92% of Businesses Increase GenAI Spend for Growth Metrics

By 2026, a lot of brands won't be using GenAI to "save time." They'll be using it to hit the numbers the board actually cares about.

BLUF: Research suggests brands are shifting GenAI from an ops tool to a growth engine—optimizing for revenue, conversion, and pipeline impact first, and operational efficiency second. If you're a CMO, the winning move is to instrument GenAI like any other growth channel: clear metrics, tight governance, and fast feedback loops.

Why growth metrics are beating efficiency as GenAI's "job to be done"

So here's the thing: "efficiency" is an easy story to tell, but it's rarely the story that gets budgets renewed.

The investment trend makes that clear. According to Master of Code Global, 92% of businesses plan to increase GenAI investments between 2025–2027. That kind of momentum doesn't happen because teams shaved a few hours off content production. It happens because leadership expects measurable growth outcomes.

Adoption is also moving from experimentation to everyday usage. A 2024 McKinsey Global Survey on AI shows 72% of organizations use AI in at least one business function, up from about 50% in previous years, and marketing and sales ranks among the top functions for adoption (as reported in McKinsey's State of AI report). When marketing is one of the top functions adopting GenAI, the implied KPI isn't "time saved." It's demand.

And marketers themselves are already operating that way. According to SEO.com, 88% of digital marketers surveyed use AI daily, and 56% of marketers' companies use AI. Daily usage is a signal: GenAI is becoming part of the growth workflow, not a side project.

What "GenAI for growth" looks like in practice (and what to measure)

If GenAI is being prioritized for growth metrics, you'll see it show up in three places: acquisition, conversion, and expansion.

Acquisition: GenAI accelerates campaign iteration—more landing page variants, more audience-specific creative, faster testing cycles. The metric shift here is from "time-to-launch" to cost_per_lead, pipeline_created, and CAC_payback.

Conversion: GenAI is increasingly used to personalize messaging and reduce friction across the funnel (product education, objection handling, follow-ups). According to Master of Code Global, 70% of surveyed companies report revenue growth and 61% report higher conversion rates from GenAI implementation. These are self-reported figures that may not generalize to every organization, but they offer a useful benchmark: if your GenAI initiatives can't connect to conversion lift, you're probably optimizing the wrong thing.

Expansion: In eCommerce especially, the growth orientation is obvious. According to Master of Code Global, 78% of brands in eCommerce have implemented or plan AI integration—often tied to recommendations, merchandising, and lifecycle messaging. That maps directly to AOV, repeat_purchase_rate, and LTV, not just internal productivity.

The practical takeaway: if your GenAI roadmap is mostly "make more stuff faster," you're leaving the real upside on the table. Tie GenAI to a revenue line, and suddenly the prioritization conversation gets easy.

The hidden risk: growth-first GenAI without operational discipline

Now the part most teams learn the hard way.

When GenAI is judged primarily on growth metrics, teams can start shipping faster than they can control quality. That's where brand risk creeps in—not through big dramatic failures, but through small inconsistencies that compound: off-brand tone, inaccurate claims, mismatched offers, messy attribution.

The broader market is scaling quickly, which raises the stakes. According to Statista, the AI market is projected to reach significant growth by 2026, and industry surveys consistently show the majority of leading companies are investing in AI capabilities. Translation: your competitors (and your customers) will be surrounded by AI-shaped experiences. Expectations will rise, and tolerance for sloppy execution will drop.

So yes—optimize for growth. But build the operational muscle that keeps growth from turning into chaos:

• Governance: what GenAI can and can't say, and where humans must approve.
• Measurement: consistent definitions for conversion, incrementality, and pipeline.
• Inputs: clean product data, up-to-date positioning, and a single source of truth.

Without those, you may see short-term lift—and long-term brand debt.

Key Insight: If GenAI isn't accountable to revenue and controlled by clear guardrails, you'll either get "safe" outputs that don't move the needle—or fast outputs that create brand risk.

A real-world signal: brands are using GenAI to scale personalization, not just content volume

Want a concrete example of "growth over efficiency"? Look at how major eCommerce brands have leaned into AI-driven personalization.

For instance, Amazon has long used AI to power product recommendations and merchandising experiences (a widely documented approach in its customer experience and retail operations). The point isn't that every brand should copy Amazon's stack. It's that the value creation model is clear: personalization that increases conversion and basket size.

That's the pattern GenAI is pushing downstream to more teams:

• More tailored on-site experiences
• Faster testing of offers and messaging by segment
• More responsive lifecycle flows (email/SMS/on-site prompts)

And the adoption data supports that direction. When a significant share of companies are already using GenAI in marketing and sales (per McKinsey's 2024 AI survey), the "next step" is less about producing more assets and more about producing better-performing assets—then proving it with attribution.

Key Takeaways:

• Instrument GenAI initiatives around pipeline, conversion_rate, and LTV, not just hours saved.
• Build governance and brand guardrails before scaling GenAI across channels.
• Prioritize use cases that improve customer experience (personalization, education, follow-up), not only content production.

GenAI budgets are likely to keep rising through 2026 if teams can show measurable growth impact—especially as adoption becomes standard operating procedure. The brands that win won't be the ones with the most AI experiments. They'll be the ones that can answer one simple question with confidence: Which GenAI use cases are driving growth, and how do we know?