McKinsey's State of AI 2025: The Marketing Visibility Gap (And How to Close It)

Everyone's Adopting AI. Few Are Seeing Results. Here's Why.

The McKinsey Numbers That Should Worry You

McKinsey's State of AI 2025 report dropped with headline numbers that sound like a success story:

• 78% of organizations now use AI in at least one business function (up from 72% in early 2024)
• Marketing & sales saw the biggest adoption surge—more than doubling from 2023
• 62% of organizations are experimenting with AI agents
• $2.6–$4.4 trillion in annual value potential from AI across marketing, sales, and customer operations

But buried in the data is a sobering reality:

Over 80% of respondents say their organizations are NOT seeing tangible impact on enterprise-level EBIT from their use of gen AI.

Read that again.

Four out of five companies using AI aren't seeing measurable business results.

The problem isn't AI adoption. The problem is the gap between using AI and being visible to AI.

The Adoption-Visibility Disconnect

Here's what McKinsey's data reveals when you read between the lines:

Everyone's Creating AI-Generated Content

Marketing & sales doubled their AI adoption. Companies are using gen AI for:
- Content creation
- Campaign optimization
- Customer communications
- Product descriptions
- Social media

But creating content with AI is only half the equation.

Few Are Optimizing for AI Discovery

The other half—the half that drives actual business results—is ensuring that AI systems can discover, understand, and cite your content.

This is the visibility gap:

McKinsey's high performers—the 20% seeing real results—aren't just using AI. They're redesigning workflows and building systems that compound over time.

Why "AI-Generated" Doesn't Mean "AI-Visible"

Here's the uncomfortable truth most marketing teams miss:

The AI you use to create content is not the same AI that helps customers find you.

• Creation AI: GPT-4, Claude, Gemini—what you use to write
• Discovery AI: ChatGPT Search, Perplexity, Google AI Overviews—what customers use to find

When someone asks ChatGPT "What's the best [your category]?" or searches "top [your product type] 2025," the answer doesn't come from your AI-generated blog posts.

It comes from structured data, entity signals, and machine-readable infrastructure that most companies don't have.

This is why 80%+ aren't seeing EBIT impact. They're using AI to create, but they're invisible to the AI that matters—the AI customers use to make purchasing decisions.

McKinsey's High Performers: What They Do Differently

McKinsey identified a clear pattern among the companies seeing real AI results:

1. They Set Growth Objectives, Not Just Efficiency

"80% of respondents say their companies set efficiency as an objective of AI initiatives, but the companies seeing the most value from AI often set growth or innovation as additional objectives."

Most companies use AI to do the same things faster. High performers use AI to unlock new capabilities and capture new markets.

For marketing, this means: Don't just use AI to write faster. Use AI to become visible in places you couldn't reach before—AI search, LLM recommendations, voice assistants.

2. They Redesign Workflows

"Half of those AI high performers intend to use AI to transform their businesses, and most are redesigning workflows."

High performers don't bolt AI onto existing processes. They rethink the entire workflow.

For marketing, this means: The traditional workflow (write → publish → wait for Google) is obsolete. The new workflow must include structured data, AI discovery protocols, and machine-readable infrastructure.

3. They Scale AI Agents

"High performers have advanced further with their use of AI agents than others have. In most business functions, AI high performers are at least three times more likely than their peers to report that they are scaling their use of agents."

AI agents—autonomous systems that execute multi-step tasks—are where the compounding value lives.

For marketing, this means: Automated content generation, structured data deployment, instant indexing, and AI crawler optimization running continuously without manual intervention.

The Five-Layer Stack: Closing the Visibility Gap

At Pressonify, we've codified what AI high performers do into the Five-Layer Optimization Stack:

Most companies operate at Layers 1-2. They optimize for traditional search and maybe featured snippets.

High performers operate at Layers 3-5. They ensure AI systems can discover, understand, and recommend their brands.

This is why 80% see no EBIT impact—they're optimizing for 2015 search, not 2025 AI.

What "AI-Ready" Marketing Infrastructure Actually Looks Like

McKinsey notes that 70% of high performers cite data management as a key obstacle. The infrastructure gap is real.

Here's what AI-ready marketing infrastructure requires:

1. Structured Data (Schema.org)

AI systems don't read prose—they parse structured data. Every piece of content needs:

Most marketing teams create content without any of this. AI systems can't reliably parse or cite unstructured content.

2. AI Discovery Protocol (ADP 2.1)

AI crawlers need machine-readable entry points:

These endpoints tell AI systems: "Here's everything you need to understand and cite us."

Without them, you're hoping AI crawlers figure out your content on their own. Most won't.

