Most organizations have spent the past two years asking the same question about AI: What can it make? Course content, translated modules, scenario scripts, voiceovers. The results have been real and the time savings genuine.
But AI-generated content is still just content. Someone still has to find it, assign it, track it and figure out whether it actually moved the needle. The administrative burden does not disappear because the authoring got faster. And the learner who needs guidance right now, in the middle of a real workflow, still has to stop what they are doing and go searching.
That is the limit of AI as a content tool. It is also the starting point for AI agents.
What Agents Actually Do
An AI agent is not a content generator. It is an active participant in a workflow. It takes inputs, reasons about them and does something: recommends, answers, triggers, analyzes. The distinction matters because the value proposition is fundamentally different.
Brandon Hall Group™ research on HCM Excellence Award® winning AI learning programs makes this concrete. In every program studied, 100% of organizations achieved measurable improvements in learning outcomes, operational efficiency and cost savings. But the programs that delivered the strongest results shared a specific characteristic: They did not rely on a single AI capability. They combined natural language processing, machine learning, adaptive algorithms and contextual assistance into integrated systems. Thirty-six percent already employed multi-agent architectures, with specialized agents handling distinct functions across the learning ecosystem.
The payoff was operational. Efficiency gains typically hit within three to six months of implementation and averaged 15 to 25 percent reductions in training staff time on administrative work. That capacity went toward personalized coaching and strategic planning. Those gains do not trace back to faster content creation. They trace back to agents handling work that previously required a person.
The Problem with Generic AI
Enterprise AI tools built for everyone have a structural problem in corporate learning: They do not know your organization.
They do not know that your compliance catalog was reorganized last quarter. They do not know that your onboarding path for field technicians differs from the one for call center staff. They cannot connect to your HR system to check a learner’s development history before recommending a next step. They handle generic queries well and organizational ones poorly.
The imc Agent Builder, built natively into the imc Learning Suite, is designed around the opposite premise. L&D teams create, configure and manage their own agents, ones grounded in the organization’s actual data, processes and learning catalog. As Christian Wachter, Co-CEO of Scheer IMC, puts it: “Our customers build their own agents. They know their processes, their data and the people who work with them. That expertise cannot be replaced by generic solutions. We provide the platform, the tools and the methodological guidance.”
The result is an agent that knows what your organization knows and can act on it.
Agents for Every Role That Touches Learning
The imc Agent Builder is built around three deployment scenarios, each targeting a different pressure point in enterprise L&D.
For learners, the agent evaluates prior experience and available learning time, then recommends relevant content from the organization’s own catalog with direct links. The recommendation reflects where the learner actually is, not a keyword match against a course library.
For platform users, the agent resolves operational questions in real time: how to register colleagues for a course, what the next required compliance training is, where to find a specific resource. It can answer directly, walk through a process, or trigger a service request. It knows the organization’s workflows, not just the platform’s help documentation.
For L&D and administrative teams, the agent handles structured analysis. An administrator asking whether the compliance catalog has gaps gets a usable overview, not a list of search results to sort. That is where the research-documented 91 percent rate of administrative burden reduction among top programs comes from: not from creating content faster, but from cutting the cognitive and operational overhead around it.
Built to Stay Manageable
One of the more consequential decisions in the imc Agent Builder’s architecture is what it avoids: a single all-purpose agent trying to do everything.
Roman Muth, CTO of Scheer IMC, is direct about why: “When a process changes, you adapt individual building blocks rather than the entire system. That significantly improves long-term manageability.” The platform uses a modular design where specialized agents handle specific roles, each accessing only the content and processes relevant to its function. The result is better traceability, better stability and easier adaptation as the organization’s needs evolve.
The headless architecture of the imc Learning Suite means agents are not tied to a single interface. They operate wherever employees already work: the learning portal, the intranet, service systems. AI becomes part of existing workflows rather than a separate application to manage. Organizations can connect agents to HR systems, ERP solutions and custom data sources via MCP servers and built-in governance controls handle PII protection, content filtering and access restrictions.
That last piece matters. Brandon Hall Group™ research is clear that organizations achieving excellence in AI learning treat governance as a foundational requirement, not a compliance checkbox addressed after deployment.
Getting from Pilot to Scale
Brandon Hall Group™ research on top-performing programs is consistent on one point: user experience design and change management predict adoption success more reliably than technical sophistication. The organizations that get the most from AI agents invest in collaborative development, real user testing and transparent communication about what the agents can and cannot do.
Scheer IMC, a Brandon Hall Group™ Eminence Partner, builds this into the imc Agent Builder delivery model through four structured phases: identifying where AI agents create genuine value in the specific organizational context, designing agent behavior and data integration, piloting with real users under monitored conditions and scaling with the explicit goal of enabling organizations to manage and extend their agent ecosystems independently.
That independence is the point. The organizations sustaining AI learning gains are not the ones that deployed an agent and moved on. They are the ones that can adapt as their needs change, add use cases and adjust agent behavior without starting over. Ninety-one percent of top programs in Brandon Hall Group™ research outlined continued AI investment strategies and expansion plans. The infrastructure has to support that.
The Shift Worth Making
Most enterprise AI spending in L&D is still at the content layer. The organizations moving ahead are at a different layer, one where agents actively participate in workflows, know the organization’s data and do work on behalf of learners, managers and administrators in real time.
The imc Agent Builder is built for that layer. It puts configuration and optimization in the hands of the L&D team. It connects to the systems that make agents genuinely useful. And it scales without requiring a rebuild every time something changes.
AI-generated content was a reasonable first step. Agents are where the real productivity gains are.
