Affect Performance Team

|News|Nov 11, 2026

Optimizely Introduces Autonomous Optimization and Dynamic Experiences

On October 14, 2026, Optimizely announced two new AI products: Autonomous Optimization for continuous experience optimization and Dynamic Experiences for generating personalized web pages in real time.

Autonomous Optimization uses AI to identify opportunities to improve websites, create and launch experimental variations, analyze visitor behavior, and continue optimizing toward defined business goals.

Dynamic Experiences builds the content and structure of web pages around individual visitors' intent, referral sources, on-site behavior, and other available signals.

Both products extend Optimizely's existing Experimentation and Personalization capabilities, automating processes that previously required manually preparing tests, audience segments, and page variations.

The central difference is a shift from predefined experiments and personalization journeys to systems that can independently adapt web experiences within established boundaries.

Autonomous Optimization: Continuous AI-Powered Website Optimization

Autonomous Optimization is designed to automate the full conversion rate optimization (CRO) cycle.

In a traditional CRO program, specialists analyze user behavior, develop hypotheses, create page variations, run A/B tests, and evaluate the results.

Once one experiment ends, the team selects the next hypothesis and repeats the process.

Autonomous Optimization can automate a substantial part of this work.

A marketer selects a page or user journey, defines the business objective, and establishes limits on permitted changes.

AI then analyzes available behavioral and experimental data, identifies improvement opportunities, creates variations, and launches tests.

The system uses the resulting performance data to guide further changes.

How Autonomous Optimization Works

Optimizely describes four main stages.

1. Set the Goal: Define the objective.

The marketer selects the page or user journey to optimize.

For example, the objective might be to increase conversion rate on a product page or generate more leads from a landing page.

At the same time, the marketer defines the boundaries for permitted changes.

2. Read the Signals: Analyze visitor behavior.

The system examines available visitor activity data.

In particular, it investigates where people interact with a page, encounter obstacles, abandon the journey, or convert.

This helps identify potential problems along the user journey.

3. Launch Better Experiences: Create and test variations.

AI generates new versions of the experience aligned with the defined goal.

The system can modify approved page elements and launch the corresponding experiments.

4. Keep Improving: Continuously improve.

Based on the results, the system evaluates variation performance, removes less effective options, and continues optimizing.

The process therefore does not end with one completed A/B test.

Each cycle provides data for the next.

Managing Goals and Guardrails

A key feature of Autonomous Optimization is the use of guardrails that define the boundaries of independent AI actions.

For example, a company could allow the system to optimize headlines, the placement of selected content blocks, and calls to action.

Legally significant information, sales terms, or other critical elements could be excluded from permitted changes.

The marketing team retains control over business objectives and the scope of optimization.

However, the specific guardrail settings, change approval rules, and mechanisms for stopping experiments still need to be verified during the closed beta.

It would be incorrect to assume that every existing control in Optimizely Experimentation is automatically supported by the new autonomous mode.

How Autonomous Optimization Differs From Traditional A/B Testing

Optimizely already provides experiment creation and execution tools, including its Visual Editor, AI Variation Development Agent, and results analysis.

In September 2026, the company also expanded Idea Builder: AI can propose an experiment and prepare a variation, primary metric, and audience.

In that workflow, however, a specialist reviews the proposed test and launches it manually.

Autonomous Optimization goes further.

The system is not limited to creating one experiment in response to a marketer's request.

It is intended to analyze outcomes independently and continue the optimization cycle.

The main distinctions are:

Traditional A/B Testing: A marketer develops a hypothesis, prepares variations, runs the test, and decides what to do based on the results.
AI-Assisted Experimentation: AI helps develop the hypothesis, create variations, and analyze results, but a specialist manages the sequence of experiments.
Autonomous Optimization: A marketer establishes the objective and constraints, while AI repeatedly identifies opportunities, creates changes, and tests them.

This could potentially extend CRO programs beyond a handful of the highest-traffic pages.

For example, an online store could experiment not only with its homepage and checkout but also with numerous category and product pages.

However, the effectiveness of this approach depends on traffic volume, reliable conversion measurement, and sound statistical decisions.

