It's early in the morning, and the work day is about to begin. Without any human intervention, the marketing agent has identified a weak campaign element, replaced the ineffective visual, informed all stakeholders, and documented the effect on KPIs in real time. Welcome to a world where business processes don't wait for humans to find time.
From information silos to real-time decisions
In most companies, data relevant to decision-making is scattered across various sources, such as analytics tools, CRM systems, content databases and project management software. When decisions need to be made, information often has to be compiled manually and evaluated. By then the data is frequently out of date. Another common problem is that, even when data is stored centrally, confusing dashboards can make interpretation difficult. It is not uncommon for days or weeks to pass before a decision is made.
AI agents excel precisely at these interfaces, where a lot of time has been lost in the past. They facilitate new processes based on current data, significantly streamlining interfaces between departments. Instead of manually transferring data or duplicating work in multiple systems, an agent system orchestrates the entire process – from analysis to implementation.
Consider campaign management. Here, a system comprising several AI agents works together: a higher-level supervisory agent controls the entire process, while specialized agents handle analysis, creation, compliance, implementation and reporting, among other things. This team of agents continuously monitors performance of all ongoing measures. If it detects that a particular visual is significantly lagging behind the predefined KPIs, it immediately launches a root cause analysis based on target group behavior, channel performance, and historical comparison data. Within seconds, the agent specializing in asset production suggests an alternative visual and the compliance agent automatically checks CI and legal compliance, allowing the material to be forwarded to the relevant human team for final approval. Once approved, the agent team independently takes care of implementation. All affected departments are then informed about the changes and their initial impact. This turns a potential budget loss into a quick course correction, eliminating friction, waiting times and meetings.
From augmentation to autonomy
Today, most AI agents still operate in a supporting role: they provide analyses, recommendations and templates that humans then implement. However, the next step in development is already underway – controlled autonomy.
In this phase, AI agents implement optimizations’ independently, document every decision and only seek human approval when necessary. This creates a new form of interaction: humans define goals and framework conditions, while AI agents ensure that these are implemented optimally in real time. The result is greater efficiency, better performance and marketing that continuously improves itself.
Autonomy does not mean 'unlimited freedom', but rather acting within clearly defined boundaries. Budget limits, brand safety guidelines, legal and compliance requirements, KPIs and approval thresholds ensure that all optimizations are transparent, compliant and in line with company strategy.
More than just an efficiency project
The use of AI agents is not purely a question of cost or time. It is about a paradigm shift in the way companies are managed:
- Decisions are based on current, complete data – not on gut feeling.
- Departmental boundaries are becoming less relevant because processes are interlinked in real time.
- Teams can focus their energy on strategy, creativity and innovation, while routine tasks are automated.
In order for AI agents to reach their full potential, companies need to bear two things in mind. Firstly, the maturity level of individual processes must be realistically assessed – some are ready for autonomous implementation, while others still require human control. Secondly, cultural change must be actively managed by adapting roles, clarifying responsibilities, and establishing trust in automated decisions.
The race is on
AI agents are already working effectively, quickly and precisely in many areas. The difference lies in how consistently companies utilize these capabilities.
Those who embrace AI agents now will gain not only operational efficiency, but also a strategic advantage, namely the ability to respond to market changes and customer behavior more quickly and accurately than the competition.
Therefore, the crucial question is no longer, ‘Do we need AI agents?’
Rather, ‘How quickly can we design our processes so that they improve themselves?’
