Blog posts by Opti Chronicles

Optimizely’s Summer ’26 Roadmap: The CMS Is Starting to Look Less Like a Publishing Tool and More Like Marketing Infrastructure

Thu, 09 Jul 2026 17:06:01 GMT

Optimizely’s Summer ’26 Product Roadmap event was not just a list of product updates. At least, that is not the part I found most interesting.

The more useful takeaway was the way Optimizely is framing the next phase of digital experience: not only around marketers creating content, or customers consuming it, but also around AI agents discovering, retrieving, interpreting, and eventually acting on that content.

That may sound abstract at first. In practice, it is very real.

Most marketing and digital teams are already using AI in some form. Someone drafts copy in ChatGPT, Claude, Gemini, or Copilot. Someone else uses AI to summarize research, produce metadata, create campaign variations, or generate a first version of an article. Then the work moves back into the CMS, where the real operating process begins: page structure, metadata, approvals, brand review, localization, publishing, analytics, optimization, and eventually the next round of revisions.

The AI helps, but it usually sits beside the workflow. It does not really understand the CMS. It does not know the content model. It does not have reliable memory of the brand’s taxonomy, page patterns, permissions, approval rules, or performance history. So the team still carries the coordination burden.

That gap — between AI as a useful side tool and AI as part of the operating model — was the thread that ran through the sessions I attended.

The event opened with Optimizely’s product and AI leadership sharing recent product highlights and what is coming next, especially around AI. Optimizely Academy describes the Summer ’26 Product Roadmap as a 90-minute event with live demos, product deep dives, and an education track across CMS, CMP, Experimentation, Commerce, and Opal AI. From there, the event moved into product breakouts and practical education sessions. I joined the CMS breakout, followed by the SEO, GEO, AEO, and AI Visibility session, which focused on how teams can understand what AI agents are doing on their websites and how to act on that information.

For digital leaders, marketing VPs, and MarTech decision makers, the event raised a practical question: what happens when the CMS is no longer only the place where teams publish pages, but the system that helps humans, customers, and AI agents interact with the brand?

That is where this roadmap gets interesting.

The three experiences Optimizely is organizing around

One of the clearest messages from the opening session was that Optimizely is organizing its product thinking around three experiences: the marketer experience, the customer or digital experience, and the agent experience.

The marketer experience is the day-to-day reality of the teams inside the platform: content creators, campaign managers, product marketers, digital producers, SEO teams, analysts, and developers. These are the people dealing with content requests, campaign deadlines, approval cycles, localization needs, personalization rules, and reporting pressure.

The customer experience is the more familiar DXP conversation: what customers, prospects, members, partners, or buyers see when they interact with the brand. This includes websites, landing pages, product pages, personalized journeys, account-based marketing pages, forms, search experiences, and commerce flows.

The new piece is the agent experience. In the opening session, Optimizely described agents as a new type of actor in the system, one that needs the right access, visibility, and governance. That point matters. AI agents are not just another reporting segment. They are becoming part of the discovery and interaction layer around digital experiences. Optimizely’s message was that brands need to understand and optimize for them without losing control of governance, reliability, or trust.

That is a useful framing because it avoids treating AI as a generic productivity add-on. It also avoids the common mistake of talking about AI only as a content generation tool. The larger issue is not whether AI can draft a paragraph. It can. The larger issue is whether AI can work inside the same operating model that enterprise marketing teams already depend on.

And that means governance, context, workflows, permissions, measurement, and accountability.

The CMS is being asked to do a different job

The CMS breakout made this shift more concrete.

Optimizely’s CMS team described the current reality many teams face: AI is often layered on top of the CMS, not built into the workflow. A marketer drafts something in an AI tool, copies it into the CMS, manually optimizes for SEO or GEO, publishes it, looks at insights somewhere else, then comes back to make updates. There is nothing inherently wrong with those steps. The issue is that the work remains fragmented.

The AI does not know which page it is working on. It does not know the content type. It does not know the approval flow. It does not know whether the content follows the organization’s structure, metadata standards, or brand guidelines.

That is why the CMS discussion centered on the idea of an “agentic CMS.” In the session, Optimizely described this as a CMS where AI does not just sit next to the platform, but operates inside it, using context, memory, governance, and structured organizational knowledge.

The phrase “agentic CMS” can sound like product marketing language, so it is worth translating.

In plain terms, the CMS is being positioned less as a page factory and more as the structured knowledge layer for the brand. Historically, teams treated the CMS as the place where the output lived: pages, articles, landing pages, campaign content, and digital experiences. In the direction Optimizely described, the CMS increasingly becomes the place where the inputs are governed: content models, taxonomy, metadata, brand facts, assets, design systems, localization rules, and reusable content structures.

That is a meaningful distinction.

If the CMS only manages output, AI helps generate more things to publish. If the CMS manages structured knowledge, AI can potentially help create, adapt, localize, optimize, and govern digital experiences using the same context the organization already depends on.

This is also where expectations need to be realistic. The shift does not happen by turning on an AI feature. It depends on whether the organization has clean content models, disciplined metadata, usable taxonomies, clear ownership, and governance rules that can be translated into workflows. If the content operation is messy, AI may expose that mess faster than it fixes it.

For leaders who want to get ahead of this, the first move is not necessarily to launch a large AI program. It is to inspect the foundation. Are key content types clearly defined? Is metadata consistently applied? Do categories and tags make sense for both human users and AI agents? Is ownership clear when content needs to be reviewed, updated, localized, or retired? And are content creation and approval workflows documented well enough that an agent-assisted process could follow them without creating confusion?

Starting there helps teams surface weaknesses early and build a stronger foundation for AI-assisted operations. That is not a reason to avoid AI. It is a reason to clean up the operating model before scaling it.

Virtual teammates are the more interesting idea

The most memorable concept from the opening session was the idea of virtual teammates.

Optimizely described virtual teammates as more than single-purpose agents. The example shared was closer to a persistent role that can collaborate with humans, use tools the user has access to, deploy agents or workflow agents, and participate in a team process. In the demo narrative, a virtual teammate proactively created a Q&A-style asset for an article, involved human peers in the discussion, incorporated feedback, and then required human approval before publishing.

That last part is important. The useful version of agentic marketing is not “AI publishes whatever it wants.” That may be exciting in a demo, but it is not how serious enterprise teams operate.

Marketing work is collaborative by nature. There are brand reviews, legal considerations, SEO inputs, product messaging changes, accessibility checks, regional market needs, and approval workflows. The more regulated or complex the organization, the more important those controls become.

This is why the human-in-the-loop workflow conversation stood out. Optimizely discussed bringing workflows into Opal so agents and virtual teammates can operate with the right checks in place before publishing. The event also referenced brand packs and channel packs, which are intended to help organize brand information and adapt assets for different channels.

That is a healthier framing of AI adoption. It is not about replacing the marketing team with agents. It is about identifying which parts of the workflow can be delegated, accelerated, or monitored — while keeping humans responsible for judgment, risk, quality, and final approval.

For a marketing VP, this should be the real evaluation question: can this reduce operational drag without creating governance debt?

The answer should not be based only on whether the demo looks impressive. Teams should look at practical indicators: workflow cycle time from brief to publication, where approval bottlenecks appear, how often content needs rework after review, how many exceptions still require manual intervention, and whether agent-assisted outputs have a clear audit trail. These are not glamorous metrics, but they reveal whether AI is improving the operating model or simply adding another layer of activity.

