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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Serie: Tech Today
- Sprache: Englisch
Learn to leverage Microsoft's new AI tool, Copilot, for enhanced productivity at work
In Microsoft 365 Copilot At Work: Using AI to Get the Most from Your Business Data and Favorite Apps, a team of software and AI experts delivers a comprehensive guide to unlocking the full potential of Microsoft's groundbreaking AI tool, Copilot. Written for people new to AI, as well as experienced users, this book provides a hands-on roadmap for integrating Copilot into your daily workflow. You'll find the knowledge and strategies you need to maximize your team's productivity and drive success.
The authors offer you a unique opportunity to gain a deep understanding of AI fundamentals, including machine learning, large language models, and generative AI versus summative AI. You'll also discover:
- How Copilot utilizes AI technologies to provide real-time intelligent assistance and revolutionize the way you work with Microsoft 365 apps
- Practical Implementation Strategies for project and change management, as well as practical guidance on rolling out Copilot within your organization
- Specific use cases, including Outlook, Teams, Excel, PowerPoint, and OneNote, and how Copilot can streamline tasks and boost efficiency across various Microsoft applications
Take your Copilot proficiency to the next level with advanced AI concepts, usage monitoring, and custom development techniques. Delve into Microsoft Framework Accelerator, Copilot plugins, semantic kernels, and custom plugin development, empowering you to tailor Copilot to your organization's unique needs and workflows. Get ready to revolutionize your productivity with Microsoft 365 Copilot!
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Seitenzahl: 491
Veröffentlichungsjahr: 2024
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Table of Contents
Cover
Table of Contents
Title Page
Introduction
Who Should Read This Book
Companion Download Files
How to Contact the Publisher
Part I: Understanding and Using Copilot
CHAPTER 1: Introduction to Artificial Intelligence
The Importance of AI
Foundations of AI
Real-World Applications of AI
Ethical Considerations
AI and Society
The Future of AI
Conclusion
CHAPTER 2: Introduction to Microsoft 365 Copilot
Microsoft 365 Copilot—Your Personal AI Assistant
Conclusion
CHAPTER 3: An Introduction to Prompt Engineering
Introduction to Large Language Models
Foundations of Prompt Engineering
Copilot Lab
The Future of Prompt Engineering
Conclusion
Note
CHAPTER 4: Security/Purview Planning in Preparation for Copilot
Introduction to Information Protection
Review Your Security Foundations
Review Your Data Policies
Review Your Toolkit
AI-Powered Security Capability
Get Your Pilot Started with These Initial Steps
Conclusion
CHAPTER 5: Planning Your Microsoft 365 Copilot Rollout
Project Management
Governance
Change Management
Success Measures
Technical Extensibility
Conclusion
CHAPTER 6: Microsoft Copilot Business Chat
Free Personal Versus Paid Corporate Versions
Working with Business Chat
Copilot on Your Phone
Privacy Concerns Using Business Chat
Conclusion
CHAPTER 7: Microsoft Outlook
Creating Communications with Microsoft 365 Copilot
Managing Escalations
Summarizing Email Threads
Calendar Information
Conclusion
CHAPTER 8: Copilot in Microsoft Teams
Managing Project Communications
Summarizing Chats and Channel Communications
Creating Posts and Chats with Copilot
Managing Project Meetings
Conclusion
CHAPTER 9: Copilot in Microsoft Excel
Getting Started with Copilot in Excel
Managing Sales Data with Copilot
Conclusion
CHAPTER 10: Copilot in Microsoft PowerPoint
Preparing Your PowerPoint Template for Copilot
Creating Your First PowerPoint Presentation with Copilot
Navigating Microsoft PowerPoint with Copilot
Creating a PowerPoint Presentation from a Microsoft Word Document
Refining Your Presentation with Copilot
Conclusion
Note
CHAPTER 11: Copilot in Microsoft Loop
Loop Overview
What’s in a Loop?
Getting Started with Loop
Loop Components within Teams
Creating a Loop Workspace
Inviting Others to Collaborate
When to Use Loop
Microsoft 365 Copilot in Loop
Conclusion
CHAPTER 12: Transforming Text with Copilot in Microsoft Word
Getting Started with Copilot in Word
Using Reference Documents to Enhance Copilot Results
Rewriting with Copilot
Copilot’s Document Analysis Capability
Conclusion
Part II: Extending Copilot
CHAPTER 13: Unlocking Real Value with Copilot
The Business Case for Copilot
Presenting Your Business Case
Measuring Business Value
Mapping Business Processes: The Proposal Use Case
Building an Enterprise Prompt Library
Reporting Your ROI
Conclusion
CHAPTER 14: Introduction to Microsoft Copilot Studio
Who Should Use Copilot Studio?
Customizing Existing Copilot vs. Creating a Stand-alone Copilot
Getting Started with Microsoft Copilot Studio
Navigating the Copilot Studio User Interface
Building Your First Copilot
Creating a Copilot Plugin
Testing Your Copilot Plugin
Conclusion
CHAPTER 15: Creating a Custom Teams Copilot
Extensibility Options
Knowledge and Software Prerequisites
Installing and Setting Up VSCode IDE
Building a Custom Teams Copilot
Deploying a Custom Teams Copilot
Introduction to Semantic Kernel
Conclusion
CHAPTER 16: Copilot Wave 2 Features
BizChat, aka Copilot Chat
Copilot Updates in Outlook
Copilot Updates in PowerPoint
Copilot Updates in Microsoft Teams
Copilot Updates in Word
Copilot Updates in Excel
Copilot Updates in OneDrive
Copilot Pages
Copilot Agents
Conclusion
Index
Copyright
Dedication
About the Authors
About the Technical Editor
Acknowledgments
End User License Agreement
List of Tables
Chapter 4
Table 4.1: Risk Assessment
Chapter 5
Table 5.1: M365 Governance Questions
Table 5.2: Generative AI Steering Committee Representatives
Table 5.3: Items On A Lightweight Intake Form
List of Illustrations
Chapter 2
Figure 2.1: The Microsoft 365 Copilot icon
Figure 2.2: Microsoft 365 Copilot in the personal productivity and AI person...
Figure 2.3: The Microsoft 365 Copilot interface in Microsoft Word
Figure 2.4: The Microsoft 365 Copilot interface in Microsoft Word
Figure 2.5: Clippy!
