Lenzing Group

Marketing has always been about understanding what people need, what they value, and what makes them trust a brand. Artificial Intelligence isn’t changing that. What it is changing is the operating model of digital marketing teams: how fast we can move, how consistently we can execute, and how much manual effort is still necessary.

The most immediate global impact is simple: AI saves on manpower needed and enables task to be completed more efficiently. That single reality is driving agency replacement and internal teams empowerment at the same time. Companies can use AI to reduce external scopes for repeatable deliverables, while others are strengthening in-house capability because AI lets smaller teams produce more without scaling headcount linearly.

This is also why leaders need to set the right expectations early. It’s less about utilizing AI for MROI, but more about using AI to enable task completion efficiency, saving on manpower needed. The current, practical value of AI is operational: more efficient business processes and few bottlenecks. As study shows, there is still no case of AI usage generating additional revenue for a company yet. For now, AI is not a revenue generating tool, but an empowerment tool. When teams accept that framing, they stop chasing AI as a “silver bullet” and start using it intentionally to lift productivity and quality.

A clear example is performance marketing iteration. AI minimizes trial and error when remarketing and marketing to target audiences by using Large Language Model (LLM) to analyze audience sentiments. Instead of relying on manual scanning of comments, reviews, and scattered feedback to guess what audiences feel, marketers can use LLMs to synthesize patterns at scale, such as common objections, recurring motivators, and the language that different segments actually use. Testing still matters, but AI helps teams reach better hypotheses faster and reduce wasteful cycles.

Where AI adds the most value across the funnel

Across the funnel, AI’s strongest impact shows up in execution speed and operational consistency. AI automation and processes quicken business efficiency and go-to-market timeline. That means faster campaign turnarounds, faster localization and adaptation, and faster iteration across creatives and messaging. This is especially true when teams standardize AI-assisted workflows with programs such as Microsoft Copilot. This will help improve the planning and reporting layer that often gets squeezed by daily delivery pressure. Summarization and fact checking for more comprehensive and detail-oriented planning and reporting helps teams convert messy inputs, such as research notes, stakeholder feedback, analytics narratives, and meeting summaries, into clearer briefs and more structured updates. It does not replace judgment, but it can reduce oversight gaps and improve the completeness of decision materials.

Ultimately, the impact of AI in marketing is best described as manpower replacement and empowerment, especially with today’s development of agentic AI. Some work is genuinely replaceable; much of it is better thought of as “enabled” work, which is faster and more consistent. Today’s agentic AI can help internal team members run day-to-day tasks as if they have an agent helping them personally. That “agent” may help draft content, reformat assets for channels, summarize insights, or support reporting, so the human marketers can focus on direction, prioritization, and brand strategies.

The skills marketers must develop to work effectively with AI

AI adoption is not happening in a stable environment, as it’s a new trend of digital disruption that causes tons of redundancy across sectors. Therefore, marketing team needs to upskill in order to keep up with the continuous learning needed for AI adoption. Tools evolve quickly, and the teams that treat AI as a one-time rollout will fall behind those who build a learning culture.

The most important capability is an operating mindset. The marketing team must have an AI-centric mindset and have knowledge of what tools can help with the tasks at hand. With that, the team can distinguish what tasks can be automated and streamlined via AI tools and what tasks should be manually conducted, thus planning marketing operations efficiently. This is the difference between “using AI” and actually redesigning workflows around it.

Execution quality also depends on communication skills with the model. Proper use of LLM such as proper prompt engineering should be the new basis of digital marketing team. This supports content development and ease of tonality and language change of the same content via AI prompt, enabling the team to adapt a single piece of content to different platforms and countries without restarting from scratch each time.

Underneath all of this sits the foundation that keeps capability compounding over time: awareness, and an interest to consistently learn and upskill. Without it, AI use becomes shallow, inconsistent, and risky.

Personalization at scale without losing brand marketing consistency

The use of AI in marketing makes personalization easier to produce, but scale without structure creates brand drift. The discipline starts with truth, not assumptions. Thus, brand perception marketing research should still be conducted to understand current audiences’ standpoints. These findings become the criteria to categorize audiences into demographic segmentations based on background, location, gender, preferences, interests, and more.

From there, LLM models can further help by summarizing audiences’ sentiments in different categories based on their preferences and interests, creating look-alike audiences which can help expand the target market. This is where AI is genuinely useful: it accelerates the interpretation of qualitative feedback and supports scalable growth while keeping segmentation grounded in real audience signals.

However, personalization must be governed. A practical example is to have at least 50% of the personalized marketing campaign to consist of necessary brand marketing compliant materials that need to be communicated in a rightful manner, including campaign message, brand identity, mission statement, brand value, product criteria, and more. The remaining portion can be tailored for the different demographic segmentations identified, and this is where A/B testing is essential to optimize for each segment with the most optimal ad setup tailored to gain their interest—without compromising the brand core.

The biggest misconception about AI in digital marketing

The biggest misconception remains that AI can do everything for the marketer and automate all tasks, that it doesn’t need 4-eyes review, and that it can replace the need of manpower. This is where brand marketing risk and credibility issues begin. LLM is essentially reading ideas and materials humans have developed over our recorded history. It can’t yet make anything brand new. AI can accelerate and amplify, but human oversight is what protects accuracy, compliance, and brand integrity. This is what keeps marketing authentic rather than merely fast. Remember, AI tools are just enablers; it’s always still human first.