The GenAI Playbook
for business leaders

Generative AI is a class of artificial intelligence systems that create new, original content — text, images, code, or data — based on patterns learned from training data. Unlike traditional AI that analyzes and predicts, Generative AI produces novel output that closely mimics human-created content.

Agentic AI is an AI system built around an autonomous agent loop: the model receives a goal, reasons about the next step, takes an action (calling a tool, searching, writing code), observes the result, and repeats until the goal is met. Unlike a single prompt–response exchange, an agent runs over many cycles, maintains state, and can recover from failures.

The Model Context Protocol (MCP) is an open standard for exposing tools, resources, and prompts to AI applications. Open-sourced by Anthropic in 2024, it lets any AI model discover and call external tools uniformly — so you write the integration once and use it across all agents and models.

Successful GenAI implementation starts with identifying high-impact use cases, structuring clean data, building internal proofs-of-concept, measuring ROI, and ensuring security and regulatory compliance. The GenAI Playbook walks through each step with practical examples across five phases.

Secure AI agents by treating tool output as untrusted (defense against prompt injection), scoping tools per task with allowlists, requiring human approval for destructive actions, validating tool arguments, rate-limiting, isolating credentials, and logging every tool call for audit.

Generative AI can hallucinate, struggle with deterministic tasks, and raise data-privacy and compliance risks. Some workloads — precise calculations, regulated decisions, or tasks requiring guaranteed accuracy — are better served by traditional automation. Agents add scaffolding (planning, tool-use, self-check) that contains hallucination but doesn't eliminate it.