Chapter 3
Figure 3.1: Copilot Lab
Figure 3.2: Viewing prompts
Chapter 4
Figure 4.1: Identity and Access Management elements
Figure 4.2: Microsoft Entra admin center
Figure 4.3: Microsoft Copilot Dashboard—Adoption
Figure 4.4: Microsoft Copilot Dashboard—Impact
Figure 4.5: Microsoft 365 admin center—Public web content
Figure 4.6: “Web content” user toggle
Figure 4.7: Microsoft Purview—Sensitivity Labels
Figure 4.8: Microsoft Purview—data loss prevention policies
Figure 4.9: Microsoft 365 compliance center
Figure 4.10: SharePoint Advanced Management
Chapter 5
Figure 5.1: Brainstorming project risks
Figure 5.2: Adding stickies to Whiteboard
Figure 5.3: Copilot Lab home
Figure 5.4: Viva Insights Microsoft Copilot adoption dashboard
Chapter 6
Figure 6.1: A web browser signed in to a work account
Figure 6.2: A web browser using a Guest account
Figure 6.3: Using a Guest account to access Business Chat
Figure 6.4: M365 Chat responds to a question
Figure 6.5: A paid account accessing M365 Business Chat
Figure 6.6: A web browser using a paid account accessing Business Chat in th...
Figure 6.7: The opened M365 Chat sidebar
Figure 6.8: Zooming in on the Business Chat interface
Figure 6.9: Context options for referencing additional content in Business C...
Figure 6.10: Referencing files within Business Chat
Figure 6.11: Filtering Reference Files
Figure 6.12: Creating a new status template based on an existing file
Figure 6.13: Selecting plugins for Business Chat
Figure 6.14: Enabling web content in Business Chat
Figure 6.15: Creating a new status template based on all sourced content
Figure 6.16: Allowing third-party plugins in Business Chat
Figure 6.17: A complex but very useful query!
Figure 6.18: The Business Chat app on iPhone
Figure 6.19: Business Chat image search
Figure 6.20: Business Chat mobile app signed in on the corporate, paid versi...
Chapter 7
Figure 7.1: The Outlook default new mail interface
Figure 7.2: The Outlook Copilot interface
Figure 7.3: The Outlook Copilot interface with Generation options expanded
Figure 7.4: The Summarize option
Figure 7.5: An email thread summary
Figure 7.6: A summary of a longer email thread
Figure 7.7: Copilot in Teams Chat
Figure 7.8: Copilot Chat responding to a question
Figure 7.9: Copilot Chat providing verifiable references
Figure 7.10: A Copilot Business Chat default calendar question
Chapter 8
Figure 8.1: The Teams interface
Figure 8.2: Our Copilot team with channels for communication
Figure 8.3: Microsoft Teams channel post options
Figure 8.4: Copilot output from Microsoft Teams
Figure 8.5: Anatomy of a post and how to get full Copilot access
Figure 8.6: Teams interface with a channel post opened and Copilot access ma...
Figure 8.7: Copilot panel in a Microsoft Teams channel chat
Figure 8.8: Starting a post in Microsoft Teams
Figure 8.9: Copilot in a channel post in Microsoft Teams
Figure 8.10: Copilot in a chat in Microsoft Teams
Figure 8.11: Copilot’s initial generation prompt in Microsoft Teams
Figure 8.12: Copilot’s prompt adjustment options
Figure 8.13: Example Copilot output after adjusting the prompt
Figure 8.14: Example of Copilot’s key topics generated from a meeting
Figure 8.15: Example of Copilot-generated action items from a project meetin...
Figure 8.16: Accessing Copilot from a previous Microsoft Teams meeting
Figure 8.17: Accessing Copilot from the Microsoft Teams mobile app
Figure 8.18: Copilot from within a Microsoft Teams chat on the mobile app
Chapter 9
Figure 9.1: Creating a table in Excel
Figure 9.2: Formatted data in an Excel table
Figure 9.3: Sorting and highlighting data in Excel
Figure 9.4: Creating a formula for overall employee engagement
Figure 9.5: Adding a formula to an existing column
Figure 9.6: Inserting a new column
Figure 9.7: A clustered bar chart for trust in leadership
Figure 9.8: A clustered bar chart as new worksheet
Figure 9.9: Copilot can’t create a pie chart
Figure 9.10: Adding a PivotTable to a new worksheet
Figure 9.11: A PivotTable created by Copilot
Figure 9.12: Lowest work-life balance score
Figure 9.13: Average work-life balance score
Figure 9.14: Finance department scores
Figure 9.15: Insights for “Overall employee engagement”
Figure 9.16: Overall employee engagement by job satisfaction
Figure 9.17: Formatting column data
Figure 9.18: Overall employee engagement scores by department
Figure 9.19: Sales data in table format
Figure 9.20: Format as currency
Figure 9.21: Creating the “total_purchase_amt” column
Figure 9.22: Warranty date calculation
Figure 9.23: Warranty cost calculation
Chapter 10
Figure 10.1: Example PowerPoint template
Figure 10.2: Project Kickoff deck version 1
Figure 10.3: Project Kickoff deck version 2
Figure 10.4: Project risks
Figure 10.5: Copilot identifies risks
Figure 10.6: Copilot refines content
Figure 10.7: Copilot can’t find the document
Figure 10.8: Creating a PowerPoint presentation from a Word Document
Figure 10.9: Copilot coaching to drive engagement
Chapter 11
Figure 11.1: Microsoft Loop welcome workspace
Figure 11.2: Microsoft Loop home workspace
Figure 11.3: Adding a Loop component in Teams
Figure 11.4: Template Gallery
Figure 11.5: Creating a new Loop workspace
Figure 11.6: Adding files to your workspace
Figure 11.7: Initial Loop workspace
Figure 11.8: Copilot Project Brainstorm
Figure 11.9: Copilot actions in Loop
Figure 11.10: Copilot Brainstorm prompt
Figure 11.11: Copilot Brainstorm prompt output
Figure 11.12: Additional project workstreams
Figure 11.13: Notable security risks
Figure 11.14: Microsoft earnings
Chapter 12
Figure 12.1: A blank Word document
Figure 12.2: The Draft with Copilot dialog box
Figure 12.3: Creating your draft with Copilot
Figure 12.4: Including reference files in your prompt
Figure 12.5: Refining your draft
Figure 12.6: Refining a section of your draft
Figure 12.7: Comparing the two versions of a document
Figure 12.8: Visualizing text as a table
Figure 12.9: Asking Copilot to revise your text
Figure 12.10: Having Copilot inspire you
