AI-Powered AOR Playbook for Portfolio Brands

Reckitt just made a classic “portfolio move”: McCann New York is now the U.S. creative agency of record (AOR) for four Essential Home brands—Woolite, Resolve, Rid-X, and Easy-Off. On the surface, that’s an agency win and a procurement decision. Under the hood, it’s a signal that large advertisers are tightening how they manage multi-brand complexity.

Here’s why I care (and why you should if you sit in marketing ops, procurement, or growth): running four household brands isn’t four separate marketing problems. It’s one system with shared constraints—budget pacing, retail calendars, promo windows, claims and regulatory review, production capacity, and vendor performance. This is where AI-driven insights stop being a “nice-to-have” and become the difference between a portfolio that compounds and a portfolio that wastes spend.

This post breaks down what an expanded AOR relationship actually changes, how to run it like a supply chain and procurement leader (not just a brand leader), and how AI helps you plan, measure, and negotiate across brands without flattening their identities.

Why a multi-brand AOR relationship is a procurement problem

A single AOR across multiple brands is about reducing coordination costs and improving consistency. That’s the procurement framing: fewer handoffs, fewer suppliers, clearer accountability, and better rate cards. But the real payoff is operational.

When one agency owns multiple brands, you can standardize:

• Creative production workflows (briefing, versioning, approvals)
• Measurement frameworks (consistent test design and reporting)
• Asset reuse (templates, footage libraries, modular creative)
• Vendor management (production partners, creators, studios)

In supply chain terms, this is like consolidating suppliers to reduce lead time variance. The brands still need distinct positioning, but the “factory” making marketing work can be shared.

The hidden cost you’re trying to eliminate: portfolio friction

Most portfolio advertisers don’t lose money because their ads are bad. They lose money because the system is slow.

Common friction points:

• Every brand uses different naming conventions, KPIs, and creative formats.
• Insights live in silos (brand A learns something brand B never sees).
• Production is re-invented for each campaign instead of modularized.
• Agency performance is judged subjectively instead of with comparable metrics.

A consolidated AOR relationship is a step toward fixing that—if you pair it with the right data and governance.

AI’s real job here: turn brand strategy into an operating system

AI in a portfolio context isn’t about replacing creative teams. It’s about making decisions faster and with fewer blind spots. Think of it as the analytics layer that connects:

• Consumer behavior signals (search, social, retailer data, call center, reviews)
• Creative performance (attention, engagement, conversion, lift)
• Commercial constraints (promotions, inventory, seasonality, pricing)

If you’re part of an “AI in Supply Chain & Procurement” series, this should feel familiar: the goal is the same as demand planning—predict, allocate, and reduce waste.

Where AI helps most in a portfolio AOR setup

1) Portfolio-level insight mining AI can cluster customer feedback and review text across brands to surface patterns you’d miss in brand-only reporting.

Example: If Easy-Off reviews spike around “fume concerns” while Woolite spikes around “scent sensitivity,” a portfolio insight might be that household shoppers are increasingly risk-averse about irritants. That can affect claims language, creative tone, and influencer selection across multiple brands.

2) Creative variant planning that doesn’t explode production Generative AI is useful when it reduces production complexity, not when it creates infinite versions no one can QA.

The winning pattern I’ve seen: modular creative.

• One master concept
• Swappable modules (headline, end card, product shot, CTA)
• Rules on what can vary by audience, retailer, or season

This is essentially marketing’s version of configuring products from shared parts.

3) Faster, more objective AOR performance analytics AOR relationships get messy when each brand picks its own scorecard. AI-driven performance analytics can standardize evaluation.

A practical portfolio scorecard might track:

• Time-to-first-draft (cycle time)
• Cost per approved asset (unit economics)
• Brand lift or incremental sales (impact)
• Rework rate due to compliance or claims (quality)

Put bluntly: if you can’t measure throughput and quality, you’re not managing an agency—you’re hosting it.

Managing four brands like a demand planner (not four separate campaigns)

Essential Home brands are heavily seasonal and promotion-driven. December and January are a perfect example: shoppers reset routines, tackle deep cleaning, and buy household basics. The marketing plan should mirror demand planning.

Here’s a portfolio-friendly approach.

Build a single “portfolio calendar” tied to retail reality

Start with a master calendar that includes:

• Retail promotion windows
• Major shopping moments (New Year reset, spring cleaning, back-to-school)
• Known inventory constraints and production lead times
• Compliance review time buffers

Then map each brand’s priority moments:

• Easy-Off: holiday cooking aftermath, spring cleaning, move-in/move-out
• Resolve: winter indoor messes, pet seasonality, back-to-school
• Woolite: winter delicates, gifting season wardrobe refresh, spring transitions
• Rid-X: home maintenance moments (often triggered by “uh-oh” emergencies)

This is supply chain thinking applied to creative: you’re aligning output to demand signals and availability.

