Scroll LinkedIn for five minutes and you’ll see it: everyone is “doing AI.”
Teams are experimenting with AI content, AI chatbots, AI analytics, AI everything. Yet when you zoom in on the website — still the primary digital storefront for most businesses — a lot of leaders quietly admit:
“We’re excited about AI, but we’re not really seeing it move the needle on our site yet.”
According to The 2026 State of the Website report, 95% of marketing and technology leaders say they face barriers to adopting AI on their websites.
At the same time:
- 92% of technical leaders believe AI is critical for website innovation over the next two years
- And 100% of marketing teams who can’t successfully adopt AI fail to execute projects on time or on budget.
So the opportunity is obvious… but something’s clearly broken.
This gap between AI potential and real website impact is what the report calls the AI adoption gap — and it’s the difference between teams that talk about AI and teams that quietly use it to win.
Let’s unpack what’s going on, and how to fix it.
The upside: what AI can (actually) do for your website
Leaders aren’t confused about the upside. They see it clearly.
In the report:
- Technical leaders say AI can unlock new levels of productivity, efficiency, and innovation for their websites — from automating manual processes to better analytics and personalization.
- Marketing leaders are especially excited about:
- Improving SEO and AEO (Answer Engine Optimization)
- Better analytics and reporting
- AI-powered assistants and automation
- Enhanced personalization
With search shifting toward AI-powered and LLM-based experiences, AEO is becoming urgent. Teams now need structured, context-rich content that answer engines can easily parse and feature — not just keyword-stuffed blog posts.
So if the opportunity is clear and the will is there… why aren’t more websites actually using AI in a meaningful way?
The AI adoption gap, explained
The report sums it up bluntly:
Leaders see the promise of AI… but outdated systems and unclear strategies are slowing impact.
Even though teams are motivated, they hit a wall when it’s time to move from “AI brainstorming” to shipping AI-powered experiences on the live site.
The research shows two big categories of blockers: hesitations and implementation barriers.
Let’s walk through both.
Barrier #1: Hesitations (security, clarity, and confidence)
1. Security and compliance fears
AI isn’t just a new tool — it’s a new attack surface.
- 54% of technical leaders say they’re concerned about using AI tools in production, especially around security.
They’re right to be cautious: data leakage, compliance issues, and governance gaps are real risks. Many organizations are risk-averse, and without clear guardrails, “no” feels safer than “go.”
2. Resource gaps and lack of direction
Then there’s the human and strategic side:
- 64% of respondents say they hesitate to adopt AI due to resource gaps, uncertainty, or lack of clear direction.
Common patterns:
- “We don’t have anyone who really owns AI strategy for the website.”
- “We’re not sure what the best first use case is.”
- “We tried a tool, but it just added complexity.”
Without a clear roadmap, AI feels like more work — not less.
3. ROI is fuzzy
Even when teams do experiment:
- 67% of marketing leaders struggle to calculate accurate ROI for AI tools.
If your analytics stack doesn’t tell you whether AI experiments are driving traffic, conversions, or revenue, it’s hard to justify more investment. So projects stall at “interesting pilot” instead of scaling into core workflows.
Barrier #2: Implementation and technical debt
Even if leaders are mentally sold on AI, their tech stacks often aren’t.
The report finds that:
- 73% of organizations face technical and integration issues that affect their ability to adopt AI.
And when they do try to implement AI:
- Teams face technical hurdles, security/data issues, and brand protection concerns, often caused by platforms that weren’t designed for AI-driven work.
Common symptoms:
- Patchwork tools everywhere
- One tool for content, another for design, a separate SEO platform, a testing tool, analytics somewhere else — and AI hacked into the middle. Each integration adds fragility.
- Legacy CMSs and custom buildsOlder systems weren’t built with AI or AEO in mind. They make it hard to:
- Store structured content
- Expose clean APIs to AI tools
- Iterate quickly on new experiences
- Limited in-house expertiseEven if the tools are there, teams often lack people who understand both:
- The technical side (APIs, data, security), and
- The marketing side (user journeys, messaging, conversion).
The result? AI remains a collection of isolated experiments, not part of a cohesive website strategy.
The paradox: experiments everywhere, impact nowhere
Here’s the twist: it’s not that teams are doing nothing.
In fact:
- 64% of marketing leaders say they’re actively creating content with LLMs and AEO/SEO in mind.
- 69% of teams are optimizing website content to stay visible across LLMs and AI-powered search results.
So there are experiments happening — AI-written content, AEO tweaks, maybe an assistant or AI search pilot.