If the answer is yes, there is business value. If the answer is “we can generate more content, but nobody knows who approved it or why,” then the organization has only moved the bottleneck.

CMS 13 and Opal are becoming part of the same conversation

The CMS breakout also spent time on CMS 13 and how it connects to the broader Opal and agentic CMS direction. In the transcript, CMS 13 was described as fully available and positioned as a release that brings customers closer to the newer Opal and Visual Builder experience. Optimizely’s CMS 13 documentation describes it as a cloud-first content management system with architecture and capabilities intended for content managers, administrators, and developers. Optimizely also describes Opal in CMS 13 as an agent orchestration platform embedded in CMS 13 that helps users explore questions, automate common tasks, and make decisions without leaving the CMS.

For decision makers, the point is not to memorize every feature. The more useful takeaway is that Optimizely is trying to connect content creation, content operations, workflow, search, analytics, and AI assistance into a more coherent operating environment.

The Page Builder agent is a good example. In the session, Optimizely described it as an agent that can look at existing pages and content types, understand how pages are usually composed, and help create a first version of a page from different inputs such as a prompt, brief, CMP task, or design.

That could be useful, but not because marketers are desperate to avoid drag-and-drop interfaces. The real value is reducing the distance between campaign intent and publishable experience. In many organizations, a campaign brief still has to travel through too many disconnected steps before it becomes a page. If AI can help produce a strong first version inside the CMS, using the right content types and patterns, the team can spend more time refining the experience and less time assembling the basics.

The same is true for CMS and CMP integration. A better connection between planning, task management, preview, approval, and publishing is not glamorous, but it matters. Content operations usually break in the handoffs. Any roadmap that reduces those handoffs deserves attention.

Still, there is a caution here. “Available,” “in beta,” “coming soon,” and “roadmap” are not the same thing. The event included a mix of recent releases, current capabilities, and future direction. Any team planning around these capabilities should validate availability, licensing, dependencies, and timing directly with Optimizely before building a business case or delivery plan around them.

That is not skepticism. That is just good MarTech governance.

AI visibility is becoming part of the discoverability conversation

The SEO, GEO, AEO, and AI Visibility session was probably the most practical part of the event for marketing leaders.

The session started from a reality many teams are already noticing: discovery is changing. The traditional model was simple enough to understand. A person searched in Google, saw links, clicked through to a site, and then the website had a chance to educate, persuade, or convert. That model still exists, but it is no longer the only path.

More people are asking questions inside AI platforms. AI systems may retrieve information from a website, summarize it, cite it, or use it to answer a user without the user ever visiting the site directly. The session framed this as a world where every website has two audiences: human visitors and AI agents.

That is a useful way to explain the issue to leadership teams. Traditional analytics tools are still valuable for human behavior, but they may not tell the full story of server-side AI agent activity. If an AI system retrieves your content to answer a question, that interaction may not look like a normal website visit in client-side analytics.

This is where Optimizely’s Agent Visibility Analytics discussion came in. The session described two types of AI visibility tooling. The first is CDN log-level data, which is factual data showing AI agent requests to specific pages. The second is Conductor-style AI visibility data, which helps show how the brand appears across AI search experiences compared with competitors. The session was careful to explain that the Conductor-style data is simulated or panel-style, while log-level data is factual request data.

Optimizely’s support documentation describes the Agent Visibility Analytics dashboard as a way to view AI platform traffic and intent, powered by CDN log data and Opal-generated insights. It also states that the dashboard can help teams track AI traffic trends and intent, identify which AI platforms visit the site most often, and see which webpages are most popular among those platforms. Optimizely Academy’s SEO, GEO, AEO, and AI Visibility session description also frames the topic around seeing what AI agents are doing on a website and integrating technical optimization into content creation and publication workflows.

That distinction is worth keeping. There is already plenty of noise in the AEO and GEO space. Some tools make strong claims about “AI visibility” without making it clear whether they are measuring real traffic, simulated prompts, competitive panels, citations, or something else. A mature strategy probably needs more than one signal.

The way I would explain it is this: log data helps you understand what AI agents are doing on your site; AI visibility data helps you understand how your brand is showing up in answer experiences. One tells you about behavior on your owned property. The other gives you a view into market visibility.

Both matter, but they answer different questions.

From visibility to action: the part teams should care about

The more valuable part of the AI Visibility session was not the dashboard itself. Dashboards are easy to admire and hard to operationalize. The interesting part was the workflow from visibility to action.

In the demo, Optimizely showed how Agent Visibility Analytics can help identify which AI platforms are visiting a website, which pages they are hitting, and why those requests may be happening. The session described intent categories such as training, search/indexing, and user action or real-time retrieval. The example then showed Opal being used to ask questions of the underlying data, such as identifying pages that are being trained on heavily but not indexed. From there, the workflow could point to agents that help with next steps, such as metadata implementation, FAQ creation, or content refresh.

That is where the business value starts to appear.

A marketing team does not need another isolated AI report. They need a way to decide what to fix first. Which pages are being crawled but not surfaced? Which pages are invisible to agents? Which topics are competitors winning in AI answers? Which content needs clearer structure, better metadata, stronger factual answers, or FAQ formatting? Which product pages are important to the business but not showing up in AI-driven discovery?

A practical prioritization model starts with business impact. Revenue-generating product pages, high-intent comparison pages, critical support documentation, and campaign landing pages should usually receive more attention than low-value archive content. From there, teams can weigh effort against potential gain: some fixes may be as simple as improving metadata, adding clearer Q&A structure, or refreshing outdated content; others may require deeper content modeling, technical SEO, or workflow changes. The last filter is strategic timing. If a major campaign, product launch, or competitive push is coming, AI visibility work should align with that moment rather than sit as a disconnected optimization task.

For example, imagine a B2B software company where pricing and product feature pages are major lead drivers. The team might start by making sure those pages are structured with clear metadata, direct answers, and FAQ sections so both human users and AI systems can interpret them more easily. At the same time, they might notice that a set of long-tail support articles are often surfaced by AI platforms but contain outdated information. Because updating those articles requires relatively low effort and may improve how AI systems interpret them, those become a sensible next priority. Then, with a product launch planned for the next quarter, the team shifts focus to refreshing content related to the new features so that AI-driven answers and competitive comparisons are less likely to rely on stale information.

That kind of prioritization helps teams avoid spreading effort too thin. It also keeps AEO and GEO from becoming another quarterly audit that produces a deck and then disappears into backlog purgatory. The better model is a continuous operating loop: detect, prioritize, improve, measure, and repeat.

That is easier said than done, of course. Teams still need ownership. They need content standards. They need technical support. They need to know which recommendations are worth acting on and which ones are noise. But the direction is right: visibility alone is not enough. Actionability is the real test.

Why this matters beyond the product roadmap

The practical implication is that this is not just a CMS or AI feature conversation. It touches how marketing teams plan work, how content gets governed, how digital experiences are measured, and how brands remain discoverable when AI systems increasingly sit between the customer and the website.

That is why the roadmap matters beyond product teams. It gives marketing, digital, and technology leaders a reason to revisit some operating-model questions that are easy to postpone until they become painful.

The first is whether AI in MarTech is still being treated as task assistance or whether it is starting to become workflow orchestration. The distinction matters. Task assistance helps someone write faster. Workflow orchestration helps the team move work through planning, creation, approval, publishing, measurement, and optimization with less friction.

