Chapter 7: Emails and instant messaging in the workplace

Introduction

This chapter introduces how generative AI can make workplace email and instant messaging more efficient through assistance, automation, better writing, and critical judgment.

• AI communication productivity: Explain how generative AI can reduce the burden of workplace email and make instant messaging more efficient by assisting with organization, response drafting, and routine tasks.
  Why it matters: Using AI thoughtfully can turn communication tools from distractions into productivity enhancers.
• Workplace communication effectiveness: Describe why effective AI-supported communication matters particularly in remote work, where it can strengthen working relationships and support job effectiveness.
  Why it matters: Clear, efficient communication affects both collaboration and how effectively you work in a remote environment.
• Chapter communication roadmap: State that the chapter will cover AI for email, instant messaging, integrations and automations, writing style, critical thinking, and the future of AI communication.
  Why it matters: This roadmap helps you place each communication technique in the broader workflow covered by the chapter.

Key terms: generative AI, email communications, instant messaging, personal assistant, productivity enhancers, remote work environments, effective communication, working relationships, integrations and automations, writing style

7.1 Enhancing email management with AI

This section frames email overload and security risks as workplace problems AI can help address, while defining the Copilot terms used in the chapter.

• AI-assisted email management: Explain why workplace email creates productivity and security challenges and how emerging AI email features can help. State that current early features focus on summarizing conversations and assisting with email drafting, whether built into an email client or accessed through third-party tools and chatbots.
  Why it matters: This establishes where AI can reduce inbox burden without pretending that email itself is no longer necessary.
• Microsoft Copilot naming distinctions: Distinguish the three Microsoft Copilot names used in this chapter: M365 Copilot is Microsoft 365 Chat for enterprise subscriptions integrated with the M365 suite; Copilot for Outlook is the integrated Outlook buttons; and Copilot or Copilot Pro refers to publicly available consumer products.
  Why it matters: Using the right Copilot name prevents confusion when the chapter discusses different Microsoft AI interfaces.

Key terms: alert fatigue, phishing, Google Gemini, Microsoft Copilot, Outlook integration, email summarization, M365 Copilot, Microsoft 365 Chat, Copilot for Outlook, Copilot

7.1.1 AI-enhanced email summarization and drafting

This section shows how AI email integrations and chatbots can summarize conversations, clarify technical language, and draft audience-appropriate workplace messages.

• AI email tool selection: Distinguish integrated email AI tools from isolated general-purpose chatbots, including what each enables for email tasks. Explain why trying the same task across services can be useful when output quality, conciseness, or flexibility differs.
  Why it matters: Choosing the right kind of AI access affects both the convenience and usefulness of your email workflow.
• Actionable email conversation summaries: Describe how to prompt an email-capable AI to summarize a set of conversations with attention to main points, decisions, action items, and deadlines. State why a specific summary request is more useful than a broad one and why its results must still be checked.
  Why it matters: An actionable summary can help you catch required work and deadlines in a busy inbox, but it cannot be treated as complete or perfectly accurate.
• Audience-ready maintenance emails: Trace the workflow of using AI to turn a technical email into a general workplace communication: first explain unfamiliar jargon, then provide the source email and audience-specific details for a draft. Describe how the resulting maintenance notice can communicate the schedule, purpose, expected impact, validator responsibilities, and meeting-invite next step in plain language.
  Why it matters: This workflow helps technical teams communicate operational work clearly to people who do not share the same specialized vocabulary.

Key terms: email integration, general-purpose chatbot, Google Gemini, Microsoft 365 Copilot, Gmail, Outlook, email summarization, email conversation, action items, deadlines

7.1.2 Not ready for prime time

This section explains the current limits of AI email assistants and identifies email and calendar capabilities that remain unreliable or unavailable.

• Email search reliability limits: Explain why chatbots such as M365 Copilot and Google Gemini can be unreliable when searching large email collections. Describe how scanning hundreds or thousands of emails can reduce accuracy by omitting relevant material or being overwhelmed by irrelevant messages.
  Why it matters: Knowing these limits helps you avoid treating an AI-generated email summary as a complete or dependable search result.
• Email integration constraints: Describe the difference between chatbots integrated directly into applications and the more limited current Outlook experience. Explain that Outlook users are restricted to specific options and concise prompts, which can help novice users and UI design but constrains broader email-assistant capability.
  Why it matters: The way an assistant is integrated into an email application determines how flexibly you can use it for real work.
• Future email and calendar automation: Describe the desired but currently limited AI capabilities for managing email rules, folders, and calendars. State that calendar accuracy is a concern, M365 Copilot and Google Gemini cannot create meetings because of privacy concerns, and third-party calendar AI can introduce security risk.
  Why it matters: Understanding what is not safely or reliably automated yet helps you choose appropriate tools and avoid exposing workplace calendar data to unnecessary risk.

