{"id":2767,"date":"2026-09-03T21:07:57","date_gmt":"2026-09-04T02:07:57","guid":{"rendered":"https:\/\/www.miaprova.com\/blog\/?p=2767"},"modified":"2026-09-03T21:07:58","modified_gmt":"2026-09-04T02:07:58","slug":"adobe-is-a-leader-in-forresters-2026-experience-optimization-wave-heres-what-target-teams-should-take-from-it","status":"publish","type":"post","link":"https:\/\/www.miaprova.com\/blog\/adobe-is-a-leader-in-forresters-2026-experience-optimization-wave-heres-what-target-teams-should-take-from-it\/","title":{"rendered":"Adobe Is a Leader in Forrester’s 2026 Experience Optimization Wave. Here’s What Target Teams Should Take From It."},"content":{"rendered":"\n

By Brian Hawkins, Head of Optimization Technology, Adswerve<\/strong><\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

Forrester recently published The Forrester Wave™: Experience Optimization Solutions, Q3 2026<\/em>, and Adobe was named a Leader.<\/p>\n\n\n\n

That headline will make the rounds on its own.<\/p>\n\n\n\n

What I find more interesting is what Forrester chose to evaluate\u2014and how the definition of experience optimization itself has changed.<\/p>\n\n\n\n

Because that tells us a lot more about where enterprise optimization is heading than any individual position on a Wave graphic.<\/p>\n\n\n\n

The report is gated, and you can request it directly from Adobe here: https:\/\/business.adobe.com\/resources\/reports\/forrester-experience-optimization-solutions.html<\/a><\/p>\n\n\n\n

I am going to stay at a high level and leave Forrester’s specific scoring and language where they belong: in the report.<\/p>\n\n\n\n

Experience optimization has become a much bigger category<\/strong><\/h2>\n\n\n\n

This was my biggest takeaway.<\/p>\n\n\n\n

For years, many of us in this industry largely associated experience optimization with experimentation, personalization, recommendations, and the teams responsible for operating those programs.<\/p>\n\n\n\n

That definition is expanding quickly.<\/p>\n\n\n\n

In the 2026 evaluation, Adobe’s experience optimization portfolio is centered on Adobe Journey Optimizer and Adobe Target within Adobe CX Enterprise<\/strong>.<\/p>\n\n\n\n

Adobe Experience Platform provides the underlying customer data foundation, while Customer Journey Analytics adds another important part of the measurement layer.<\/p>\n\n\n\n

That framing matters.<\/p>\n\n\n\n

Experience optimization is no longer just about determining which version of a webpage performs better.<\/p>\n\n\n\n

It is becoming a connected discipline spanning customer data, experimentation, personalization, analytics, recommendations, decisioning, journey orchestration, and increasingly AI.<\/p>\n\n\n\n

For those of us who have spent years working with Adobe Target, I think that is worth paying attention to.<\/p>\n\n\n\n

It does not make Target less important.<\/p>\n\n\n\n

Target remains one of Adobe’s core engines for experimentation and personalization, particularly across web and application experiences.<\/p>\n\n\n\n

But Target increasingly operates as part of a much broader optimization ecosystem.<\/p>\n\n\n\n

And I think that is the right evolution.<\/p>\n\n\n\n

The technology is often ahead of the operating model<\/strong><\/h2>\n\n\n\n

This is where I stop talking specifically about the Forrester report and start talking about what I see in enterprise optimization programs.<\/p>\n\n\n\n

I have been working with Adobe Target since it was Offermatica, and I have spent the last several years working with organizations trying to connect experimentation to AEP, Real-Time CDP, Journey Optimizer, Customer Journey Analytics, Adobe Analytics, and the broader Adobe ecosystem.<\/p>\n\n\n\n