The second is whether the CMS is being treated as infrastructure or merely as a publishing destination. There has been a tendency in some organizations to treat the CMS as plumbing: important, but not strategic. That view is becoming harder to defend. If the CMS becomes the structured knowledge layer that supports pages, personalization, AI agents, search, localization, and content governance, then CMS maturity becomes a business capability, not just a technology concern.

The third is whether discoverability is being measured broadly enough. Traditional SEO is still relevant, but it is no longer the whole picture. Brands need to understand how AI systems retrieve, interpret, and cite their content. They also need to know where competitors are gaining visibility in AI-generated answers.

The fourth is whether governance is strong enough to support agentic workflows. Human-in-the-loop approvals, brand rules, role-based access, auditability, and clear ownership are not administrative overhead. They are what make AI-assisted work usable in an enterprise environment. Without them, teams may move faster, but not necessarily better.

And the final point is probably the most practical one: readiness matters. The organizations that will get the most value from this direction are not necessarily the ones that adopt the newest AI capability first. They are the ones with cleaner content models, clearer governance, stronger content operations, and a realistic path for adoption.

A few questions marketing leaders should ask now

Before jumping into agentic content workflows, I would start with a few plain questions.

Is our CMS structured well enough for AI to use it responsibly? If page types, metadata, taxonomies, and reusable content are inconsistent, AI will have a weaker foundation to work with.

Do we know which content actually matters for AI discoverability? Not every page deserves the same optimization effort. Product, pricing, support, documentation, comparison, and educational content may play very different roles depending on the business.

Can we tell the difference between human traffic and AI agent activity? If not, the organization may be missing a growing part of the discovery picture.

Do we have approval workflows that can support AI-assisted work? If an agent proposes metadata updates, FAQ content, or page changes, who reviews them? Who approves them? Who is accountable?

Are our teams ready to work differently? This may be the least technical question, but it is usually the hardest one. AI adoption changes responsibilities, not just tools.

Change management should start with a clear definition of what AI is expected to improve, which teams are affected, how approvals will work, and how success will be measured. From there, teams should pilot the new workflow in a controlled area, gather feedback, adjust roles and responsibilities, and only then scale it more broadly. Most importantly, leaders should communicate early and often about what is changing and why, while making space for the people doing the work to point out where the process still needs adjustment.

Final thought

What stood out from Optimizely’s Summer ’26 roadmap was not one individual feature. It was the larger direction: CMS, Opal, analytics, content operations, experimentation, personalization, and AI visibility are being pulled into a more connected story.

That story is not “AI will do marketing for you.” Good. That would be too simplistic.

The better story is that marketing teams are being pushed to rethink how digital experiences are created, governed, discovered, and optimized when both humans and AI agents are part of the audience.

For some organizations, that will feel early. For others, especially those already struggling with content scale, localization, campaign velocity, or SEO/AEO uncertainty, it will feel overdue.

Either way, the conversation has moved. The CMS is no longer just where content gets published. Increasingly, it is where the brand’s digital knowledge, governance, and AI readiness come together.

That is the part worth paying attention to.

References / Sources

Optimizely Summer ’26 Product Roadmap notes, July 9, 2026.

Optimizely Academy, “Summer ’26 Product Roadmap Virtual Event.”
https://academy.optimizely.com/student/page/3469092-summer-26-product-roadmap-virtual-event

Optimizely Academy, “SEO, GEO, AEO, and AI visibility: Using agentic workflows and intelligence to optimize your content.”
https://academy.optimizely.com/student/page/3499549-seo-geo-aeo-and-ai-visibility-using-agentic-workflows-and-intelligence-to-optimize-your-content

Optimizely Developer Documentation, “CMS 13 overview.”
https://docs.developers.optimizely.com/content-management-system/v13.0.0-CMS/docs/cms-13-overview

Optimizely Developer Documentation, “Optimizely Opal in CMS 13.”
https://docs.developers.optimizely.com/content-management-system/v13.0.0-CMS/docs/optimizely-opal-in-cms-13

Optimizely Support Help Center, “Agent Visibility Analytics dashboard in Optimizely Analytics.”
https://support.optimizely.com/hc/en-us/articles/45787354895757-Agent-Visibility-Analytics-dashboard-in-Optimizely-Analytics

Optimizely PaaS CMS Administrator Certification (2025): What to Expect and How to Prepare

Tue, 02 Jun 2026 16:55:30 GMT

The Optimizely PaaS CMS Administrator certification is best understood as training for the operational side of Optimizely CMS. It focuses on how the platform is configured, governed, and maintained as content operations become more complex.

It differs from the developer certification in scope. Developer certifications focus on building and extending the platform. The administrator certification focuses on how the CMS is used and managed in practice, including configuration, content operations, permissions, assets, audiences, projects, publishing, and system tools.

For individuals, it provides a structured way to understand Optimizely CMS beyond day-to-day content editing. For organizations, the value is practical: fewer governance gaps, better content reuse, more predictable publishing processes, and less dependency on developers for tasks that should be managed inside the CMS.

Where CMS Platforms Start to Become Harder to Manage

Most CMS implementations begin in a manageable state. A smaller group of users creates content, the structure is relatively simple, and informal coordination is often enough. That can work for a while.

The challenge appears as more teams, regions, languages, campaigns, and content types are added. A CMS that once felt easy to manage starts to carry more operational weight. Content can become duplicated rather than reused. Permissions may become inconsistent across different areas of the site. Assets may be uploaded repeatedly because users cannot find or trust what already exists. Publishing may become harder to coordinate across teams.

These issues are not necessarily signs that the platform is failing. More often, they are signs that the organization needs stronger CMS administration.

What the Certification Actually Helps With

The certification does not introduce new product features or unlock additional functionality in Optimizely CMS. Instead, it focuses on how existing capabilities are configured and used.

That distinction matters.

In many organizations, features such as permissions, projects, audiences, content structures, and asset management are already available but not applied consistently. The value of the certification comes from understanding how these capabilities work together and how to use them in a structured, sustainable way.

Core Areas Covered

The certification aligns with Optimizely Academy training, including PaaS CMS for Administrators and PaaS CMS Essentials for Editors. Together, these provide both an administrative view of the platform and a practical understanding of how content teams work in the CMS.

Configuration covers how the CMS environment is set up and managed. This includes websites, languages, categories, plug-ins, edit tabs, and other system settings that influence how the platform behaves. The administrator training includes managing websites and languages, as well as system configuration tools such as Categories, Plug-in Manager, and Edit Tabs.

Tools focus on the administrative features used to maintain the CMS. This includes export and import, search configuration, change logs, license information, and other tools that support troubleshooting and system management.

Understanding the editing interface is included because administrators need to understand how users actually work inside the CMS. The editor-focused training covers the CMS layout, dashboard, navigation panel, assets panel, on-page editing, and all properties view.

Create and Manage Content focuses on pages, blocks, forms, assets, reports, and content organization. This matters because administrative decisions affect the daily workflow of editors and marketers.

Versioning and Publishing covers how content moves from draft to publication, including version comparison, scheduling, approval workflows, and publishing options. These capabilities help teams manage content accuracy and release timing.

Managing Assets covers media, folders, reusable blocks, metadata, permissions, and asset organization. This is often where content governance becomes visible because poor asset structure quickly leads to duplication and inconsistency.

