If you’re stuck for time, the infographic below breaks things down into their component parts. Essentially, AI agents are designed to handle complex operations, analyze data, and make decisions based on predefined rules or learning algorithms.
The question remains, though: What are the current use cases for agentic AI? Where are some areas where agentic AI is almost ready for primetime, but not quite?
Current use cases for agentic AI
Here are some specific areas where this technology is thriving in the current moment.
Content generation and adaptation Agentic AI is really hitting its stride here, handling tasks like automated writing and video production with finesse. It's using advanced Natural Language Processing (NLP) and machine learning to churn out relevant content and personalize it on the fly.
Automated campaign optimization AI-powered systems, like SET, are constantly fine-tuning campaigns in real-time. They're not just optimizing; they're learning from data to refine strategies, boost ROI, and maximize ad spend efficiency.
Lead management & scoring AI is automating the tedious work of qualifying leads by analyzing customer behaviors across multiple touchpoints. It's predicting which leads are most likely to convert, making sales teams more effective and efficient.
Data unification & insights generation AI is bringing together data from various sources to give marketers deep insights and predictive analytics. Platforms like BERA.ai are leading the charge, providing unified dashboards and actionable insights that drive strategic decisions.
Personalized internal reporting & alerts AI is automating the creation of personalized reports and real-time alerts based on key metrics. This saves time and enables faster, data-driven decision-making across marketing teams.
Resource allocation & forecasting Forecasting models powered by AI are revolutionizing resource allocation by predicting market trends and campaign performance. This means marketers can optimize budgets and resources more effectively, responding swiftly to changes in the market.
Workflow automation AI is streamlining workflows by handling routine tasks like campaign setup, task management, and project coordination. It's like having a super-enthusiastic assistant that keeps everything running smoothly in the background (but who won’t be able to go pick up your coffee order...yet).
Audience segmentation AI is refining audience segmentation based on behavioral, demographic, and psychographic data. This enables marketers to target their messages with pinpoint accuracy, boosting engagement and conversion rates.
Agentic AI areas that need a little more time
Keep in mind that AI is evolving with shocking speed, so what’s aspirational today could be a reality tomorrow. That said, here are some areas where agentic AI looks promising, but isn’t quite ready for primetime.
High-level strategy & brand consistency Automating creative decisions to maintain brand identity across different demographics is still a work in progress, and a place where a human-in-the-loop is essential. That said, tools like Propellers are proof that AI is a viable, high-level creative brainstorming partner.
Interoperability & standardization Making different marketing tools and platforms speak the same language requires standardized APIs and better interoperability. It's about creating a seamless flow of data and processes across diverse tech stacks.That’s one reason why the Stagwell Marketing Cloud Platform, designed to elevate the integration of disparate products, is so exciting.
Trust & ethical governance Marketers are understandably cautious about AI agents autonomously shaping campaigns due to concerns about bias, privacy, and brand safety. Establishing clear ethical guidelines and governance frameworks is essential for building trust and ensuring responsible AI use, and often the exuberance of technological progress trumps the boring stuff (like building appropriate guardrails).
Of course, another obstacle to the advance of agentic AI involves cultural and organizational acceptance. That means fostering a culture where AI is seen as a valuable tool, rather than a threat to human expertise.
What does the future hold?
Look for agentic AI advances in areas like:
Conversational AI for customer support: Advancements in natural language processing (NLP) and sentiment analysis are paving the way for AI agents that can provide empathetic and contextually aware responses in customer service scenarios.
Autonomous decision-making in marketing strategy: Look for AI agents to sharpen their skills when it comes to optimizing marketing campaigns, allocating resources, and adjusting strategies based on predictive insights—though still under the watchful eye of old-fashioned human expertise.