The capabilities available today are extraordinary.<\/p>\n\n\n\n

Real-time customer profiles.<\/p>\n\n\n\n

Identity resolution.<\/p>\n\n\n\n

Cross-channel audiences.<\/p>\n\n\n\n

Experimentation.<\/p>\n\n\n\n

Personalization.<\/p>\n\n\n\n

Journey orchestration.<\/p>\n\n\n\n

Customer Journey Analytics.<\/p>\n\n\n\n

Increasingly sophisticated AI.<\/p>\n\n\n\n

The technology foundation is incredibly strong.<\/p>\n\n\n\n

Where organizations often struggle is operating all of those capabilities as one optimization program<\/strong>.<\/p>\n\n\n\n

A Target activity may be owned by one team.<\/p>\n\n\n\n

An AJO journey may be owned by another.<\/p>\n\n\n\n

AEP audiences and datasets may belong to a data organization.<\/p>\n\n\n\n

Measurement may live in Adobe Analytics or CJA with another group entirely.<\/p>\n\n\n\n

Each team can be doing its job well and the overall system can still become fragmented.<\/p>\n\n\n\n

That fragmentation produces some very predictable problems.<\/p>\n\n\n\n

Two teams can unknowingly run overlapping experiences.<\/p>\n\n\n\n

An audience can materially change and the team using it may not realize it.<\/p>\n\n\n\n

A batch ingestion issue can affect personalization downstream without the experience owner immediately understanding what happened.<\/p>\n\n\n\n

An experiment can generate an important insight that never influences the next journey or campaign because the team creating that experience never saw the result.<\/p>\n\n\n\n

A successful optimization program can accumulate hundreds or thousands of activities, audiences, journeys, datasets, and learnings without having a great way to understand how all of those pieces relate to one another.<\/p>\n\n\n\n

Those are not failures of Adobe’s technology.<\/p>\n\n\n\n

They are signs that the operating model around experience optimization has to evolve alongside the platform<\/strong>.<\/p>\n\n\n\n

And I think that is becoming one of the biggest opportunities in our industry.<\/p>\n\n\n\n

Three questions I would ask about your own optimization program<\/strong><\/h2>\n\n\n\n

One thing I liked about the Forrester report is that several of its buyer considerations work equally well as a maturity assessment for organizations that have already selected their technology.<\/p>\n\n\n\n

I would ask three questions.<\/p>\n\n\n\n

1. How far does AI reach beyond content generation?<\/strong><\/h3>\n\n\n\n

Generating copy, imagery, audiences, and experience variations is rapidly becoming table stakes.<\/p>\n\n\n\n

The more interesting question is what AI can do across the optimization lifecycle itself.<\/p>\n\n\n\n

Can an agent identify an opportunity?<\/p>\n\n\n\n

Can it investigate performance?<\/p>\n\n\n\n

Can it understand the audiences involved?<\/p>\n\n\n\n

Can it identify something unusual in the data supporting an experience?<\/p>\n\n\n\n

Can it recommend the next experiment?<\/p>\n\n\n\n

Can it help configure that experiment?<\/p>\n\n\n\n

Can it analyze the outcome and use what it learned to recommend what happens next?<\/p>\n\n\n\n

Eventually, can it perform portions of that lifecycle autonomously within the governance and permissions established by the organization?<\/p>\n\n\n\n

That is where this gets really interesting.<\/p>\n\n\n\n

2. What does autonomous optimization realistically look like for your organization?<\/strong><\/h3>\n\n\n\n

Forrester spends considerable attention on the movement toward agentic and increasingly autonomous optimization.<\/p>\n\n\n\n

I think the word realistically<\/strong> matters here.<\/p>\n\n\n\n

Some organizations may eventually allow agents to create experiences, configure experiments, manage audiences, analyze outcomes, and initiate follow-up actions with minimal human intervention.<\/p>\n\n\n\n

Others will want humans approving nearly every meaningful action.<\/p>\n\n\n\n

Both models can work.<\/p>\n\n\n\n

The important question is where your organization wants the human in the loop.<\/p>\n\n\n\n

Because simply adding an AI assistant to an inefficient operating model does not fix the operating model.<\/p>\n\n\n\n

