On the surface, agentic AI marketing might seem like another incremental stage in the evolution of MarTech. However, it’s the moment where AI stops merely predicting what might work and actually begins acting on its own to execute and orchestrate campaigns in real-time. While every media business is chasing “efficiency gains,” this shift from AI being an assistant to a revenue operator demands a fundamentally different architecture mindset. But first, we must understand how agentic AI differs from traditional marketing AI.
Media companies have been using AI in bits already. For forecasting impressions, generating audience segments, optimising bid recommendations, predicting which screens deliver higher lift…
But that’s still Traditional AI. It “recommends”. It tells you what could work. And then your optimisation team executes.
Agentic AI is a different league. It doesn’t wait for humans to act. These are autonomous agents that decide, launch, optimise, shift budgets, and pause tactics. All these are independent of human intervention.
For example, traditional AI would say:
“These 4 screen clusters are likely to convert better during evening traffic — consider allocating more budget there.”
But agentic AI would say:
“I’ve already shifted budget into those 4 clusters between 5–8 pm, paused morning spend, and launched 3 new variants to test against commuters, based on real-time performance.”
The next million-dollar question you might have is: Is my current MarTech ready for Agentic AI?
Today’s MarTech stacks were mainly designed to store data and report on campaigns. But agentic AI requires a stack that it can act on in real time. More importantly, it should happen across channels, buying platforms, inventory, pricing, targeting, bidding, and optimisation.
The truth is: most legacy stacks simply cannot support that level of autonomy. This is not because of what they don’t have. But because of what they still depend on.
And until those foundational gaps are fixed, agentic AI will remain a slide-deck vision and not an operational revenue engine. Here are three such foundational gaps that you need to address first before agentic AI adoption:
Your audience data, campaign outcomes, pricing signals, inventory utilisation, and audience cohorts mostly live in separate systems. Agentic AI can’t “decide” or “execute” if it cannot access a unified context.
Every new tool requires custom connectors and API plumbing. These brittle integrations break easily. Autonomous agents cannot run on a stack that breaks every time a field or platform changes.
Most “automation” today means: trigger this action if this condition happens. Agentic AI requires: multi-step reasoning, continuous optimisation, and closed-loop execution; not just conditional workflow.
To address the above three gaps and agentic AI to actually execute (and not just analyse), you need to rebuild foundations in three core areas.
If agents don’t have a single, consistent, connected view of your audiences, content, channels, performance outcomes, pricing signals, and historical context, they can’t make decisions with confidence. Most media environments are still fragmented. Data sits in BI dashboards, CRM exports, CMS logs, campaign tools, legacy platforms, publisher systems, etc.
Agentic AI needs a harmonised, queryable, shared data model. Not stitched-together spreadsheets and siloed databases.
Agents cannot stay locked inside one tool. They need to talk to systems and act across them, including campaign platforms, inventory tools, content systems, billing, CRM, attribution engines, activation layers, and audience stores.
This requires more than APIs. It requires a composable, event-driven integration layer that supports multi-system operations without human intervention.
Automation also has to evolve from simple conditional triggers (“if X happens → do Y”) to autonomous workflows where AI can take multi-step actions and optimise continuously. Without this integration fabric, agentic AI becomes just another analytical overlay, not a revenue operator.
Autonomy only works when there are boundaries. If agents are going to make decisions around spend, pacing, targeting, content usage, prioritisation, or creative distribution, there must be guardrails that define acceptable ranges and rules.
This includes:
Governance isn’t there to restrict AI. It’s there to ensure that autonomous decisions remain aligned with brand integrity, legal frameworks, audience expectations, and business strategy.
Together, these three pillars modernise your stack plus create the operational substrate that autonomous agents can safely run on. And this is where Salesforce becomes more than just a marketing platform. It becomes the control layer that unifies data and orchestrates cross-system execution.