Key terms: M365 Copilot, Google Gemini, accuracy, summary, relevant information, irrelevant emails, integration, GitHub Copilot, Microsoft Teams, Gmail

7.1.3 Email security

This section shows how to use AI to analyze email source and headers for phishing indicators while applying cautious judgment to suspicious messages.

• Phishing risk awareness: Explain why emails that seem legitimate can still be malicious and identify the situations that call for caution, such as unexpected links, requests for sensitive information, or an unusual message.
  Why it matters: Recognizing suspicious circumstances helps you avoid malware, credential theft, and other email-based attacks.
• AI-assisted header analysis: Describe how to obtain an email's source or headers in Outlook for Mac or PC and use an AI service to analyze that copied content for phishing. State that the user does not need to manually understand the raw header text.
  Why it matters: AI can turn hard-to-read email metadata into an actionable assessment of whether a message may be fraudulent.
• Email authentication failures: Explain how failed SPF, DKIM, and DMARC checks can support an AI assessment that an email may be phishing, including the basic purpose of each protocol.
  Why it matters: Authentication failures provide technical evidence that a sender may be spoofed or that a message cannot be trusted as claimed.
• Phishing evidence evaluation: Describe the non-authentication clues that strengthened the phishing assessment: a mismatched SharePoint-like domain, suspicious links, urgency through a Review button, an external-sender warning, and potentially inconsistent Received-header routes. Explain that the appropriate response is to avoid links or downloads, report the message, and verify through official channels.
  Why it matters: Combining technical checks with message-content clues supports safer decisions than relying on any single signal.
• AI analysis comparison: Distinguish the example responses from M365 Copilot and ChatGPT Plus: Copilot gave a satisfactory but more concise phishing assessment, while ChatGPT Plus gave a more detailed explanation and explicitly recognized the nonofficial SharePoint-like domain. State that AIs based on the same GPT-4 model can respond differently because of different training data and implementation behavior.
  Why it matters: Comparing AI outputs helps you select or supplement tools when one response misses useful security detail.

Key terms: phishing attacks, malware, sensitive information, external email, email header, source code, View Source, Properties, M365 Copilot, SPF

7.2 Using AI in instant messaging

This section introduces AI-assisted instant messaging as a way to reduce distraction and manage workplace conversations more effectively.

• AI-assisted IM management: Explain why instant messaging can become a distraction or time sink and how AI can help manage messages more effectively, similarly to AI assistance for email.
  Why it matters: AI can help you stay responsive to important conversations without manually tracking every message.
• AI capabilities for Teams: Describe the AI uses this section introduces for Microsoft Teams: summarizing conversations, identifying action items, suggesting responses, and enabling simple automations and integrations; state that the concepts can often apply to other IM platforms.
  Why it matters: These capabilities can turn busy chat conversations into clearer summaries, follow-up work, and more efficient communication workflows.

Key terms: instant messaging (IM), AI, email, Microsoft Teams, summarize conversations, action items, suggest responses, automations, integrations

7.2.1 Meeting transcription and recap with AI

This section shows how to transcribe meetings and use AI, especially Microsoft Teams Copilot, to create, refine, and safely share recaps, action items, and reusable documentation.

• AI meeting recap workflow: Describe how meeting transcription can produce automated recaps and action items, and distinguish the capabilities of transcription-only meetings from fully recorded Teams meetings.
  Why it matters: AI-generated recaps reduce manual note-taking and make meeting outcomes easier to find and act on.
• Teams Copilot recap process: Trace the process for generating and exploring a meeting recap in Microsoft Teams: start recording or transcription, wait for the transcript, open View Recap, and use Copilot and AI Notes to inspect or ask about the meeting.
  Why it matters: Knowing the Teams workflow lets you turn a completed meeting into accessible notes and answers without manually reviewing everything.
• Iterative documentation expansion: Explain how to turn a training-meeting recap into detailed documentation by asking follow-up questions about individual summary bullets, compiling the expanded answers, and optionally translating the result.
  Why it matters: A short recap can become procedures, training material, or other documentation when AI is prompted for specific missing detail.
• Transcript privacy and limitations: State the consent, privacy, and quality considerations for AI meeting transcription, including notifying participants, respecting chatbot token limits, and protecting sensitive transcripts when using third-party services.
  Why it matters: Meeting transcripts can contain sensitive workplace information, and long or externally shared transcripts can create privacy and accuracy risks.