It may just make the inefficiency move faster.<\/p>\n\n\n\n

3. Is your data foundation actually unified in practice?<\/strong><\/h3>\n\n\n\n

Adobe has built a very powerful data foundation with AEP.<\/p>\n\n\n\n

But having a unified customer-data platform and operating your optimization program as if the data is unified<\/strong> are not necessarily the same thing.<\/p>\n\n\n\n

Are the same customer signals consistently available across experimentation, personalization, journey orchestration, recommendations, and measurement?<\/p>\n\n\n\n

Can experience owners understand the health of the audiences they are activating?<\/p>\n\n\n\n

Can they see when an upstream dataset or ingestion process changes?<\/p>\n\n\n\n

Do teams know when the same audience is being used across several experiences?<\/p>\n\n\n\n

Can they understand the dependencies between data and activation?<\/p>\n\n\n\n

Adobe provides the architecture to make much of this possible.<\/p>\n\n\n\n

The question for individual organizations is whether their implementation and operating processes are actually taking advantage of it.<\/p>\n\n\n\n

That is worth measuring rather than assuming.<\/p>\n\n\n\n

Where MiaProva fits<\/strong><\/h2>\n\n\n\n

This evolution of experience optimization is one of the biggest reasons we have expanded MiaProva beyond traditional experimentation management.<\/p>\n\n\n\n

MiaProva is designed to provide a program-level management, intelligence, and observability layer across the Adobe optimization ecosystem<\/strong>.<\/p>\n\n\n\n

On the experimentation side, that means helping organizations manage Adobe Target as a program rather than simply as a collection of individual activities.<\/p>\n\n\n\n

Teams can understand what is running, what is coming, where experiences may overlap, what has been learned, which activities require attention, and what should happen next.<\/p>\n\n\n\n

As organizations adopt AEP and Real-Time CDP, that same program-level visibility can extend into the data powering those experiences.<\/p>\n\n\n\n

Audience health.<\/p>\n\n\n\n

Audience complexity.<\/p>\n\n\n\n

Dataset activity.<\/p>\n\n\n\n

Batch ingestion.<\/p>\n\n\n\n

Governance.<\/p>\n\n\n\n

Consumption.<\/p>\n\n\n\n

Operational signals that can affect downstream personalization.<\/p>\n\n\n\n

And as Target and Journey Optimizer increasingly operate as complementary parts of the same experience optimization portfolio, I believe organizations need better ways to understand experimentation and journey orchestration together.<\/p>\n\n\n\n

That is why we have been building capabilities such as an activation calendar that allows teams to view experimentation and journey activity on a common timeline.<\/p>\n\n\n\n

The goal is not to recreate Adobe’s applications.<\/p>\n\n\n\n

It is to help organizations understand and operate the optimization program that spans them.<\/p>\n\n\n\n

The agentic piece may be the most important part<\/strong><\/h2>\n\n\n\n

Forrester’s description of where experience optimization is heading is particularly interesting to me because of its emphasis on agentic AI.<\/p>\n\n\n\n

Adobe is clearly investing heavily in this direction as well.<\/p>\n\n\n\n

But agents become much more useful when they can safely operate across the systems involved in an optimization program.<\/p>\n\n\n\n

An optimization agent may need to understand an Adobe Target activity.<\/p>\n\n\n\n

Then inspect the audience powering it.<\/p>\n\n\n\n

Then understand the underlying AEP data.<\/p>\n\n\n\n

Then query CJA or Adobe Analytics to understand performance.<\/p>\n\n\n\n

Then determine whether something changed.<\/p>\n\n\n\n

Then recommend\u2014or eventually execute\u2014the next action.<\/p>\n\n\n\n

That requires more than an AI chat interface.<\/p>\n\n\n\n

It requires governed, permissioned access to the systems and context involved in the optimization lifecycle.<\/p>\n\n\n\n

This is an area where we have been investing heavily with MiaProva.<\/p>\n\n\n\n

Our MCP tooling makes Adobe Target, Adobe Experience Platform, Real-Time CDP, Customer Journey Analytics, and Adobe Analytics available as agent-accessible surfaces.<\/p>\n\n\n\n

