White Paper Program

Execution Standards

Lead with the decision. Open with the question an executive is trying to answer, then the insight. Context comes second.
Anchor to a real company. Use the named example as the worked case so the argument is concrete, not abstract.
Source everything. Prefer primary sources — filings, earnings calls, company and vendor documentation, regulator guidance, and credible analyst research. Footnote all claims.
Quantify the stakes. Size the opportunity, cost, or gap wherever the evidence allows.
Stay vendor- and model-agnostic. The posture is that of an adviser to buyers, not a seller of platforms.
Deliver a takeaway. Each paper includes at least one framework, one-page model, or decision matrix the reader can reuse.
Define a first move. Where applicable, close with a concrete 90-day action rather than a call to “explore.”
Structure and length. Executive summary → decision → framework → worked example → implications → first move. Target 8–15 pages; define jargon on first use.

The Projects

All five projects are to be executed and delivered by August 15, 2026.

Project 1

Agentic Commerce (anchor: Nike)

Scope. Define what agentic commerce is and what Nike’s journey reveals about the capabilities required to compete in it.

Research. Nike’s AI strategy, data modernization, and first-party data strategy (Nike Direct, membership and loyalty, the shift away from third-party retail); its approach to hyper-personalization and the role of personalization and product-discovery platforms such as Reflektion and Lucidworks; and Nike’s relevant AI acquisitions and partnerships as evidence of capability-building.

Deliverables.

A clear definition of agentic commerce, distinct from “AI-powered” commerce generally.
A capability-stack map: modernized data → first-party data → personalization → agentic experiences.
Nike as the worked example throughout.

Outcome. What separates leaders from the field, and where most enterprises fall short on the path to agentic commerce.

Project 2

Agentic Commerce Applied to Retail Banking & Wealth Management

Scope. Translate the agentic-commerce and personalization playbook from Project 1 into retail banking and wealth management. Builds directly on Project 1; flag transferable insights while that research is underway.

Deliverables.

A mapping of the agentic-commerce capability stack onto retail banking and wealth-management contexts.
The personalization and first-party-data lessons that transfer, and the ones that do not.

Outcome. A credible case for how a financial institution adapts the leading commerce playbook to its own customer relationships.

Project 3

The Decision Layer: Own vs. Rent (anchor: OneMain Financial)

Scope. The foundational paper of the series, structured as a working-session deliverable anchored to OneMain Financial.

Deliverables.

An own-vs-rent strategy — what to build versus buy, how to stay model-agnostic, and how to avoid vendor lock-in.
The AI operating model on one page — how outcomes, decision owners, architecture, and governance connect, including the role of a central AI team and the cross-functional model around it.
A first 90-day move — one high-value OneMain use case, plus a clear read on where the lending-decision chain breaks today.
The headline finding — for the first time, a measure of how much of an organization like OneMain Financial’s decisioning is consistent and traceable, and where the largest gap is.

Outcome. The finding sizes the opportunity and tells leadership exactly what — and whether — to fund next.

Project 4

AI Model Safety for US Banks

Scope. Anthropic and others are beginning to publish AI safety guidance, but it is general-purpose, not industry-specific. There is no playbook for AI model safety written for US banks. This project fills that gap.

Deliverables.

Model-safety principles translated into the specific regulatory, risk, and model-governance context US banks operate in.
A resource a bank’s risk and AI leaders can use directly.

Outcome. The first industry-specific reference for AI model safety in US banking.

Project 5

GCC in India vs. Alternatives — Banks & Financial Services

Scope. Whether a bank or financial-services firm should stand up a Global Capability Center (GCC) in India versus pursuing alternatives such as an AI-led roadmap and automation.

Deliverables.

The business case — strategic rationale and trade-offs.
The CFO view — cost, ROI, and capital implications.
The COO view — operations, talent, delivery, and risk.
The evidence — analyst data, cost comparisons, and current trends, rather than conventional wisdom.

Outcome. A grounded, three-lens decision framework for the GCC-versus-alternatives question.

Program Overview

Execution Standards

The Projects

Sequencing & Priorities