Key terms: meeting transcription, recap, action items, AI Notes, recording, Copilot for Microsoft Teams, Microsoft Teams, transcription, Copilot icon, View Recap

7.3 Integrations and automation with AI

Introduces Microsoft Teams Workflows as a simplified Power Automate capability for building prebuilt or customized, cross-application automations through connectors.

• Teams Workflows foundations: Explain what Microsoft Teams Workflows are and how they relate to Power Automate. State that Teams exposes a subset of Power Automate to make implementing workflows with Teams simpler.
  Why it matters: This helps you recognize where Teams automation comes from and what it is designed to simplify.
• Workflow creation options: Describe the two ways to create a Teams workflow: choosing a prebuilt workflow or customizing a flow for a specific need.
  Why it matters: Knowing both options helps you start quickly with a template or adapt automation to the task you actually need.
• Connector-based integrations: Explain that connectors are prebuilt integrations that let different applications and services communicate without complex coding, including services beyond Microsoft technologies.
  Why it matters: Connectors are what let a Teams workflow extend beyond one application and automate work across services.
• Connector pricing awareness: State that many flow templates focus on Microsoft 365 products, while connector availability and cost vary, so premium-indicated connectors may not be free.
  Why it matters: Checking the premium indicator prevents unexpected costs when choosing an integration.

Key terms: Microsoft Teams Workflows, Power Automate, workflows, prebuilt workflows, customize a flow, connectors, prebuilt integrations, applications, services, M365 products

7.3.1 Teams workflows in action

This section shows how to configure and adapt prebuilt Teams workflows for email-sentiment notifications and meeting reminders without advanced AI or coding knowledge.

• Email sentiment channel workflow: Describe how to create the prebuilt “Analyze email sentiment with AI Builder and send results to Teams” flow and adapt it to analyze emails from a chosen Outlook folder and share results in a Teams channel.
  Why it matters: This lets a team notice and respond quickly to sentiment in messages sent to a shared distribution list.
• Workflow filtering and delivery settings: Explain how sender filtering, Outlook rules, and Teams posting options control which emails the sentiment workflow processes and where its result appears.
  Why it matters: Correct filtering and delivery settings prevent irrelevant emails and make notifications visible to the people who need them.
• Flow testing and troubleshooting: Describe how to test the email-sentiment flow and diagnose a missing Teams result by checking flow errors, allowing time for execution, and verifying that email rules routed the message to the correct folder.
  Why it matters: A workflow is only useful when you can verify that it ran and isolate why a notification did not appear.
• Meeting reminder Teams flow: Describe how to build a meeting reminder flow using the Outlook upcoming-event trigger and a Teams message to the Flow bot, including setting a look-ahead time and composing a message with the Outlook Subject dynamic content.
  Why it matters: A visible Teams reminder can be more noticeable than an Outlook popup and reduce missed or late meetings.
• No-code Teams AI capabilities: State that Teams provides prebuilt workflows, connectors, and evolving SharePoint Agents that can automate communication tasks or answer questions from organization documents without requiring advanced knowledge or coding.
  Why it matters: Knowing that these capabilities exist helps IT professionals start with available services rather than assuming every AI workflow must be built from scratch.

Key terms: Workflows, AI Automation, Analyze email sentiment with AI Builder and send results to , connectors, trigger, When a new email arrives, From field, Show Advanced Options, Outlook rule, Folder

7.3.2 Webhooks

This section introduces webhooks as event-driven integrations and illustrates how AI can accelerate their implementation with custom code.

