Practical steps that won't disappoint your users
AI pressure is everywhere, but media intelligence platforms feel it more than most. Your product already ingests massive amounts of information and converts it into insights fast, so naturally, customers expect AI to make it even faster.
The mistake? Rushing to add a central chatbot that replaces all user interaction at once. This approach typically overpromises and underdelivers.
The assumption is that now is the time to shift from user-driven workflows to agent-first automation in one full swing. But users aren't going away. They'll keep demanding control, transparency, and the ability to shape outcomes.
The real goal isn't to replace users. It's to show them AI can do everything they can, gradually, with their control and approval.
Build Your Foundation First
If you want AI agents to do what humans do, your system must handle the basics flawlessly. AI agents amplify whatever mess you already have.
What needs to be done:
- Data mapping: High-fidelity tagging of companies, people, topics, and sentiment
- Clean feeds: No duplicates, consistent metadata, reliable source quality
- Permission logic: AI operates under the exact same access rules as human users
- Audit trails: Complete logging of what the AI did and why
This isn't exciting work, but it makes difference.
Start with quick wins
1. Smart Search
A chatbot that "runs the whole product" is rarely the right first move. Instead, start with a lightweight overlay that helps users find information faster without disrupting existing workflows.
Replace manual filtering with natural language: "Show me negative coverage of Tesla in German media this month."
Keys to success:
- Don't overpromise what it can do
- Start with search, summaries, and maybe simple reports
- Always explain limitations upfront
- Use UI/UX to guide expectations and prevent frustration
2. Contextual Copilots
To build truly effective agents, map your user workflows and identify where friction lives. In media intelligence, the bottleneck is usually the hours of manual research required before users can even start writing a report.
The solution: let AI anticipate the next move.
- Put AI where work happens: Don't hide it in a separate tab. Add a "Draft" panel inside the dashboard.
- Connect it to existing wokrflows: If a user is viewing a brand page, offer to draft a profile update or a "What changed since last month?" summary.
Context is everything. When you understand why users follow certain paths, you can help them get there faster.
Move to Specialized Agents
Once you've proven AI can handle basics, the temptation is to "just automate everything." Resist this. The gap between expectations and implementation can still work against you.
Start with one agent, one workflow, one success metric. This creates a blueprint for future expansion.
Two Specific Use Cases
The Competitive Intelligence Agent
Scans competitor coverage to build a timeline of meaningful events: product launches, pricing changes, regulatory mentions. Produces a summary + evidence + suggested follow-up.
The Data Quality Agent
Detects incomplete entity data or inconsistent sentiment tags. Suggests fixes with high-confidence sources, but only applies updates after human approval.
In both cases, users aren't overwhelmed. The UI prompts them to employ the agent when it matters.
The Path to Agent-Native Platforms
As specialized agents prove their value, they can combine into multi-step operations, e.g planning a product launch, refining global alerts, conducting competitive analysis.
At this stage, you don't need to redesign your UI. The interface stays familiar, but agents gain more "tools" to run steps on the user's behalf.
What "Agent-Native" Really Means
The ultimate goal for media intelligence isn't just to "add AI." It's to bridge the gap between data and insights.
In the past, your platform was like a library where users did all the research. In the agent-native model, it becomes a digital newsroom where research is done by AI and users supervise outcomes.
The transition happens through defined agency: giving AI specific "tools" to perform the same granular actions a user can.
This approach forces much-needed clarity. Instead of hoping a chatbot "figures it out," you define exactly what the agent can do, which sources it trusts, and when it must pause for human approval.
Agent-native products aren't built overnight. They evolve with your workflows. At Senvio we follow the same principels to make suret that as your system gains more autonomy, you gain more control, not less. See more
