It’s often referred to as the “AI execution gap,” which is the space between what AI creates and what actually gets delivered, launched or acted on. Agentic AI is designed to close that gap. Instead of stopping at output, it works across steps. It remembers the objective, organizes next actions and coordinates the supporting tasks. It doesn’t replace human strategy or creative thinking, but it does ensure the work keeps moving without someone having to manually drive every step. AI Knows the Goal (and Doesn’t Need Reminders) The word “agent” is getting thrown around a lot right now, and it’s not always clear what it means. Agentic AI refers to fully autonomous systems that can check your email, respond to meeting invites, schedule calendar events and even use your actual credit card to place online orders. It understands goals, manages steps and follows through without needing constant prompting. In channel marketing, agents are not replacing partner enablement or vendor oversight. Instead, they are shouldering the operational load. It’s like having a thousand digital team members ready to execute instantly, around the clock. Structured’s system, for example, includes more than 20 AI agents that work together seamlessly across campaign development. In the near future, there will be the possibility of nearly unlimited agents. The only limitation is our imagination (and we can build an agent for that, too.) A Campaign Creator agent might begin by drafting messaging tailored to a partner’s audience and objectives. Once complete, it hands the work off to the Brand Guide agent, which checks for alignment with visual standards and brand tone. From there, a Localization agent adapts the content into different languages and formats for global distribution. Each agent supports a defined role, one that would normally require manual input or human review. Instead of managing these steps one by one, a person reviews the combined output at the end, refining or approving the full campaign in context. That’s what sets agentic systems apart. They work more like a team than a tool, allowing users to continue with higher-value tasks. Partners Don’t Speak Funnel If you want a clear view of where agentic AI can make an immediate difference, look at channel partners. Most partners aren’t marketers. They’re product specialists, salespeople or technical advisors. They’re experts in what they sell, but they’re not fluent in campaign strategy, digital funnels or content planning. So, when they’re handed a portal full of templates and tools, they don’t know where to start. And they’re too busy to try and figure it out. Many partner platforms assume a level of marketing fluency that just doesn’t exist in the field. The result is low engagement, underused resources and too much reliance on one-size-fitsall content. Agentic AI changes that dynamic. The agent becomes the expert and understands the partner’s role, industry, audience and goals. So, instead of asking a partner to choose the right format or map out a funnel, it lets them “just ask” for help in their own words and then does the work of translating that into a complete, ready-to-launch campaign. In a space where traditional tools have stalled, agentic systems give partners access to something they’ve never had before: a marketer that’s always available, always brand-safe and ready to get things done. And the impact doesn’t stop with the partner. Vendors see the benefit in real business terms, including faster campaign launches, higher partner engagement and better use of existing marketing resources. When more partners activate more often, the entire channel becomes more productive and profitable. In essence, agents can help unlock revenue that would otherwise remain dormant in underutilized partner channels. Introducing new technology often raises a question: will this mean fewer people or less creativity? The answer is no when it’s done thoughtfully and purposefully. Agentic AI handles repetitive work like formatting, compliance checks, localization, campaign setup, freeing people to focus on higher-value tasks such as building new product lines, exploring global markets, and pursuing ideas they have shelved for years. When teams aren’t overwhelmed and can work with agency, performance improves. People bring more energy, creativity and loyalty, all of which boost long-term results. AI isn’t replacing the team. It supports the team by offering focus, clarity and a chance to lead. Ultimately, GenAI brings ideas to life. Agentic AI carries them across the finish line. The result is supercharged partner performance. o Daniel Nissan is Founder & CEO of Structured. Source: B2U Agents enabled by generative AI could function as hyperefficient virtual coworkers Source: McKinsey & Co. Illustration of how an agent system might execute a workflow, from prompt to output Using natural language, the user prompts the generative AI agent system to complet a task. The agent system interprets the prompt and builds a work plan. A manager agent subdivides the project into tasks assigned to specialist agents; they gather and analyze data from multiple sources and collaborate with one another to execute their individual missions. The agent team shares the draft output with the user. The agent team receives user feedback, then iterates and refines output accordingly. External systems: Agents interact with databases and systems– both organizational and external data–to complete the task. Manager agent Specialist agents Start End Analyst agent Checker agent Planner agent 1 2 3 4 The steep drop from pilots to production for task-specific GenAI tools reveals the GenAI divide Source: MIT Project NANDA Why GenAI pilots fall: top barriers to scaling AI in the enterprise Users were asked to rate each issue on a scale of 1-10 0 1 2 3 4 5 6 7 8 9 10 Source: MIT Project NANDA Challenging change management Lack of executive sponsorship Poor user experience Model output quality concerns Unwillingness to adopt new tools How executives select GenAI vendors Derived from interviews and coded by category 2.5% ADOPTERS 13.5% MAJORITY 34% MAJORITY 34% 16% General-Purpose LLMs 80% 60% 50% 20% 40% 5% Investigated Piloted Successfully Implemented Embedded or Task-Specific GenAI 44 CHANNELVISION | FALL 2025
RkJQdWJsaXNoZXIy NTg4Njc=