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AI for Non-Technical Managers

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AI for Non-Technical Managers

Last Updated : July 30, 2025
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About Course

This comprehensive course is designed to demystify
artificial intelligence for business leaders and managers
who do not have a technical background. Participants
will gain a practical, actionable understanding of AI’s
core concepts, including capabilities like predictive
analytics, automation, and natural language
understanding, as well as its realistic limitations. This
foundational knowledge will empower them to
confidently initiate and contribute to AI projects, bridge
communication gaps with technical teams, and make
informed strategic decisions regarding AI adoption and
investment within their organizations

What Will You Learn?

  • Grasp Core AI Concepts: Comprehend fundamental AI terminology and methodologies, such as machine learning, deep learning, and data science, without requiring programming or advanced statistical skills.
  • Identify Strategic AI Applications: Pinpoint practical and high-impact AI use cases directly relevant to your industry (e.g., optimizing supply chains, enhancing customer experience, automating routine tasks) and current business challenges.
  • Communicate Effectively with Technical Teams: Develop the vocabulary and understanding needed to articulate business problems clearly to data
  • scientists and AI engineers, interpret technical recommendations, and provide constructive feedback.
  • Evaluate AI Proposals and Solutions: Confidently assess internal AI project proposals and external vendor solutions by understanding key evaluation
  • criteria such as data requirements, model accuracy, implementation feasibility, scalability, and return on investment (ROI).
  • Manage AI Projects for Business Impact: Learn the unique aspects of managing AI initiatives, from setting realistic expectations and timelines to allocating resources, mitigating risks, and measuring tangible business outcomes and value generated.
  • Address Ethical and Risk Considerations: Recognize, analyze, and strategically address the ethical implications and potential risks associated with AI applications, including data privacy, algorithmic bias, fairness, transparency, and compliance with emerging regulations.

Course Content

AI Fundamentals for Business Leaders
This module provides a jargon-free introduction to the essential components of AI, machine learning (supervised, unsupervised, reinforcement learning), and data science. It focuses on demystifying common AI terminology like "algorithms," "datasets," and "models" within relevant business contexts. Participants will gain a clear understanding of the types of business problems AI can realistically solve (e.g., forecasting sales, identifying fraud) versus common misconceptions and inflated expectations.

Strategic AI Use Cases Across Industries
Explore a diverse portfolio of successful AI implementations across various business functions and sectors. This includes examples in retail (e.g., personalized recommendations, inventory optimization), manufacturing (e.g., predictive maintenance, quality control), healthcare (e.g., diagnostic support, operational efficiency), finance (e.g., fraud detection, risk assessment), and customer service (e.g., intelligent chatbots, sentiment analysis). The module emphasizes how AI consistently creates value by transforming customer experiences, streamlining operations, and enabling new business models, helping managers identify similar high-impact opportunities within their own organizations.

Effective Collaboration with AI Teams
Master practical strategies for fostering productive communication and collaboration with data scientists, AI engineers, and IT professionals. Learn how to define clear, measurable business problems and objectives for AI solutions (e.g., "reduce customer churn by 10%"), translate high-level business needs into actionable project requirements, and understand the data and infrastructure constraints involved. This module also covers setting realistic expectations for AI project timelines and outcomes, fostering a culture of cross- functional collaboration, and leveraging agile methodologies adapted for AI development.

Evaluating AI Solutions and Vendors
Acquire a practical, systematic framework for assessing AI proposals, internal prototypes, and external vendor offerings. This includes understanding key questions regarding data quality and availability, model accuracy and robustness, technical integration complexity, scalability requirements, and ongoing maintenance. Participants will learn to critically interpret AI demonstrations and pilot results, perform basic cost-benefit analyses, and make informed make-or- buy decisions for acquiring or developing AI capabilities aligned with business strategy.

Managing AI Projects and Driving Adoption
Address the unique considerations for managing AI project lifecycles, from ideation to deployment and beyond. This module covers setting realistic timelines and milestones for iterative AI development, effectively allocating resources (human and computational) for AI initiatives, and identifying common pitfalls such as scope creep or data scarcity. Emphasis is placed on implementing effective change management strategies specifically tailored for AI adoption, ensuring organizational readiness, user acceptance, and successful integration of AI tools into daily workflows.

Responsible AI for Business Managers
Gain a critical understanding of the business, ethical, and societal risks associated with AI implementation. This module delves into practical aspects of data privacy concerns (e.g., GDPR, CCPA implications), robust data governance practices, and strategies for identifying and mitigating algorithmic bias and ensuring fairness in AI outputs. It highlights the importance of transparency and explainability in AI decision-making for stakeholder trust. Participants will learn to develop and enforce responsible AI guidelines for their teams and navigate evolving regulatory and compliance considerations to ensure sustainable and ethical AI deployment

Capstone Project
Develop a comprehensive AI opportunity assessment and implementation plan specifically tailored for a chosen organization (your own company or a detailed case study). This in-depth project requires identifying at least three high-potential AI applications within a specific business function (e.g., marketing, operations, HR), prioritizing them based on quantifiable business impact and technical feasibility, and outlining the necessary data, technology infrastructure, and human resources for their development and deployment. The plan must also include a robust roadmap for responsible AI adoption, addressing ethical considerations, change management strategies, and a methodology for measuring tangible ROI. You will present your plan as a professional, business-focused executive briefing, supported by detailed documentation and financial projections.

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