Redesigning Workflows for Gen AI for Managers

Learn to manage people in the Gen AI era, including hiring and performance management. Delegate and coordinate AI work, prioritize tasks, and shape Gen AI workers' skills.

Align expectations across managers, leaders, and teams while fostering a culture of experimentation and psychological safety, and operationally adopt Gen AI with clear workflows and ROI measures.

Delegate and coordinate Gen AI work by prioritizing Tier 1 tasks, structuring the reasoning for workflows, and managing adoption to maximize ROI across generating, distilling, and automating tasks.

Learn to hire and manage Gen AI workers using a 5 hard skills and 5 soft traits framework to decompose work, prompt precisely, validate outputs, and share knowledge.

Learn how to design Gen AI and talent roadmaps by choosing to hire, augment, or automate, weighing risk, ROI, and task specificity to balance speed, errors, and reliability.

Explore how to manage Gen AI work, hire and develop AI-aware teams, delegate and coordinate AI tasks, and roadmap talent amid the hiring, augmenting, or automating choices.

Explore how to implement Gen AI in an organization by selecting high-impact use cases, piloting initiatives, designing tasks and workflows, and establishing human oversight and validation.

Choose repeatable, low-risk pilots with clear validation to prove readiness for Gen AI. Define the pilot: use case, workflow, data, validation, and metrics for learning ROI.

Discover how AI champions translate strategy into practice, enable continuous learning, reduce friction, and accelerate Gen AI adoption through shared prompts and proven practices.

Design clear task and workflow structures for Gen AI use, splitting work by risk and repeatability, and defining inputs, outputs, and human handoffs to ensure quality and controllable failure.

Learn how to implement formal human oversight to validate Gen AI outputs, focusing on risk-based validation at early stages, escalation, and accountability.

Reflect on the four core Gen AI implementation lessons, including pilots and use cases, AI champions and enablement, task design, and human oversight, and the questions for consolidating learning.

Identify how to implement Gen AI across an organization using structures, templates, prompts, and metaprompts, and apply knowledge sharing and archetype workflows for consistent, scalable results.

Explore the seven Gen AI archetype workflows—researcher, generator, evaluator, advisor, simulator, operator, orchestrator—with templates, inputs, constraints, and outputs for specialized, tool-agnostic use.

Share replicable prompts, templates, and structured knowledge to stabilize Gen AI outputs across teams. Build a lightweight knowledge base as the single source of truth and infrastructure.

Learn how metaprompts and templates generalize prompts with dynamic fields to scale across use cases, preserving output structure and consistency while guiding inputs, outputs, and validation.

Explore three core lessons for Gen AI implementation: workflows for the seven major archetypes, knowledge sharing as infrastructure, and scalable metaprompts and templates.

Design and implement Gen AI solutions by examining data biases, distinguishing Gen AI from other AI, and understanding retrieval-augmented generation and related trade-offs.

Identify and mitigate biases in data used for Gen AI by sunsetting old documents, versioning embeddings, and tuning retrieval to favor impact and evidence over frequency.

Gen AI is not universal; use it selectively alongside discriminative AI, automation, and human judgment. Design hybrid workflows that match tasks to generative, discriminative, or deterministic components.

Explore how retrieval-augmented generation uses context to improve Gen AI answers through chunking, embeddings, top-k retrieval, and reranking. Design with metadata, versioning, pruning, and governance to sustain data retrieval quality.

Explore design choices in Gen AI implementation, addressing data biases, comparing Gen AI with other AI, and dissecting retrieval and RAG components for robust knowledge retrieval.