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Most FutureLearn courses run multiple times. Every run of a course has a set start date but you can join it and work through it after it starts. Find out more This course is designed for individuals interested in artificial intelligence (AI) and its use in business. Please note that the individuals detailed in the ‘Who will you learn with?’ section below, are current staff members and may be subject to change.

Topics Covered

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Most FutureLearn courses run multiple times. Every run of a course has a set start date but you can join it and work through it after it starts. Find out more This course is designed for individuals interested in artificial intelligence (AI) and its use in business. Please note that the individuals detailed in the ‘Who will you learn with?’ section below, are current staff members and may be subject to change.

Topics Covered

  • Data sources
  • Knowledge acquisition
  • Knowledge representation
  • Types of machine learning algorithms
  • Decision-making processes
  • Value creation

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Reviews summary

Ai for business: strategic overview & decision-making

According to students, this course offers a solid introduction to integrating AI into business strategy and decision-making. Learners consistently praise its ability to demystify AI concepts for non-technical professionals, focusing on strategic implications and value creation. The explanations of machine learning types and decision-making processes are often highlighted as incredibly clear and helpful. While many find it perfect for understanding AI's impact, some intermediate learners suggest it remains too high-level, desiring deeper technical dives or more hands-on examples. Overall, it's seen as a valuable resource for grasping the essentials of AI in a business context.
Strong on strategic implications, less on hands-on implementation.
"I found it more theoretical than practical, which might disappoint if you're looking for hands-on business AI tools."
"The case studies were particularly helpful in showing real-world applications, which I appreciated."
"I would have loved more interactive exercises or templates for practical application."
Complex AI ideas are made accessible and easy to understand.
"The instructor made complex ideas accessible for someone without a technical background."
"I found the explanations of machine learning types and decision-making processes incredibly clear and actionable."
"This course provided me with a very solid introduction to using AI for business decisions."
Ideal for strategic application, not deep technical detail.
"As a business manager, I needed to understand how AI can genuinely impact our strategy, and this course delivered."
"This course is perfect for non-technical business professionals like me to grasp AI's strategic implications."
"I found this course demystified AI concepts and connected them directly to business planning."
Some examples may not reflect the very latest advancements.
"Some content felt slightly dated, and I was looking for cutting-edge applications."
"My only minor critique is that some technical parts could have been explained a bit deeper, perhaps with more current examples."
"I didn't find the practical applications as innovative or current as I had hoped."
Offers a broad introduction, but lacks advanced technical depth.
"It's good for absolute beginners like me, but not for those looking to deepen existing knowledge."
"The course provided a good overview, but I felt it stayed too high-level for my needs."
"I was hoping for more actionable insights or hands-on examples for implementation."

Career center

Learners who complete Using Artificial Intelligence (AI) Technologies for Business Planning and Decision-making will develop knowledge and skills that may be useful to these careers:

