Introduction

According to McKinsey, 78% of organizations now use generative AI in at least one business function. But as adoption (and general AI overwhelm) rises, we’re all asking ourselves three key questions:

• How is everyone else using AI?
• How can AI create value for my organization?
• Are we currently unlocking its full potential?

This report offers insight into how Optimizely Opal is being used across our customer base and what benefits those customers are getting. With three months of usage data from nearly 900 companies, insights from early adopters, and the latest industry research, we explore how AI is actually being used and what benefits adopters are getting from usage.

Find out what’s working, what’s evolving, and what best-in-class AI marketing practices look like today.

About our research

Data sources

Our insights are drawn from three primary data sources:

• Internal Optimizely Opal data: Analyzed usage data across 47,000 Optimizely Opal interactions from customers who adopted Optimizely Opal after its public launch, covering feature adoption patterns, user behavior, and measurable performance outcomes.
• Customer stories from early adopters: Firsthand insights from nearly 900 Optimizely Opal adopters, highlighting their implementation experiences, challenges overcome, and the tangible impacts achieved.
• Industry research: Third-party research and surveys from leading firms like McKinsey and Gartner, providing external context around broader AI adoption trends and transformations in marketing.

Methodology

To ensure statistical rigor and a clean, understandable output, this analysis employed a methodology with a few key components:

Key takeaways

The state of AI: Adoption and impact today

Industry transformation at scale

Companies across industries are achieving unprecedented gains by integrating AI into their core workflows.

What is Optimizely Opal?

Today’s marketers are expected to do more than ever—most of the time with fragmented systems, limited (or messy) integrations, and increasing pressure to deliver. At Optimizely, we believe there’s a better, less disconnected, and more integrated way.

Optimizely Opal is more than just another AI assistant or chatbot: it's an extension of your workforce, enabling marketing and digital teams to effortlessly scale the way they work and redefine what’s possible.

From ideation to optimization to analysis—and every step of the marketing lifecycle in between—Optimizely Opal is your agentic, fully-autonomous, and infinitely-scalable AI specialist, helping you complete any marketing and digital job to be done.

Optimizely Opal unlocks infinite scale by working both with you and for you: automating tasks on your behalf, offering proactive insights and suggestions for optimization, creating brand-specific content, and solving your bottlenecks with custom guardrails and control.

Since its launch in May 2025, nearly 900 companies have adopted Optimizely Opal to embed AI throughout their marketing workflows. Top adopters include Diligent, Robinson Club, Elite Hotels of Sweden, and Road Scholar, representing over $2B in annual revenue.

Industry recognition

Industry Analysts are taking notice. Optimizely was named a Leader in the 2025 Gartner® Magic Quadrant™ for Content Marketing Platforms. The report states “Optimizely evolved its stand-alone AI-enabled features into a network of AI agents automating the entire content production and delivery process. It reduced potential AI risks by enhancing its compliance and security features with role-based access control, which is ideal for large enterprises.”

How it’s being used

Experimentation accounts for 58.74% of credit usage, with Content Marketing Platform (CMP) representing 26.58%. The remaining 14.68% is distributed across additional workflows including Optimizely Opal itself, CMS, Data Platform, and Configured Commerce. This distribution shows that approximately 85% of all Optimizely Opal credits are concentrated in just two primary use cases: experimentation workflows and content marketing activities.