Audiences covers how user groups are created and managed for targeted content experiences. This includes audience criteria, personalization, and content visibility.

Projects covers how teams group related content changes, collaborate, track progress, and publish updates together. This is especially useful for campaigns, launches, and coordinated site updates.

Gadgets covers configurable interface components that support navigation, task tracking, version history, project visibility, and content management workflows.

When This Certification Becomes Valuable

This certification becomes especially useful when the CMS is no longer simple. That usually happens when multiple teams are creating content, when the organization manages more than one site or language, or when personalization and coordinated publishing become part of regular operations.

It is also relevant for organizations that need stronger governance, clearer ownership, and more predictable publishing processes. In regulated or high-visibility environments, this becomes even more important because permissions, workflows, and content visibility need to be managed carefully.

For a very small team managing a simple single-site implementation, the certification may not be urgent. But as the number of editors, regions, assets, workflows, and campaigns grows, administration becomes less optional.

Who Should Consider It

This certification is most relevant for CMS administrators, content architects, digital platform owners, and technical project managers who work closely with Optimizely CMS teams. It can also be useful for developers who want a stronger understanding of how the platform is operated after implementation.

It is less urgent for pure content editors who only need to create and publish content within an already well-managed environment. Developers focused strictly on implementation may want to prioritize developer certifications first, then use this certification to broaden their operational understanding.

How It Complements Developers and Partners

The administrator certification does not replace developer expertise or implementation partner support.

Developers build and extend the platform. Administrators help ensure the platform is configured and managed properly over time. Partners may help design and implement the solution, but internal teams still need enough platform knowledge to maintain governance, support users, and make informed day-to-day decisions.

A strong Optimizely operating model usually needs all three: implementation expertise, developer capability, and internal administrative ownership.

Business Value for Organizations

The business value of this certification is not the badge itself. The value comes from having people who understand how to prevent common CMS problems before they become expensive.

That can mean reducing reliance on developers for routine CMS configuration, improving content reuse, making publishing cycles more predictable, and reducing the risk of mistakes caused by unclear permissions or unmanaged workflows.

It can also help organizations use existing Optimizely CMS capabilities more effectively. Features such as audiences, projects, forms, assets, and language management are more valuable when teams understand how to apply them consistently.

Recommended Preparation

Before taking the exam, complete the PaaS CMS for Administrators training, the PaaS CMS Essentials for Editors training, and the available PaaS CMS event recordings in Optimizely Academy.

The official documentation is also useful for reinforcing the topics covered in training:

Getting Started with Optimizely CMS
https://docs.developers.optimizely.com/content-management-system/docs/getting-started

Create Content
https://support.optimizely.com/hc/en-us/sections/4413216725517-Create-content

Manage Content
https://support.optimizely.com/hc/en-us/sections/4413209581197-Manage-content

Assets
https://support.optimizely.com/hc/en-us/sections/4413209393421-Assets

Audiences
https://support.optimizely.com/hc/en-us/sections/20737827567117-Audiences

Projects
https://support.optimizely.com/hc/en-us/sections/4413218123917-Projects

Languages
https://support.optimizely.com/hc/en-us/sections/4413223265933-Languages
https://support.optimizely.com/hc/en-us/articles/4413192302477-Manage-website-languages

Sites
https://support.optimizely.com/hc/en-us/articles/4413192303629-Manage-websites

Forms
https://support.optimizely.com/hc/en-us/sections/4413199603469-Forms

User Scenarios
https://support.optimizely.com/hc/en-us/sections/4421938507277-User-Scenarios

Administration of CMS
https://support.optimizely.com/hc/en-us/articles/4413200627981-Administration-of-CMS

Performance Edge
https://support.optimizely.com/hc/en-us/categories/39072730760461-Performance-Edge

Scheduled Jobs
https://support.optimizely.com/hc/en-us/articles/4413192306317-Scheduled-jobs

Change Log
https://support.optimizely.com/hc/en-us/articles/4413205653005-Change-Log

Reports
https://support.optimizely.com/hc/en-us/articles/4413200675469-Reports

Content Types
https://support.optimizely.com/hc/en-us/articles/4413200629133-Content-types

Final Thoughts

The Optimizely PaaS CMS Administrator certification is useful because it connects platform configuration with real content operations.

It helps people understand not only what the CMS can do, but how those capabilities should be managed when teams, content, languages, assets, and workflows grow. That is where many CMS environments become harder to maintain.

For individuals, the certification builds a more complete understanding of Optimizely CMS. For organizations, it supports a stronger operating model around the platform. The best reason to pursue it is not to collect another credential, but to improve how the CMS is governed, maintained, and used over time.

Commerce 15 and CMS 13: Optimizely’s Next Step Toward AI-Powered, Graph-First Commerce

Thu, 07 May 2026 22:55:12 GMT

Optimizely is preparing to release Commerce 15 in mid-May 2026, positioning this as a foundational shift—not just an upgrade. The direction is clear: move from fragmented tooling and isolated AI features toward a graph-first, AI-embedded commerce platform where search, content, and operations are unified.

The headline changes are not cosmetic. Visual Builder replaces legacy editing paradigms, OptiGraph becomes the default search and data layer, and Opal evolves from a set of tools into a core operational interface for commerce teams. At the same time, the upgrade path introduces real architectural considerations—especially for organizations still dependent on Find, legacy APIs, or external service layers.

For decision-makers, the implication is straightforward: Commerce 15 is where Optimizely is investing future innovation, particularly around AI agents and automation. The question is less “should we upgrade?” and more “how do we prepare to adopt a fundamentally different operating model?”

Market Context: Commerce Is Moving from Systems to Systems of Intelligence

Modern commerce platforms are no longer judged solely on catalog management, checkout performance, or integration flexibility. The competitive frontier has shifted toward:

Speed of content and campaign execution
Search and discovery quality (increasingly semantic and AI-driven)
Operational efficiency for merchandisers and marketers
Embedded intelligence across workflows

What’s often missed is that most commerce stacks still operate as systems of record, not systems of intelligence. Data exists—but it isn’t continuously activated.

Commerce 15 reflects a broader industry trend:

moving from “tools that support workflows” to “platforms that participate in workflows.”

The Optimizely Perspective: Commerce 15 as a Graph-First, AI-Embedded Platform

Commerce 15 is being framed as a modern, AI-powered, graph-first commerce platform

Three structural shifts define this positioning:

1. Graph as the foundation (not an add-on)

OptiGraph replaces Find as the default search and data layer, enabling unified indexing across CMS, commerce, DAM, and CMP.

2. AI embedded into workflows

Opal is no longer a side feature. It becomes part of day-to-day commerce operations, from product management to promotions.

3. Experience creation without developer bottlenecks

Visual Builder and integrated tooling shift control toward business users—particularly merchandisers.

This combination is what makes Commerce 15 feel less like a version upgrade and more like a platform reset.

What’s New (and Why It Matters)

Visual Builder: Merchandiser-Led Experience Creation

Visual Builder replaces the traditional on-page editor and is now embedded directly into commerce workflows.

Build PDPs, category pages, and promotions visually
Access all blocks, templates, and components in one interface
Reduce dependency on developer tickets

The practical impact is significant:

Faster campaign launches
Lower backlog pressure on engineering
More experimentation at the merchandising level

Or, as framed in April 28th Optimizely session Introduction to Commerce 15:

“Visual Builder gives your developers their time back.”