But turning this into sustained competitive advantage requires more than enthusiasm. It demands:
- A clear strategy
- The right platform foundation
- And solid governance
Without those, AI remains a flashy side project instead of a core driver of website performance.
How to actually turn AI hype into website results
So how do you close the AI adoption gap in practice?
Here’s a simple framework you can apply over the next 6–12 months.
Step 1: Start with business outcomes, not tools
Before you pick an AI vendor, answer:
- What user journey do we want to improve first?
- (e.g. demo bookings, self-serve onboarding, lead qualification, product discovery)
- What measurable outcome do we want?
- (e.g. +15% in qualified leads, +10% in conversion rate, -30% in time to publish, +20% in organic traffic from AI-powered search)
This keeps you focused on impact, not novelty.
Step 2: Pick 2–3 concrete website use cases
Don’t boil the ocean. Choose a small set of high-leverage use cases aligned to those outcomes. For example:
- AEO-optimized content workflows
- AI-assisted briefs and outlines that structure content for answer engines
- Automated schema suggestions and internal linking ideas
- AI-assisted content production (with human review)
- Drafting first passes for landing pages, FAQs, or resource descriptions
- Repurposing long-form content into snippets, summaries, or email copy
- On-site search and discovery
- AI-powered search that understands natural language and intent
- “Ask the site” experiences that surface relevant pages and resources
- Personalization blocks
- AI-suggested content or CTAs based on behavior or segment
- Different messaging for new vs returning visitors
The goal: ship one or two of these to production, not fifteen in a backlog.
Step 3: Get your foundation right (platform + content)
This is where a lot of teams get stuck — and where the report is very clear:
Breaking the cycle requires platforms that eliminate technical barriers and make AI-driven innovation practical, not aspirational.
Practically, that means:
- A website platform that plays nicely with AI
- Clean APIs
- Structured content models
- Native or easily integrated AI capabilities
- Strong permissioning and governance
- A content model that’s structured and consistent
- Clear content types (e.g. solutions, industries, use cases, resources)
- Standardized fields for metadata, FAQs, benefits, features, etc.
- Content that’s easy for both LLMs and traditional search engines to understand
If your current setup is a tangle of plugins and legacy templates, consider whether it’s time to modernize. This is often the real unlock.
Step 4: Build AI governance into your workflows
To reduce hesitation, make AI feel safe and boring — in a good way.
Create simple, documented rules:
- Data & privacy
- What data can and cannot be sent to third-party AI tools?
- Which tools are approved?
- Brand & quality
- AI is allowed to draft, humans must approve.
- Define tone of voice, messaging pillars, and red lines.
- Review & rollout
- Set clear review steps for any AI-generated content or UX.
- Start with limited audiences or non-critical pages, then widen rollout.
This lowers the psychological and real risk. It turns “we’re nervous about security and brand” into “we know exactly how to do this safely.”
Step 5: Measure, learn, and scale
Finally, make sure you can prove what’s working. That’s how you escape pilot purgatory.
- Set specific metrics per use case
- AEO content: impressions from AI-driven search, assisted conversions
- AI content workflows: time-to-publish, content volume, quality scores
- AI search/personalization: on-page engagement, conversion rate, support deflection
- Run A/B or holdout tests where possible
- Show whether AI-driven variants actually improve performance vs control.
- Make decisions from data, not vibes
- This is how you get buy-in for the next round of investment.
When leaders can see that AI-driven changes are measurably improving traffic, engagement, or revenue, the conversation shifts from “Is AI worth the risk?” to “How fast can we scale this?”
What closing the AI adoption gap looks like
Organizations that bridge this gap over the next year will look very different from those that don’t.
On the winning side:
- Marketing teams are confident and autonomous, using AI as a standard part of their workflow — not a novelty.
- Technical teams focus on platform, governance, and performance, not constant manual interventions.
- The website is structured for AEO, discoverable across AI-powered search, and continuously improving based on AI-assisted analytics.
- AI initiatives are tied directly to business metrics, not just “innovation theater.”
On the other side, teams will still be:
- Debating which tools to use
- Worrying about security and brand risk
- Fighting with legacy systems
- And wondering why competitors’ websites are quietly pulling ahead
The good news? You don’t need a massive transformation to get started.
Pick one journey. Pick one or two AI use cases. Make sure your platform and governance can support them. Measure impact. Then repeat.
That’s how you move from AI hype to actual website results — and step out of the 95% still stuck at the starting line.
