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Digital Budgets in the Age of AI: What to Fund First

Chris Osterhout SVP of Strategy
#Digital Strategy, #Artificial Intelligence

Published on September 17, 2025

Most digital budgets are stuck in the past. To thrive in the age of AI, marketers must rethink priorities—starting with machine readability, measurable outcomes, and disciplined AI spend.

Why Budgets Are Already Out of Date

Most marketing budgets are still structured for yesterday’s digital world: SEO retainers, campaign spend, martech renewals. That structure made sense when digital transformation meant optimizing for Google rankings or swapping out CMS platforms every few years.

But 2026 won’t reward the same formulas. Search engines are shifting toward AI-powered answers. Customers expect instant, context-aware interactions. And the cost of AI usage—not just licenses but compute cycles and API calls—can spiral without planning.

The question isn’t how much to spend on AI. It’s what to fund first, what to cut, and what to hold in reserve. This hierarchy gives digital marketing directors a sequence to follow when rethinking their budgets for the age of AI.

Step 1: Fund the Table Stakes

Before you chase pilots or shiny new tools, ask yourself a blunt question: Can AI systems even read, interpret, and surface your content? If not, everything else you budget for will underperform.

The rise of Answer Engine Optimization (AEO)

Traditional SEO budgets focused on ranking for keywords. But AI-driven search doesn’t reward backlinks or keyword density in the same way. Large models need structured, machine-readable signals to understand your content. That’s where Answer Engine Optimization (AEO) comes in.

What this means for your budget:

Structured microdata and schema markup. Every product, service, and article should carry structured tags that help machines categorize and contextualize it.
llms.txt and similar directives. These emerging standards let you control how large models interact with your content, just as robots.txt once shaped crawler behavior.
Consistent taxonomies. If your content tags are a mess, models can’t make sense of them. Budget time and resources to clean and standardize metadata across your digital properties.

These aren’t “nice-to-haves.” They’re the new baseline. If your 2026 budget doesn’t carve out dollars for AEO, your content risks becoming invisible in AI-powered search results.

Why this matters now

Marketers who treat AEO as optional are repeating the mistake companies made in the early 2000s by ignoring SEO. Those that invested early captured disproportionate visibility. The same will happen here.

Step 2: Budget for AI-Based Business Outcomes

Once your foundation is in place, shift focus from tools to measurable outcomes. AI isn’t just a line item—it’s a way to drive results tied directly to your KPIs.

Replace outdated tools with AI-driven systems

Instead of stacking new software on top of old, look at where AI can replace underperforming tools:

Site search. Most legacy site searches frustrate users. AI retrieval trained on your own content makes finding information easier and boosts conversions.
Customer support chat. Chatbots trained on your private vector database can deflect tickets, cut response times, and reduce service costs.
Content automation. Use AI to draft, summarize, and repackage content so staff can focus on higher-value work.

But beware the vendor AI upsell

Nearly every platform in your stack—email, CMS, analytics—is now offering “AI-powered” upgrades. Many of these features exist to drive the vendor’s revenue, not yours.

Take email marketing tools as an example. Several now promote “AI subject line generators” as a premium upgrade. But in practice, those subject lines often perform no better than a simple A/B test your team can already run. You end up paying for the appearance of sophistication rather than the actual lift.

Before shifting budget toward vendor AI add-ons, ask:

Does this feature tie to a measurable business outcome?
Could the same result be tested more cheaply with a focused pilot?
Am I paying for their roadmap, or my results?

Fund outcomes, not slogans. If a vendor AI feature clearly drives conversions or saves time, budget for it. If it doesn’t, that money is better spent on pilots you control.

Step 3: Manage AI Spend with Discipline

Here’s where most budgets go off the rails. The cost of using AI can dwarf the cost of buying it. API calls, compute cycles, and storage add up quickly if they aren’t managed. On the other side, pilots that show promise often stall because there’s no budget line to scale them.

Cap your proof budget

When you test a new AI use case, set a hard ceiling on what you’ll spend to prove value. Enough to validate results, not enough to sink your budget. For example:

Allocate $50K to replace site search with an AI-driven model.
Run it for 3–6 months, measuring impact on conversions and support calls.
If it hits the metrics, you unlock additional budget from your reserve.

This prevents runaway costs while keeping teams focused on proving business value.

Create a reserve for scaling winners

If a pilot works, you need to expand quickly. Waiting for next year’s budget cycle kills momentum. That’s why 5–10% of your digital budget should be held back as a flexible reserve. Use it to double down on successful pilots: expanding chatbot coverage, rolling out AI search across product lines, or scaling personalization efforts.

Don’t forget the hidden plumbing

Every AI outcome relies on infrastructure you can’t ignore:

Private vector databases. To constrain AI to your own content and reduce hallucinations.
Monitoring frameworks. To catch accuracy or compliance issues before they reach customers.
Governance. To ensure AI usage aligns with brand guidelines and regulations.

This spend feels invisible, but it’s what separates sustainable AI adoption from brittle experiments.

The Hierarchy That Keeps Budgets Grounded

Budgeting for AI isn’t about sprinkling a few tools into old line items. It’s about knowing what comes first.

Table stakes. Make your digital presence machine-readable or risk invisibility.
Business outcomes. Fund AI initiatives that directly lift conversions, reduce costs, or save time—not just vendor upsells dressed as innovation.
Discipline. Cap pilot spend, hold a reserve for scaling winners, and invest in the unseen infrastructure.

The question isn’t “how much do we spend on AI?” The question is “how do we restructure budgets to align with the new mechanics of digital value creation?” Follow this hierarchy, and you’ll avoid the trap of funding vendor roadmaps instead of your own results.

(For a companion piece on how to get AI pilots moving once the budget is in place, read Breaking AI Paralysis.)

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