The AI Problem Isn’t “Can We Build a Model?” It’s “Can We Scale Outcomes?”
Most organizations can spin up an AI pilot. Far fewer can operationalize AI so it reliably improves decisions, productivity, and speed-to-market across the enterprise—without ballooning risk, cost, or technical debt.
Procter & Gamble (P&G) offers a useful blueprint. Not because they’re experimenting with generative AI (everyone is), but because they’ve built a repeatable system—an “AI factory”—to industrialize how AI is developed, deployed, governed, and operated. Reportedly, that factory cut their time to model deployment by roughly six months.
For executives and technical leaders, P&G’s approach points to a clear conclusion: AI at scale is less about individual use cases and more about building a production-grade capability.
At MILL5, we help organizations do exactly that through three integrated offerings—Strategy, Build, and Operate—so AI moves from isolated pilots to measurable, governed business value.
What P&G Did Differently—and Why It Matters
P&G’s AI evolution is notable for three decisions that many companies delay too long.
1) They Built an “AI Factory” to Standardize Delivery
P&G observed that bespoke algorithm development and custom deployments were creating delays that had “material financial impact.” Their answer: a standardized vehicle of data sources, tools, methods, and security protocols to rapidly develop, test, deploy, and monitor algorithms in production.
Technically, that’s an operating platform: repeatable pipelines, shared components, consistent guardrails, and built-in observability. Organizationally, it’s a commitment to treat AI like software delivery—productized, measurable, and maintainable.
Key takeaway: If your AI work relies on heroics (custom glue code, one-off environments, manual approvals), scaling will be slow and fragile—even if the pilot “works.”
2) They Treated AI as Products Employees Use, Not Just Models Engineers Build
P&G didn’t stop at “model development.” They created internal AI products that map to common enterprise needs:
- chatPG: secure employee access to multiple LLMs, selectable by business context
- imagePG: multimodal generation and analysis to support advertising and other workflows
- askPG: a generative interface over curated internal unstructured knowledge
- insightsPG: a generative front-end to business data (“talk to your data”)
This matters because adoption follows usability. AI becomes valuable when it’s embedded in the way people work: inside customer service flows, supply chain planning cycles, R&D experimentation loops, marketing concept development, and performance management.
Key takeaway: A “successful AI program” often looks like a portfolio of well-governed internal products—not a stack of disconnected notebooks.
3) They Balanced Analytical AI, Generative AI, and Agentic AI Based on Outcomes
P&G explicitly recognizes that a “huge percentage of value” still comes from analytical AI (e.g., supply chain and media decisioning), even as generative and agentic approaches expand what’s possible.
They’re also experimenting with agentic AI in pilots—while emphasizing the ongoing importance of a human in the loop.
Key takeaway: The best AI roadmap isn’t “GenAI everywhere.” It’s the right modality for the right outcome, with governance designed into the workflow.
P&G Use Cases: The Business Outcomes That Make AI Real
A useful way to ground an AI roadmap is to start where P&G did: measurable outcomes tied to business pain.
Consumer Experience: Personalization That Reduces Friction
P&G’s Pampers My Perfect Fit uses an AI-driven questionnaire to recommend diaper fit, reportedly 90% accurate at preventing leaks—one of the biggest consumer pain points in the category.
What to learn: Consumer-facing AI doesn’t have to be “flashy.” It has to reduce friction and improve confidence at decision points.
Supply Chain: Optimization That Prevents Lost Sales
In Brazil, P&G described an analytical AI system that splits and schedules customer orders into truck-size loads prioritized by shelf need, reducing out-of-stock occurrences by 15%.
What to learn: Some of the highest-return AI still looks like advanced analytics—because it directly reduces operational leakage.
Customer Service: Speed and Consistency at Scale
P&G’s Project Genie synthesizes information from articles and help documents to support 800+ customer service reps, reducing question processing time.
What to learn: Knowledge-grounded assistants are among the fastest paths to value—when security, permissions, and content governance are real (not an afterthought).
R&D: Compressing Experimentation Cycles
P&G’s Perfume Development Digital Suite uses AI and advanced data processing to create new fragrances five times faster, analyzing millions of data points and guiding formulation and testing via rapid prototyping/experimentation.
What to learn: AI can shift innovation economics—not by replacing scientists, but by accelerating iteration loops.
The Technical Blueprint Behind the “AI Factory” Concept
When leaders hear “AI factory,” the productive question is: What capabilities make scaling faster and safer?