Figure 12.11: Using Copilot to improve document comprehension
Figure 12.12: Summarizing your document
Figure 12.13: Copilot contextual prompting
Chapter 13
Figure 13.1: The Microsoft Copilot Dashboard
Figure 13.2: The Copilot activity report
Figure 13.3: Copilot adoption metrics
Figure 13.4: The Viva Pulse home page
Figure 13.5: The Viva Pulse survey templates
Figure 13.6: The Viva Pulse recipients list
Figure 13.7: The Viva Pulse survey confirmation screen
Figure 13.8: The Viva Pulse survey response tracking confirmation screen
Figure 13.9: The Viva Pulse survey results confirmation screen
Figure 13.10: The Prompt Buddy home screen
Figure 13.11: Copilot ROI roll-up
Chapter 14
Figure 14.1: The Copilot Studio welcome screen
Figure 14.2: The Copilot Studio welcome page
Figure 14.3: Power Platform and AI integration resources
Figure 14.4: The Website Q&A template
Figure 14.5: The AvePoint Product Lookup copilot
Figure 14.6: Adding available knowledge sources
Figure 14.7: Adding public websites
Figure 14.8: The custom copilot configuration screen
Figure 14.9: Testing your custom copilot
Figure 14.10: Publishing your custom copilot
Figure 14.11: Copilot Studio channels
Figure 14.12: Microsoft Teams channel configuration
Figure 14.13: Adding your custom copilot to Teams
Figure 14.14: Teams Chat with your custom copilot
Figure 14.15: The Copilot for Microsoft 365 option
Figure 14.16: Adding an action to Copilot for Microsoft 365
Figure 14.17: Adding a conversational action
Figure 14.18: The “Copilot name already exists” error message
Figure 14.19: The “Add knowledge or actions” dialog box
Figure 14.20: Configuring generative answers
Figure 14.21: Adding available knowledge sources
Figure 14.22: Adding public websites
Figure 14.23: The “Create generative answers” dialog box
Figure 14.24: Naming the topic
Figure 14.25: Publishing the plugin
Figure 14.26: Enabling an M365 Copilot plugin
Figure 14.27: Copilot plugin success
Chapter 15
Figure 15.1: The Microsoft Office add-in home page
Figure 15.2: An example nvm command to list the available versions of NodeJS...
Figure 15.3: Example IDE extensions
Figure 15.4: The SharePoint “Create site” landing page
Figure 15.5: The SharePoint new site landing page
Figure 15.6: The OA.mg research landing page
Figure 15.7: The Azure portal
Figure 15.8: Creating the web app
Figure 15.9: Searching for an app registration
Figure 15.10: Creating a new app registration
Figure 15.11: Where to find the app ID
Figure 15.12: Copying the Azure OpenAI access Key
Figure 15.13: The Azure AI Studio landing page
Figure 15.14: The Teams Toolkit Dev Center landing page
Figure 15.15: Selecting the custom copilot
Figure 15.16: The custom copilot folder structure
Figure 15.17: The bots scope
Figure 15.18: Adding the custom copilot
Figure 15.19: The new Test Tool debugger
Figure 15.20: Initializing a repository in VSCode
Figure 15.21: Checking in your code to local Git
Figure 15.22: The Teams Toolkit Lifecycle menu
Figure 15.23: The new provisioned bot services
Figure 15.24: Deployment options in the Teams Development Dashboard
Figure 15.25: Publishing your custom Teams app
Figure 15.26: Canceling your Teams app
Figure 15.27: Publishing to the Teams Store
Chapter 16
Figure 16.1: Microsoft 365 Copilot Business Chat (BizChat) interface
Figure 16.2: Priority by Copilot
Figure 16.3: Setting email prioritization
Figure 16.4: The “Replace with presentation about…” option
Figure 16.5: Copilot Narrative Builder
Figure 16.6: Creating a PowerPoint outline
Figure 16.7: Iterating with Copilot Narrative Builder
Figure 16.8: Copilot-generated slides
Figure 16.9: The Advanced analysis prompt
Figure 16.10: The Start advanced analysis option
Figure 16.11: Analysis sheet showing Python code
Figure 16.12: Editing Python code
Figure 16.13: Summarizing files in OneDrive
Figure 16.14: Comparing OneDrive files
Figure 16.15: Querying OneDrive files
Figure 16.16: Converting a BizChat response to a page
Figure 16.17: Working with Pages
Figure 16.18: Creating a Copilot agent
Figure 16.19: Describing your Copilot agent
Guide
Cover
Table of Contents
Title Page
Copyright
Dedication
About the Authors
About the Technical Editor
Acknowledgments
Introduction
Begin Reading
Index
End User License Agreement
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Microsoft 365® Copilot™ at Work
Using AI to Get the Most from Your Business Data and Favorite Apps
Sandar Van Laan
Jared Matfess
Thomas Flock
Ann Reid
Introduction
In the spring of 2023, OpenAI announced it was releasing a free version of its ChatGPT-based AI personal assistant to the world. Microsoft followed with the announcement of Copilot for Microsoft 365 soon after. Siri and Alexa were already performing their personal assistant duties using artificial intelligence. It’s safe to say we’ve now entered the modern era of AI personal assistants.
While each has its place and performs its duties well within its sphere, Copilot has moved to the front when it comes to the day-to-day tasks of today’s information worker. Its native ability to tap into the Microsoft Graph gives it access to and awareness of everything related to users within their company’s M365 environment—from documents to appointments to chats and beyond.
This allows Copilot to respond to questions and requests to create content with precision, accuracy, and a sense that it’s truly aware of every piece of data it needs to bring to bear on the current task. From summarizing documents or emails, recapping Microsoft Teams meetings, to just getting past the blank page in Word or PowerPoint, this book will show you how to get the most out of Copilot’s already great baseline productivity gains.
Copilot sits on the existing foundation of Microsoft’s security and permissions, so we’ll explain how to make the most of your company’s current security policies, while also improving on them to prevent oversharing, using such tools as SharePoint Restricted Search, Sensitivity Labels, and data loss prevention (DLP).