3. Entity Signal Consistency

AI systems build understanding through entity recognition. Inconsistent naming, geography, or service descriptions confuse entity resolution.

What this means: Every mention of your brand, products, and services must use consistent terminology across all content. AI needs a clean, unambiguous dataset.

4. AI Crawler Permissions

Many companies inadvertently block AI crawlers in their robots.txt:

# Most companies (blocking AI)
User-agent: GPTBot
Disallow: /

# High performers (enabling AI)
User-agent: GPTBot
Allow: /

User-agent: ClaudeBot
Allow: /

User-agent: PerplexityBot
Allow: /

If you're blocking AI crawlers, you're invisible to AI search. Period.

The $2.6–$4.4 Trillion Opportunity

McKinsey's estimate of AI value potential isn't theoretical. It's the gap between current performance and what's achievable:

"The largest value pools include customer operations, marketing & sales, software engineering, and R&D—contributing to an estimated $2.6–$4.4 trillion annual impact potential."

For marketing specifically, this value comes from:

1. Reduced customer acquisition costs (AI recommendations replace paid ads)
2. Increased organic discovery (AI search surfaces your brand for intent-based queries)
3. Higher conversion rates (AI-cited brands carry implicit trust)
4. Lower content production costs (AI-generated, AI-optimized content at scale)

But you only capture this value if AI systems can find and recommend you.

How Pressonify Bridges the Gap

Pressonify.ai was built specifically to close the visibility gap McKinsey's data reveals.

For Teams Using AI to Create Content

Every press release published through Pressonify includes:

• 3-8 Schema.org schemas automatically generated
• Knowledge graph inclusion (28 synced entities)
• llms.txt and llms-full.txt listing
• JSON Feed for AI feed readers
• IndexNow instant indexing
• Do-follow backlinks with proper attribution

This is the infrastructure most teams don't have and can't build themselves.

For Teams Redesigning Workflows

Pressonify operationalizes the Five-Layer Stack:

Instead of manually implementing each layer, you publish once and get all five.

For Teams Scaling AI Agents

Pressonify's 16-agent architecture handles:

• Content generation (Claude Sonnet 4.5)
• SEO optimization (3-8 schemas)
• Fact verification (anti-hallucination)
• Journalist matching (4,827 contacts)
• Fraud detection (4-layer system)

This is the "agent scaling" McKinsey identifies as a high-performer trait—automated systems that execute multi-step workflows.

The Cost Comparison McKinsey Implies

McKinsey's data shows high performers invest in infrastructure. But the cost comparison is stark:

Traditional Approach (What Most Companies Do)

Pressonify Approach (What High Performers Do)

McKinsey shows that high performers get 3x+ better results. The question is whether you're investing in infrastructure that compounds or one-off efforts that don't.

The 80/20 of AI Marketing

McKinsey's data reveals a clear 80/20 split:

• 80% of companies use AI for efficiency (doing the same things faster)
• 20% of companies use AI for transformation (doing new things entirely)

The 80% are using AI to write content faster while remaining invisible to AI search.

The 20% are building infrastructure that makes them the default recommendation when AI systems answer customer queries.

The choice is yours: Use AI to create, or use AI to be found.

How to Check Your AI Visibility

Before investing in more AI content tools, check whether AI can actually find you.

Our free AI Visibility Checker analyzes your domain across five categories:

Your score tells you where you sit:

• 90-100: You're in the top 1% (high performer territory)
• 75-89: Good, but gaps to close
• 50-74: Significant visibility issues
• Below 50: AI systems likely can't find you

Check your AI visibility now → (5 seconds, free)

The Bottom Line

McKinsey's State of AI 2025 confirms what we've seen in practice:

1. AI adoption is ubiquitous (78% of organizations)
2. Marketing is leading (biggest adoption surge)
3. Results are rare (80%+ see no EBIT impact)
4. High performers are different (infrastructure, workflows, agents)

The gap isn't AI adoption. The gap is AI visibility.

Companies creating AI content but invisible to AI search are the 80%.

Companies building AI-native infrastructure—structured data, discovery protocols, machine-readable endpoints—are the 20%.

Pressonify exists to move you from the 80% to the 20%.

Check your AI visibility → and see where you stand.

Or skip ahead: Publish an AI-optimized press release → and get the full Five-Layer Stack from day one.

The $2.6–$4.4 trillion opportunity is real. The question is whether AI can find you.

FAQ

Published December 16, 2025 | Pressonify.ai

Sources: McKinsey State of AI 2025, McKinsey State of AI 2024

This article demonstrates ADP 2.1 compliance with structured data, proper Schema.org markup, and AI-optimized content architecture.