• Webhook event-driven integration: Explain how a webhook exchanges information between unrelated applications when an event occurs. Contrast sending data to a webhook URL with repeatedly polling for information, and describe how the receiving service performs a predefined action.
  Why it matters: Webhooks let you connect workplace tools so information can trigger useful actions automatically.
• Webhook payload implementation: Describe the basic implementation flow for a webhook: obtain useful information, post it as a JSON object to a webhook URL, and have the receiving service process it. State that implementation can require advanced knowledge and custom code.
  Why it matters: Knowing the basic data flow helps you turn a repetitive information-sharing task into an automated integration.
• AI-assisted webhook coding: Describe how an AI chatbot can guide webhook implementation by generating starter code and explaining steps, while the user reviews, tweaks, and completes imperfect output. Use the ServiceNow-to-Teams PowerShell example to illustrate querying open tickets through REST API calls and posting the results to a Teams webhook.
  Why it matters: AI can substantially reduce the time needed to build an integration, but it provides a starting point rather than flawless finished code.

Key terms: webhooks, polling, webhook URL, event, predefined action, JSON object, custom code, receiving service, AI chatbots, ChatGPT

7.4 Improving your writing while maintaining your voice

This section compares practical ways to use AI to improve workplace writing while preserving meaningful human authorship and personal voice.

• Active authorship and AI skepticism: Explain why AI-assisted writing needs active human involvement, including why obvious AI phrasing can undermine authenticity and why AI-detection tools should not be treated as definitive.
  Why it matters: You need to remain responsible for what your message says rather than assuming AI output or AI detectors is reliable.
• Three AI writing approaches: Describe the three approaches to AI-assisted writing: generating a minimal-input draft and editing it, supplying a detailed prompt for a controlled draft, and drafting the content yourself before asking AI to review and improve it. Distinguish their effort, control, and appropriate use cases.
  Why it matters: Choosing the right workflow prevents you from spending more time repairing an AI draft than you would spend writing the message yourself.
• Writer-first review workflow: Explain why the section prefers creating a nonperfect human draft first and using AI to preserve its relevant details and structure while improving grammar, style, readability, and clarity.
  Why it matters: Using AI as an assistant for revision helps retain your actual message instead of replacing it with impressive-sounding but empty wording.
• Tool selection and writing scope: Describe the section's tool preferences: Grammarly and Apple Writing Tools are favored mainly for inline editing across platforms or applications, while Claude Writing Styles is preferred for generating new content and addressing writing at paragraph or page scope. State that the best choice depends on the user's workflow.
  Why it matters: Matching a tool to the scale and style of editing you need makes AI writing support more useful in daily work.

Key terms: AI-assisted writing, AI detection technology, authenticity, minimal input, comprehensive details, detailed prompt, review and improvement, inline editing, preserve all relevant details and structure, grammar and style

7.4.1 Grammarly

This section presents Grammarly as an integrated AI writing assistant for real-time grammar, spelling, tone, and style improvement in professional communication.

• Grammarly writing assistance: Describe how Grammarly uses AI and natural language processing to help users write, rewrite, and check their writing, especially for professional emails and non-native English speakers.
  Why it matters: Grammarly can improve workplace writing without requiring the user to leave the application where they are composing.
• Integrated platform availability: Explain why Grammarly's availability as a browser extension, desktop app, and mobile app makes it convenient compared with copying text between a chatbot and another application.
  Why it matters: Integrated writing assistance lets you improve text in the workflow and device you already use.
• Tone and style guidance: Describe how a user can set a target tone and receive writing-style ratings plus suggestions for grammar, spelling, tone, and alternative wording.
  Why it matters: Tone and style feedback helps a workplace message fit its intended audience as well as read correctly.
• Suggestion-level editing control: Explain that Grammarly supports real-time corrections and rephrasing while the user writes, with the user able to accept or reject each individual suggestion; state that the section finds this more effective than chatbots for those tasks.
  Why it matters: Reviewing each live suggestion preserves the writer's control over the final wording and voice.
• Free and premium options: State that Grammarly has a free version and that the section recommends considering its premium features.
  Why it matters: Knowing that both free and premium options exist helps readers decide how deeply to adopt the tool.

Key terms: Grammarly, AI, natural language processing, professional emails, non-native English speakers, browser extension, desktop app, mobile app, integrated, target tone

7.4.2 Apple Intelligence Writing Tools

This section introduces Apple Intelligence Writing Tools for editing, transforming, and composing text across Apple platforms while stressing cautious, authentic use.