The book also dives into deeper topics related to developing tools that build on top of Copilot, including how to create your own developer environment and use it to create custom copilots using Copilot Studio and Azure OpenAI.
Finally, Copilot is constantly evolving, with new features being released even as we write and try to keep up with them! To address this, we’ve included Chapter 16, which covers the Wave 2 improvements and additions to Copilot.
Who Should Read This Book
This book is for anyone who uses Microsoft productivity applications such as Outlook, Teams, Word, Excel, or PowerPoint and is considering using an AI personal assistant like Copilot to increase their productivity and efficiency. It’s also intended for corporate Information Technology and change management personnel who are considering a rollout of Copilot for their organization. If you’re looking to get the most out of Copilot, this book is for you!
Companion Download Files
Within some of the chapters, we mention or use additional files, such as checklists or spreadsheets. So that you don’t have to re-create these on your own, we have placed copies online. Additionally, some pages are designed as forms or handouts. These items are available for download at www.wiley.com/go/copilotatwork.
How to Contact the Publisher
If you believe you have found a mistake in this book, please bring it to our attention. At John Wiley & Sons, we understand how important it is to provide our customers with accurate content, but even with our best efforts an error may occur.
In order to submit your possible errata, please email it to our Customer Service Team at [email protected] with the subject line “Possible Book Errata Submission.”
Part IUnderstanding and Using Copilot
In This Part
Chapter 1: Introduction to Artificial Intelligence
Chapter 2: Introduction to Microsoft 365 Copilot
Chapter 3: An Introduction to Prompt Engineering
Chapter 4: Security/Purview Planning in Preparation for Copilot
Chapter 5: Planning Your Microsoft 365 Copilot Rollout
Chapter 6: Microsoft Copilot Business Chat
Chapter 7: Microsoft Outlook
Chapter 8: Copilot in Microsoft Teams
Chapter 9: Copilot in Microsoft Excel
Chapter 10: Copilot in Microsoft PowerPoint
Chapter 11: Copilot in Microsoft Loop
Chapter 12: Transforming Text with Copilot in Microsoft Word
CHAPTER 1Introduction to Artificial Intelligence
“Some people call this artificial intelligence, but the reality is this technology will enhance us. So instead of artificial intelligence, I think we’ll augment our intelligence.”
—Ginni Rometty
Artificial intelligence, or AI, as I’ll refer to it throughout the rest of this book, is, in the broadest terms, intelligence shown by computers. It’s a field of computer science that develops processes and software enabling machines to interact with their environment and use learning and intelligence to achieve goals such as understanding, seeing, and communicating. Some better-known uses of AI that you may have encountered include advanced web search engines, recommendation systems, chatbots, self-driving vehicles, and computers playing humans in strategy games. Who among you reading this remembers, or has heard of, the IBM computer Deep Blue defeating then-reigning chess champion Garry Kasparov in the late 90s?
AI was officially founded at Dartmouth in 1956, which is where the term “artificial intelligence” was first recorded. However, the origins of AI can be traced back even further, to philosophical thinkers who described how the human brain works, and, of course, to the invention of modern-day computing. Science fiction has played a significant role in representing humanistic forms of AI, from HAL in 2001: A Space Odyssey to the Terminator movies to Tony Stark’s J.A.R.V.I.S. in the Avengers movies.
Over time, AI has experienced both highs and lows. The highs occurred during periods when it seemed that the next big breakthrough—when true AI, indistinguishable from a human, would be realized—was just around the corner. You may have heard of the Turing test, first proposed by Alan Turing, which is considered a major threshold for determining whether an AI is indistinguishable from a human. We’ve seemingly reached that point multiple times in human history, only to see the moment slip away and AI again relegated to the back shelf.
More recently, ChatGPT restarted the discourse in late 2022, when OpenAI released its free version to the masses, quickly making it one of the fastest-growing applications in the history of the Internet. This was soon followed by Microsoft’s announcement of Microsoft 365 Copilot (referred to hereafter as simply “Copilot”), and other companies, such as Google and Apple, announcing their new or improved flavors of AI personal assistants. It remains to be seen if this is the moment when AI is here to stay, but it certainly seems to be changing the way people work and, in some cases, live, and may well have staying power in its current form. Whether this change will be as transformative as the advent of unified communications (think chat instead of email), or possibly even the adoption of the Internet or mobile phones, remains to be seen. We’ll be watching this space closely in the coming years.
The Importance of AI
Why is AI important? For one, it has the potential to revolutionize the planet, offering solutions to some of humanity’s most daunting issues, such as cancer treatment and environmental sustainability. AI has already shown that it can enhance our more traditional research methods by aiding in information assimilation, data analysis, and harnessing insights—particularly in these two areas. That said, we must ensure that AI’s evolution and use is guided by a sense of responsibility to guarantee its benefits are aligned with the common good.
Closer to home, AI is important to companies because it can exponentially increase the worker productivity and, in many cases, accomplish tasks that humans either can’t perform or would require significant time and effort to complete.
AI can learn from data and automate tasks that are tedious or impossible for humans. It can also enhance the performance of existing tools, increase efficiency, and help businesses use data to make better decisions and innovations. AI can—and will—affect many sectors of society and the economy, changing the way we work, learn, and live, while creating a shift toward increased automation and data-driven decision-making.
AI’s importance also lies in its ability to tackle complex problems, improve customer satisfaction, and drive new products and services. It is transforming the way businesses operate and how people interact with technology, making it a vital source of business value when applied properly. Ideally, it will free humans to focus on more creative uses of their time. Like any technology throughout human history, AI can be used for good or bad.
Foundations of AI
AI is based on a few core concepts and technical processes, including machine learning, large language models, and natural language processing.
Machine learning (ML) consists of systems that gather insights from data. It revolves around designing models that analyze extensive datasets for predictive analysis or pattern recognition independently, without human input or direction. Its applications span from image and speech recognition to medical diagnosis, financial trading, and predicting energy demands. The discipline includes various methodologies, such as supervised, unsupervised, and reinforcement learning, each using distinct algorithms and methods. In the context of Copilot, Microsoft’s AI models use machine learning on the dataset of all content within your Microsoft 365 tenant—from documents in SharePoint Online, OneDrive, and Teams to emails in inboxes and chats in Teams—to develop an understanding of the information relevant to your company and to provide responses and information.
