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Use Case Identification
Learn how to identify and prioritize the business problems where AI can create meaningful value, then define the work and outcomes clearly enough to guide execution.
Kendall teaches teams how to identify the work AI should perform, gather and structure the business context it needs, and build assistants and agents that use that context to produce accurate, useful results.
Designed for enterprise teams building AI assistants, agents, and AI-enabled workflows around real business work.
Most AI systems have access to powerful models and enterprise data. What they often lack is the operational knowledge that determines how work actually gets done: roles, rules, decisions, constraints, exceptions, and the judgment people apply along the way.
Kendall teaches teams how to capture and structure that context so AI can produce more accurate, useful results in real business workflows.
Learn how Context 360 worksKendall combines practical training in use case identification, context curation, AI system development, and Context Operations so teams can build, improve, and scale AI with confidence.
Learn how to identify and prioritize the business problems where AI can create meaningful value, then define the work and outcomes clearly enough to guide execution.
Learn how to uncover and structure the roles, rules, processes, decisions, exceptions, and knowledge AI needs to perform accurately.
Learn how to deploy that context in AI systems, test performance against real work, and improve the system until it delivers useful results.
Learn how to manage context as an ongoing enterprise asset, including ownership, updates, quality, traceability, and reuse across AI systems.
The goal is not classroom knowledge. It is an internal team that knows how to identify the right problems, prepare the right context, deploy it in AI systems, and execute the process repeatedly.
Explore the Kendall FrameworkKendall helps teams turn real business problems into accurate, production-ready AI systems while building a repeatable capability they can scale across the organization.
Build assistants and agents around the real roles, rules, processes, decisions, constraints, and exceptions that shape the work, so outputs are more relevant and dependable.
Give teams a practical method for moving from a defined business problem to a working AI solution without getting stuck in endless experimentation.
Develop people who know how to gather the right context, deploy it in AI systems, and solve real business problems with a repeatable, governable approach.
The Kendall Project helped us move from scattered AI experimentation to execution. Through executive training, team-wide AI literacy, and context sprints, they gave us a shared language, a practical framework, and the confidence to turn AI into an organizational asset.
This training is designed for enterprise teams that are moving beyond AI experimentation and want to build the internal capability to identify valuable problems, prepare the right business context, and put AI assistants and agents to work on real business needs.
No. The training is designed to help business and functional teams work effectively with AI without requiring them to become software developers. Technical teams can participate, but the focus is on business problems, context, workflow knowledge, and practical AI execution.
Teams learn how to identify and prioritize AI use cases, capture and structure the context behind the work, deploy that context in AI assistants and agents, test performance against real business tasks, and improve the system based on what they learn.
An AI assistant is deeply trained on a defined problem area and uses constrained business context to help people make decisions and perform work accurately. An AI agent relies on that same context, rules, and understanding, but adds a degree of autonomy, allowing it to take actions and complete tasks within defined boundaries.
Context curation is the practice of identifying, capturing, structuring, and validating the business knowledge AI needs to perform accurately. That can include roles, rules, processes, decisions, constraints, exceptions, approved sources, and the judgment people apply in real work.
Prompt engineering focuses on how people communicate with AI. Kendall training goes further by teaching teams how to define the business problem, prepare the operational context the AI depends on, deploy that context in AI systems, and build a repeatable approach for solving real business problems.
Yes. The Kendall approach is tool- and vendor-agnostic. The training is designed to help teams apply better problem definition, context, and operating discipline across the AI platforms and tools their organization chooses to use.
Context Operations is the ongoing discipline of managing the business context AI systems depend on. It includes ownership, quality, updates, traceability, reuse, and the processes required to keep context useful as the organization and its AI systems evolve.
The goal is not simply course completion. Your organization builds practical internal capability: people who know how to identify the right problems, gather and structure the right context, deploy it in AI systems, and repeat the process in a more accurate, governable way.
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