Use AI to forecast “creative demand,” not just product demand

Most teams forecast sales, but not creative workload. That’s why agencies burn out and timelines slip.

A basic AI-assisted creative demand forecast can estimate:

• Number of assets needed by channel (CTV, social, retail media)
• Variant count by retailer requirements
• Localization needs
• Review cycles based on past turnaround times

The result is a more accurate resourcing plan—internally and with your AOR.

Standardize what should be standardized

Portfolio brands often fear standardization because they think it kills differentiation. The trick is to standardize plumbing, not personality.

Standardize:

• Brief templates
• Performance reporting
• Naming conventions and asset metadata
• Experiment design (A/B rules, sample size thresholds)

Protect:

• Brand voice
• Claims boundaries
• Visual identity rules

A strong portfolio doesn’t look the same everywhere. It operates the same everywhere.

What “AI-enabled AOR” looks like in practice (a simple blueprint)

If McCann is expanding scope across four brands, the operational opportunity is to stand up a shared system that makes every brand faster.

Step 1: Create a shared measurement spine

Pick a few metrics every brand must report, then allow brand-specific add-ons.

A good “spine”:

• Incremental sales or modeled contribution
• Reach and frequency quality
• Cost efficiency (CPM, CPA, ROAS where appropriate)
• Creative effectiveness signals (view-through, attention proxies, thumb-stop)

This makes portfolio optimization possible. Without it, you’re comparing apples to different fruit.

Step 2: Implement an asset supply chain

Treat assets like inventory. You need findability, version control, and reuse.

Minimum viable stack:

• A digital asset management system with strong metadata
• Clear versioning rules (what’s “master,” what’s “derived”)
• Rights and usage tracking
• A structured taxonomy (brand, channel, audience, claim type, date)

AI helps here by auto-tagging content, detecting duplicates, and surfacing “best performing” components to reuse.

Step 3: Use AI to personalize within guardrails

Personalization is where many portfolios overreach. The smarter move is bounded personalization:

• 3–5 audience segments per brand
• Pre-approved claim language variants
• Channel-specific templates

AI can recommend which creative module to pair with which segment, but your guardrails keep compliance and brand safety intact.

Step 4: Turn agency governance into a quarterly business review (QBR) that actually drives change

A QBR shouldn’t be a slideshow recap. It should be a decision meeting.

Agenda that works:

1. What drove incremental impact (by brand and channel)
2. What slowed the system (cycle time, rework, bottlenecks)
3. What we’ll stop doing next quarter
4. Experiments to run (and what success looks like)
5. Procurement review: rates, utilization, and forecasted needs

AI can automate the “what happened” reporting so humans spend time on “what we’ll do next.”

People also ask: practical questions portfolio teams ask in December

How do we keep four brands from blending into one voice?

Use shared processes but separate brand playbooks: voice, visuals, claims, and “do/don’t” examples. Train AI tools on brand-specific guidelines, not a generic portfolio corpus.

Do we need one AI model for the whole portfolio?

Not necessarily. Many teams use one analytics layer for cross-brand reporting, then brand-specific assistants/workflows for creative and insights. The point is interoperability and governance.

What’s the first AI use case that pays for itself?

Creative operations. Auto-tagging assets, summarizing feedback, generating first-draft variants inside templates, and speeding compliance reviews typically produce measurable time savings within a quarter.

What Reckitt’s move suggests for 2026 planning

AOR consolidation across multiple brands is a bet on coordination and speed. But speed only shows up when you treat marketing like an operational system: forecast demand, standardize processes, measure throughput, and optimize suppliers.

If you’re planning 2026 budgets right now, here’s the stance I’d take: portfolio marketing without AI-enabled governance will keep getting more expensive, not more effective. Channel fragmentation isn’t slowing down, retail media requirements aren’t getting simpler, and content volume expectations are climbing.

The next step is straightforward: audit your creative supply chain the same way you’d audit a physical supply chain. Where are the bottlenecks? Where does work get duplicated? Which vendor metrics are opinion-based instead of evidence-based?

If your AOR relationship is expanding—like Reckitt and McCann—this is the moment to bake in AI-driven performance analytics and portfolio-level insight workflows. Otherwise you’ll just scale the chaos.

What would change in your marketing operation if you could forecast “creative demand” as confidently as you forecast product demand?