{"id":23363,"date":"2026-11-10T05:31:56","date_gmt":"2026-11-10T05:31:56","guid":{"rendered":"https:\/\/www.sifytechnologies.com\/us\/?p=23363"},"modified":"2026-11-10T13:48:12","modified_gmt":"2026-11-10T13:48:12","slug":"five-takeaways-from-a-year-of-ai-inside-two-enterprise-learning-teams","status":"publish","type":"post","link":"https:\/\/www.sifytechnologies.com\/us\/digital-learning\/five-takeaways-from-a-year-of-ai-inside-two-enterprise-learning-teams\/","title":{"rendered":"Five Takeaways From a Year of AI Inside Two Enterprise Learning Teams"},"content":{"rendered":"[vc_row][vc_column][vc_column_text]When we opened our recent webinar with Training Industry<\/a>, we asked the audience what they hoped AI would do for L&D. Most of the replies came down to doing more with less.[\/vc_column_text][vc_empty_space height=”16px”][vc_column_text]Learning teams have been asked to scale faster for years, so that hope makes sense. Yet the two teams on our panel described a year that went well beyond output. Nikita Singh, Senior Leader of Learning and Certifications at Cisco, and Mazie Baskin, VP of Anaplan Academy, have each built AI into how their teams design and deliver training. Their results came from tackling problems the business already had, bringing their people along and keeping humans responsible for quality. Below are five things we took away from their stories.[\/vc_column_text][vc_empty_space height=”28px”][vc_column_text]1. Start with a problem the business already has
\n<\/strong>[\/vc_column_text][vc_empty_space height=”8px”][vc_column_text]A year ago, many teams were still debating whether to use AI at all, or worrying that everyone else was ahead. The more useful question now is how to make it work in your own organization. Since every team is solving something different, every team starts from a different place.[\/vc_column_text][vc_empty_space height=”16px”][vc_column_text]Nikita’s team builds Cisco’s technical and certification training. Their first step was helping the team write better prompts. Over time they went further, moving to a content-as-code model, where the team writes courses the way software developers write code, in developer tools with AI agents working alongside them. They then built AI “skills,” playbooks that package the team’s style, brand and editorial guidelines, for editorial review, design, metadata and labs.[\/vc_column_text][vc_empty_space height=”16px”][vc_column_text]Anaplan’s challenge looked quite different. As Anaplan grew from a platform into an applications company with many more products, Mazie’s team had to move training away from “where do I click” toward how to implement well. So they mapped the competencies implementers need at each level and used AI to build training around them. Each team began with a problem it understood well, and AI gave it a new way to solve that problem.[\/vc_column_text][vc_empty_space height=”28px”][vc_column_text]2. Treat adoption as a people challenge<\/strong>[\/vc_column_text][vc_empty_space height=”8px”][vc_column_text]Ask teams what slows them down and most will name change management and readiness. The technology itself comes up far less. Adoption also spreads gradually, moving team by team and person by person as comfort grows.[\/vc_column_text][vc_empty_space height=”16px”][vc_column_text]Nikita was candid about how emotional this period has been for many people on her team, who wonder where their roles are heading. Her team frames AI as something that extends their expertise, and they’re putting the time it frees into new skills and more creative work.[\/vc_column_text][vc_empty_space height=”16px”][vc_column_text]Mazie starts with something more personal. She asks people what they love doing and where they want their careers to go. Once people get hands-on, the fear tends to fade and experimentation follows. One team member who used to score practical certifications wrote an AI agent to take over the scoring, and he now leads automation for the team.[\/vc_column_text][vc_empty_space height=”16px”][vc_column_text]As a learning partner that helps teams scale production, we also hear the same question from our own instructional designers. We answer the same way, by looking at how AI can make their work easier and their output better.[\/vc_column_text][vc_empty_space height=”28px”][vc_column_text]3. Choose tools task by task, and govern them closely<\/strong>[\/vc_column_text][vc_empty_space height=”8px”][vc_column_text]Much of the early talk about AI centered on finding the one right model. Teams with more experience have found that different tools suit different work, and that costs swing widely depending on the task and the model behind it.[\/vc_column_text][vc_empty_space height=”16px”][vc_column_text]Both panelists pick their tools deliberately. Mazie’s team uses Claude for configuration-heavy work and Synthesia for video. At Cisco, Nikita’s team judges each model on the quality of its output and on how many tokens it uses, which is what drives the cost of each task. Token usage hardly came up a year ago; today it shapes how her team designs prompts and agents.[\/vc_column_text][vc_empty_space height=”16px”][vc_column_text]Governance carries just as much weight. At Cisco, a governance committee approves tools, and Nikita’s team stepped back from voice cloning and digital avatars because of security and consent concerns. Anaplan runs every tool through a security review and checks content as it comes out. Mazie was frank that reviewing AI output closely is demanding work that needs someone detail-oriented.[\/vc_column_text][vc_empty_space height=”16px”][vc_column_text]Learners are watching for this as well. Many have doubts about the accuracy of AI-generated content and want to know a person has checked it. In regulated sectors such as pharma and financial services, scrutiny begins earlier, with questions about which models to use, where data goes and what good governance should look like.[\/vc_column_text][vc_empty_space height=”28px”][vc_column_text]4. Look beyond ROI<\/strong>[\/vc_column_text][vc_empty_space height=”8px”][vc_column_text]The learning teams at Cisco and Anaplan both produce training faster now. AI drafts first versions for SMEs and instructional designers to review and refine, and quality checks have become more consistent. For Mazie’s team, AI made it possible to get field readiness kits, enablement and certifications to partners in time for a wave of new product launches, which gave the whole company more agility.[\/vc_column_text][vc_empty_space height=”16px”][vc_column_text]Nikita added a useful caution. Steps such as publication don’t use AI yet, so gains in one stage don’t always carry through, and end-to-end ROI is still hard to see.[\/vc_column_text][vc_empty_space height=”16px”][vc_column_text]Some of the benefits teams care about most resist measurement. Greater agility, more room to experiment and the chance to put human expertise where it counts all came up in the discussion.[\/vc_column_text][vc_empty_space height=”28px”][vc_column_text]5. Reinvest the time in the learning experience
\n<\/strong>[\/vc_column_text][vc_empty_space height=”8px”][vc_column_text]What excited us most was how both teams are using the time they’ve gained. Nikita’s SMEs had long wanted to build graded experiential labs, but grading every possible scenario was too complex until AI could help. Her team is also designing onboarding simulations in which AI agents play the boss and the customer while a new hire troubleshoots a network. Mazie’s team uses AI-generated video hosts as tour guides through the product, so technical training feels more like a guided experience.[\/vc_column_text][vc_empty_space height=”16px”][vc_column_text]Questions from the audience followed the same thread. People wanted to know how to make dry regulatory content engaging, how to spot skills gaps before training starts and how to show real change in performance. Each of those comes back to learner experience and business impact. That is where AI gives L&D the most room to grow, helping learning teams work closer to the business and solve its problems faster.[\/vc_column_text][vc_empty_space height=”28px”][vc_column_text]Watch the full conversation<\/strong>[\/vc_column_text][vc_empty_space height=”8px”][vc_column_text]The webinar recording<\/a> goes deeper into each team’s story and includes the full audience Q&A.[\/vc_column_text][\/vc_column][\/vc_row]\n","protected":false},"excerpt":{"rendered":"

When we opened our recent webinar with Training Industry, we asked the audience what they hoped AI would do for L&D. 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