Core use cases powered by Optimizely Opal

brand_optimizationOptimization

From ideation to analysis, Optimizely Opal optimizes the experimentation lifecycle:

• Experiment Ideation Agent: Provides test ideas for web pages and creative elements.
• Experiment Planning Agent: Generates complete test plans with hypotheses, KPIs, and estimated durations.
• Variation Development Agent: Makes automatic edits to variations based on a prompt.
• Experiment Summary Agent: Summarizes test results and recommends next steps.
• Personalization Ideation Agent: Provides ideas for personalization campaigns.

brand_documentContent marketing

AI-powered, on-brand content tailored to specific industries, campaigns, and languages:

• Industry Marketer Agent: Creates industry-specific content eliminating the need for comprehensive research, various industry experts, and duplicative work.
• Market Research Agent: Conducts research, compiles insights, and then provides campaign suggestions.
• Keyword Research Agent: Provides suggested keywords based on top business priorities, generates a plan for content clusters, and builds out optimized content in seconds.
• AI Campaign Kits: Generates end-to-end campaigns, including briefs, tasks, supporting content, and social promotion.
• AI Translations: Translates content into multiple languages, instantly.

brand_approval-workflowBrand and workflow customization across products

Optimizely Opal isn't siloed. Instead, it's woven throughout Optimizely One:

• Fully integrated: Embedded in the UI across Optimizely’s product suite.
• Consistent messaging: Brand tone, voice, and content guardrails are set once, then used everywhere.
• Marketing automation: Purpose-built agents to support common marketing and digital use cases.

Firmographic data: Where is Optimizely Opal being used?

• The US dominates adoption (41.1%), with the UK and Australia close behind. European markets show strong uptake across Germany, Sweden, and the Netherlands.
• Emerging adoption in India and Poland points to global scalability.
• Overall, usage spans 50+ countries, reinforcing that marketing teams around the world are prioritizing smarter, faster ways of working.

Industry uptake: What are the main industries adopting Optimizely Opal?

Retail leads adoption at 17.4%, driven by the sector's constant demand for fresh, scalable content, hyper-personalized experiences, and rapid campaign execution.

• Software (11.8%) and Financial Services (9.6%) follow closely, while Business Services rounds out the top four at 9.1%.
• Notable adoption across traditionally conservative sectors—Healthcare & Medical (3.2%), Education (3.6%), and Insurance (3.6%)—signals a broader industry shift toward marketing modernization and operational agility.
• The long tail of adoption across diverse industries reflects a growing consensus: AI-powered marketing is transitioning from experimental investment to essential capability across virtually every sector.

Digital maturity: How digitally mature are adopters of Optimizely Opal?

• The majority of adopters (51.2%) come from digitally-advanced organizations, while more than one-third (37.1%) represent mid-maturity companies leveraging AI to accelerate their digital transformation journey.
• Notably, 11.6% of adoption stems from low digital maturity organizations, demonstrating AI's relatively accessible entry point compared to other advanced technologies.
• The spread suggests that AI is no longer reserved for tech-forward organizations—it’s becoming a lever for transformation at all stages of digital readiness.

What roles are using Optimizely Opal?

Understanding who is using Optimizely Opal—not just which companies, but which roles and teams—provides crucial context for its impact on marketing organizations. Our data reveals an adoption pattern that defies traditional technology rollout models, with usage spreading horizontally across roles rather than remaining confined within specialized teams.

• Senior Leadership/Executives
• Product Marketing Teams
• Customer Success
• Content Marketing
• Digital Marketing
• Field Marketing
• Brand & Creative Teams
• Events Teams

Who is getting the most out of the infinite workforce?

Not all users are equal. Some users are finding significantly more use cases and generating significantly more value than others.

• To be in the top 10% of Optimizely Opal users (power users), you must engage with Optimizely Opal at least 60 times.
• Hitting the top 25% requires just around 30 interactions—a sign of early-stage adoption curve.
• The top 1% use Optimizely Opal 198+ times, often embedding AI into daily workflows.
• At the extreme end, maximum usage reaches 698 interactions, demonstrating how deeply some organizations have integrated AI into their marketing operations.