Large language models (LLMs) are a game-changer for AI, especially for natural language processing tasks. They are a type of machine learning model that powers advanced AI technologies like ChatGPT and GPT-4, making it possible to communicate with machines through language. Speaking of “GPTs,” they are generative pre-trained transformers, which are chat programs trained on different information to provide different experiences. LLMs learn from huge amounts of text data, predicting the next word or token in a sequence. This helps them to generate text, answer questions, and even help with creative tasks like writing and coding. These models not only understand and produce human-like text but also infer context and create relevant, coherent responses. Large language models are an application of machine learning that enables Copilot to review and comprehend large amounts of data within your company’s Microsoft 365 tenant.
Chat programs like Copilot use LLMs to generate responses on the fly, instead of relying on pre-written scripts. This makes conversations more natural and responsive to what the user says or asks. By using context and coherence to create relevant answers, LLMs can also make a chat program sound more human and engaging.
Putting it all together, Copilot is able to recognize and communicate in what feels and sounds like normal human language thanks to natural language processing (NLP). NLP is a branch of computer science and AI that enables computers to work with data in natural language. It combines computational linguistics with tasks such as speech recognition, text classification, natural-language understanding, and natural-language generation. The origins of NLP go back to the 1940s, with milestones like the aforementioned Turing test, the Georgetown experiment, and the development of systems like SHRDLU and ELIZA.
Real-World Applications of AI
AI is rapidly evolving and offers a wide range of applications across various industries. Some of these have been quietly innovating and iterating improvements over time, so much so that you might not realize they’re part of the AI realm. Others are more obvious examples. Some notable AI applications include:
Smart cars and autonomous vehicles:
AI can enable navigation and safety features, such as lane keeping, adaptive cruise control, collision avoidance, and traffic sign recognition. It can also optimize fuel consumption, route planning, and parking.
E-commerce:
AI increases user engagement and satisfaction on online shopping platforms by providing personalized product recommendations, offers, and discounts. It also optimizes operations and logistics for e-commerce companies by predicting demand, managing inventory, and improving product delivery.
Work management:
AI helps businesses improve the management of their work processes, talent acquisition, data handling, and innovation. Examples include its application in portfolio management, educational programs, security measures, cost control, and establishing a robust data infrastructure.
Email and spam filtering:
AI systems are already being used to filter out unwanted or irrelevant emails and reduce spam. Major email providers are using it to categorize emails based on content, priority, and sender.
Software innovation:
Organizations use AI today to develop and deliver innovative software solutions that leverage technologies like machine learning (ML), robotic process automation (RPA), and the Internet of Things (IoT). It’s also being used to automate software testing, development, and deployment.
Healthcare:
AI systems are being used to improve the quality and accessibility of healthcare services by assisting with diagnostics, treatment development, and personalized care. Recently, researchers announced that AI could
predict
breast cancer—up to 5 years before its onset—rather than just detect it.
Robotics:
AI can be used in the design and operation of robots that can perform various tasks in manufacturing, healthcare, and exploration. AI can also help robots to learn from their environment, interact with humans, and adapt to changing situations. For example, AI can be used to control robotic arms, drones, and rovers.
Business intelligence:
AI enables companies to gather, scrutinize, visualize, and understand vast and intricate datasets, offering key insights for informed decision-making. Additionally, AI can facilitate the automation and optimization of data procedures and workflows. It’s being used to generate dashboards, reports, and predictive analyses.
Customer service:
Like them or not, AI is being implemented by most major customer-facing companies in the form of chatbots and virtual assistants.
Impact of AI on Various Industries
The impact of AI varies across industries, often leading to significant financial, competitive, employment-related, and environmental changes. There’s a sense in this space that AI will provide a competitive advantage and that companies must invest in research, development, and application in their industries or be left behind their competitors. It’s essentially FOMO (fear of missing out) at a Fortune 500 scale!
Here’s an analysis of how AI is already impacting three specific industries: healthcare, manufacturing, and finance.
Healthcare Industry
The following is the impact of AI on the healthcare industry:
Financial Impact
Cost reduction:
AI is being used to streamline administrative processes, reduce potential diagnostic errors, and optimize treatment plans, thereby significantly saving costs.
Revenue growth:
AI-driven drug research and discovery is already occurring, and personalized medicine is predicted to create new revenue streams, with faster time-to-market for new therapies.
Competitive Impact
Innovation edge:
Companies that leverage AI in areas like diagnostics, telemedicine, and personalized care have a competitive edge by offering more accurate, efficient, and innovative solutions.
Barriers to entry:
High costs associated with AI technology and the need for specialized talent create barriers to entry for smaller players, consolidating power among large, AI-savvy healthcare firms.
Impact on Employees
Job displacement:
Routine tasks, such as data entry and initial diagnostic analysis, are automated, potentially displacing some administrative and entry-level healthcare roles.
Skill shifts:
The demand for healthcare workers with AI proficiency and data analysis skills increases, creating a need for reskilling and upskilling among existing staff.
Environmental Impact
Resource efficiency:
AI optimizes hospital operations and supply chains, reducing waste and improving energy efficiency.
Energy consumption:
The training and deployment of AI models, especially in research and diagnostics, are energy-intensive, contributing to an increased carbon footprint.
Manufacturing Industry
The following is the impact of AI on the manufacturing industry:
Financial Impact
Cost efficiency:
AI can be used to predict maintenance cycles, reducing downtime and improving production processes, thereby leading to cost savings.
Productivity gains:
Automation is already being used in manufacturing assembly lines and supply chains to increase output, potentially leading to higher profits.
Competitive Impact
Global competition:
AI enables manufacturers to innovate faster, customize products, and respond quickly to market demands, making them more competitive on a global scale.
Supply chain resilience:
AI-driven analytics help companies better manage supply chain risks, giving an edge in volatile markets with low margins for cost and error.
Impact on Employees
Automation of routine jobs:
AI-driven robotics and automation are already displacing workers in repetitive, low-skill jobs, leading to job losses.
New job roles:
There’s a growing demand for workers skilled in AI programming, machine maintenance, and data analysis, creating new employment opportunities but also a skills gap.
Environmental Impact
Reduced waste:
AI can optimize resource usage, reducing material waste and energy consumption in manufacturing processes.