External Content: Breaking Down System Silos

Commerce 15 introduces stronger support for pulling data from external systems (ERP, PIM, etc.) into CMS.

Centralized product and content management
Reduced need for custom integration layers
Single UI for merchandisers

This is less about convenience and more about operational coherence.
When product data, content, and customer context live together, personalization and campaign execution become materially easier.

Embedded DAM: Content Operations at Scale

The embedded DAM brings:

AI-powered tagging
Automated renditions
Integrated asset workflows

For SKU-heavy organizations, this addresses a persistent bottleneck: managing media at scale without introducing content chaos.

CMP-to-CMS Publishing: Closing the Campaign Gap

Campaign workflows now connect directly to CMS:

Content moves from planning to publishing without manual handoffs
Templates and previews streamline execution
Alignment between marketing and web teams improves

This is a subtle but important shift—reducing the friction between strategy and execution.

OptiGraph: The Shift from Search Engine to Experience Layer

OptiGraph replaces Find as the default search and navigation engine.

Key capabilities include:

Unified indexing across platforms
Semantic, intent-aware search
Support for AI-driven context (Opal)

“Graph is now acting as the de facto experience/discovery layer and internal search provider instead of Find.”

This matters because search is no longer just about retrieval—it’s about interpretation and relevance in context.

Opal: From Feature to Operating Model

The most strategic change in Commerce 15 is how AI is positioned.

Available now:

Opal Chat for content and optimization
Product catalog translations
Promotions agent
Data-fetching tools via Graph

Expected next:

Product catalog management agent (bulk operations)
Expanded Opal Chat for commerce workflows
Google AI Search (leveraging vector-based retrieval)

The narrative is clear:

AI is moving from isolated capabilities into embedded commerce workflows.

In practice, this means:

Merchandisers can generate and optimize content in context
Promotions can be created via natural language
Catalog operations can be automated at scale

Commerce 14 vs Commerce 15: A Strategic Decision Point

One of the more important signals from the announcement:

Commerce 14 will receive limited future out-of-the-box agent support
Commerce 15 becomes the primary platform for all future AI innovation

This creates a clear fork in the road:

Stay on Commerce 14 → maintain stability, rely on custom solutions
Move to Commerce 15 → access future Opal agents and platform capabilities

For most organizations, this isn’t just a technical upgrade—it’s a roadmap decision.

What Merchants and Teams Gain

For Merchandisers

Greater autonomy (Visual Builder, Opal agents)
Faster campaign execution
Less reliance on dev cycles

For Marketing Teams

Direct CMP-to-CMS workflows
Better alignment between planning and publishing
Improved personalization potential

For Engineering

Reduced content-related workload
Opportunity to focus on higher-value architecture and integrations

For Leadership

Faster time-to-market
Improved operational efficiency
Platform aligned with AI-driven future capabilities

Migration Reality: What You Need to Plan For

This is where many organizations underestimate the effort.

1. Find → OptiGraph Migration

This is not optional.

Search logic must be re-implemented
Indexing strategies must be redesigned
Category and content rendering may need refactoring

2. API and Architecture Changes

Deprecated APIs require review
Content Delivery APIs are migration-only (not future-facing)
New development must align with a Graph-first approach

3. OptiID Becomes Mandatory

OptiID is required for:

Content Manager
DAM
Opal
Connect Platform

Organizations not already using it must plan for identity and access transitions.

4. Service API Timing

Not available at launch (mid-May)
Expected later in Q2 2026

If your implementation depends on Service API or PIM connectors, timing your upgrade becomes critical.

Risks and Trade-offs (Pragmatic View)

Even with a strong platform direction, there are real considerations:

Migration complexity (especially search and indexing)
Dependency alignment (Service API, connectors)
Organizational readiness (AI adoption, workflow changes)
Learning curve for new tooling (Graph, Opal, Visual Builder)

That said, these are not unusual for a major platform evolution—they’re the cost of moving to a more modern architecture.

What’s Often Missed

Most discussions will focus on features. The more interesting shift is operational:

Commerce 15 is designed to make merchandisers more self-sufficient and AI part of daily execution, not strategy decks.

That changes:

How teams work
How fast they move
How value is created

Conclusion: A Platform Shift Worth Planning For Now

Commerce 15 represents a decisive move by Optimizely:

From search engine → experience graph
From tools → embedded intelligence
From developer-led workflows → merchandiser-led execution

For decision-makers, the takeaway is not just “this is a new version.”

It’s this:

Commerce 15 defines where Optimizely is going—and where your commerce capabilities can go if you align early.

Next Steps for Leaders

Assess your current dependencies
- Find usage
- Service API reliance
- Identity model (OptiID readiness)
Engage your implementation partner early
- Review upgrade complexity
- Validate timelines against Q2 dependencies
Explore preview packages
- Understand Graph and Visual Builder in practice
- Evaluate Opal use cases relevant to your business
Align internal teams
- Prepare merchandisers for new workflows
- Define governance for AI usage (human-in-the-loop, approvals, auditing)

From Prompting to Production: Optimizely Opal University Cohort and the Future of Agentic MarTech

Tue, 28 Apr 2026 16:59:46 GMT

Most organizations today are still playing with AI. They experiment with prompts, test ideas in isolated chats, and occasionally automate a task or two. It creates value, but it’s inconsistent, difficult to scale, and almost impossible to govern in a meaningful way.

What the Optimizely Opal University Cohort makes clear is that we’re moving beyond that phase. This is no longer about using AI—it’s about operationalizing it.

Over the course of the workshop, the experience wasn’t just about building agents. It was about understanding what it actually takes to make AI reliable, reusable, and embedded into the day-to-day workflows of marketing and digital teams. And that distinction matters, because the organizations that figure this out won’t just be more efficient—they’ll fundamentally change how work gets done.

The Real Shift: From Conversations to Systems

One of the most important ideas reinforced early in the cohort seems simple on the surface, but it has far-reaching implications once you start applying it in practice:

Chats are disposable. Agents are systems.

That shift alone reframes how you think about AI. In most teams today, someone writes a prompt, tweaks it until the output looks acceptable, uses it once, and then repeats the process the next time they need something similar. It works, but it doesn’t scale, and more importantly, it doesn’t create institutional knowledge.

With Opal, you’re encouraged to step back and ask a different question: instead of solving the problem once, how do you design something that solves it consistently?

The workshop used a restaurant analogy to explain this, which actually holds up surprisingly well when you think about it more deeply. The prompt becomes your recipe, the instructions are your ingredients, the tools are your kitchen equipment, and evaluations act as your quality control. What you’re ultimately producing is not just an answer, but a repeatable outcome that can be delivered again and again with a predictable level of quality .

What’s often missed in the broader AI conversation is that this isn’t really about prompting anymore. It’s about system design, and that’s where Opal starts to feel meaningfully different.

The Agent Creation Experience: Fast, Structured, and Intentionally Imperfect

One of the more impressive aspects of the workshop is how quickly you can go from idea to working agent. The two-tab workflow—building in Opal University and then deploying in the Opal platform—removes a lot of the friction that typically comes with setting these things up.

In practice, this approach can save hours of manual effort, especially when compared to building something similar from scratch . But what stood out wasn’t just the speed—it was the expectation that speed alone isn’t the goal.

