Marketing teams are nowadays being squeezed from both sides. Customer acquisition is getting harder and more expensive, while expectations for personalization keep rising. At the same time, most organizations are still running campaigns with a mix of spreadsheets, disconnected tools, and workflows that require too many manual handoffs.
In our recent webinar with Stefan Jacák (CEO, YNK Media) and David Vyskocil (CEO, Samba.ai), one message came through clearly: the next efficiency leap will not come from “more automation rules”. It will come from AI agents that can prepare most of the work, propose better decisions, and help teams execute faster across channels, while humans stay in control of strategy and approvals.
Chatbots, automation, and AI agents are not the same thing
Many leaders hear “AI” and picture a chatbot. That’s useful, but it is not the operating model change that will reshape campaign execution. A practical way to see the difference:
| Capability | Chatbot | Classic automation | AI agent approach |
|---|---|---|---|
| Typical output | Answers, drafts | If/then flows | Multi-step work delivered as a result |
| Decision-making | Reactive to prompts | Predefined rules | Goal-driven, proposes actions and next steps |
| Best use | Q&A, content help | Repeatable scenarios | Planning, segmentation, content prep, orchestration |
As mentioned in the webinar, the definition of an AI agent is simple: a set of functions that solves a specific problem independently by breaking work into tasks and coordinating the right tools. The practical implication for CMOs is huge: instead of starting every campaign from zero, teams can start with 70 to 80 percent of the work already prepared, then validate and refine it.
From rigid flows to adaptive micro-segments
Traditional lifecycle marketing often relies on a limited set of segments and prebuilt flows. That model works, but it is constrained by human capacity. The shift discussed in the webinar is toward micro-segmentation and dynamic follow-ups, where messaging adapts to behavior across very small audiences.
The difference is not “more emails”. The difference is relevance at scale: the system prepares copy variants, product blocks, and follow-up logic per segment, and marketers focus on governance, brand voice, and business priorities.
Where teams see value first: proven use cases
Stefan has shared several of his practical experiments that prove to work and teams can implement them without waiting for a perfect future state:
- Faster reporting and insights: pulling campaign or monthly performance with one prompt and structuring it into a usable layout.
- Assisted segmentation and automation design: creating segment logic and workflow drafts via prompts, then validating and adjusting.
- Newsletter production at speed: generating drafts directly into HTML templates (with current limitations in drag-and-drop builders).
- AI-driven triggers that reduce manual scheduling: shopping intent and repeat-order campaigns where timing and product selection are decided by data, not gut feeling.
- Smarter lead capture and web personalization: using behavior on-site to tailor the experience and increase the chance of converting anonymous traffic into known contacts.
What AI agents look like in Samba.ai
To make AI agents useful, you need a solid data foundation and execution layer. Samba.ai is a customer data platform (CDP) that combines AI-driven analytics with marketing execution, so teams can unify customer profiles and activate personalization across channels.
The most relevant capabilities for an “agent-ready” setup in Samba are:
- Unified customer view for segmentation and decisioning
- Flow Campaigns for omnichannel orchestration (email, SMS, push, webhooks)
- Autopilot-style automation that reduces manual work on targeting and recommendations
- Web personalization and lead-gen experiences that support growth even when acquisition becomes more challenging
An adoption roadmap for CMOs
- Start with one high-impact lifecycle area, usually retention or repeat purchases, and define success metrics that matter to finance (incremental revenue, margin, churn reduction).
- Fix the foundation: unify customer and product data, align events, and agree on segmentation principles.
- Choose two “agent-augmented” workflows to pilot, for example reporting + one automated trigger campaign, and design a clear approval process.
- Scale what works: expand from email into true omnichannel orchestration, and invest in brand voice, QA, and governance so speed does not damage trust.
The bottom line
AI agents are not replacing marketing teams. They are changing the starting line. The winners will be the teams who turn repetitive production into an AI-assisted process, then reinvest saved time into strategy, experimentation, and better customer experiences.
If you want to see what an agent-ready retention setup looks like in practice, explore how Samba.ai supports unified customer data, omnichannel orchestration, and AI-driven personalization, then book a demo to map your use cases to a realistic rollout plan.
