For many digital and marketing leaders, the conversation around AI has evolved from excitement to a greater focus on practicalities.
This year we have seen digital leaders shift to asking more grounded questions. How do we use AI responsibly? How do we measure its impact?
The answers are not found in high level strategy decks or isolated AI experiments . It is emerging within the systems and workflows that already power marketing operations. This is where AI agents are quietly redefining what real, practical transformation looks like.
The rise of AI Agents
Unlike one-off generative tools, AI agents are designed to work inside an organisation’s existing digital ecosystem. They understand structured processes, act on your data, and deliver measurable results. Think of them as digital teammates that manage repeatable tasks while learning from performance data.
For marketing and digital teams, this means agents that can:
- Build landing pages or content briefs automatically
- Route and tag creative assets
- Analyse campaign performance and recommend next steps
- Optimise personalisation and SEO in real time
According to McKinsey, organisations that embed AI within structured workflows see an increase in customer satisfaction by 15–20%, boost revenue by 5–8%, and reduce the cost to serve by up to 30%, making it one of the clearest examples of measurable AI-powered marketing impact.
Rather than experimenting on the edge, the most valuable and least risky AI opportunities lie within everyday marketing processes.
Why use cases in marketing operations are a logical place to start?
Marketing Operations sits at the crossroads of content, technology, and performance. It is where data flows are defined, creative assets are approved, and results are measured. It is also where inefficiencies quietly multiply, through manual reviews, rework, and disconnected tools.
This is exactly where AI agents create impact. They bring automation with context, improving accuracy, speed, and consistency across campaign setup, personalisation, and reporting.
According to Deloitte’s State of Generative AI in the Enterprise survey, almost all organisations report measurable ROI from their most advanced Gen AI initiatives, with 20% reporting ROI in excess of 30%.
Where Opal AI changes the game
Optimizely’s Opal AI turns this potential into reality. It introduces an intelligent orchestration layer that works across the Optimizely platform, connecting content, commerce, experimentation, and data intelligence.
For retail, manufacturing, and B2B organisations, this enables:
1. Streamlined campaign execution
Opal’s pre-built agents can automate campaign setup, metadata tagging, and QA, reducing delivery times from days to hours while maintaining governance and visibility.
2. Scalable personalisation
Agents can learn from live performance data to adjust content, layouts, and calls to action, ensuring every customer interaction remains relevant and measurable.
3. Data-driven decision making
Because Opal works within the existing analytics layer, every action is tracked and tied to real business outcomes, giving marketing leaders both clarity and control.
4. Faster experimentation
Agents can propose and run A/B tests autonomously, then summarise findings to help teams refine content and improve performance continuously.
What industry analysts are saying
Analysts agree that the next wave of AI transformation will be operational, not experimental. Gartner predicts that by 2026, more than 40% of enterprise marketing teams will use AI agents to manage workflows and optimise performance.
BCG highlights that companies using embed AI across their business workflows (rather than running isolated pilots) are achieving approximately 3–3.6 × higher returns compared to less-mature peers.
Forrester notes that intelligent automation across digital experience platforms is now a key differentiator in digital maturity.
AI’s value is no longer about what it can imagine. It is about what it can execute.
From ambition to action
Many AI initiatives fail not because of technology but because of scale without structure. The most successful digital leaders take a different approach. They start narrow, learn quickly, and scale confidently.
By focusing on Marketing Operations, they build evidence through small, safe experiments that prove where AI adds real value. This builds trust internally, secures investment, and creates momentum for wider transformation.
Platforms such as Optimizely with Opal AI make this practical. They provide the safety, structure, and measurability needed to innovate responsibly.
Practical steps for digital leaders
If you are exploring how to use AI agents in your organisation, begin with four principles.
1. Anchor each pilot to a measurable workflow: campaign setup, tagging, and QA are excellent places to start.
2. Stay within trusted, scalable systems: use platforms such as Optimizely Opal that protect data and maintain governance.
3. Measure outcomes, not activity: track efficiency, accuracy, and engagement improvements, not just AI output.
4. Scale with evidence: use early success stories to expand adoption sustainably across teams.
Turning proof into progress
At DotCentric, we work with digital and marketing leaders to turn AI potential into measurable performance. We help define where AI can have the greatest impact, design intelligent workflows using Optimizely, and run focused AI Value sprints that prove ROI within four weeks.
For us, the goal of AI is not to automate creativity but to empower it. When AI agents handle the repetitive work, teams gain the time and space to innovate, connect, and grow. That is what true transformation looks like.
Continue the conversation
To explore how digital leaders are putting AI to work inside their organisations, listen to the latest episode of Connecting the dots, DotCentric's Digital Transformation Podcast. We discuss the future of AI agents and how platforms like Optimizely Opal are changing the way digital teams operate.
