Optimizely's agentic AI retools the entire marketing workflow
Marketing teams are being asked to grow output without growing headcount. Optimizely is leaning into that reality with Opal, its agentic AI platform that now supports every stage of the marketing lifecycle - ideation, creation, testing, publishing and optimization.
Brands like Starbucks and Nike already use Optimizely's digital experience stack. The shift is clear: move from AI demos to real, production-grade results.
From AI experiments to enterprise results
Leadership is pushing AI-first operations. Many teams are told to prove an agent can't do the work before they get new hires.
The message for marketers: pilots aren't enough. What matters is automating meaningful slices of work and showing measurable gains in throughput, quality and revenue impact.
Context beats generic AI
Generic prompts create what many call "AI slop" - content that looks fine in a demo and falls apart in production. The fix is context engineering: feed the model only the essentials that drive brand-consistent outcomes.
That means codifying brand voice, personas, product nuance, claims that can and can't be made, competitive angles and internal best practices. Too much context overwhelms the model; too little gives you bland copy.
Why Optimizely chose Gemini
Optimizely selected Google's Gemini as its core model for multimodal work across text, image and video. The large context window and flexible tiers (Flash, Pro, Thinking) help align speed and cost to each task.
Enterprise controls around privacy, security and governance were also key. For more on Gemini's capabilities, see Google's model reference here.
Agent-to-agent interoperability across your MarTech stack
Most stacks are sprawling. Optimizely is pushing A2A (agent-to-agent) interoperability so Opal can talk to Google agents and third-party agents across tools you already use.
That composability is being open-sourced via the Linux Foundation, expanding the kinds of workflows you can automate end to end. Learn about the Linux Foundation's role in open source here.
What this looks like in your workflow
- Content planning: Agents build briefs, pull past performance and propose angles mapped to personas.
- Creation: Drafts are produced in your brand voice with product and compliance context applied.
- Experimentation: Variants are generated and routed into A/B tests with guardrails set by your team.
- Distribution: Copy adapts to channel constraints and audience segments automatically.
- Analytics: Agents summarize performance, flag wins, and recommend next tests - all in the tools your team already uses.
A 30/60/90-day plan for marketers
- Days 1-30: Pick three workflows with repeatable steps (e.g., campaign briefs, SEO outlines, ad variants). Document brand voice, claims, and compliance rules into a "context pack." Define success metrics.
- Days 31-60: Pilot inside real workflows. Compare agent output vs. baseline for quality, time saved and performance. Add review checkpoints and tighten context.
- Days 61-90: Scale to more channels. Connect agents to testing and analytics. Automate handoffs with A2A, and publish a playbook so the team can self-serve.
Quality, governance and adoption
- Quality: Establish a reference library of "gold standard" content. Agents learn from what wins, not just what's written.
- Governance: Centralize brand rules, disclaimers and blocked phrases. Log prompts and outputs for audits.
- Adoption: Put agents where people work - inside CMS, planning boards, testing tools - so no one has to context switch.
Metrics that matter
- Content throughput per marketer
- Time to first draft and time to publish
- Variant coverage across segments and channels
- Lift in conversion, CTR and revenue per visit
- Percentage of workflow fully automated with guardrails
Why this matters now
Agentic AI isn't about flashy demos. It's about compressing cycle times, multiplying test velocity and tightening the loop between insight and execution.
With the right context and interoperable agents, you get scale without losing your brand's edge - and you get it inside the tools your team already uses.
Level up your team's AI skills
If you're building an AI-first marketing org, formal training helps speed up adoption and consistency. Explore the AI certification for marketing specialists here.
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