Virtual Opticon 2026: Where AI Can Improve DXP Delivery

Consider a campaign whose first draft takes ten minutes to generate but then waits five days for review. Faster drafting has helped one task. The campaign still reaches the customer five days later.

As an attendee at Opticon Online on October 21, I was particularly interested in how AI could address that delivery problem. Across the customer stories and product keynote, the strongest case for AI was its ability to take responsibility for recurring work across a digital experience program. The value depends on which work we assign it, what context it receives, and how we measure the result.

Optimizely framed the discussion around three experiences: the marketer doing the work, the customer using the experience, and the AI agent increasingly researching or acting on a customer's behalf. Its October Agent Platform updates make that direction more tangible. Here is how I would translate it into DXP decisions.

Measure the time it takes to reach a decision

Robert Chatwani's DocuSign presentation focused on cycle time: the path from a hypothesis to a live experiment, a decision, and the next test. His recommendations were specific: bring the relevant functions together, assign a directly responsible individual with decision authority, and remove the handoffs where work stalls.

For a DXP team, this changes where an AI pilot should start. Map one recurring process before choosing an agent. Record the time spent producing work, waiting for review, resolving missing information, and implementing the decision. If most of the delay sits in an approval queue, another copy generator addresses the wrong part of that process.

A useful pilot might assemble the source material, perform an initial brand check, and route a complete review package to a named approver. Measure elapsed time through approval and publication, alongside the amount of rework. That tells you whether the process improved.

Experimentation needs a further distinction. AI can shorten preparation and analysis work. A valid decision still needs appropriate measurement, traffic, and observation time. Faster launches do not automatically produce reliable learning sooner.

Personalization also needs an explicit decision framework: which observed signal changes the experience, what content is eligible, and what visitors receive when the signal is absent. More content variants are useful when they support a relevant customer choice and an outcome the team can evaluate.

Make approved context part of the delivery process

KPMG's sequence was one of the most useful examples in the event. Lauren Boyman described consolidating intake, incorporating editorial and brand review into workflows, and establishing a shared digital asset library with metadata and usage information. Specialized agents followed that foundation.

That sequence belongs in a DXP roadmap. Before asking AI to reuse an asset or adapt a claim, establish which version is approved, where it can be used, and who maintains it. Otherwise, automation can distribute outdated information more efficiently.

Brand Packs address part of this requirement. Optimizely documents them as structured brand assets and rules that become a skill when published. A default pack supplies brand context unless another is selected. Variants extend the production workflow by creating channel-specific image versions.

The distinction between brand guidance and approval deserves care. Publishing a Brand Pack gives agents approved instructions; it does not establish that every future output is factually accurate, legally acceptable, or suitable for every market. Teams still need to define which outputs require review.

I would give the pack a business owner and a review process. Test it against representative outputs, including difficult cases such as an expired offer or a claim that requires qualification. For image variants, inspect cropping, legibility, and required disclosures. The useful measure is how much work passes review without correction.

The Skills Directory can help distribute reusable guidance across the organization. Administrators can add organization skills, while users can add personal skills. That creates a practical reason to make approved organizational guidance easy to find and maintain, especially when personal instructions also exist.

Give Virtual Teammates a bounded job and an accountable owner

October's Web Manager and Content Marketer additions move the discussion toward recurring responsibilities. The Web Manager documentation describes compliance audits, site-health work, and website updates. Its jobs run through scheduled or triggered workflows; enabled jobs and connected tools determine what is available. The documentation also says it asks for approval before acting on your behalf. 

The Content Marketer monitors trends and competitors and can use connected Salesforce data to identify pipeline-related content gaps. The opportunity is to give editorial teams evidence for prioritization. A competitor publishing on a topic is a signal to investigate, not sufficient reason to publish the same thing.

For either role, I would define a short operating brief: the pages or topics in scope, the approved sources, the actions it can propose or execute, the person who resolves exceptions, and the conditions that stop a run.

Then follow the work through to completion. An audit that generates fifty findings has created a queue. Track how many findings were accepted, corrected, verified, and prevented from recurring. For content recommendations, track whether briefs address an agreed audience need and progress through review. Activity counts alone will overstate the value.

The platform's workflow-priority controls also deserve attention as usage grows. They distinguish interactive work from background runs when execution capacity is constrained. A team needs to decide which processes can wait before scheduling large volumes of automated work.

Evaluate orchestration before expanding a migration

The keynote positioned Agent Platform as an orchestration layer across Optimizely applications and external systems, including competing content platforms. The October update adds connections spanning advertising, analytics, collaboration, and content tools. 

For organizations with an established technology stack, this creates an option worth evaluating: improve a workflow across the systems already in use before adding a broader platform replacement to the plan.

Connector availability is only the start of that assessment. Validate the actual operations the workflow needs, the permissions required, and how failures are handled. A connector that retrieves campaign data does not, by itself, prove that the complete review and publishing process can run through it.

A contained workflow gives the team a way to test that boundary. For example, investigate whether an agent can gather performance data, prepare a recommendation, and create a review task while publication remains in the established CMS workflow. The result should inform the architecture decision.

Prepare for agents completing customer tasks

The agent-experience discussion extends beyond visibility in AI-generated answers. A prospective customer may ask an assistant to find a suitable product, locate its documentation, or arrange a meeting. The website then needs to support the task as well as explain the offering.

Optimizely's WebMCP material describes a proposed standard for exposing structured website capabilities to agents. Its Agent Ready Experiences implementation is currently described as a closed beta. This warrants a controlled pilot and a check of current availability before committing a production roadmap.

I would start with one journey, such as finding a product and retrieving its current technical document. Check whether the product identifiers, versions, eligibility rules, and document relationships are explicit and consistent. If the journey progresses to an action, define the authorization and confirmation requirements.

Measure successful task completion, incorrect selections, and points where a person must take over. Being cited in an AI answer and helping a customer finish a task are separate outcomes. Both need their own evidence.

Build the business case around accepted work

The evolution from Opal toward Mark, including domain-specific models and a marketing context layer, brings AI economics into the DXP discussion. The keynote described some model capabilities as forthcoming, particularly models trained for customers' own agents. Those plans should remain separate from capabilities verified in a customer's environment.

Optimizely reported a 67% benchmark all-pass rate against 60% for Claude Code at half the cost in the cited default configurations. That is a seven-percentage-point gap on that evaluation. The separate claim of up to tenfold model cost efficiency came from early testing. Neither establishes the savings a particular customer will achieve, and multiplying them into a twentyfold business-case assumption is not justified without a combined test. 

For a pilot, calculate cost per accepted output or completed workflow. Include platform consumption, setup and maintenance, review time, corrections, and failed runs. Compare that with the baseline for the same work. Cheap generation can still produce an expensive process if reviewers must reconstruct the answer.

The next thirty days can be straightforward: select one recurring workflow with a visible delay, assign an owner, establish the baseline, and run a bounded pilot using approved context. Expand when it reduces elapsed time or effort while meeting the agreed quality standard.

Seth Godin's closing session brought the discussion back to the audience: who the work serves, what change it should create, and whether people want to hear from the brand. That is a useful test for the time AI gives back. Use it to understand customers better, improve an experience, or test a substantive idea. A higher publishing count is a weak return if the work gives customers no additional reason to care.

About the Author

I am Augusto Davalos, a Senior Project Manager at Oshyn, a digital transformation consultancy helping brands get more value from composable DXP platforms such as Optimizely. My work sits at the intersection of digital strategy, agile delivery, content operations, and business outcomes. I share practical perspectives on Optimizely, DXP, MarTech, and digital leadership.