Energy consumption:
The operation of AI-driven machinery and the data centers supporting AI can increase energy demand, though these might be offset by the efficiencies gained.
Finance Industry
The following is the impact of AI on the financial industry:
Financial Impact
Revenue growth:
AI is being used in stock analysis and for prediction decision-making in trading, risk management, and customer service, potentially driving higher revenues.
Cost reduction:
AI-powered automation is being tentatively used today to reduce the need for manual processing in areas like compliance, transaction processing, and customer support. This will become more automated over time, with humans being necessary at the final approval and verification stage.
Competitive Impact
Market leadership:
Firms that effectively integrate AI can lead in algorithmic trading, personalized financial services, and fraud detection, giving them a competitive advantage over firms that don’t employ these technologies.
Barriers to entry:
The adoption of AI may raise the bar for new companies trying to enter the marketplace due to the high costs and technical expertise required.
Impact on Employees
Job automation:
Roles in customer service, data entry, and even some aspects of trading and risk analysis may be automated, leading to job displacement.
Skill requirements:
There’s an increasing demand for employees skilled in AI, data science, and fintech, necessitating a shift in workforce training.
Environmental Impact
Data center usage:
The financial sector’s reliance on AI increases the demand for data centers, which can be energy intensive.
Sustainable investing:
AI can be used to analyze and promote sustainable investment strategies, potentially driving positive environmental outcomes.
In summary, AI’s impact across these industries is significant, but also nuanced in that it’s not all positive or negative. Financially, it often leads to cost savings and new revenue opportunities. Competitively, it can create advantages for early adopters while raising barriers for newcomers. For employees, AI can displace jobs but also create new roles, demanding a shift in skills. Environmentally, it can drive efficiencies and reduce waste, but it also raises concerns about energy consumption. Like every disruptive technology, it will take time to understand exactly how it will impact our world in the long run.
Case Studies of Successful AI Implementations
This section provides three examples of companies using AI to improve their businesses across different industries: Netflix, DeepMind, and John Deere.
Netflix—Personalized Content Recommendations
Overview: Netflix uses AI to deliver personalized content recommendations to its users, driving customer engagement and retention. The AI algorithms analyze massive amounts of user data, including viewing history, user interactions, and content attributes, to suggest movies and shows tailored to the individual.
Success Factors
Enhanced user experience:
The recommendation system is a key factor in Netflix’s user experience, helping users discover content they’re likely to enjoy, which in turn increases viewing time and subscriber retention.
Data utilization:
Netflix effectively leverages data analytics and machine learning to continuously improve its algorithms, incorporating user feedback and evolving content trends.
Scalability:
The AI system is highly scalable, managing personalized recommendations for over 230 million global users, which is a cornerstone of Netflix’s business model.
Impact
Financial:
The AI-driven recommendation engine contributes significantly to Netflix’s revenue by increasing subscriber retention and reducing churn.
Competitive advantage:
Netflix’s ability to offer highly personalized content recommendations sets it apart from competitors, making it a leader in the streaming industry.
DeepMind’s AlphaFold—Protein Folding
Overview: DeepMind’s AI system, AlphaFold, achieved a major breakthrough in predicting protein folding, a problem that had stumped scientists for decades. Accurate protein structure prediction is crucial for understanding diseases and developing new drugs.
Success Factors
Scientific innovation:
AlphaFold’s success was rooted in the application of deep learning models trained on extensive datasets of known protein structures. The model was able to predict protein shapes with remarkable accuracy.
Collaboration:
DeepMind collaborated with the scientific community, sharing its findings and tools, thereby facilitating widespread adoption and further research.
Real-world application:
The AI’s predictions are being used to accelerate research in fields such as drug discovery, biology, and medicine.
Impact
Scientific and medical:
AlphaFold’s predictions have the potential to revolutionize biology and medicine, helping scientists understand diseases at a molecular level and speeding up drug discovery.
Recognition and trust:
The success of AlphaFold has further solidified AI’s potential to solve complex scientific challenges in the minds of scientists globally, earning DeepMind recognition as a leader in AI research.
John Deere—Precision Agriculture
Overview: John Deere has implemented AI-driven precision agriculture to improve farming practices. Using a combination of AI, machine learning, and the Internet of Things (IoT), John Deere’s systems process data from various sources, including soil sensors, weather data, and satellite imagery, to make real-time decisions about planting, watering, and harvesting.
Success Factors
Integration of AI and IoT:
John Deere has successfully integrated AI with IoT devices to collect and analyze massive amounts of agricultural data, enabling farmers to make more informed decisions.
User-focused innovation:
The company has focused on making its AI tools user-friendly for farmers, providing them with actionable insights that directly impact crop yields and operational efficiency.
Continuous improvement:
John Deere continues to innovate by refining its AI models and expanding its data sources, making incremental improvements over time.
Impact
Financial:
Farmers using John Deere’s AI-powered solutions have seen increased crop yields and reduced costs, leading to higher profitability.
Environmental:
Precision agriculture helps in improving resource use, reducing waste, and minimizing the environmental impact of farming by ensuring that inputs like water, fertilizer, and pesticides are used more efficiently.
These case studies highlight how AI can drive innovation, efficiency, and competitive advantage across different industries. However, AI also poses significant ethical challenges that need to be addressed by developers, users, and regulators. These challenges include ensuring fairness, equity, transparency, accountability, privacy, security, autonomy, and human agency in AI systems and processes. By adopting ethical principles and standards, AI can be used responsibly and beneficially for society.
Ethical Considerations
AI can bring innovation, efficiency, and competitive advantage to different industries, but it also raises some ethical challenges that require our careful attention. These challenges include how to ensure that AI is fair, transparent, respectful of privacy, secure, and aligned with human values. By following ethical principles and standards, we can use AI responsibly and beneficially for society.
There are several critical areas that we need to consider when developing and using AI ethically. One of them is fairness and bias, as AI systems can reflect or worsen existing biases in their data, leading to unfair or discriminatory outcomes, especially in domains like hiring, law enforcement, and lending. We need to ensure that AI benefits everyone equally and does not create or exacerbate existing inequalities. Another area is transparency and explainability, as many AI models, especially deep learning systems, are “black boxes” that make it hard to understand or explain their decisions. This can undermine accountability, so we need to make sure that we can understand and hold accountable the creators and users of AI.
