The Small Team Advantage
Picture a three-person customer service team in Tulsa, Oklahoma, handling 10,000 support tickets monthly — the same volume that previously required a 25-person department. Same response times. Same customer satisfaction scores. Same resolution rates. They’re working with AI agents that can diagnose problems, draft responses, and even escalate complex issues automatically.
They’re partnering with AI agents that can analyze, adapt, and execute complex workflows independently. While most organizations debate whether to adopt basic automation, forward-thinking teams have moved beyond simple tools to true AI collaboration.
Work is being fundamentally reimagined, and L&D sits at the center of this shift.
TL;DR — Agentic AI goes beyond basic automation to create autonomous systems that can handle complex, multi-step workflows with minimal human oversight. Unlike traditional AI tools that react to prompts, agentic AI proactively observes, decides, and executes entire processes independently. For L&D teams, this technology could transform content creation, learning coaching, curation, feedback, and analytics — enabling smaller teams to achieve exponential productivity gains while focusing on strategic work that requires human creativity and judgment.
Why Workforce Efficiency Matters More Than Ever
Historically, productivity stagnation has challenged every industry. Finance teams spend hours on manual data entry and reconciliation. Marketing departments struggle to create personalized campaigns at scale. Customer service teams can barely keep up with ticket volumes. IT departments are drowning in routine maintenance requests that pull them away from strategic projects.
Organizations everywhere are looking for opportunities to automate tasks and increase productivity. Given the level of uncertainty in markets today, this push for efficiency makes sense. From Silicon Valley startups to Fortune 500 companies across the United States, organizations have tried everything — more software, better processes, additional headcount. Yet teams still feel overwhelmed, burned out, and unable to focus on the work that actually moves the needle.
The productivity challenge hits L&D particularly hard. Brandon Hall Group™ research reveals that 57% of L&D teams rate time as a “significant” or “heavy” constraint on their capacity. Despite decades of productivity tools, learning teams are still drowning in administrative tasks, content creation bottlenecks, and scaling challenges.
First wave AI is helping to automate or speed up repetitive tasks — chatbots that answer FAQs, algorithms that personalize content recommendations, systems that route support tickets. These tools handle individual tasks efficiently, but they require constant human input and can only work within narrow, predefined parameters. Teams are still overwhelmed because the productivity gains from basic automation aren’t enough to solve fundamental capacity constraints.
Agentic AI represents a different approach entirely. Instead of just automating individual tasks, these systems can understand broader goals, make decisions across multiple steps, and adapt their approach based on outcomes — all with minimal human oversight.
Beyond Automation: What Makes Agentic AI Different
Traditional AI tools react to prompts. Agentic AI proactively observes workflows, triggers actions, and executes multi-step processes independently with minimal human oversight. It’s goal-oriented, adaptive, and autonomous in ways that previous AI wasn’t.
The distinction is clear: Instead of asking an AI to “create a course outline,” you might soon find yourself telling an agentic AI ecosystem, “We need to upskill 500 sales reps on the new product launch by Q2.” The agent then researches the product, analyzes past training effectiveness, designs the curriculum, creates assessments, schedules delivery, and monitors progress — all while you focus on strategic decisions.
EI Powered by MPS, a Brandon Hall Group™ Smartchoice® Preferred Provider, has been exploring how this level of AI integration transforms not just what we teach, but how we design and deliver learning experiences at scale.
Redefining Team Productivity
The math here is compelling: smaller teams + right systems + people + AI agents = exponential productivity gains.
Small teams are already using agentic AI to handle customer outreach workflows, automate IT support tasks like password resets and ticket routing, and reduce software development sprint times from days to hours. From tech companies in Austin to financial services firms in New York, a compact team of 3-5 people can now dynamically deploy agents across multiple functions that previously required separate departments.
The shift isn’t just about headcount — it’s about capability. When AI agents handle research, first drafts, data analysis, and operational tasks, humans can focus on creativity, leadership, collaboration, and strategic thinking. Success requires full process redesign, not just tool upgrades.
L&D’s Pivotal Role in the Agentic Age
Learning and development has a dual opportunity in this transformation.
Preparing the Workforce: Someone has to train people to work effectively with AI agents. This means developing human-AI collaboration skills, establishing best practices for agentic AI adoption, and helping teams navigate the transition. The skills needed aren’t intuitive — they require strategic thinking to guide AI toward meaningful outcomes, quality discernment to evaluate AI outputs, and ethical reasoning to make decisions AI cannot handle alone. Understanding AI trends in L&D becomes essential for leaders preparing their teams for this shift.
Transforming L&D Operations: L&D teams can leverage agentic AI in their own work. Content development at scale becomes possible when agents handle research and first drafts. Intelligent personalization can adjust learning paths in real-time. Strategic analytics can predict performance gaps and recommend interventions automatically. Operational tasks like scheduling, tracking, and reporting can run autonomously.
