Three years after the launch of ChatGPT, marketing’s AI conversation is shifting again. Attention is moving beyond generative AI toward the promise of AI agents and agentic AI. Martech vendors are racing to introduce on-platform agents (and agent building platforms) to autonomously perceive goals, make decisions, and take action across workflows.
Of course, the vendor and influencer hype around agentic AI is currently outpacing the operational reality of the marketing function.
According to Gartner research, many martech solutions marketed as AI agents still function primarily as AI assistants that complete prompted tasks rather than independently reasoning and acting toward business goals. This distinction matters. AI assistants can generate content or execute predefined steps but lack autonomy to independently pursue outcomes.
AI agents, by contrast, are designed to:
- Perceive goals and environmental signals
- Make decisions based on available data
- Take action across internal and external systems
- Adapt execution within governed parameters
Treating assistants as agents inflates expectations and causes pilot programs to fail to deliver meaningful performance gains at scale. The challenge for CMOs is not simply deciding whether to adopt agentic AI. It is determining how to adopt it without damaging marketing’s credibility and performance in the process.
Outcome Driven Use Cases Create Real Value
Gartner’s 2025 Marketing Technology Survey shows that only 15% of organizations qualify as martech high performers based on their ability to meet strategic objectives and demonstrate positive ROI from technology investments. One of the most important differences between these leaders and their peers is how they pilot agentic AI use cases.
Lower-performing organizations tend to focus on production-level activities such as content asset creation or asset transformation and enrichment. These applications can improve throughput, but rarely drive end-to-end business impact. Because AI agents are inherently goal oriented, the greatest performance gains occur when autonomy compounds across an entire workflow rather than accelerating an isolated task.
High-performing martech leaders prioritize outcome driven initiatives such as campaign optimization or B2B account qualification. In practice, that means starting with end to end process mapping for a limited number of performance critical workflows and prioritizing use cases based on customer and business impact rather than time savings alone.
Autonomy Is a Spectrum, Not a Switch
Another critical element in successful use case pilots is recognizing the immaturity of agentic AI deployments in vendor platforms. In reality, autonomy exists on a spectrum. Systems may operate with limited independence in narrow contexts while still requiring human oversight in others. Failing to recognize that nuance often leads to internal overpromising to executive stakeholders, fragmented pilot deployments across functions, rework as early initiatives scale, and governance debt that accumulates faster than value.
Vendor messaging rarely clarifies these limitations. Product branding and feature lists frequently imply autonomous execution even when systems still rely on human direction for critical decisions. Organizations pursuing investment in AI agents from technology vendors will need to be prepared to evaluate these agents by degree of autonomy, not labels.
This includes verifying what decisions or actions occur independently, how actions are logged, and where escalation paths exist if autonomous execution produces unexpected outcomes. The shift from feature evaluation to autonomy evaluation allows marketing leaders to increase autonomy deliberately, rather than assuming it from day one.
Data and Architectural Readiness Determines Results
Scaling agentic AI is not a feature rollout but an operating model change that requires collaboration across marketing, IT and data leadership. As marketing moves from assistive AI toward higher levels of autonomous decision making, performance becomes constrained by the environment in which agents operate. Data quality, system integration and architectural composability determine whether autonomous actions can be executed safely and at scale.
Organizations that invest in governed data platforms, composable martech architectures, scalable integration methods, and formal governance models are significantly better positioned to translate early experimentation into durable performance improvements. Without these foundations, agentic capabilities may be technically present but operationally unusable. This can lead to integration delays, security exposure, and reputational risk if autonomous actions cannot be explained or controlled.
Preparing Marketing to Work With Agents
Agentic AI will likely pressure marketing’s operating model before it reliably improves marketing outcomes. Poorly governed autonomy can create fragmented orchestration, unclear accountability and frontline resistance if job security fears or trust gaps are not addressed before deployment.
High-performing organizations are already responding by formalizing enablement through centers of excellence, specialized implementation teams, and ongoing skills development programs. In addition to technical readiness, marketing teams must build practical competencies in AI use case identification, prompting and output discernment to collaborate effectively with autonomous systems.
Gartner predicts that by 2028, 60% of brands will use agentic AI to facilitate streamlined one-to-one interactions that collapse traditional channel architectures and redefine customer journeys. Organizations that benefit most from that shift will be the ones that prepare their operating models to scale autonomy responsibly as the technology matures.
Julia Multedo and Michael McCune are experts in the Gartner Marketing Practice, specializing in marketing technology strategy and operations. Learn more about how to scale agentic AI at the Gartner Marketing Symposium/Xpo, June 8-10, in Denver.
