AI in Advertising: How to Optimize Your Workflow With AI Agents

‍How is AI changing advertising workflows?

AI is changing advertising workflows by taking pressure off the “connecting” work that sits between tools and teams. That work is what usually slows execution down: chasing context, reconciling conflicting numbers, finding the owner of an issue, and rebuilding the same views of performance over and over.

In a typical week, problems rarely show up as “one big failure”, but as small things that consume hours: pacing not within the plan in one platform, resolving tracking issues that bounce between analytics and activation, naming inconsistencies that break reporting, or a change that happened mid-flight that nobody can fully trace. Very often the team spends its time in coordination mode instead feeling enabled to optimize and increase the impact.

Where AI can make the difference is in the operating rhythm of campaigns: monitoring, checking, summarizing, and routing work to the right people.

Here’s what “AI changing the workflow” actually looks like in practical terms:

• Earlier detection of issues: pacing anomalies, frequency spikes, delivery drops, sudden CPM movement

• Faster context gathering: pulling spend, delivery, targeting, creative versions, and recent changes into one view

• Clearer ownership: routing alerts and tasks to the right role (media owner, analytics, account lead)

• More consistent reporting: the same KPIs, defined the same way, shared in the same format across teams and clients

• Continuous monitoring: checking setup and changes against standards instead of relying on a one-time checklist before launch

A concrete example: when pacing drifts in a DSP, the hard part often isn’t “spotting it,” it’s understanding what changed and what to do next. The useful contribution from an agent is the heavy lifting around context: what moved, what else changed around the same time (budget, audience, bid strategy, creative swap), and what the team normally does in that situation based on existing rules.

AI becomes far more valuable when the basics are already stable: consistent KPI definitions, clear naming standards, and a shared understanding of who owns which decisions. Only if this foundation is there, agents outputs become reliable and usable. ‍

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How can I use AI agents to optimize my advertising process?

Using AI agents to optimize your advertising process works best when you start with one workflow that repeats every week and has a measurable outcome. Most teams get traction fastest by choosing an operational workflow, not a large strategic one-off project.

A practical starting point for many advertisers is pacing and in-flight monitoring, because it is frequent, time-sensitive, and easy to measure. Another strong starting point is campaign setup, because small setup errors create expensive downstream work.

A simple way to approach this, without turning it into a big internal program: ‍

1) Pick one workflow with a clear outcome

Good candidates tend to share three traits:

• the work repeats weekly or daily

• the work relies on cross-platform context

• the work creates escalation when it breaks

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2) Write down the rules people already follow

Even if not set in documents, the rules of advertising operations usually exist today in every organization. Often, they just live in habit, Slack messages, and tribal knowledge. Agents become useful when those rules are made explicit.

For pacing, rules might include:

• how far off plan counts as a problem (for spend, delivery, conversions)

• which levers are allowed (budget shifts, audience expansion, creative rotation, frequency caps)

• what requires approval and from whom

• what the escalation path looks like for high-risk changesFor campaign setup and readiness, rules might include:

• naming conventions and taxonomy

• UTMs and tagging requirements

• creative specs and platform checks

• tracking validation steps and required fields

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3) Give the agent a job that supports decisions

A good agent does not “make strategy.” It prepares the team to act with less manual effort.

In practice, that job often looks like:

• monitor defined signals

• compare them to agreed thresholds

• pull the relevant context from multiple systems

• produce a short, action-ready summary

• log what was seen and what was recommended

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That last point matters more than most teams expect. Decision logging and traceability are what make an agent part of operations.

The following list summarizes more use cases that tend to pay off quickly:

• Readiness and QA checks: standards validation before launch and after changes

• In-flight monitoring: pacing, frequency, delivery anomalies flagged early with context

• Cross-platform tracking: consistent KPI views across multiple channels, building the foundation for true AI-based cross-platform optimization recommendations

• Status updates: in-flight summaries that stay current without manual rebuilds and automated report creation

• Governance checks: approvals, spend rules, naming and taxonomy enforcement

One practical filter helps keep this grounded: focus on work that is repeatable and measurable, and where the team already agrees on what “good” looks like. That is how you get results quickly and build internal trust. ‍

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Where MINT fits

MINT is built on three layers: workflows, data, and agentic AI. Agentic AI is the layer getting the spotlight right now, but the foundation sits in workflows and data. Advertisers need those two layers anyway to run campaigns with consistency, governance, and control across platforms.

That also makes MINT relevant in two common real-world setups:

• If you already have an established tech stack and infrastructure, MINT can be added on top to connect workflows and consolidate campaign-relevant data across platforms. Agentic AI then supports day-to-day execution in a way that stays consistent and accountable.

• If your team relies on heavy manual coordination between tools, MINT can reduce the fragmentation by providing a structured operating layer that helps standardize workflows and data first, so automation becomes dependable in daily work.

In other words, agents create value fastest when they sit inside a system that already knows what the work is, what the data means, and how decisions get made. That is the point where agents stop feeling like a novelty and start behaving like part of the operating model.

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