There’s a consistent message throughout the sessions: getting an agent to work is relatively easy; getting it to work well takes iteration.

That gap between 80% and 95% quality becomes very real once you start testing outputs. It’s where you begin to notice inconsistencies, edge cases, and subtle misalignments with your expectations. And it’s also where most of the real work happens.

The workflow itself reinforces good habits. You generate a structured prompt using a dedicated builder, validate the output before committing, export it as JSON, and then bring it into the Opal platform for testing and refinement . It’s not just efficient—it introduces a level of discipline that most teams don’t naturally apply when working with AI.

What Actually Makes Agents Work in Practice

As the sessions progressed, a pattern started to emerge. It wasn’t any single feature that made agents effective—it was how a few key components worked together.

Instructions: Where Consistency Lives

If prompts define what an agent does, instructions define how it behaves over time.

Opal allows you to establish both instance-wide instructions—things like brand voice or company guidelines—and more personal layers that reflect individual preferences. You can even generate these from an existing website, effectively turning your digital presence into a baseline for how your agents communicate .

This is one of those features that sounds operational, but it has strategic implications. It means you’re no longer relying on individuals to remember how to phrase things or maintain consistency across outputs. Instead, that consistency is embedded into the system.

That said, there’s an important caveat. When instructions conflict, outputs can become unpredictable. It’s a reminder that governance isn’t optional—it’s foundational.

Evaluations: Defining What “Good” Looks Like

If there’s one capability that feels underappreciated in most AI discussions, it’s evaluation. Not in the abstract sense, but as a structured, repeatable way to define quality.

Opal’s approach to evals is both simple and powerful. You link examples of what good output looks like, and the agent uses those examples both to score itself and to guide future responses. Over time, this creates a feedback loop that improves consistency and reliability .

What’s particularly useful is how this forces teams to be explicit. You can’t just say “this looks good”—you have to define it. And once you do, you can measure against it.

In practice, this also introduces nuance. Structured outputs might require higher thresholds—90% or more—while creative work needs more flexibility. That balance is something teams will need to learn, but having the mechanism in place is a significant step forward.

Workflow Agents: Where AI Becomes Operational

This is where things start to feel less like a feature set and more like a platform.

Workflow agents allow you to connect multiple agents together, passing outputs from one to the next and triggering them based on real-world events. You can initiate workflows through chat, webhooks, email, or scheduled runs, which opens up a wide range of possibilities .

What makes this particularly compelling is how it mirrors actual business processes. Instead of thinking about isolated tasks, you start thinking about sequences—how information flows, how decisions are made, and where automation can meaningfully reduce effort.

The newsletter-to-LinkedIn example from the workshop illustrates this well. An incoming email triggers a workflow, which transforms the content, applies formatting improvements, and prepares it for distribution. It’s a relatively simple use case, but it captures the essence of what’s possible when you move beyond single-agent interactions .

Reusability Through Variables

Another detail that becomes increasingly important at scale is the use of variables.

By parameterizing inputs—things like URLs, languages, or datasets—you can reuse the same agent across different contexts without rebuilding it from scratch. This might seem like a small feature, but it’s what turns agents into reusable assets rather than one-off solutions.

The Use Cases That Actually Matter

What made the workshop particularly valuable was seeing how these capabilities translate into real work.

Some use cases were more obvious, like competitive analysis or lead generation, but others highlighted where agents can quietly drive significant value.

For example, transforming meeting notes into structured user stories is not glamorous, but it’s incredibly useful. It reduces manual effort, ensures consistency, and accelerates downstream work. Similarly, automating reporting workflows or translating strategic goals into measurable KPIs addresses very real operational bottlenecks.

More forward-looking use cases, like AEO and GEO optimization, point to where the industry is heading. As AI-driven search becomes more prevalent, understanding how brands appear in those environments—and how to improve that presence—will become a critical capability. Agents are particularly well-suited to this kind of analysis because they can combine research, evaluation, and recommendation into a single workflow.

What’s Actually Impressive (And What’s Not)

It’s easy to get caught up in features, but what stood out here was less about what Opal can do and more about how it encourages teams to think.

There’s a clear emphasis on iteration over perfection, which aligns with how real teams operate. There’s also a deliberate separation between different components—creation, instructions, evaluations—which reduces complexity as usage grows.

At the same time, it’s important to recognize that this isn’t a fully abstracted experience. You still need to think about how agents interact, how outputs are structured, and how workflows are designed. In other words, the platform gives you the building blocks, but it doesn’t remove the need for strategy.

Implementation Realities: Where Things Get Hard

If there’s one consistent theme that emerged, it’s that technology is only part of the equation.

From a people perspective, someone needs to take ownership of agent design and governance. Without that, it’s easy for things to become fragmented or inconsistent.

Process-wise, teams need to define when to use agents versus chats, how to evaluate outputs, and how to maintain and improve what they’ve built. These aren’t decisions that can be deferred—they shape how effective the system becomes over time.

On the technical side, integration and orchestration introduce their own challenges. Even something as straightforward as email triggers can have limitations that require workarounds, as seen during the workshop .

And then there’s governance. If agents are going to play a meaningful role in operations, there needs to be visibility into how they’re performing, control over what data they access, and clear accountability for their outputs.

The Bigger Picture: Why This Matters Now

What the Opal University Cohort ultimately highlights is not just a set of capabilities, but a shift in how AI fits into the MarTech ecosystem.

We’re moving from a world where AI is something you occasionally use, to one where it becomes part of how work gets done. That shift requires new ways of thinking about design, quality, and governance.

The organizations that adapt will find themselves operating differently. They’ll move faster, not just because they’re more efficient, but because they’ve reduced the friction between idea and execution. They’ll also be more consistent, because their processes are embedded into the systems they use.

And perhaps most importantly, they’ll be better positioned to adapt as the technology continues to evolve.

Final Thoughts

The experience of going through the Opal University Cohort makes one thing clear: the future of AI in marketing and digital experience is not about better prompts. It’s about better systems.

Opal is not trying to replace how teams work—it’s trying to reshape it in a way that makes AI usable at scale. That’s a much harder problem to solve, but it’s also the one that matters.

For teams willing to invest the time to understand and apply these concepts, the payoff is not just incremental improvement. It’s a fundamentally different way of operating—one where AI is no longer an experiment, but an integral part of the organization’s capability.

Take this opportunity and join the waitlist to be a part of a Cohort: https://www.optimizely.com/ai-marketing-certificate/

Optimizely CMS 13: A Strategic Reset for Content, AI, and Composable Marketing

Tue, 14 Apr 2026 19:50:12 GMT

Optimizely CMS 13 is not just another version upgrade—it represents a deliberate shift toward a connected, AI-enabled, and API-driven content platform.

Released March 31, 2026, CMS 13 introduces:

A Graph-first architecture for content delivery
Embedded AI workflows via Opal
A Visual Builder for marketer-led content creation
A standardized approach to integrating external systems

This isn’t about improving the CMS editor, it is about redefining how content operates across your entire digital ecosystem.

Market Context: The CMS Is No Longer the Center

Most enterprise CMS platforms were built for:

Page-based publishing
Centralized content storage
Custom integrations for everything else

That model is increasingly misaligned with reality.

Today:

Content lives across CMPs, DAMs, commerce platforms, and PIMs
Experiences span websites, apps, marketplaces, and AI interfaces
Marketing teams need speed without engineering dependency
AI requires structured, accessible content across systems

The implication is clear:

The CMS is no longer the system of record—it’s the orchestrator of distributed content.