Agentic AI in Action: Potential L&D Use Cases
As this technology evolves, we’re beginning to see what’s possible when AI systems can handle complex, multi-step workflows independently. Here are five areas where agentic AI could transform L&D operations:
Content Creation
- AI agents analyze performance data to identify skill gaps and research best practices from approved sources
- Generate learning objectives aligned with business goals and create initial drafts across multiple formats
- Produce assessments that measure meaningful outcomes and adapt content based on learner responses
- Allow L&D teams to focus on refining and personalizing rather than starting from scratch
Learning Coaches
- AI coaches track individual learner progress and provide personalized support when someone struggles
- Answer questions, suggest resources, and adjust instruction pace based on comprehension patterns
- Maintain context across learning sessions and escalate complex issues to human instructors
- Scale personalized guidance without overwhelming your team
Curation Agents
- AI systems continuously scan internal knowledge bases and external resources for relevant learning content
- Evaluate materials for quality and alignment with learning objectives
- Organize content into searchable, tagged collections and identify library gaps
- Surface current resources when business priorities shift
Feedback Systems
- AI feedback agents analyze learner interactions and assessment results to provide immediate, constructive feedback
- Identify areas where learners need additional support and suggest targeted interventions
- Track feedback effectiveness and provide insights to instructors about course performance
- Create continuous improvement loops for both learners and content
Analytics Intelligence
- AI analytics agents correlate training completion with performance metrics and predict intervention effectiveness
- Generate executive dashboards highlighting trends and early warning signs
- Flag learners who need additional support and recommend resource allocation changes
- Monitor learning ecosystem health and alert teams to emerging issues
Building the Right Capabilities
Success with agentic AI requires new competencies. Humans must develop strategic thinking to set goals for AI agents, quality evaluation skills to refine outputs, and ethical reasoning to maintain oversight. The focus shifts to creativity, leadership, and complex problem-solving — skills that AI cannot easily replicate.
Process redesign becomes essential. This requires reimagining entire workflows, redefining roles for human-agent collaboration, and establishing governance frameworks for autonomous systems. Successful L&D transformation depends on maintaining human connection even as efficiency improves through AI.
Your Path Forward
Organizations that embrace agentic AI in L&D won’t just improve their training programs — they’ll fundamentally change their ability to adapt, innovate, and compete. Whether you’re leading learning initiatives in Chicago, managing corporate training programs in Dallas, or developing talent strategies in Seattle, the opportunity is the same.
Start with one high-impact, low-risk process. Content research, initial drafts, or learner progress analysis are perfect pilots. Build AI literacy within your team, focusing on human-AI collaboration skills rather than just tool usage. Redesign your metrics to focus on business outcomes rather than activity measures.
The future of work depends upon unleashing human potential through intelligent collaboration with AI. Smaller teams with smarter systems will define the next decade of learning and development. The question is whether your L&D function will lead this transformation and define the next decade of learning and development.
Your learners are counting on you to prepare them for a world where human-AI collaboration is the norm. Your organization needs you to demonstrate what’s possible when the right people, systems, and AI agents work together. That future starts with the choices you make today.
FAQs on Learning and Agentic AI:
What’s the difference between agentic AI and regular AI tools? Regular AI tools respond to specific prompts and require constant human input to function. Agentic AI can understand broader goals, make decisions across multiple steps, and adapt its approach based on outcomes with minimal human oversight. It’s the difference between asking AI to “write a course outline” versus telling it “upskill 500 sales reps by Q2” and having it handle the entire process.
Is agentic AI technology available now? Exciting advances in agentic AI are happening every day, with new capabilities emerging regularly. However, some of the use cases described above may not quite exist in the market yet. Organizations should focus on building AI literacy and preparing for gradual implementation as these technologies continue to mature and become more widely available.
How can L&D teams prepare for agentic AI adoption? Start by building AI literacy within your team and experimenting with current AI tools to understand human-AI collaboration. Focus on process redesign rather than just tool adoption. Identify high-impact, low-risk processes where you could pilot agentic AI when it becomes available, such as content research or learner progress analysis.
Will agentic AI replace L&D professionals? No, agentic AI is designed to augment human capabilities, not replace them. While AI handles routine tasks, humans focus on creativity, strategic thinking, complex problem-solving, and relationship building. The goal is to free L&D professionals from administrative work so they can concentrate on high-value activities that require human judgment and empathy.
What skills will L&D professionals need in an agentic AI world? Key skills include strategic thinking to set goals for AI agents, quality evaluation abilities to refine AI outputs, and ethical reasoning to maintain oversight. L&D professionals will also need to develop human-AI collaboration skills and the ability to redesign workflows for optimal human-agent partnerships.
How should organizations measure success with agentic AI? Success metrics should focus on business outcomes rather than activity measures. Look at productivity gains, time savings, improved learner outcomes, and the ability to scale personalized learning experiences. The key is measuring how AI helps humans achieve better results, not just how much work AI completes.
