Advanced AI Tools for Business: A Hands-On Workshop

Artificial intelligence capabilities are advancing rapidly, and many professionals have moved beyond experimenting with basic prompts and chatbots. Organizations now need practical ways to integrate AI into real workflows that improve productivity, accelerate analysis, and support better decision-making.

In this advanced hands-on workshop, you will build on your foundational AI knowledge and learn how to design structured AI workflows and agent-based processes. Rather than focusing on individual prompts, you will explore how modern AI systems combine reasoning models, data, and multimodal inputs to perform more complex business tasks.

Through practical exercises and real-world scenarios, you will learn how to design repeatable AI workflows, evaluate AI outputs, and apply governance guardrails so AI can be used responsibly and effectively within your organization.

• Design structured AI workflows that automate multi-step tasks and improve productivity across business functions
• Understand how AI agents operate and how they can be used to support research, analysis, reporting, and operational processes
• Apply context engineering techniques that improve AI accuracy, reliability, and usefulness in real business scenarios
• Experiment with multimodal AI workflows that combine text, images, documents, and data inputs
• Evaluate AI outputs using structured frameworks that improve trust, quality, and decision-making
• Recognize emerging AI risks including prompt injection, misinformation, and governance challenges
• Develop practical guardrails that ensure responsible and secure use of AI tools within your organization

• Moving from prompts to AI workflows: Learn how to break complex work into structured AI-powered workflows that combine reasoning, data, and tool integrations to produce higher quality outputs.
• Designing AI agents for business tasks: Explore how modern AI agents plan tasks, call tools, and iterate toward goals. Learn how agents can automate research, reporting, and operational tasks.
• Context engineering and reasoning models: Discover how structured context improves AI performance and how reasoning models support more complex problem-solving and analysis.
• Multimodal AI applications: Experiment with AI systems that combine text, images, documents, and datasets to support business workflows.
• Evaluating and improving AI outputs: Apply frameworks that measure accuracy, usefulness, bias risk, and business value in AI generated content.
• AI governance and responsible adoption: Understand emerging risks including prompt injection, data exposure, and misuse of AI systems and develop strategies to implement effective governance.

Learning Objectives

• Understand how AI workflows differ from simple prompting and how they can improve business processes.
• Design AI agents that automate multi-step tasks such as research, analysis, and reporting.
• Apply context engineering strategies to improve the quality and reliability of AI outputs.
• Experiment with multimodal AI systems that combine different types of inputs.
• Evaluate AI outputs using structured criteria to improve trust and decision-making.
• Recognize governance, security, and ethical risks associated with enterprise AI adoption.

From Prompts to AI Workflows

• Examine how most organizations currently use AI tools and why simple prompts often fail to produce reliable results.
• Break complex business tasks into structured workflows that combine AI reasoning, data inputs, and human review.
• Practice converting a basic prompt into a multi-step AI workflow that produces more reliable and useful outputs.
• Identify common business tasks that benefit from workflow automation.

Designing AI Agents for Business Tasks

• Explore how modern AI agents plan tasks, use tools, and iterate toward goals.
• Understand the architecture of AI agents including goals, planning, memory, and evaluation.
• Experiment with designing an AI agent that supports a real business task such as competitive research or reporting.
• Evaluate where agents can create the most value in your organization.

Context Engineering and Reasoning Models

• Learn how structured context dramatically improves AI performance.
• Apply frameworks that define roles, goals, constraints, and outputs to guide AI systems.
• Explore how reasoning models can support deeper analysis and complex problem-solving.
• Practice improving weak prompts using context engineering techniques.

Multimodal AI Workflows

• Discover how modern AI systems combine text, images, audio, and data to generate richer insights.
• Design multimodal workflows that integrate multiple inputs to support marketing, product development, and operational analysis.
• Evaluate the opportunities and risks associated with multimodal AI.

Evaluating AI Outputs and Managing Risk

• Understand why evaluation frameworks are essential for reliable AI deployment.
• Develop scoring models that assess accuracy, completeness, bias, and business value.
• Identify emerging risks such as prompt injection and data exposure.
• Design governance approaches that ensure responsible and secure use of AI tools.