CMS 13 is Optimizely’s answer to that shift.

What Is Optimizely Graph?

Optimizely Graph is the central layer that connects, indexes, and delivers content across your ecosystem.

Think of it as a unified content engine that sits between your systems (CMS, DAM, commerce, etc.) and your channels (web, apps, AI).

Why this matters?

1. Content is no longer siloed
Graph enables content from multiple systems to be accessed and used together in a single experience.

2. It replaces legacy search and delivery models
CMS 13 deprecates Search & Navigation and positions Graph as the primary content delivery mechanism.

3. It powers AI across the platform
Opal’s AI capabilities—including content creation and optimization—depend on Graph indexing and understanding your content.

4. It improves performance and scalability
Graph acts as a high-performance delivery layer, enabling faster and more consistent content retrieval.

The Strategic Shift

Before: CMS → APIs → Channels

Now: Graph → unified content → channels + AI + integrations

This is not a technical distinction—it’s a platform-level change in how content is accessed and activated.

The Three Strategic Shifts in CMS 13

Optimizely frames CMS 13 around three outcomes:

Faster content creation
Better delivery capabilities
AI-powered workflows

Let’s break it down to what that means.

1. Visual Builder: Marketing Speed Without Developer Dependency

CMS 13 introduces a Visual Builder that enables marketers to create experiences directly.

Grid-based layout system (rows, columns, sections)
Real-time preview across breakpoints
Reusable templates (“blueprints”)
Governance through predefined styling controls

From a business perspective:

Campaigns launch faster
Less reliance on development teams
Brand consistency is maintained through guardrails

Existing CMS 12 content still works, teams can adopt this incrementally. (With some required updates and changes, e.g. Framework updated to .NET10.0, check DB compatibility, Optimizely Graph is needed to use CMS 13, among others)

2. Graph-First Delivery: The Foundation for Everything Else

Graph is no longer optional, it’s the backbone of CMS 13.

Powers search, APIs, and content delivery
Enables semantic search and AI use cases
Replaces legacy search infrastructure
Standardizes how content is accessed across channels

This aligns CMS 13 with modern architectures where:

Content is API-driven
Frontends are decoupled
Experiences are assembled dynamically

3. External Content: Composable CMS in Practice

CMS 13 introduces a new external content model, enabling content from other systems to be used directly.

Integrations with commerce, PIM, DAM, and more
Content remains in the source system (no duplication)
Real-time updates reflected in experiences
Standardized integration via Optimizely Connect Platform

This eliminates a long-standing problem: Custom integrations that become outdated and expensive to maintain.

4. Embedded DAM: From Asset Storage to Asset Strategy

CMS 13 includes an enterprise-grade DAM directly in the CMS experience.

Centralized asset management
Metadata, tagging, and renditions
Unified asset access across workflows

This reduces operational friction: fewer tools, fewer handoffs, and better governance.

5. Opal AI: Embedded, Not Bolted On

CMS 13 integrates Opal AI directly into editorial workflows. Opal enables:

Content creation and publishing directly within CMS
SEO & AEO optimization and automated recommendations
Content management operations (create, update, manage variations)

Additional capabilities include metadata and schema generation, content audits and refresh recommendations, and AI-assisted content modeling and page creation.

The key shift:

AI is no longer a side tool
It becomes part of how content gets created and optimized daily

How It All Fits Together?

CMS 13 operates across three coordinated layers:

1. Creation Layer

Visual Builder
CMP integration
Embedded DAM

2. Content Layer

Graph as the unified index
External content integration
Structured content models

3. Intelligence Layer

Opal AI agents
Semantic search and analytics
AI-driven optimization workflows

The implication is that content becomes structured, connected, and AI-readable by design.

What This Means for Different Stakeholders?

Marketing Leaders

Faster campaign execution
AI-assisted content production
Consistent brand governance

Digital Experience Managers

Unified content access across systems
Improved discoverability and reuse
Reduced operational complexity

Engineering Leaders

Less custom integration work
Standardized APIs via Graph
More focus on architecture, less on maintenance

Implementation Considerations

1. This Is a Replatforming Decision

While CMS 12 → 13 is described as an evolution, the reality is:

Graph adoption is required
Search must be migrated
Architecture changes impact delivery models

2. Content Modeling Must Evolve

To unlock value:

Move from page-centric to component-based design
Structure content for reuse and AI consumption

3. Cloud Becomes the Default

Graph is a SaaS service
Opal and DAM depend on cloud capabilities
Opti ID is required for key features

4. AI Governance Is Critical

Organizations must define:

Approval workflows for AI-generated content
Brand and compliance guardrails
Data access policies for AI agents

Risks and Trade-offs

Transition Complexity: New architecture, new workflows, new mindset.

Dependency on Graph: Graph is foundational, there is no fallback to legacy models.

Organizational Readiness: Success depends on people and process, not just technology.

Cloud Constraints: On-premise environments face limitations for full feature adoption.

Key Decisions

CMS 13 reflects a broader industry shift:

From CMS as a publishing tool → to CMS as a content orchestration platform

The real question for decision-makers is not: “Should we upgrade?”

It’s: “Are we ready to operate in a connected, AI-driven content ecosystem?”

Final Takeaway

CMS 13 is not incremental, it’s directional.

It aligns with where MarTech is going:

API-first architectures
AI embedded in workflows
Composable ecosystems over monolithic platforms

The organizations that win won’t just adopt CMS 13.

They’ll use it to rethink how content flows across their entire business.

References:

My Takeaway from Optimizely Opal Agents in Action 2026 - What Agentic AI Means for the Future of Digital Marketing

Fri, 06 Mar 2026 15:48:32 GMT

I would like to share with you what stayed in my head after this amazing virtual event organized by Optimizely. Agents in Action 2026, a live demonstration of Optimizely's AI assistant platform, Opal. The session showcased how AI agents embedded across the Optimizely ecosystem can conduct research, analyze documents, orchestrate tools, and automate operational workflows for marketing teams.

Artificial intelligence in marketing is entering a new phase. For the past two years, the focus has largely been on generative AI—tools that produce copy, images, summaries, and marketing ideas. But as organizations begin integrating these technologies into their workflows, a more powerful model is emerging: agentic AI. Agentic systems go beyond answering questions. They can plan tasks, call tools, retrieve data, analyze information, and execute multi-step workflows.

Optimizely's demonstration highlighted a shift that is beginning to reshape digital marketing operations: the move from isolated AI features to AI agents that perform work inside enterprise platforms.

Industry research suggests the timing is not accidental. Organizations are rapidly experimenting with AI but still struggle to integrate it deeply into operational workflows. According to McKinsey’s latest global survey on AI adoption, many companies have begun using AI widely but have not yet embedded it deeply enough into processes to generate full enterprise value (McKinsey).

What Optimizely showed with Opal agents points toward the next stage of AI maturity—where marketing teams work alongside AI agents that automate research, analysis, and operational tasks.

The Rise of Agentic AI in Marketing

Most early generative AI tools behaved like conversational assistants. You ask a question, yhe system produces an answer.

That model has proven useful for content generation, brainstorming, and summarization, but enterprise environments require more than output generation. They require systems that can execute processes.

