Writing a book can be exhilarating, but it can also be overwhelming. Whether you’re crafting a novel, a memoir, or a nonfiction book, starting with a clear outline can set you up for success. Think of it as a roadmap: It gives you direction, keeps you organized, and helps you maintain momentum through the writing process.

In this guide, we’ll walk you through everything you need to know about outlining a book. You’ll learn why outlines matter, explore different outlining styles and methods, and get a simple, step-by-step process to create your own outline.

Table of contents

Benefits of outlining your book

Do I need an outline?

Types of outlines

Outlining methods

How to outline your book

Tips for writing a strong book outline

Conclusion

Book outline FAQs

Benefits of outlining your book

Outlining is an important step in the writing process. For some writers, it might feel like unnecessary extra work, but it can actually make the writing process much smoother. A well-structured outline gives you a clear path forward so you can focus on your creative expression.

Provides structure

• Organizes your ideas in a logical, easy-to-follow sequence
• Ensures your story or argument flows smoothly from beginning to end
• Helps identify gaps or weak points in your overall narrative early on

Helps you focus

• Keeps your writing aligned with your main themes and goals
• Prevents you from including irrelevant detail or veering off topic
• Makes it easier to maintain consistency in tone, pacing, and voice

Saves time

• Speeds up writing with a clear plan for what comes next
• Reduces the need for major changes during revisions
• Helps you schedule your writing more efficiently

Prevents writer’s block

• Provides a clear next step when you’re unsure what to write next
• Allows you to skip around to outlined sections if you get stuck
• Offers a visual indication of your progress

Do I need an outline?

Every writer approaches their craft differently. Some are meticulous planners, mapping out every component of their story, characters, setting, and theme before writing. These writers are sometimes called plotters—writers who plot out their writing. At the other end of the spectrum are the pantsers—writers who don’t plan their writing, preferring to discover the story as they write it. They “fly by the seat of their pants.”

Very few writers are entirely plotters or pantsers. Most fall somewhere in between. As you develop your writing skills, you’ll figure out the balance that works for you. Maybe you prefer writing only a detailed synopsis of your book before writing. Alternately, you may find that a detailed outline of your characters, plot, and themes works best for you.

Book outlining types

Effective outlines for your book can come in a variety of forms. The two most common outline types are linear and visual outlines. Experiment to figure out which works best for you.

• Linear outline: A sequential, hierarchical method of organizing your narrative using headings and subheadings. Often this takes the form of a bulleted or alphanumeric list.
• Visual outline: A method of organizing your narrative using images, icons, and lines to indicate relationships. Frequently, writers use a technique called mind mapping to build visual outlines.

Book outlining methods

There are a number of different outlining methods writers use for structuring their books. Each has strengths for different writing styles, genres, and story structures. Here are a few of the most popular.

The snowflake method

Developed by author Randy Ingermanson, this method starts with a one-sentence summary of your book. From there, you expand that idea into a paragraph, then into detailed character descriptions, scenes, and storylines.

It’s great for writers crafting complex plots or character-driven narratives.

The 3-act structure

The three-act structure is one of the most common story structures. It divides your narrative into three distinct sections:

1. Setup: Introduces characters and the setting
2. Confrontation: Develops a conflict and sets the stakes
3. Resolution: Resolves the conflict and concludes the story

This method is a basic but effective strategy for building tension and making sure your narrative has a strong arc and satisfying conclusion.

The hero’s journey

Popularized by writer and scholar Joseph Campbell, the hero’s journey outlines a 12-step journey of transformation. Common in mythology, fantasy, and adventure stories, it follows a protagonist who overcomes trials and emerges changed.

The hero’s journey is ideal for quest-driven narratives or stories focused on a character’s growth.

The synopsis method

The synopsis method involves writing a short summary of your entire book before you begin drafting. This summary typically includes major plot points, character arcs, key themes, and the ending, all condensed into one to three pages.