Use this as a quick self-diagnostic to know whether your organisation is structurally ready for agentic AI, not just theoretically interested in it.
When your goal is to move beyond analytics and into autonomous marketing operations, you need a platform that brings together data, activation and control. Salesforce Marketing Cloud (SMC) is positioned exactly for that role. From ingesting and unifying data to powering AI-driven execution, it handles all.
Here’s how SMC becomes the command layer for agentic AI:
Marketing Cloud helps “unlock trapped data”, giving you contextual information about each customer or audience segment in real-time. It lets you build a centralised data model (via its Customer Data Platform / Data 360 offering) and then activate that data across channels.
For agentic AI, this means the agent doesn’t need to hunt for signals. It can act because the context is already in place.
With built-in AI capabilities through “Agentforce”, Marketing Cloud enables you to generate campaign briefs, define segments, build journeys, and even create content.
In an “agentic” scenario, this is where the “decision- → execution” loop happens. Rather than a human approving every piece, the agent can design and launch autonomously, leveraging flows and orchestration already embedded in the platform.
Marketing Cloud emphasises “personalise the right moment with actionable data,” delivering personalised experiences across touchpoints. For media businesses, this means your AI agent can tailor content, offers, placements, and cadence based on unified audience profiles, all delivered seamlessly through the same system.
Marketing Cloud also positions itself as a platform to “connect marketing to every department”. It aligns marketing, sales, service, loyalty and advertising.
That cross-department integration is critical for agentic AI: when your agent can act in marketing, pricing, engagement and retention streams, you need a “single system of record” that spans those functions.
Rather than layering AI on top of a fractured martech stack, Marketing Cloud provides the foundation where agentic systems access context, execute decisions and operate within governance, all from a unified command centre.
Agentic AI cannot simply be “switched on.” It needs a MarTech foundation that is structurally capable of autonomous execution. As a Salesforce consulting partner, Brysa specialises in preparing marketing ecosystems so that AI agents can operate safely, contextually and at scale, not in theory, but in production.
Our Salesforce consulting services and implementation services do not revolve only around tool installation; they also focus on infrastructure transformation. We follow these 4 steps to make your MarTech infrastructure AI-ready:
We begin with a deep diagnostic, mapping your current MarTech stack. We identify systems, bottlenecks, data silos, duplication, and integration gaps. This reveals what blocks autonomy today and what architectural changes are needed to make agentic AI viable tomorrow.
Because Salesforce becomes the command layer for agentic AI, we focus heavily on restructuring Salesforce clouds (Marketing Cloud, Data Cloud, Sales/Service Cloud, etc.) so that they can serve as the “operational brain.”
For instance, our Salesforce MCAE package focuses on restructuring data models, simplifying journeys, refining automation logic, tightening governance rules, and ensuring real-time orchestration capability. This ensures autonomous agents have a safe, stable, consistent base to operate within.
Autonomous agents are only as good as the inputs they consume. Brysa integrates Salesforce with the client’s wider ecosystem, standardises identity and records, removes duplication, connects BI + CRM + campaign systems, and creates the unified context layer required for autonomous decisioning.
Next comes supervised rollout - pilots with budget ceilings, compliance boundaries, escalation rules, approval triggers, and parameter limits. This allows organisations to prove outcomes with low risk and gradually transition from “AI-assisted” → to “AI-operated” without losing control.
A strong example of our impact comes from our engagement with View2Fill, a media photography & videography company in the UK. View2Fill approached us because manual workflows were limiting growth capacity. We redesigned core processes, implemented Salesforce, connected stakeholders (internal team, external freelancers, client workflow), and automated multiple operational steps.
View2Fill was able to scale capacity significantly without increasing admin load, streamline coordination across teams, and improve turnaround speeds, paving the way for future agent-driven automation because foundational fragmentation was removed.
Ready to architect your MarTech for autonomous advertising? Contact us and let’s build your agentic-ready infrastructure together.