Based on P&G’s described approach, an enterprise-grade AI factory typically includes:
Data + Knowledge Foundations
- Governed access to structured and unstructured data
- Metadata, taxonomy, and permissions that make retrieval accurate and safe
Model Access Abstraction
- Secure access to one or more LLM providers/models (with routing by task, data sensitivity, latency, and cost)
Evaluation and Risk Controls (LLMOps/MLOps)
- Automated testing for quality, safety, and regressions
- Monitoring for drift, hallucinations, and policy violations
- Audit logs for regulated workflows
Deployment Accelerators
- Standard APIs and integration patterns
- Workflow automation hooks (CRM, ERP, service desk, marketing ops tools)
Agent Operations (Where Relevant)
P&G notes their factory now incorporates agentic capabilities such as monitoring agentic systems at scale, registering agents, and connecting agents/tools using protocols including the Agent2Agent Protocol and the Model Context Protocol.
Bottom line: Scaling AI is an engineering and operating-model challenge as much as it is a data science challenge.
How MILL5 Helps You Apply This Playbook: Strategy, Build, Operate
MILL5 Strategy: Align Outcomes, Architecture, and Risk—Before Spend Explodes
We help leadership teams answer four make-or-break questions:
- Where will AI drive measurable impact in 6–12 months?
- Use-case portfolio and prioritization across operations, customer, and innovation.
- What data and knowledge do we trust—and what must be fixed?
- Readiness assessment for data quality, permissioning, and content governance.
- Which AI modality fits which workflow?
- Analytical vs. generative vs. agentic—based on value, risk, and feasibility.
- How do we govern responsibly without slowing delivery to a crawl?
- Practical governance: human-in-the-loop design, evaluation standards, auditability, and security-by-default patterns.
Strategy outputs you can take to the board:
- A funded roadmap with metrics (ROI model + KPIs)
- Reference architecture for your “AI factory”
- Governance and risk controls appropriate to your industry
MILL5 Build: Stand Up the “AI Factory” and Ship AI Products People Actually Use
This is where strategy becomes repeatable delivery.
Core build components we implement:
- Secure LLM access layer (multi-model, policy-based routing)
- Retrieval-augmented generation (RAG) patterns for internal knowledge assistants
- Conversational analytics patterns (semantic layer + governed metrics so “talk to your data” doesn’t become “guess at your data”)
- LLMOps/MLOps toolchain (evaluation harness, monitoring, cost controls)
- Integration into business systems (service desk, CRM, supply chain planning, marketing ops)
- Agentic workflow foundations where appropriate (tool permissions, guardrails, escalation)
What we deliver in practice:
- A production-ready internal assistant (service, sales, ops, HR)
- A governed “ask your data” experience for leaders and analysts
- One high-impact workflow automation tied to measurable outcomes (e.g., reduce handle time, reduce OOS, improve forecast accuracy, compress cycle time)
MILL5 Operate: Keep AI Safe, Performant, and Cost-Effective—After Launch
AI value erodes when models drift, content changes, and adoption stalls. We provide the operational muscle to sustain outcomes:
- Continuous evaluation and regression testing
- Observability for latency, cost, and quality
- Prompt/model versioning and change management
- Incident response and governance reporting
- Enablement and adoption programs (role-based training and playbooks)
P&G’s own structure separates building the factory from scaling and operating the algorithms within it—an operating-model lesson many enterprises learn late.
A Practical 90-Day Plan (Modeled on What Works)
If your organization is serious about scaling AI beyond pilots, this is a proven way to start:
Days 0–30: Decide what matters
- Prioritize 3–5 use cases tied to measurable KPIs
- Define governance, security, and evaluation standards
- Align owners and operating model
Days 31–60: Build the core factory + one flagship product
- Stand up secure model access + RAG foundation
- Launch a knowledge assistant or service copilot into a real workflow
- Implement monitoring and feedback loops from day one
Days 61–90: Prove value and make scaling repeatable
- Add an analytics experience (“talk to your data” with governed definitions)
- Extend to a second workflow where the platform repeats the pattern
- Establish the runbook for ongoing operations
The Point of the P&G Story
P&G didn’t “win at AI” by chasing the newest model. They built a capability that turns AI into a repeatable engine for customer value, operational performance, and faster innovation—and they invested in the people and governance to make it stick (including significant workforce upskilling).
If you want AI outcomes—not just AI experiments—MILL5 can help you design the roadmap, build the factory, and operate it day-to-day.
If you’re ready to move from pilots to production, ask the MILL5 team about an AI Factory Accelerator: a focused engagement to prioritize use cases, stand up the core platform, and launch the first production workflow in 90 days. Contact our AI specialists at ai@mill5.com.
Source Note: This article is informed by MIT Sloan Management Review’s reporting on Procter & Gamble’s AI approach and use cases, including its “AI factory,” internal AI products (chatPG, askPG, imagePG, insightsPG), and reported outcome examples.
