Contents
- AI technical skills and how they are changing marketers’ role at work
- What is changing in AI — and what that means for what marketers should learn
- Looking deeper: measuring AI by job performance, not just by “knowing how to use it”
- Vietnam’s market: AI will be taken more seriously when it is tied to specific outputs
- What to do with AI so technical skills truly create an advantage
- References
AI is doing one very clear thing for marketers: pushing technical skills from “nice to have” to “hard to work without.” As tools, workflows, and ways of measuring performance change, marketers not only need to know how to use AI, but also how to verify it, read data, and choose the right way to learn so they can apply it for real.
- Key points:
- Technical skills are being redefined as hard skills, meaning learning to do the job rather than just understanding the concept.
- AI is creating new pressure on how people learn: it is no longer just about choosing a course, but choosing a path that can turn into job-ready capability.
- For marketers, the question is no longer “Do you use AI?” but “How far can you use it, how do you measure it, and where are the risks?”
- The Vietnamese market will benefit most when businesses connect AI training with operational problems and performance reporting.
AI technical skills and how they are changing marketers’ role at work
At a foundational level, Simplilearn describes technical skills as hard skills, meaning abilities gained through training or learning to complete a specific task. This understanding matters a great deal for marketers because AI is moving many tasks that were once considered “soft” into a space that requires clear technical execution: knowing how to write prompts is not enough; you also need to check outputs, read dashboards, handle data, and work with digital tools. Simplilearn’s article “What Are Technical Skills? Examples & Types Explained” captures that spirit well: technical skills are directly tied to the job, not just knowledge to remember for fun. Source: Simplilearn.
On the operational side, Simplilearn’s content on getting into IT without a formal degree shows that the job market is increasingly prioritizing the ability to do the work over the title on a résumé. Although “How to Get an IT Job Without a Degree?” is not specifically about marketing, its logic maps closely to AI: value lies in practical capability. For marketers, that means someone who can use AI to draft copy, catch errors, test content variations, or pull insights from data will stand out more easily than someone who only talks about AI as a trend. Source: Simplilearn.
What is changing in AI — and what that means for what marketers should learn
AI is no longer just a matter of “knowing it in time”; it is becoming a matter of “learning it in a way that gets results.” For marketers, this changes how learning content is chosen: instead of collecting lots of short courses, they should prioritize capabilities that can be tied to real tasks, such as prompt testing, evaluating output quality, understanding tool limitations, and knowing how to turn AI results into marketing action. That conclusion does not come from a single source, but is reinforced by both Simplilearn articles: one defines technical skills as hard skills tied to specific tasks, while the other emphasizes that the market prioritizes people who can do the work. Put together, they show that learning AI as “knowing the tool name” will quickly become outdated if it is not paired with the ability to apply it. Source: Simplilearn, Simplilearn.
This is especially important for Vietnamese marketing teams because most businesses are not short on tools; they are short on people who know how to turn tools into processes. Learning AI should therefore follow a sequence: understand what the tool can do, test it on a small problem, measure the results, and then scale up. Without that sequence, AI can easily become a showpiece in an internal report.
Looking deeper: measuring AI by job performance, not just by “knowing how to use it”
Both Simplilearn sources point to the same broader issue: skills only matter when they change the way work gets done. So when measuring the effectiveness of AI training, marketers should stop relying on impressions like “the course is finished,” “the tool is installed,” or “the team is familiar with it.” They need to look at more work-relevant indicators: Has the time needed to complete a task gone down? Are there fewer rounds of content revisions? Is the quality of the initial draft better? Can the team handle repetitive tasks on its own instead of waiting for someone else? Simplilearn’s understanding of technical skills as hard skills helps separate learning to know from learning to do. Source: Simplilearn.
At the labor-market level, the other source shows that recognition of practical capability is rising. That means companies also need to change how they write AI training reports. Instead of reporting “how many sessions were held,” businesses should report “after training, who can do what faster, more accurately, and with less dependence on others.” For marketers, this is a very practical shift because learning budgets are often scrutinized for both cost and operational impact. In short: measure AI by where it makes the team stronger, not by how many hours of training have been spent.
Vietnam’s market: AI will be taken more seriously when it is tied to specific outputs
In Vietnam, AI is moving faster at the experimentation layer than at the operational layer. Many marketing teams have tried tools, but not every team has turned them into a stable capability for the whole group. The two Simplilearn sources point to an important lesson: technical skills are learnable, but they must be learned in a way that serves real tasks. For the Vietnamese market, this fits the reality of small and medium-sized businesses very well: they may be reluctant to buy AI training programs just to “keep up with trends,” but they are willing to invest if training helps shorten content production time, improve reporting quality, or reduce dependence on a few tool-savvy individuals. Source: Simplilearn, Simplilearn.
The market’s turning point in Vietnam will be very specific: businesses that turn AI into a shared skill set across the team will rely less on “the person who knows the tool,” and will find it easier to standardize hiring, training, and handoffs. That is where AI moves out of the “just trying it for fun” stage and into a real work capability.
What to do with AI so technical skills truly create an advantage
- Choose one specific marketing task to apply AI to, instead of learning many tools in a scattered way.
- Set clear measurement criteria: time, number of revisions, output quality, and the team’s level of independence.
- Prioritize training in skills that can be checked, such as prompting, output evaluation, data handling, and tool coordination.
- Build a process so AI knowledge does not stay with one person, but becomes the team’s shared way of working.
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This article focuses on AI technical skills for marketers with a perspective for the Vietnamese market.
References
- Simplilearn.com — What Are Technical Skills? Examples & Types Explained
- Simplilearn.com — How to Get an IT Job Without a Degree? [Facts & Top Jobs]