In a month, these top users typically:

• Send 70+ messages
• Create 45+ tasks
• Create 11+ campaigns
• Create 10+ articles
  
  

The benefit of AI: What companies gain from an infinite workforce

To understand Optimizely Opal's true impact, we analyzed performance data from early adopters using a Difference-in-Differences methodology. The results show clear performance improvements that scale with both usage frequency and depth of engagement across optimization and content marketing functions.

brand_ab-testing Digital optimization

Experimentation programs benefit fundamentally from either more or better experiments. The DiD analysis revealed improvements across both vectors in the headline benchmarks:

+ 78.66% created experiments
+ 2.38% concluded experiments
+ 24.05% created personalization campaigns
+ 11.97% concluded personalization campaigns
+ 9.26% win rate
+ 1.38% conclusivity rate

The gap between creation and concluded uplift may stem from the limited reporting window or from early adopters using Optimizely Opal mainly for idea generation, though further analysis suggests that greater engagement leads to more concluded experiments.

Win rate and conclusivity also rise, signaling modest quality gains. Average uplift dips slightly—a natural effect of higher velocity—but is far outweighed by the surge in volume.

Analysis of usage and funnel data shows that the best users of experimentation use Optimizely Opal throughout the entire experimentation lifecycle:

• 10.95% of experiments are created from ideas by Optimizely Opal
• 6.80% of experiments are summarized using Optimizely Opal
• 19.54% of experiments are Optimizely Opal-driven follow-ups

The analysis found that not only was there an uplift in nearly all major experimentation metrics and strong usage throughout the experimentation lifecycle, but also the higher the usage, the greater the uplift.

• The more teams use Optimizely Opal—measured by both active days of usage and credits consumed—the more experiments they create and conclude, scaling linearly with increased engagement.
• The steeper uplift slope for concluded experiments compared to created experiments suggests that increased Optimizely Opal usage is not only driving more experimentation activity, but also improving teams’ ability to bring experiments to a meaningful outcome.

Users can calculate the exact projected uplift of their programs using the equations below, where ‘y’ (the dependent variable) is the expected uplift and ‘x’ (the independent variable) is active days of usage or credit consumed.

Customer story: Unlocking experimentation at scale

One Fortune 500 financial services company dramatically accelerated their experimentation program after adopting Optimizely Opal. In the year before adoption, their experimentation volume was modest due to switching systems. But in the following year— with access to Optimizely’s advanced experimentation platform and the added power of Optimizely Opal AI—their team increased experiment velocity by nearly 80x.

One Fortune 500 financial services company dramatically accelerated their experimentation program after adopting Optimizely Opal. In the year before adoption, their experimentation volume was modest due to switching systems. But in the following year— with access to Optimizely’s advanced experimentation platform and the added power of Optimizely Opal AI—their team increased experiment velocity by nearly 80x.

This rapid ramp-up showcases how combining robust experimentation infrastructure with AI-driven acceleration unlocks scale and impact that simply wasn’t possible before.

brand_promotion Content orchestration

Similar to experimentation programs, content marketers create more value by either producing more or better revenue-generating content. Companies can also realize value by time or labor savings when marketers become more efficient. The DiD analysis shows benefit across all three of those value drivers:

• + 7.05% campaigns created
• + 12.82% tasks created
• − 53.73% time per campaign
• − 15.41% time per task
• + 7.41% engagement time

The sharp reduction in campaign and task completion times—paired with higher output volumes—shows that CMP users are achieving genuine workflow acceleration, not just working faster. At the same time, increased engagement time suggests teams are using Optimizely Opal to produce higher-quality content that holds visitors’ attention and delivers greater value.

We found these same trends reflected in the funnel data. Content created by Optimizely Opal is getting used by marketers more often than not:

• Optimizely Opal-generated content (both images and text) is being actively adopted by users, not just tested or used for brainstorming.
• Text generation is particularly successful, with almost all generated outputs being used.
• Image generation adoption is strong but with room for further improvement as LLMs ability to create images catches up to their ability to create high-quality text.