• Apple Writing Tools capabilities: Describe what Apple Intelligence Writing Tools can do for text on Apple platforms, including rewriting, tone changes, summarization, key points, and turning paragraphs into lists or tables.
  Why it matters: These capabilities can speed up everyday workplace writing without requiring a separate writing application.
• ChatGPT compose integration: Explain that the Compose option integrates with ChatGPT at no cost, while signing in with an OpenAI account provides advanced functionality.
  Why it matters: Knowing the access options helps Apple users choose an appropriate way to use AI composition features.
• Cautious authentic AI use: Justify using caution and good judgment with Apple Intelligence and explain why writers should preserve their own style and viewpoint even when accepting AI suggestions.
  Why it matters: An AI-polished message can create privacy concerns or backfire if it no longer credibly represents the person who sent it.
• Apple ecosystem availability: State that Apple Intelligence Writing Tools are free with Apple operating systems and explain that their deep Apple ecosystem integration may drive rapid adoption despite being less widely available than Grammarly.
  Why it matters: Availability and platform integration affect which AI writing tool is practical for a workplace user.

Key terms: Apple Intelligence Writing Tools, rewrite, More Friendly, More Professional, More Concise, summarize, Compose, ChatGPT, OpenAI account, advanced functionality

7.5 Maintaining critical thinking and good judgement

This section argues that AI should supplement—not replace—human judgment, requiring verification, continued independent thinking, and careful protection of sensitive data.

• Critical evaluation of AI outputs: Explain why AI-generated information must be verified for accuracy before it is used in workplace tasks. Describe that AI can make mistakes and that unsafe or incorrect recommendations may not always be obvious.
  Why it matters: Checking AI output before acting on it helps prevent plausible-sounding errors from affecting your work.
• Independent thinking and skill retention: Describe the risk of overrelying on chatbots: it can reduce practice in figuring things out independently and may leave previously developed skills out of practice. Distinguish using AI as support from allowing it to do all the thinking.
  Why it matters: Maintaining your own problem-solving practice helps you retain the expertise needed to assess and use AI well.
• Sensitive-data protection in prompts: Describe how AI use in email and instant messaging can expose sensitive organizational data, especially through third-party services. State that users should follow organizational acceptable-use and secure-communication policies and remove sensitive details from prompts when necessary.
  Why it matters: Protecting data in AI prompts reduces the risk of exposing company systems, people, and confidential information.

Key terms: critical thinking, verify, accuracy, AI chatbot, overreliance, supplement your capabilities, Use it or lose it, data privacy, secure communication, acceptable use policies

7.6 The future of AI communications

This section forecasts deeper AI integration into workplace and mobile communications, reducing administrative work and leading into AI-assisted, human-centered support.

• Future AI communication integration: Describe the section’s forecast that AI will become more deeply integrated into work applications and mobile devices, taking on communication-related administrative work. Explain that capabilities such as calendar management, email filtering, and document organization are expected to reduce tedious daily tasks, although some desired uses are not yet technologically ready.
  Why it matters: This frames AI communications as an evolving assistant capability rather than a set of fully mature tools available today.
• Personal assistants and specialized agents: Explain the anticipated role of AI personal assistants on mobile devices and increasingly specialized AI agents. State that personal assistants may know users well enough to remind them about places and tasks, while agents may perform more complex specialized work.
  Why it matters: These developments point toward AI that proactively supports work and daily coordination, not merely reacts to individual prompts.
• Human-centered support transition: State how the section connects future AI communications to the next chapter: AI can help diagnose support problems accurately and craft appropriate responses, while users still need empathetic, human-centered support.
  Why it matters: The transition emphasizes that better AI-assisted communication should improve support without replacing empathy and judgment.

Key terms: deeper integration, work applications, mobile devices, calendar management, filtering emails, organizing documents, personal assistants, AI agents, specialized tasks, technical problem solving

7.7 Prompts used in this chapter

This section provides prompt ideas for drafting and adapting emails about a complex IT implementation for technical and general audiences.

• Technical email scenario construction: Describe how to construct an example email about a complicated IT issue, such as implementing a new IPS, including technical jargon, bullet points, and completed versus remaining steps.
  Why it matters: A realistic, detailed scenario gives an AI enough context to produce a useful technical email.
• Audience-specific email adaptation: Explain how AI can condense a jargon-heavy technical email into a network-engineer-to-network-engineer message and draft a separate version suitable for general communication.
  Why it matters: Adapting the same technical information to its audience makes workplace communication clearer and more appropriate.

Key terms: technical jargon, bullet points, IPS, completed steps, remaining steps, condense, technical email, network engineer, general communication

7.8 Summary

This summary recaps how AI can improve workplace email, messaging, writing, automation, and meeting follow-up while requiring security, human oversight, and realistic expectations.