It’s best for writers who find linear outlining challenging but still want a plan for their book.

How to outline your book in 7 steps

Before you begin your outline, decide on the outlining type and method or methods you’ll employ. You may want to experiment to determine which combination works best for you.

1  Define your book’s purpose

What are you trying to achieve with your book? For instance, you can inform, entertain, inspire, or persuade. Your purpose will help guide your voice, tone, and structure.

2  Identify your audience

Who are you writing for? What do they already know? What do they expect to learn or experience from your book? Getting specific about your audience helps you make sure your writing speaks directly to them.

3  Create your central idea or thesis

Summarize your book’s core idea in one or two sentences. For fiction, this might include your main character and their challenge. For nonfiction, it should outline the key argument or insight. Refer to this regularly to keep your outline aligned with this central idea.

4  Craft your story, character, or topic arc

For fiction, chart how your protagonist grows or changes. In nonfiction, think about how your topic unfolds and builds. This arc gives your book a compelling through line that keeps readers engaged.

5  Build your book’s structure

Using your chosen method, lay out the big-picture framework of your book. Focus on major beats like plot points, key scenes, or conflicts. Don’t include the fine details just yet. As you add more specifics, this framework will give you something to build on.

6  Add high-level details

Begin fleshing out each section with more context: scenes, character dynamics, emotions, examples, or facts. These elements will bring depth and continuity to your outline and help you visualize where your narrative is going.

7  Organize and refine your outline

Arrange your content into chapters or logical sections. Ensure a smooth flow and clear transitions from one section to the next. Aim for a structure that naturally builds toward a satisfying conclusion.

Tips for writing a strong book outline

Use these tips to get even more out of your book outlining process.

• Stay flexible: Whether you’re more of a plotter or a pantser, remember that your outline is a guide, not a recipe. Adjust and refine it as your ideas and writing evolve.
• Use tools to help: Tools like Scrivener and Milanote are built to help you organize and revise your ideas. Grammarly can generate a first-draft outline based on your ideas or notes if you need help getting started.
• Get feedback: Share your outline with a trusted friend or mentor. Outside perspectives can help you spot opportunities for improvement or unlock new ideas.

Conclusion

Outlining is one of the most powerful tools a writer can use to bring clarity, direction, and structure to a book. Even if you consider yourself a pantser, a little preplanning can make the writing process smoother and more productive.

Take the time to explore different methods, mix and match techniques, and customize your approach to suit your unique writing style. Remember: Your outline isn’t set in stone—it’s a flexible roadmap that helps you move forward with confidence.

Book outline FAQs

What is the best method for outlining a novel?

There’s no single best method. The snowflake method is great for detailed planners, while the three-act structure suits plot-driven writers. Try a few approaches to see what works best for you.

Can I outline a nonfiction book the same way as a novel?

Not exactly. Nonfiction outlines focus more on organizing information, while fiction outlines emphasize narrative flow and character development.

How long should a book outline be?

It should last as long as necessary. It should give you enough structure to stay on track while still leaving room for creativity.

Do I need to follow my outline exactly?

No. Think of your outline as a flexible guide. If your story or argument evolves, your outline can evolve with it.

What’s the easiest outlining tool for first-time authors?

Grammarly is user-friendly and great for beginners. Its AI can help you brainstorm, outline, draft, and revise your writing. It’s your writing partner through every stage of your writing process.

The post How to Outline a Book in 7 Steps appeared first on Grammarly Blog.

The challenge: Navigating AI’s impact on education

Grammarly Authorship: A student-first approach

• Clear categorization: Authorship distinguishes between human-written content, AI-generated content, modified content, and pasted material.
• Analytical insights: Reports clearly display writing categories through intuitive, color-coded breakdowns.
• Authoring playback: This feature visually demonstrates each step of a student’s writing process, fostering trust and deeper engagement.