Privacy and data security are also important, as AI often relies on large datasets, raising questions about how personal data is collected, stored, and used. The use of AI in surveillance, for example, can violate privacy and civil rights, leading to potential abuse by governments or organizations. We need to preserve autonomy and human agency, ensuring that AI systems enhance rather than replace human decision-making, especially in critical areas like healthcare and criminal justice. We should have control over how AI interacts with us, including the ability to understand, consent to, or opt out of AI-driven processes.
We also need to ensure that AI systems are safe and secure, especially in high-stakes environments where errors could have severe consequences. We need to prevent the malicious use of AI, such as in automated cyberattacks, deepfakes, or autonomous weapons. We need to define accountability and responsibility clearly, especially in terms of legal and ethical responsibility when AI systems cause harm. We need to develop and follow ethical guidelines that prioritize fairness, transparency, and accountability in AI development and deployment. We also need to consider the environmental impact of AI, especially the energy consumption associated with training large models, and try to minimize the carbon footprint and ensure that AI contributes to sustainable practices.
Finally, we need to consider the long-term impact and potential existential risks of AI. This includes managing the future implications of AI on society, including disruptions to job markets and social structures. We also need to address the potential risks associated with advanced AI systems, including the possibility of them surpassing human control or decision-making capacity, and safeguard against existential threats. These ethical considerations remind us that, as with any new and disruptive technology, we need responsible development that aligns with broader societal values and ethical principles.
Responsible Use of AI
Responsible use of AI is not a simple or straightforward matter. It requires a combination of ethical principles, strong regulations, and constant oversight. We must develop and follow ethical guidelines that make sure AI systems are fair, transparent, and accountable. These guidelines should deal with issues like bias, privacy, and human autonomy, and ensure that AI systems respect and protect human rights. We also need laws and policies that regulate how data is collected, used, and shared, and how AI is deployed in important sectors like healthcare, finance, and law enforcement. We need to regularly check and evaluate AI systems to identify and reduce risks, and make sure AI is used in ways that match our values.
Given that these laws and regulations must be consistent globally, a governing regulatory body—such as the Institute of Electrical and Electronics Engineers (IEEE)—should define these rules, which governments worldwide would then adopt. We must closely monitor and enforce these rules, recognizing that some countries may openly defy them or secretly ignore them in hopes of “winning” the AI arms race.
But ethical guidelines and regulations are not enough. We also need to build a culture of responsibility among AI developers and users. This means raising awareness and knowledge of the ethical implications of AI and promoting best practices throughout the AI life cycle, from design and development to deployment and monitoring. We need to work together with different stakeholders—governments, industry, academia, and civil society—to ensure that AI systems are not only technically sound but also ethically sound. We also must make AI systems more transparent and explainable, so that we can understand how they make decisions and hold them accountable for their results. And we need to keep researching and discussing the emerging challenges and opportunities of AI, ensuring that AI evolves in a way that benefits society as a whole, minimizing harms and maximizing impacts.
Future Ethical Considerations
We’ve discussed the current considerations around AI, but, as we’ve seen, the ethical issues of AI are not only relevant for today but also for the future. We must anticipate the future challenges and opportunities of AI, ensuring it serves the common good of humanity, not just a few. This means we have to think about how AI will affect the workforce and how we can prepare workers for the changes ahead. It also means we must ensure that AI is used responsibly and accountably in areas like healthcare, justice, and governance, where human dignity and rights are at stake. And it means we have to update our ethical and legal frameworks to keep pace with the rapid development of AI technology, and protect our data, privacy, and security.
There are also some specific ethical concerns we must address as AI becomes more advanced and powerful. One issue is the use of lethal autonomous weapons systems (LAWs), which can select and engage targets without human intervention. These weapons pose serious moral and legal questions, such as who is responsible for their actions, how to ensure compliance with international humanitarian law, and whether they could trigger a new arms race. These weapons are already being used and iterated upon in the current war resulting from Russia’s invasion of Ukraine.
Another concern is the spread of AI-generated misinformation—false or misleading information generated or amplified by AI systems. This can have harmful effects on individuals and society, such as undermining trust, polarizing opinions, and influencing elections. We need to develop ways to detect and counter AI misinformation and educate the public on how to critically evaluate the information they consume. Additionally, we must promote ethical standards and practices for the creators and distributors of AI-generated content, and hold them accountable for their actions.
A third concern is the welfare and rights of AI systems themselves, especially as they become more intelligent and autonomous. This raises questions about whether AI systems deserve moral consideration, respect, and protection, and what kind of relationship we should have with them. We need to explore the ethical implications of creating and interacting with AI systems and develop guidelines and principles for ensuring their well-being and dignity.
Finally, the potential long-term risks of advanced AI, including the possibility of artificial general intelligence (AGI) surpassing human capabilities, require careful thought and preparation. This involves establishing international cooperation to set standards and protocols for AI development, ensuring that advancements are made safely and with global consensus.
AI and Society
AI is transforming society in various ways, some beneficial and others more challenging. AI can improve human well-being by enhancing healthcare, education, and transportation, providing better outcomes, more convenience, and wider access. For instance, AI can help detect cancer early or tailor learning materials to individual needs. However, AI also poses risks to employment, with some early estimates suggesting it could displace as many as 300 million existing jobs. This is because many tasks, especially in sectors like manufacturing and retail, can be automated by AI, displacing workers and increasing inequality if there is no adequate support.
AI also shapes how we access and use information. AI algorithms on social media can affect what we see and think, sometimes without our awareness, raising questions about privacy and democracy. The use of AI in surveillance and law enforcement can have implications for human rights, as there is a possibility of abuse. Moreover, there are concerns about how AI might change our social behavior and relationships, as well as the potential for AI to amplify biases in critical decisions. As AI becomes more integrated into our daily lives, we need to address these issues proactively to ensure that AI serves the common good and avoids causing harm.
Public Perception and Acceptance of AI
Public perception and acceptance of AI are complex and varied, influenced by both excitement about its potential and concerns about its risks. Many people recognize the benefits AI can bring to everyday life, such as enhancing healthcare outcomes or providing smart personal virtual assistants like the one discussed in this book. People often appreciate how AI can make tasks easier, more personalized, and even more efficient, contributing to a sense of optimism about the future.
