Agentic AI in Marketing for Decision-Driven Growth
27 janvier 2026 • 6 Minute Read • Tod Szewczyk, VP, Services marketing
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Video: Turning Agentic AI into Measureable Marketing Impact with Optimizely Opal
20 février 2026 • 2 Minute Read • Elizabeth Spranzani, Chef de la direction technologique
AI has crossed the hype threshold in marketing. Most teams have experimented with generative tools, built prompt libraries, and launched internal AI playbooks.
These are useful starting points. Yet few teams can confidently answer a simple question: What business impact is this actually driving?
What’s holding teams back isn’t access to AI or tools. It’s how and where it’s applied.
AI is often used in isolation by optimizing tasks without changing how decisions are made, prioritized, or acted on. The result is localized efficiency rather than systemic impact.
This is where agentic AI—and specifically Optimizely Opal—changes the equation.
For a deeper look at how agentic AI enables decision-driven growth in marketing, read our perspective on Agentic AI in Marketing for Decision-Driven Growth.
In this conversation, Liz Spranzani, CTO of Verndale, and Michiel Dorjee, Director of Digital Experience at Optimizely, unpack what it takes to move from AI experimentation to measurable marketing impact.
You’ll hear how agentic AI shifts marketing from task automation to decision acceleration, why ROI depends on reinvestment (not just time saved), and how Optimizely Opal serves as a decision layer across the stack. The discussion also brings these ideas to life through real-world agent examples spanning content intelligence, conversion optimization, and lead enrichment.
Video: Turning Agentic AI into Marketing Impact with Optimizely Opal
Why Efficiency Alone Isn’t ROI
Time savings are often the first metric teams use to justify AI investments. It's also the first place the story breaks down.
Saving hours on content creation, research, or reporting matters only if that time is reinvested in higher-impact work, such as experimentation, optimization, faster launches, or improved lead quality. Without reinvestment, efficiency becomes invisible value.
Instead of asking, “What tasks can we automate?” ask, “Where are decisions slowing us down, and what happens if we remove that friction?”
Reducing decision latency is where agentic AI starts to compound, and faster insight-to-action cycles can drive measurable impact across campaigns and channels.
From AI Experiments to Operational Intelligence
Scattered AI tools and prompt libraries are useful early on, but this approach doesn't scale.
To scale AI, marketing teams need an operational model, not a static one.
Operational AI requires a model in which learning is embedded in workflows, execution is repeatable, and decisions are supported by systems, not memory. This is the shift Optimizely Opal enables.
Agentic AI creates leverage and delivers the most value in work that's:
- Signal-heavy — requires synthesizing large volumes of information
- Time-sensitive — loses value when delayed
- Outcome-tied — directly connected to performance or revenue
Marketing is full of this type of work, from content strategy to CRO to lead qualification and compliance. Opal accelerates the decisions that sit between insight and action.
How Opal Structures Agentic Work
Opal functions as a decision layer across the marketing stack, built on three core components:
- Tools — Integrations that connect Opal to systems like CMPs, CRMs, analytics, and external data sources.
- Specialized agents — Agents responsible for a single function, such as research, scoring, or analysis.
- Workflow agents — Orchestration layers that chain tools and agents into governed, repeatable processes.
This structure allows teams to start with focused use cases and scale without having to rebuild their approach.
Measuring What Actually Matters
Successful teams establish baselines early, then track how agentic workflows change outcomes in cycle times, conversion rates, lead quality, and revenue contribution.
Efficiency becomes the enabler; outcomes are the end goal. The real question becomes: “What did we unlock because we saved time?”
Agentic AI turns productivity experiments into an operational advantage with systems that can reason, adapt, and scale alongside teams. Optimizely Opal makes that advantage measurable.
Where to Go Next with Agentic AI
Turning AI into marketing impact takes more than automation. It requires clarity on where decisions slow teams down, discipline in reinvesting time savings, and systems that can scale learning across the organization.
If you’re looking to move beyond isolated AI experiments and toward operational, outcome-driven systems, explore our Optimizely Opal Readiness Assessment to learn about where agentic AI can create the most leverage in your organization.