Recognition from EdTech Breakthrough

“Grammarly Authorship gives students an easy way to show how they wrote their paper, including if and how they interacted with AI tools. While other AI detection tools leave students in the dark with no insight into the results or defense against false positives, Authorship provides deeper insights that foster two-way transparency in the writing process between educators and students. Grammarly makes it easy to improve your writing, track sources, and generate citations so your work stands out as credible, original, and impactful.”

Real-world impact: Empowering students and educators

“Authorship has tremendous potential for higher education as we try to navigate the challenges and opportunities of AI,” said Nathan Fayard, Assistant Professor of English at Indiana Wesleyan University. “Not only does Authorship help ensure students are actually doing their work, but it can also protect students from false-positives on AI detectors, giving them a way to document where the different parts of their work came from.”

• Explore how one professor at the University of Florida modernized traditional writing assignments with Grammarly Authorship.
• Learn how Rowan-Cabarrus Community College leveraged Grammarly Authorship to reduce academic integrity violations from 27 to 1.

Looking ahead: Our continued commitment

Join us

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Customer support teams are communicating more than ever—and feeling the pressure. With higher ticket volumes, more communication channels, and customers expecting fast, high-quality responses, today’s support agents are being pushed to their limits.

The result? Burnout.

In fact, 62% of support professionals say the expectation to always be connected is contributing to their stress and burnout. Over three-fourths of support agents say they’re communicating more at work than they were just a year ago, and 68% are doing it across more channels. This constant context switching—between chat, email, calls, and internal tools—leaves little time for deep focus or strategic customer care.

Unlike other knowledge workers who can spend more time on asynchronous work, CX teams are expected to be “always on,” ready to respond to customer needs in the moment. In fact, customer-facing teams spend 66% of their workweek communicating in real time—17% higher than the average worker. Yet despite these growing demands, many CX teams are still operating without the tools they need to keep up.

AI can help—but adoption is lagging

Here’s the good news: Data shows that AI can help support teams reclaim their time, reduce repetitive tasks, and communicate more effectively. The not-so-good news? Many CX teams haven’t effectively put AI to use.

Our latest report, The Productivity Shift: AI Support for Customer Support Teams, found that 84% of support agents say they lack the resources or knowledge to communicate and work effectively. And while AI adoption is growing, only 68% of CX workers use AI tools, the lowest rate among all job functions surveyed. Almost one-third (32%) of agents avoid AI altogether, 10 percentage points higher than the cross-functional average.

What’s holding them back? A portion (38%) of them aren’t sure if their company has approved AI tools for use. Others worry about privacy, compliance, or losing the human touch in customer interactions.

The hesitation is understandable—but it’s also solvable. For the teams that have adopted AI, the results are clear: It’s helping them save time, reduce workload, and improve both productivity and morale.

Where AI can make the biggest impact for CX

So where should support leaders focus their AI investments? Here are three high-impact areas to provide AI support to your customer support teams:

1. Agent communication

AI tools like Grammarly that integrate seamlessly into CX workflows help agents draft clear, on-brand, and mistake-free responses—fast. This leads to better customer understanding and a more consistent support experience. In fact, 99% of business leaders say better communication improves customer understanding, and 98% say it helps deliver more attentive support.

2. Agent productivity

From suggesting responses to surfacing relevant knowledge base content, AI can dramatically reduce the time it takes to resolve issues. 98% of leaders say better communication speeds up resolution times, and 96% say it contributes to repeat business.

3. Brand compliance

Maintaining a consistent brand voice is tough across hundreds of customer interactions per day. AI tools help agents stay on-brand while reinforcing compliance standards: 97% of leaders say improved communication boosts brand reputation, and 94% say it ensures brand guideline compliance.

What comes next

The demands on customer support teams aren’t slowing down, so their tools and workflows need to catch up. AI is no longer just about automation. It’s about enabling human agents to do their best work: responding with clarity, confidence, and speed.