Like with experimentation, the productivity benefits of Optimizely Opal usage in content marketing scale with usage:

• The more teams use Optimizely Opal—measured by both active days of usage and credits consumed—the less time they take to complete tasks and campaigns, with time savings scaling consistently as engagement increases.
• The steeper negative slope for time per campaign compared to time per task suggests that Optimizely Opal usage is driving especially large efficiency gains in campaign delivery, freeing up resources for additional creative and strategic work.

Users can calculate the exact projected uplift of their programs using the equations below, where ‘y’ (the dependent variable) is the expected uplift and ‘x’ (the independent variable) is active days of usage or credit consumed.

Customer story: Accelerating content performance at scale

A leading financial services organization rapidly accelerated its content operations after adopting Optimizely’s Content Marketing Platform with Optimizely Opal AI. In the period before adoption, campaign creation and completion were modest. But in the following months—powered by AI-driven workflow acceleration—the team achieved a 43.22% uplift in campaigns created and an over 3× increase in campaigns concluded.

A leading financial services organization rapidly accelerated its content operations after adopting Optimizely’s Content Marketing Platform with Optimizely Opal AI. In the period before adoption, campaign creation and completion were modest. But in the following months—powered by AI-driven workflow acceleration—the team achieved a 43.22% uplift in campaigns created and an over 3× increase in campaigns concluded.

This surge demonstrates how combining a unified content platform with AI can unlock new levels of scale and efficiency, enabling teams to deliver more high-quality campaigns in less time.

Best practices: How to unlock AI’s full value

The benefits of an integrated AI approach are clear, but not everyone is seeing the same level of success. It’s not quite as simple as switching a new tool on and watching the dollars pour in—it requires a transformation in how teams work.

Here are the best practices that separate AI leaders from everyone else:

AI will make good marketers great, but great marketers irreplaceable, and the difference will be largely in how well they implement AI best practices. The key is making it feel natural, not forced—an extension of what you already do well, just amplified.

AI governance: Balancing innovation with oversight

Without clear frameworks, teams face inconsistent outputs, brand risks, and compliance gaps that can quickly derail AI initiatives.

Here’s what to establish up front, along with a governance framework you can follow:

Essential building blocks

A little groundwork upfront can go a long way to improving the long-term outcomes of AI usage. Here are some of the recommended first steps:

• Start with clean data: Audit content repositories and databases before scaling AI deployment. Clean, current data is the foundation of reliable AI output—garbage in, garbage out. Providing a single source of truth—such as an approved dataset or integrated live data—ensures your AI outputs remain accurate and valuable.
• Establish brand guardrails: Use custom fine-tuning to embed brand voice and content guidelines directly into AI behavior. Define acceptable parameters upfront to prevent off-brand outputs.
• Keep humans in the loop: Implement review workflows where experienced marketers approve AI-generated content, especially for customer-facing communications.
  62%

  of marketers lack formal AI training

• Partner with legal early: Establish clear guidelines around AI content usage, IP rights, and data privacy. Stay ahead of evolving regulations to protect your brand.
• Build team capabilities: 62% of marketers lack formal AI training. Bridge this gap with practical prompt engineering education and develop “AI champions” to drive adoption.

Designing your governance model

There’s no one-size-fits-all AI governance model. Here are key factors to consider when designing the optimal governance model for your business:

• Control: Who can deploy AI tools and approve outputs? Ensure teams have mastered AI fundamentals—prompt engineering, quality assessment, brand alignment—before expanding access across the organization.
• Capabilities: Do you have the right tools and resources for proper oversight? Consider your team's technical expertise, available budget for AI platforms, and bandwidth for training. Start simple and scale capabilities as your team matures.
• Risk tolerance: How much risk can you accept from AI errors or off-brand messaging? High-stakes customer communications require stricter approval workflows, while internal efficiency tools can operate with lighter oversight.
• Connection to business priorities: Is your AI governance aligned with core business objectives? Avoid creating AI governance in isolation. Ensure your framework supports broader marketing goals and can adapt as business priorities shift.
• Data sensitivity: What sensitive data will your AI systems access? Build governance controls that scale with data sensitivity. Customer data requires stricter access controls than general marketing content.

