
Read case studyPublic products
Systems you can explore.
Independent product work across planning, finance, governance, coding workflows, and investment research.
01
PilotPlan
Turn a business problem into a researched, sourced implementation plan you can defend in a steering meeting.
- Problem
- Requirements and vendor research are scattered across documents.
- Approach
- Research options against business needs, constraints, and budget.
- System
- Architecture, sourced estimates, rollout phases, risks, PDF and Excel exports.
- Availability
- Public beta
02
Enterprise Dashboard
A multi-company FP&A workspace built on SEC-filed financials, with every figure traced to its source.
- Problem
- Financial results and business drivers are difficult to review together.
- Approach
- Connect company and fiscal-period controls to source-linked financial views.
- System
- Executive review, variance analysis, scenario planning, cash flow and reporting.
- Availability
- Public demonstration using public-company data
03
Graph Skill
Run coding-agent work as a dependency graph: parallel independent steps, cached results, retries scoped to what changed.
- Problem
- Long coding-agent runs repeat work and restart after a failed step.
- Approach
- Model dependencies, parallelize independent work, and reuse cached results.
- System
- Per-node retries, local lint/type/test gates, AI review and terminal progress.
- Availability
- Open source CLI skill
04
AetherOps
Access governance for people and AI agents: request in plain language, triage, review, provision, audit.
- Problem
- Access approvals lack consistent controls for people and AI agents.
- Approach
- Apply risk triage and policy gates before approval and fulfillment.
- System
- Owned agent identities, scoped access, connectors and exportable audit evidence.
- Workflow
- Request → triage → approve → fulfill → audit
05
INVST
Multi-asset investment research that keeps the stance, the counter-case, and the evidence gaps in one view.
- Problem
- Investment signals are noisy and their evidence is often unclear.
- Approach
- Keep the research stance, opposing arguments and source limitations together.
- System
- Multi-asset search, research evidence, source status, saved history and watchlists.
- Scope
- Investment research, not personalized financial advice