Summary of "HPE's CFO: Making Agentic AI Work in Finance"

High-level summary

Marie Myers (CFO, HPE) describes HPE’s deployment of agentic/generative AI across a ~3,600‑person finance organization to drive speed, accuracy, and scaled operational insight.
The approach treats AI as an iterative, enterprise transformation — not a point tool — with strong emphasis on data, governance, determinism, human oversight, change management and ROI discipline.

“Treat AI as an iterative, enterprise transformation — not a point tool.”

Practical starting points: transactional finance (accounts payable, credit & collections, contract analysis, internal audit) and enterprise areas with clear productivity upside (software engineering, marketing, IT).
Key platform/partners: Deloitte’s Zora platform (HPE‑branded as “Alfred”), NVIDIA (collaborated on determinism/NIM), and private‑cloud/on‑prem architecture for sensitive financial data.

ROI framework
- Separate direct (hard savings) vs indirect benefits (speed, accuracy, fraud reduction, time‑to‑insight).
- Define an “indirect bucket” and quantify where possible; use scoring to prioritize bets and stage‑gate decisions.
Stage‑gate / experiment-driven investment
- Run small pilots, iterate rapidly, track outcomes, and stop projects that don’t meet ROI/scorecard thresholds.
Workflow‑first deployment playbook
- Redesign workflows and operating models (for example, centralize FP&A) before layering agents.
- Standardize processes to enable scalable agent deployment.
Data foundation playbook
- Build and verify a single source of truth (“one time data”).
- Reconcile and clean data before AI; treat data quality as a gating factor.
Determinism & governance playbook
- Require deterministic outputs for finance queries.
- Co‑engineer or tune models (HPE worked with NVIDIA NIMs) to ensure consistent answers.
- Prefer private cloud / on‑prem when necessary for compliance/security.
Human‑in‑the‑loop governance
- Maintain human oversight for judgmental, regulatory, or high‑risk decisions.
- Define responsibilities across business owners, IT/data custodians, compliance, and audit.
Change management & capability building
- Sustained training and organizational learning (HPE trained thousands).
- Focus on culture and adoption, not just technology.

Organization & scale
- Finance organization: ~3,600 people.
- HPE trained >3,000 employees on AI capabilities.
Data & platform scale
- Alfred (HPE’s Zora instance) contains ~500,000 data elements.
- Alfred was on version 65+ during development.
Process cadence
- “Ops call”: weekly enterprise operational review (Monday midday) automated/enhanced by Alfred.
Suggested KPIs to track
- Determinism / consistency rate (same answer every time for the same query)
- Time‑to‑insight / time‑to‑decision
- Error rate / invoice error reduction / fraud incidents
- Throughput (invoices processed, collections actions)
- Forecast accuracy and variance
- Employee adoption / training completions
- Cost of models/infrastructure vs productivity gains (ROI)
- Number of agentic workflows deployed and iteration/version cadence

Platform & scope
- Started with Deloitte Zora for transactional finance (AP) and expanded into the weekly ops call via HPE’s Alfred.
Security model
- Used private‑cloud / on‑prem deployment to retain control of sensitive financial data and meet compliance requirements.
Determinism engineering
- Co‑engineered model behavior with NVIDIA to move from probabilistic to deterministic outputs for finance queries.
Workflow redesign
- Centralized FP&A and standardized end‑to‑end workflows before deploying agents.
Prioritized use cases
- Finance (early): accounts payable, credit & collections, contract/accounting reconciliation, internal audit, controls checking.
- Enterprise (high ROI): software engineering/code generation, marketing (creative/content campaigns), IT workflow automation.
Human role
- AI synthesizes and narrows areas of focus (e.g., flags top issues); humans validate, investigate, and execute corrective plans.

Build the data foundation: single source of truth; reconciled and clean datasets — non‑negotiable.
Redesign workflows first: centralize/restructure processes so agents can be effective and measurable.
Start small and iterate: pilot transactional workflows with clear metrics; apply agile development and versioning.
Insist on determinism for critical finance queries: engineer or configure models to return consistent answers.
Use private/on‑prem where compliance/security require it; otherwise evaluate hybrid models.
Bake in human‑in‑the‑loop controls for judgment, regulatory interpretation and final accountability.
Set clear stop/go criteria and monitor ROI closely; be prepared to reallocate capital.
Invest heavily in change management and continuous training (not one‑and‑done).
CFOs should lead ROI stewardship; work closely with CIOs, compliance/audit, and business owners.

Role changes: reduce manual data‑gathering roles; shift finance skills toward analysis, judgment, cross‑functional partnering, and “AI + domain” fluency.
Staffing model: fewer repetitive processing tasks; more emphasis on analytical, communication, and decision‑making capabilities.
Learning agility: capture organizational learning from pilots (including failures) to accelerate subsequent deployments.

Accountability remains human: final accountability for regulatory interpretation and finance outputs stays with people.
Determinism & explainability: finance requires deterministic answers; explainability remains important and needs governance.
Controls & audit: integrate compliance teams early; CIO/data custodians must enforce data controls.
Security: private/on‑prem deployments used to control sensitive data and meet governance requirements.

Adopt an experimentation budget with clear tolerances for “throwaway” work; keep investments manageable and closely monitored.
Expect models and infrastructure to evolve quickly; focus on building core skills & processes that transfer across model generations.
Evaluate ROI not only on hard cost savings but also on speed, accuracy, risk reduction, and capacity to redeploy human capital into higher‑value work.

CFOs: act as stewards of AI capital — measure ROI, prioritize investments, and ensure data & governance; develop conversational-to-deeper tech acumen.
CIOs: be central to deployment — data custodian, controls lead, governance steward; become a strategic partner to the business.
Board members: require AI literacy; understand risks, deployment plans, and how AI can disrupt industry economics.

Non‑determinism and explainability challenges of generative models.
Data quality as the primary blocker.
Cultural resistance and the risk of “AI slop” (dropping standards).
Rapid model lifecycle and circular financing in the AI ecosystem (chipmakers, model vendors, clouds funding one another) — judge investments by customer value and ROI.