What’s next: Where AI is going (and how to be ready for it)

While it’s already hard to imagine a world without it, AI is only going to get better from here. Here are our predictions for how AI will continue to shape marketing work, how Optimizely plans to stay ahead of those trends, and how you can be ready to take advantage of the change.

Short-term (2025):

AI embedded across marketing operations

Currently, companies are rapidly embedding AI throughout their marketing stack, moving beyond experimentation to operational deployment. Marketers now leverage AI for efficiency gains, content optimization, and workflow automation, liberating teams to focus on the creative and strategic work that drives real business impact.

Mid-term (2026-2027):

Multi-agent orchestration and AI ecosystems

In the mid-term, simple AI assistants are beginning to evolve into multi-agent systems orchestrating complex marketing workflows. Rather than one chatbot doing it all, specialized AI agents collaborate: one might analyze customer data, another generates personalized content, and another optimizes the delivery timing. Platforms like Optimizely Opal are already pioneering this approach, enabling multiple AI agents to work in concert with robust governance frameworks.

Long-term (2028 and beyond):

Generative experience optimization

Further into the future, marketing will evolve into a highly personalized, automated experience at every touchpoint. AI agents will dynamically adjust user experiences in real-time across digital platforms, while the concept of "marketing to AI"—where strategies target AI agents rather than human consumers directly—becomes mainstream. This fundamental shift will require marketers to craft messaging that resonates with both human and AI-driven decision-makers.

Free tickets

Opticon25 - World's premier marketing event starts Sept 10

Latest product launches, spiciest marketing tips, and the future of marketing and product with AI. Join us in in NYC, LDN and Online.

Concluding remarks

This report has validated what most Optimizely Opal users already know from experience: that adoption is rapid and broad, that AI results in real, measurable improvements in productivity, output, and quality, and that there are ways to make AI usage more effective.

Here are our most actionable takeaways from this Optimizely Opal study:

1. Focus on improving key metrics: The data shows that Optimizely Opal impacts experimentation velocity, average test impact, marketing team output, and content quality. Use it intentionally to improve these key metrics that drive value for the business.
2. Embed comprehensively throughout workflows: Optimizely Opal works best when used throughout the content lifecycle and scales with more use. Use it not only as an idea generator, but as a strategic partner through the whole marketing process.
3. Set up your AI program for success: AI isn’t a silver bullet. Some approaches work better than others. Adopt best practices and governance to get the most out of your investment.

Many Optimizely customers are already putting these insights into practice and seeing the benefits. As a leader in AI innovation, we’ll continue expanding our capabilities and reporting how best to use them in future analyses.

Appendix

Key definitions

• Experiments/Personalization Campaigns Created – The number of new experiments or personalization campaigns initiated within the reporting period.
• Experiments/Personalization Campaigns Concluded – The number of experiments or personalization campaigns that have reached a definitive outcome in the reporting period.
• Average Test Impact – The expected value of an experiment, calculated as win rate × uplift, used to forecast future returns and balance win rate with impact size.
• Campaigns – Coordinated sets of marketing or content activities aimed at achieving a specific goal, often spanning multiple channels and assets.
• Tasks – Individual pieces of work (e.g., write, review, edit) assigned to users within a CMP workflow.
• Engagement Time – duration a visitor actively interacts with a page—measured when the page is in focus and the user scrolls or moves the cursor, plus a 5-second grace period after activity stops.
• Credits – Optimizely Opal usage is measured in credits, consumed whenever an Optimizely Opal feature calls an AI model. Credit use varies by task complexity.

Free tickets

Opticon25 - World's premier marketing event starts Sept 10

Latest product launches, spiciest marketing tips, and the future of marketing and product with AI. Join us in in NYC, LDN and Online.