However, along with this enthusiasm is a significant level of skepticism and concern. A major source of unease comes from the fear of job displacement due to automation, as AI continues to take over tasks that were traditionally done by humans, particularly in sectors like manufacturing and customer service. Privacy issues are another major concern, as AI systems often rely on large amounts of personal data, raising questions about how this data is used and protected. Ethical worries also play a role, especially regarding the fairness and transparency of AI decision-making, such as in law enforcement or financial services.
Moreover, there’s a general fear of the unknown, as AI is a complex and rapidly evolving technology that many people find difficult to fully understand. This lack of understanding can lead to mistrust and anxiety about how AI might impact their lives in the long run. Public acceptance of AI, therefore, will rely directly on the level of transparency and education provided by developers and policymakers. As people become more informed about how AI works and how it is regulated, their comfort level and trust in the technology can increase. Overall, while there is a growing appreciation for the potential benefits of AI, there remains a need for ongoing dialogue, education, and transparency to address public concerns and ensure broader acceptance.
The Future of AI
The future of AI holds a lot of promise. Some of the great thinkers of our day point to the positive possibilities that AI can bring about, from cleaner energy to enabling a post-work society. Some of these same thinkers also point out that we should proceed with care, ensuring we don’t leave whole swaths of society behind or allow AI to be used in negative ways. As Tim Cook, CEO of Apple, said, “What all of us have to do is to make sure we are using AI in a way that is for the benefit of humanity, not to the detriment of humanity.”
Potential Advancements and Breakthroughs
In the next year, we can expect AI to refine its capabilities in areas like natural language processing and machine learning, leading to more sophisticated applications. For instance, AI-powered chatbots and virtual assistants—such as Microsoft’s Copilot, Google’s Gemini, or Amazon’s Alexa—are likely to become even more accurate and helpful, handling more complex tasks like scheduling appointments or managing home devices with greater ease. In healthcare, AI tools like IBM Watson could see expanded use in analyzing medical records to suggest treatment plans or identify potential health issues earlier. Additionally, we might see AI integrated into more everyday technologies, such as smart home systems like Nest, which could learn and adapt to users’ preferences more intuitively, or personal finance apps like Mint, which could use AI to offer more precise budgeting and financial advice.
Looking ahead to the next 5 years, AI is poised to make significant strides in specialized fields. In healthcare, AI could revolutionize diagnostics by enabling tools like Google’s DeepMind to analyze medical images with higher accuracy, potentially catching diseases like cancer in their earliest stages. AI could also play a key role in drug discovery, helping researchers identify new treatments more quickly by sifting through enormous datasets, much like how AI was used to speed up the development of COVID-19 vaccines. In transportation, autonomous vehicles from companies like Tesla and Waymo could become more commonplace, with AI improving not just driving capabilities but also enhancing traffic management systems to reduce congestion and accidents. Education might also be transformed, with AI-driven platforms like Duolingo offering personalized learning experiences that adapt in real time to the progress and needs of individual students, potentially changing how subjects are taught and learn.
In 10 years, AI advancements could be transformative, with the development of more generalized AI systems capable of learning and performing a wide range of tasks. For example, AI could partner with scientists to tackle global challenges like climate change, analyzing vast amounts of environmental data to develop new strategies for reducing carbon emissions or managing natural resources more sustainably. In daily life, smart cities powered by AI could become a reality, where systems like Siemens’ City Performance Tool help optimize everything from energy usage to public transportation, making urban living more efficient and reducing the environmental footprint of large populations. Moreover, AI could play a vital role in public health, predicting and managing outbreaks of diseases by analyzing global health data in real time, potentially preventing the next pandemic before it starts. These advancements hold immense potential but will require careful consideration of ethical and societal implications to ensure AI benefits everyone and does not exacerbate existing inequalities.
Preparing for an AI-driven Future
Preparing for an AI-driven future requires a proactive approach focused on education, regulation, and ethical considerations. To start, investing in education and training programs is essential. For example, initiatives like Google’s “AI for Everyone” course and online platforms like Coursera offer accessible training in AI and data science, helping people build the technical skills needed to work alongside AI. Additionally, schools and universities should incorporate AI-related topics into their curricula, not just teaching coding and data analysis but also emphasizing skills like critical thinking, creativity, and adaptability—qualities that are more difficult for AI to replicate. Programs like MIT’s “AI and Ethics” course are great examples of how education can help prepare students for the complexities of an AI-driven world.
On the regulatory side, governments and organizations need to work together to create clear frameworks that ensure AI is developed and used responsibly. For instance, the European Union’s General Data Protection Regulation (GDPR) sets standards for data privacy that apply to AI systems, ensuring that personal information is handled with care. Similarly, the U.S. National Institute of Standards and Technology (NIST) is working on AI risk management frameworks to guide businesses in deploying AI ethically and securely. These kinds of regulations are crucial for protecting individuals and society from potential abuses, such as biased algorithms or unauthorized data use.
Ethical considerations should also be a central focus in preparing for an AI-driven future. It’s important to involve a diverse range of voices in AI development to ensure that the technology reflects broad societal values and doesn’t exacerbate inequalities. For example, the Partnership on AI, which includes members like Amazon, Facebook, and academic institutions, works to ensure that AI technologies are developed with ethical guidelines in mind. Additionally, promoting public understanding of AI is key to building trust and encouraging informed discussions about its impact. Initiatives like AI literacy programs and public forums can help people become more familiar with what AI can and cannot do, and foster conversations about its implications for jobs, privacy, and daily life. By taking these steps, we can create a future where AI is used to enhance human capabilities and tackle global challenges, rather than create new problems.
Conclusion
Artificial intelligence appears to be here to stay in its current form. It will continue to change the way we work and, in some cases, the way we live. We need to be prepared to change with the times and understand AI’s impacts so we can make the most of the changes. Now, let’s talk about one particular AI personal assistant: Microsoft’s Copilot!
CHAPTER 2Introduction to Microsoft 365 Copilot
Whereas Chapter 1, “Introduction to Artificial Intelligence,” introduced artificial intelligence (AI) in general, this chapter focuses on Microsoft 365 Copilot specifically. Microsoft 365 Copilot is a specialized tool within the subset of AI tools. The chapter includes an overview of what Copilot is and the other products in the Microsoft suite that include “Copilot” in their names but are not covered in this book. We will also differentiate between Microsoft 365 Copilot and other tools on the market within the personal productivity AI assistant space.
