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AI is moving from a “support tool” to an “operational layer” across many marketing and sales activities. Three recent stories from MarTech show that businesses not only need to use AI faster, but also need to use it in the right places: from customer data management and high-value customer care to building internal capabilities.
What all three stories have in common is a very notable reality: marketing and sales teams can no longer view AI as a separate trend, but must see it as a foundational capability for reorganizing processes, experiences, and working skills.
The era of the “agentic CDP” is taking shape
MarTech says that after years of mergers and integration in the martech industry, CDPs are increasingly being seen not just as standalone products, but as a concept whose value lies in how it solves customer data challenges. Deals such as Twilio buying Segment, SAP buying Emarsys, Contentstack buying Lytics, and Uniphore buying ActionIQ show that customer data platforms were built to do very specific things: collect data, unify profiles, segment audiences, and activate campaigns.
What stands out in the article is that Hightouch and Databricks are both pointing to a new direction: the “agentic CDP,” meaning a CDP that can operate in an agent-like way, embedding AI into data-processing steps and making actions more automated rather than simply storing, syncing, or classifying data as before. For marketers, this means a CDP is no longer just a customer data repository, but can move closer to serving as an intelligent orchestration layer between data, segmentation, and experience activation.
That said, this is still a concept in the process of being defined. For businesses, the key question is not only “which CDP has AI,” but which step AI helps shorten on the path from data to action, and whether it fits the current workflow.
Source: MarTech, “Ready or not, welcome to the era of the agentic CDP”.
AI chatbots are only effective when they know how to “open the door” for sales
In the answer compiled by MarTechBot, the article emphasizes a very practical B2B principle: chatbots should not be used as a “barrier” to block customers, but rather as a concierge — a guide that helps lead the way. For high-value enterprise deals, a “white-glove” experience is often the default expectation. If a prospect is repeatedly kept in a loop of mechanical responses, they can easily feel that the brand does not respect their time or needs enough.
The recommended approach is to build a “seamless hand-off” mechanism — a smooth transfer from AI to humans. The chatbot should handle tasks such as capturing the initial need, screening for fit, gathering context, and preparing information so sales staff can enter the conversation with a fuller understanding.
Strategically, this is a reminder that AI in sales does not necessarily have to replace people. In large deals, AI’s value lies in reducing friction at the top of the funnel, while humans retain the role of closing trust, providing deep consultation, and handling sensitive points in negotiation.
Source: MarTech, “How to blend AI chatbots with high-touch sales”.
Learning AI on your own through a real project at home
In the third article, MarTech offers a very “everyday” but useful perspective: if a business has not yet defined its AI strategy, marketers can still proactively learn through a personal project with practical application. The author uses the example of building an AI dashboard to manage an egg incubator at home, then draws lessons on how to turn a real need into an AI skills exercise.
The message here is not that every project has to be complex, but that you should start with a problem specific enough to force the learner to think about data, processes, and outputs. When you work on a small but useful project, you are not only learning how to use AI tools, but also understanding how to design a system that can move from idea to action.
For marketers, this way of learning is especially valuable because many AI applications at work also come from very practical needs: organizing data, automating reports, classifying content, creating internal assistants, or building performance-tracking dashboards. A home project can become a safe draft before deployment in a business environment.
Source: MarTech, “Build your AI skills with a useful home project”.
A perspective for the Vietnamese market
These three stories point to a common message for Vietnamese marketers: AI is not just about “adding tools,” but about restructuring how marketing and sales operate. At the enterprise level, teams should prioritize problems with clear impact, such as unifying customer data, automating lead-screening steps, and standardizing the hand-off process between chatbots and real staff.
In a context where many Vietnamese businesses are still in the testing phase, the right approach may be to start small but measure results: an internal dashboard project, a chatbot dedicated only to lead screening, or a redesigned CDP layer built to support specific actions. If done right, AI will no longer be a “must-have trend,” but a capability that saves time, improves experience quality, and supports sustainable growth.
More importantly, marketing teams need to learn to ask the right questions before buying tools: What goal does this AI serve, what data is clean enough to run it, and where will humans sit in the new process? That is the most practical starting point for avoiding scattered investment with little value.
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Read more articles in the same category at Digital Trends.
This article focuses on AI in marketing with a perspective for the Vietnamese market.
References
- Ready or not, welcome to the era of the agentic CDP
- How to blend AI chatbots with high-touch sales
- Build your AI skills with a useful home project