That creates some fascinating possibilities.<\/p>\n\n\n\n

An agent can investigate an experiment.<\/p>\n\n\n\n

Inspect an audience.<\/p>\n\n\n\n

Check data health.<\/p>\n\n\n\n

Analyze performance.<\/p>\n\n\n\n

Look for anomalies.<\/p>\n\n\n\n

Connect those signals.<\/p>\n\n\n\n

And eventually participate in larger optimization workflows while respecting the permissions and governance already established by the organization.<\/p>\n\n\n\n

The goal is not to replace Adobe’s applications or Adobe’s AI strategy.<\/p>\n\n\n\n

It is to help organizations and agents operate more effectively across the Adobe investment they already have.<\/p>\n\n\n\n

I think that distinction is important.<\/p>\n\n\n\n

As experience optimization shifts from individual tools toward connected systems\u2014and eventually toward agent-assisted and increasingly autonomous workflows\u2014the connective tissue between those systems becomes extremely valuable.<\/p>\n\n\n\n

What I would do with the Forrester report<\/strong><\/h2>\n\n\n\n

If you are already an Adobe customer, I would not read this report only as a vendor scorecard.<\/p>\n\n\n\n

Use it as a mirror.<\/p>\n\n\n\n

Ask whether experimentation and journey orchestration are actually connected inside your organization.<\/p>\n\n\n\n

Ask whether the teams creating experiences can understand the audiences and data powering them.<\/p>\n\n\n\n

Ask whether learnings from experiments consistently influence the next experience you create.<\/p>\n\n\n\n

Ask whether your analytics, experimentation, customer data, and journey teams share the same definition of success.<\/p>\n\n\n\n

Ask whether operational issues in the underlying data can be connected quickly to the experiences they affect.<\/p>\n\n\n\n

And ask where AI can meaningfully remove work or improve decision-making rather than simply adding another interface to an already complicated process.<\/p>\n\n\n\n

Forrester evaluating Adobe Target and Adobe Journey Optimizer together within Adobe’s broader CX platform is an important signal.<\/p>\n\n\n\n

Many enterprises still operate these technologies through different teams, different backlogs, different workflows, and sometimes entirely different organizational structures.<\/p>\n\n\n\n

Closing that gap may create as much value over the next several years as adopting any individual new feature.<\/p>\n\n\n\n

Adobe has built an incredibly powerful foundation for experience optimization.<\/p>\n\n\n\n

The next opportunity is making the entire optimization program operate like a connected system.<\/p>\n\n\n\n

And if Forrester is right about where this category is headed, eventually some of the participants operating that system will not be people.<\/p>\n\n\n\n

They will be agents.<\/p>\n\n\n\n

That’s the part I think is going to get really interesting.<\/p>\n\n\n\n

If you’re working through that evolution in your own Adobe program, I’d love to compare notes.<\/p>\n","protected":false},"excerpt":{"rendered":"

By Brian Hawkins, Head of Optimization Technology, Adswerve Forrester recently published The Forrester Wave™: Experience Optimization Solutions, Q3 2026, and Adobe was named a Leader. That headline will make the rounds on its own. What I find more interesting is what Forrester chose to evaluate\u2014and how the definition of experience optimization itself has changed. Because…<\/p>\n","protected":false},"author":2,"featured_media":2768,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_kad_post_transparent":"","_kad_post_title":"","_kad_post_layout":"","_kad_post_sidebar_id":"","_kad_post_content_style":"","_kad_post_vertical_padding":"","_kad_post_feature":"","_kad_post_feature_position":"","_kad_post_header":false,"_kad_post_footer":false,"_kad_post_classname":"","footnotes":""},"categories":[6,3,363,369],"tags":[],"class_list":["post-2767","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-adobe","category-adobe-target","category-aep","category-ai"],"yoast_head":"\nAdobe Is a Leader in Forrester's 2026 Experience Optimization Wave. 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