Agentic AI systems are designed for that purpose. Instead of generating a single response, they can:

Execute multi-step research
Retrieve external information
Call APIs or tools
Analyze documents
Trigger workflows
Coordinate multiple systems

This shift reflects a broader trend in enterprise AI adoption.

McKinsey research shows that AI is increasingly used across multiple business functions—including marketing and sales—where generative AI can support activities such as knowledge synthesis, content generation, and strategic analysis (McKinsey ). But many organizations still struggle to scale these capabilities across their workflows.

The opportunity lies not in adding more AI tools, but in redesigning operational processes so AI becomes embedded within them. This is where agentic platforms begin to matter, instead of interacting with AI occasionally, teams interact with systems that continuously assist, automate, and coordinate operational tasks.

The Optimizely Opal demonstration illustrated how such systems might work in a marketing technology environment.

What Optimizely Demonstrated at Opal Agents in Action 2026

The event focused on Opal, Optimizely’s AI assistant platform integrated across its product suite, including CMS, experimentation tools, analytics capabilities, and content marketing workflows.

At its core, Opal operates as a conversational AI interface embedded directly inside the Optimizely ecosystem. Users interact with the system through a chat interface that maintains persistent conversation history and contextual awareness.

Before each interaction with the underlying language model, the platform executes a multi-step enrichment process.

This pipeline loads contextual information such as:

organizational instructions and brand guidelines
available agents and tools
operational prompts or system rules

Organizations can configure these instructions at multiple levels.

Instance-level instructions apply across the entire organization—for example, defining brand voice or communication guidelines. Personal instructions allow individual users to tailor how the system responds to their requests.

Role-based permissions control who can modify these instructions, which helps maintain governance in enterprise environments.

The demonstration showed how this contextual layer ensures AI interactions remain aligned with organizational policies and brand requirements.

Key Capabilities of Opal Agents

The most interesting aspect of the event was not the conversational interface. It was the demonstration of specialized AI agents capable of executing complex operations.

Specialized agents

Agents in Opal are created with defined roles, instructions, and tool access. When an agent runs, it operates in an isolated context. This prevents large multi-step processes from overwhelming the main conversation.

During the demonstration, a research agent was configured to analyze companies. The agent accepted a topic parameter and automatically executed multiple research passes.

In another example, the agent performed several research queries about Toyota, gathering information about company background, news, and financial performance. The system automatically executed a chain of operations:

search → scrape → analyze → summarize
The process generated a detailed report after approximately 234 seconds and involved roughly 44,000 tokens across multiple tool calls.
Execution logs displayed the reasoning cycle in real time, including predicted tool calls and returned outputs.

The demonstration highlighted how AI agents can handle research tasks that typically require multiple manual steps.

Tools and extensibility

A second important capability is Opal’s tool framework. Developers can create custom tools using the Opal Tools SDK, which supports Python and FastAPI. Functions can be exposed as tools through simple annotations, allowing the AI system to invoke them during task execution. The demonstration included a simple tool that generated random numbers based on user parameters. While trivial in itself, the example illustrated how tools return structured responses that can render as interactive user interface elements. More advanced tools are provided directly by Optimizely product teams.

Examples demonstrated during the session included:

Web search integration that performs Google searches and scrapes relevant websites
PowerPoint processing that converts slides into analyzable formats
Product-specific tools for CMS and experimentation features

The system determines when to call these tools based on their descriptions and parameter schemas. The result is an AI system capable of chaining together multiple operations without manual intervention.

Enterprise document analysis

In the event it was also demonstrated document processing capabilities.

PowerPoint files can be uploaded and converted into a structured Canvas format, allowing the system to analyze slides individually.
A code execution tool—similar to those used in other advanced AI systems—will enable analysis of spreadsheets and other Office documents through generated Python scripts.

This allows agents to read, manipulate, and extract information from enterprise files, so organizations can also attach reference documents or knowledge sources to agents. These materials become searchable through retrieval-augmented generation workflows using Optimizely’s content graph.

Workflow automation

For me the most operationally significant feature demonstrated during the event was multi-agent workflow orchestration. Agents can be chained together sequentially, passing outputs and parameters between steps.

Workflows can be triggered in several ways:

Direct chat input
Email-based triggers
Webhook integrations
Scheduled automation

One demonstration showed a research agent analyzing a topic and passing its results to an email agent. The second agent transformed the research findings into a formatted HTML email and automatically delivered it to recipients. Other enterprise use cases discussed during the event included automated market research, competitive intelligence monitoring, and recurring insight reports.

Why Agentic Marketing Matters for Digital Teams

For marketing teams, the operational implications of agentic AI could be substantial. Modern digital marketing involves a constant flow of analysis, reporting, and information gathering. Many tasks follow similar patterns:

Collect information → Analyze it → Generate output → Share results

AI agents can automate much of this process instead of manually conducting research or assembling reports, teams can initiate workflows where agents gather information, analyze it, and deliver structured insights. This does not remove human involvement, instead, it shifts where humans spend their time.

MIT Sloan research suggests that AI is most effective when it complements human work rather than replacing it, enabling people to focus on higher-value tasks such as interpretation and decision-making (MIT Sloan Management Review ). For marketing organizations, that means less time spent on operational preparation and more time spent on strategy.

Industry Validation

Several industry studies reinforce the potential impact of AI in marketing operations.

McKinsey estimates that generative AI could generate significant economic value across industries, with marketing and sales among the functions that stand to benefit the most from productivity improvements (McKinsey). The firm’s research suggests that generative AI could increase marketing productivity up to 15 percent of total marketing spend through improvements in content creation, personalization, and knowledge synthesis.

At the same time, organizations are still learning how to operationalize AI effectively. McKinsey’s latest global AI survey notes that many companies have adopted AI tools but have not yet embedded them deeply into their workflows or processes (McKinsey). This gap between experimentation and operational integration is precisely where agentic platforms may deliver value.

By embedding AI into enterprise systems and allowing agents to execute tasks, organizations can move from isolated experiments to operational transformation.

Strategic Implications for CMOs and Digital Leaders

For marketing leaders evaluating AI platforms, several strategic considerations emerge from the Opal demonstration.

First, governance becomes critical. AI systems embedded in operational workflows must operate within brand, legal, and compliance boundaries. Opal’s instruction framework illustrates how organizations can define these constraints.

Second, the ecosystem around AI matters as much as the AI itself. Platforms that support tool integration, APIs, and workflow orchestration will likely deliver more long-term value than standalone assistants.

Third, marketing operating models may evolve. As agent-driven workflows mature, teams may increasingly focus on oversight, strategy, and decision-making rather than manual research or analysis.

Finally, human supervision remains essential. The demonstration itself acknowledged that large language models can occasionally drift off topic during complex research tasks, requiring prompt refinement and oversight. Agentic systems should therefore be viewed as collaborators, not replacements.

My Final Takeaways

The Optimizely Opal Agents in Action event offered an early look at how AI agents might reshape marketing operations. Rather than presenting AI simply as a content generator, the demonstrations positioned Opal as a system capable of executing research, analyzing documents, orchestrating tools, and automating workflows across marketing platforms. This approach reflects a broader industry shift. As organizations move beyond isolated AI experiments, the focus is shifting toward operational integration—embedding AI into everyday workflows. For digital marketing teams, this may represent the next stage of platform evolution, not just tools, not just assistants, but ecosystems of AI agents working alongside human teams to execute the operational layer of marketing.