Automating experiments does not eliminate the problem of insufficient data.

Dynamic Experiences: Real-Time Website Personalization

The second announced product, Dynamic Experiences, is designed to create an individualized experience while a visitor is on the website.

Under traditional personalization models, marketers build audiences in advance and assign a particular page variation to each group.

For example, visitors from different countries might see different promotional offers.

Visitors arriving from paid search might see one landing page, while those arriving from email see another.

The number of personalization scenarios depends on how many segments and rules the marketing team has created.

Dynamic Experiences changes this operating model.

Instead of selecting one prebuilt page, AI assembles appropriate content and layout based on the individual visitor's available context.

It uses information about the visitor and their actions during the current session.

Which Signals Dynamic Experiences Uses

Optimizely identifies several sources of information.

Campaign Context: Information about the advertising campaign that brought the visitor to the site.
Referral Source: Where the visit originated.
Device: The type of device being used.
Location: Available geographic context.
On-Site Behavior: The visitor's actions during the session.
Natural-Language Input: Needs the visitor expresses in their own words.

For example, someone reaches a website after seeing an advertisement for marketing automation software.

The system may take the campaign message into account and display material relevant to the promoted topic.

If the visitor begins researching CRM integrations, the page could adapt to present integration details and related use cases.

If the visitor describes a specific task in their own words, Dynamic Experiences can use that context to further adjust the page content.

Personalization From the First Visit

Optimizely emphasizes that Dynamic Experiences can work with first-time visitors who have no previous interaction history on the website.

It does so using contextual signals available when the visitor arrives.

For example, the referring campaign may indicate which product category interests the visitor.

Region and device can influence how content is presented.

Subsequent on-site interactions help refine the system's interpretation of the visitor's needs.

This differs from personalization models that require a history of previous visits or prior classification into a customer segment.

However, not requiring visit history does not mean that the system can accurately determine every new visitor's individual preferences.

Initial personalization relies on available signals and should be refined as the interaction develops.

Generating Page Content and Structure

One of the most significant features of Dynamic Experiences is its ability to change not only individual text elements but also the overall presentation of a page.

Optimizely says the system can determine suitable content, layout, and next steps, building the front-end experience in real time.

For example, a visitor to a B2B website may be researching a product's integration with Salesforce.

Instead of showing a general page with a long feature list, the system could present integration resources, relevant use cases, and a link to technical documentation.

Another visitor may be more interested in the financial impact of implementation.

For that person, customer results, commercial benefits, and opportunities to request a cost estimate may be more relevant.

In a traditional model, these variants have to be designed in advance.

Dynamic Experiences is intended to build the corresponding user journey as the visit happens.

How Dynamic Experiences Differs From an AI Chatbot

An AI chatbot usually provides answers within a separate conversation interface.

The rest of the website typically remains unchanged.

Dynamic Experiences uses conversational and behavioral signals to adapt the web experience itself.

For example, if a visitor asks about product compatibility, the system could do more than provide a written answer: it could also change which resources and next steps appear on the page.

AI therefore becomes part of the page assembly mechanism rather than just an additional channel for visitor interaction.

However, Optimizely has not published exhaustive technical specifications identifying exactly which components can be generated dynamically or what restrictions apply to interface changes.

Dynamic Experiences should therefore not be described as a system capable of performing any website operation without additional integrations.

Content and Brand Controls

Optimizely keeps marketers in control of content sources and AI guardrails.

The company determines which materials may be used to build personalized experiences.

The marketing team also remains responsible for the accuracy of claims, brand consistency, and permitted forms of interaction.

This is especially important when handling prices, legal terms, technical product specifications, and promotional offers.

AI may select and arrange content, but commercially significant claims must remain grounded in verified information.

Practical Value for E-commerce

For online retailers, the two products address different needs.

Autonomous Optimization is designed for continuous improvements to commercial pages and shopping journeys.

For example, a store could use the system to optimize category pages, product recommendation placement, and calls to action.

Business goals might include add-to-cart rate, conversion rate, or other properly configured metrics.

Dynamic Experiences focuses on tailoring content to the needs of a particular shopper.