Reading list

We've selected 42 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Using Artificial Intelligence (AI) Technologies for Business Planning and Decision-making.
Explores formalisms for knowledge representation, providing a deep understanding of the topic.
This classic collection of essays foundational text in the study of judgment and decision-making, outlining key heuristics and biases that affect human judgment. It is essential reading for graduate students and researchers in psychology, economics, and behavioral science.
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This contemporary book explores the concept of 'noise' – unwanted variability in judgments – and its detrimental effects on decision-making in various professional fields. It's essential for graduate students and professionals seeking to deepen their understanding of judgment and improve decision quality.
Drawing on the world of poker, this book teaches readers how to make better decisions in uncertain environments by thinking in terms of probabilities and outcomes. It's highly relevant for understanding decision-making under uncertainty and is valuable for students and professionals in fields with inherent risk.
Authored by a Nobel laureate, this seminal work introduces the concept of bounded rationality, arguing that individuals make decisions based on simplified models of the world due to cognitive limitations. It cornerstone text for advanced students and researchers in economics, psychology, and artificial intelligence.
Explores the intersection of formal ontology and information systems, providing a foundation for representing knowledge in information systems.
Provides a comprehensive overview of reinforcement learning, a subfield of machine learning that deals with how agents can learn to make decisions in complex environments. It is written by two of the leading researchers in the field.
Addresses the learning strategies by describing the models of a number of different algorithms used in machine learning. Every chapter addresses a different learning algorithm and contains detailed diagrams and real-world applications.
Provides a comprehensive overview of statistical learning, a subfield of machine learning that deals with supervised learning. It is written by three of the leading researchers in the field.
Provides a comprehensive overview of data sources, including types, access methods, and preparation techniques. It is particularly valuable for its detailed coverage of data quality and data integration.
This textbook provides a comprehensive overview of artificial intelligence, including a chapter on knowledge representation.
Explores the systematic ways in which people defy rational economic predictions, offering numerous examples of irrational behavior in decision-making. It's excellent for gaining a broad understanding of behavioral economics and its impact on choices. It serves as valuable additional reading to complement theoretical concepts.
Based on a large-scale forecasting tournament, this book identifies the traits and techniques of highly accurate forecasters. It's relevant for anyone interested in improving their predictive abilities and making better decisions in uncertain environments, particularly valuable for professionals.
Examines the impact of rare and unpredictable events (black swans) on decision-making and the limitations of relying on normal distributions. It's highly relevant for understanding risk and uncertainty in complex systems and is valuable for advanced students and professionals.
Argues that an excessive number of choices can lead to decreased satisfaction, anxiety, and decision paralysis. It's highly relevant for understanding the psychological impact of choice on well-being and valuable read for all audiences.
Offers a structured and systematic approach to decision-making, outlining a process for identifying objectives, alternatives, and consequences. It's a practical guide suitable for a broad audience, from high school to professionals, looking to improve their decision-making skills.
Introduces the Naturalistic Decision Making (NDM) framework, focusing on how experienced individuals make decisions in complex, real-world settings. It offers a different perspective than traditional rational choice theory and is valuable for those interested in decision-making in high-stakes environments.
While not solely focused on personal decision-making, this book classic in understanding the factors that influence our decisions and compliance. It's highly relevant for understanding external factors that impact choices and is valuable for students and professionals in marketing, sales, and psychology.
This academic book explores the concept of bounded rationality and the use of heuristics in decision-making, offering an alternative perspective to purely rational models. It is more suitable for graduate-level students and researchers interested in the theoretical underpinnings of decision processes.
Outlines the principles the author developed and used to achieve success in life and business, including his approach to decision-making. It provides a framework for developing personal and organizational decision-making processes and is particularly relevant for professionals and those in leadership roles.
This classic book focuses on data warehousing, a specific type of data source that is optimized for data analysis. It provides a deep dive into dimensional modeling, a key technique for organizing data in data warehouses.
Applies knowledge representation and reasoning to the field of cognitive science, providing insights into human cognition.
Provides a historical perspective on the understanding and management of risk, a critical element in decision-making under uncertainty. It's valuable for gaining a deeper appreciation of the evolution of decision theory and is particularly relevant for students of finance, economics, and history.
Covers the topic of machine learning for data streams. Data streams are continuous flows of data that are too large to be stored in memory. This book shows how to use machine learning algorithms to process data streams in real time.
Focuses on data preparation, which crucial step in data analysis. It provides hands-on guidance on cleaning, transforming, and visualizing data for analysis.
Comprehensive guide to Hadoop, a distributed computing platform that is widely used for processing large datasets. It covers data sources, data ingestion, and data analysis techniques in the context of Hadoop.
Introduces data science from a business perspective, emphasizing the importance of data sources and data analysis for decision-making. It provides a broad overview of data mining techniques and how they can be applied to real-world problems.
Covers the topic of machine learning for text data. Text data special type of data that has unique characteristics. This book shows how to use machine learning algorithms to process text data.
Covers the topic of sparsity in machine learning. Sparsity property of data that has many zeros. This book shows how to use sparsity to improve the performance of machine learning algorithms.
Applies economic principles to understand decision-making in various unconventional contexts. While not a traditional decision-making textbook, it offers a unique perspective on incentives and choices, making it engaging additional reading for all levels.
Provides a comprehensive overview of big data analytics, including data sources, data processing techniques, and analytical methods. It is particularly valuable for its focus on real-world case studies and industry applications.
Introduces data visualization techniques for effectively communicating data insights. It covers a wide range of visualization types and provides hands-on examples using R and ggplot2.
Focuses on using data sources to improve marketing campaigns and customer engagement. It provides practical guidance on data collection, analysis, and interpretation for marketing professionals.
Concise guide to building data pipelines, which are essential for automating the flow of data between different sources and systems.

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