AI is becoming the operating brain of media and advertising
December 29, 2025 | 08:33
AI has moved from side tool to core operating system across the media and advertising chain. It now touches strategy, creative, targeting, buying, and reporting-often in real time. The advantage goes to the teams who structure their data, set clear objectives, and let agentic systems execute within tight guardrails.
What this means for marketers
- Creative and messaging adapt in real time based on intent, context, and performance.
- Budget shifts automatically toward audiences and placements that hit objective-based targets (CPA, ROAS, reach quality).
- Reports go from static to living dashboards with drill-downs you can query in plain language.
- Predictive models guide planning, from audience sizing to offer strategy and channel mix.
Vietnam accelerates: NIC seminar signals a clear push
On December 25 at the National Innovation Centre (NIC), the seminar "Applying AI in SME Corporate Governance for Media and Advertising" underscored a simple message: AI is now a strategic capability. NIC highlighted active research and policy consultation to build an enabling legal framework so businesses can apply AI effectively, responsibly, and sustainably. Learn more about NIC.
Adoption in Vietnam is rising-agencies are using AI for customer data analysis, performance optimisation, content personalisation, and automated reporting. The friction points are familiar: fragmented data, limited system integration, and a gap between technology and day-to-day operations, especially for SMEs. Closing those gaps is where the gains sit.
Agentic AI moves from theory to production
Seinetime, founded in Paris by Ta Anh Phuong, Nguyen Dang Phuong, and Nguyen Dac Nam, brings decades of data and AI leadership from major French enterprises. The team has built data governance systems, monetisation models, operational automation, and ad targeting optimisation at scale-experience now being applied to Vietnam's SME market.
"We do not view AI as a substitute for humans. In an Agentic AI governance model, leaders act as captains-guiding, supervising, and orchestrating the AI system across strategy and operations," said Ta Anh Phuong, CEO of Seinetime. The aim: a "second brain" that makes smarter, faster, more consistent decisions without losing human judgment.
Within the same framework, 5S Media signed a strategic cooperation agreement with Seinetime to embed agentic AI directly into the agency's operating systems. The goal is to optimise workflows, increase data transparency, raise internal efficiency, and set up AI-powered client services. "Our 'AI First Agency' strategy puts AI at the centre to amplify team capability and governance quality," said Pham Thi Thu Hang, CEO and founder of 5S Media. A demo launches in Hanoi ahead of an official release in Q1 2026.
Cloud AI is getting practical
Agent-based architectures are moving into mainstream stacks, with major cloud providers supporting orchestration, safety, and interoperable data layers. As noted by AWS leaders, the shift is toward intelligent, self-directed applications that tie to business objectives. See how agents are implemented in practice: Agents for Amazon Bedrock.
Human-centered governance and the road to March 2026
Experts agree: AI delivers sustained value when governance is clear and human-centered. Vietnam's first AI Law will take effect in March 2026-another reason to lock in standards now. For marketing leaders, that means consented data, audit-ready processes, measurable objectives, and transparent model oversight.
- Data: unify IDs, consent, retention rules, and data lineage.
- Models: document purpose, inputs, risks, and evaluation methods.
- Operations: define who approves, who monitors, and what gets paused automatically.
A practical 90-day plan for marketers
- Days 0-30: Pick one revenue-critical use case (e.g., creative iteration for top SKUs or budget pacing for paid social). Map your data sources. Define success metrics (CPA, ROAS, time-to-brief, creative hit rate).
- Days 31-60: Stand up a clean data pipeline and a single performance dashboard. Pilot an agent to automate one loop: fetch data, analyse, recommend action, and draft changes for human approval.
- Days 61-90: Expand to two more channels. Add auto-generated reporting and alerts. Set review cadences, fail-safes, and rollback rules. Document outcomes and move to controlled scale.
A lean stack that works
- Data layer: Warehouse or lake with clean IDs and consent flags.
- Decision engine: Agentic logic tied to objectives and constraints.
- Content: AI-assisted copy and visual variants with brand guardrails.
- Activation: APIs into ad platforms, email, and onsite personalization.
- Measurement: MMM/MTA hybrids, incrementality tests, and live QA.
Metrics that prove it's working
- Creative: time-to-first-asset, variant win rate, effective frequency.
- Media: CPA/ROAS stability, budget utilisation, wasted spend reduction.
- Ops: time-to-report, approvals per week, automation adoption rate.
Common pitfalls and quick fixes
- Fragmented data: Start with the 3-5 fields that drive buying and reporting. Add the rest later.
- Black-box decisions: Require explainability logs and human-in-the-loop approvals for spend and brand-sensitive actions.
- Over-automation: Cap daily budget moves and creative swaps. Escalate outliers.
- Model drift: Set monthly backtests and refresh windows. Keep a control group.
Upskill your team
If you want a structured path to apply AI directly to campaigns, briefs, and analytics, see this practical program: AI Certification for Marketing Specialists. Build the muscle once, then scale it across channels and clients.
The takeaway is simple: treat AI like an operating brain, not a novelty. Set clear objectives, wire the data, keep humans in charge, and let agents handle the busy work. That's how you win budget efficiency and creative speed-without losing control.
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