For example, a visitor arrives at an electronics store from an advertisement for TVs intended for gaming consoles.

The page could emphasize relevant features and products, then adjust recommendations based on the visitor's subsequent behavior.

In fashion e-commerce, personalization could reflect the category that interested the shopper before arriving and present a more relevant assortment.

For home improvement, content related to a particular renovation project could be more useful than a general list of product categories.

The principal commercial opportunity is to improve the performance of paid traffic by providing a more relevant experience.

However, Dynamic Experiences does not replace a product catalog, search engine, or recommendation engine.

Personalizing an assortment requires access to accurate product data and the appropriate e-commerce functionality.

Optimizely has also not yet published verified implementation results that would quantify the products' effects on conversion rate or average order value.

Applications for Service Businesses and B2B

For companies selling services, Autonomous Optimization could improve landing pages and lead generation funnels.

For example, the system could optimize how benefits are presented, where lead forms appear, and how users move through the stages of a journey.

Dynamic Experiences could tailor informational content based on the reason for the visitor's inquiry.

A person interested in a specific service could receive more relevant information instead of a general page listing every service the company offers.

For B2B SaaS companies, personalization based on referral source and visitor intent is particularly interesting.

For example, a technical specialist might look for API documentation and integrations, while a commercial decision-maker looks for implementation costs and business results.

Dynamic Experiences could potentially produce different user journeys without requiring a separate landing page for every combination of role, industry, and need.

However, inferring a visitor's role from indirect signals remains probabilistic. The system should not assign a specific job title, company, or authority to an individual without supporting data.

Availability and Limitations

Autonomous Optimization and Dynamic Experiences were introduced on October 14, 2026, but their announcement does not mean they are generally available.

Autonomous Optimization is in closed beta, as confirmed by Optimizely's official beta registration page.

The product is aimed at customers using Optimizely Experimentation.

Interested companies need to apply for beta access or contact their Optimizely representative.

General availability has not been confirmed for Dynamic Experiences either. The product is in an early stage of rollout, and participation terms should be checked directly with the vendor.

Neither product has a separate public price list on the official pages.

Pricing and access terms may depend on the existing contract, Optimizely licenses, and usage requirements.

What to Check Before Implementation

For Autonomous Optimization, correct goal configuration, data collection, and restrictions on automatic changes are essential.

Before testing, teams should determine which website elements AI may change and which metrics will define success.

For Dynamic Experiences, teams should establish which content sources are available, how personalization rules work, and what interface changes are allowed.

Particular attention is required for:

Accuracy of product and service data.
Consistency between advertising messages and landing page content.
Application of valid commercial terms.
Protection of personal data.
Ability to review personalized page variations.
Continued operation of critical user journeys.

In both cases, automation does not remove the need for quality control and statistical verification of business results.

Optimizely: A Platform for Experimentation, Personalization, and Digital Experiences

Optimizely is an enterprise digital experience platform that brings together content management, A/B testing, personalization, analytics, and digital product optimization.

Optimizely Web Experimentation supports A/B tests and multivariate experiments, page-element changes, traffic allocation between variations, and analysis of their impact on business metrics.

Optimizely Feature Experimentation provides feature flags, controlled releases, and experimentation in applications and server-side logic.

Optimizely Personalization tailors website content, offers, and recommendations to audience characteristics and behavior.

The platform also includes CMS, content marketing, digital asset management, analytics, and AI tools in Optimizely Agent Platform.

Autonomous Optimization and Dynamic Experiences extend those capabilities.

Autonomous Optimization automates recurring experiments and website optimization based on defined goals.

Dynamic Experiences assembles personalized pages and user journeys from visitor context and intent.

For businesses, the potential value is less manual CRO work and more relevant digital experiences without having to prebuild a large number of separate journeys.

At the time of the October announcement, however, general availability had not been confirmed for either product. Their actual performance, technical capabilities, and commercial terms need to be evaluated through pilot deployments.

Trust Us Transform Your Performance Marketing.

Make Every Experiment Measurable. Build a rigorous CRO program with trustworthy conversion tracking, testing, and personalized landing page measurement.

Discuss CRO Strategy