
ALT Infographic titled “Human Expertise in L&D in the Age of AI Orchestration,” presenting four areas where human expertise matters when working with AI: Learning Architecture, including shaping structure, considering examples, calibrating challenge, and validating assessments; Responsible Judgment, including verifying outputs, protecting information, disclosing AI use, and owning outcomes; Learning Quality, using the SURE framework: Simple, Useful, Resonates, and Easy; and Evidence of Learning, using learner feedback, learning outcomes, analysis, and improvement to understand impact. The infographic promotes iSpring AI Days 2026, a free virtual conference on October 28.

ALT Graphic titled “The Hidden Work AI Time Savings Miss.” It identifies five types of human work that remain across an AI-supported workflow: setting context, verifying outputs, reviewing and reworking, supervising actions, and handling exceptions. These activities require context, judgment, quality control, oversight, and accountability. The graphic asks which effort can be reduced and what doing the work well still requires.

ALT Capability Gap Decision Tree for diagnosing what is constraining business capability or performance before investing in a people solution. The six-step tree asks whether the required business outcome is clear, whether capable people would struggle in the current environment, whether the capability already exists elsewhere in the organization, whether success depends on a few individuals or teams, and whether the capability can be built internally within the available time. Depending on the answers, the recommended response is to define the outcome, fix the conditions, mobilize existing talent, build organizational capability, build internally, or buy, borrow or redesign. The final step is to make the investment decision based on the constraint identified.