The Productivity Shift: AI Support for Customer Support Teams offers a detailed look at where support teams are struggling—and how the right AI tools can make a measurable difference.

The post How AI Is Helping Customer Support Teams Avoid Burnout appeared first on Grammarly Blog.

Starting a piece of writing can feel overwhelming, especially when you’re staring at a blank page. That’s where the rough draft comes in—it’s your chance to dive in, explore your ideas, and shape your thoughts into something tangible. A rough draft isn’t about perfection; it’s about progress. It gives you the freedom to experiment with structure, test your argument, and let your creativity lead the way.

In this guide, you’ll learn why a rough draft is important, the key components of a rough draft, and tips to make your rough draft writing process easier.

Table of contents

Why is a rough draft important?

How to write a rough draft in 5 steps

Step 1: Transition from your outline

Step 2: Write your introduction

Step 3: Develop body paragraphs

Step 4: Push through the roadblocks

Step 5: Write your conclusion

Tips for writing an effective rough draft

What to avoid

How AI is changing the drafting process

Rough draft FAQs

Why is a rough draft important?

A rough draft is a critical step in the writing process. It’s your chance to write freely without worrying about anyone else seeing it. You can experiment, make mistakes, and follow ideas wherever they lead. This freedom allows you to discover your message, organize your thoughts, and build momentum.

Here are the primary benefits of a rough draft:

Builds momentum

• Experiment freely: It’s a low-pressure space to try out different styles, structures, or tones.
• Focus on content over correctness: You can concentrate on ideas before worrying about grammar or formatting.
• Improve time management: An intentional rough draft stage separates idea development from polishing, helping to keep you focused. This makes the entire writing process faster and more manageable.

Strengthens your ideas

• Clarify your argument: A rough draft helps you refine your thesis, supporting points, and overall message.
• Identify gaps: Moving from your outline to writing often helps you spot where more information, research, or clarification is needed to make your point.
• Catch major issues early on: Problems with content flow, logic, or development are more evident as you work through your rough draft.

Prepare for revisions

• See the full context: A rough draft lets you step back and evaluate how well your ideas connect and support your goals before refining.
• Get better feedback: A rough draft gives others something concrete to react to, making their input more useful and specific.
• Make editing less overwhelming: With a complete draft in hand, you can focus on improving one section at a time instead of rewriting from scratch when you receive feedback.

How to ​write a rough draft in 5 flexible steps

Writing a rough draft can be intimidating, but it should be seen as a creative phase rather than a final product. Your goal is to get ideas on the page in a loose form without worrying about grammar or transitions. Focus on developing your content and building momentum. Here are five simple steps to help you.

Think of these steps as a toolkit rather than a checklist. Write in whichever order works best for you.

Step 1: Transition from your outline

If you’ve already outlined your piece, you have a roadmap. Now, you just need to fill it in.

Start by choosing any section—introduction, body, or even conclusion—that feels easiest to tackle. You don’t have to write in order. Use your outline points to guide your paragraphs, but don’t feel locked in. If a new idea comes to mind, follow it. You can revise the outline later.

Example:

Outline point: “Main benefit of meditation = stress relief”

Draft: “One of the biggest benefits of meditation is its ability to reduce stress. Even just a few minutes a day can calm the nervous system and help you feel more [something].”

Step 2: Write your introduction

Many writers find the introduction the hardest part, so don’t stress if it takes a few tries. The goal is to introduce your topic, offer a little background, and clearly state your thesis or purpose.

Start with a hook—a compelling fact, question, or statement—that draws the reader in. Then, provide any context needed to understand your topic. Finally, present your thesis: the main argument or message of your piece.

If you find yourself stuck, you can leave the introduction for last. Writing an introduction after you have the rest of your draft is often much easier.

Example:

“Why do we always feel busier than we are? That’s the question I started asking after reading three different productivity books in one week. This piece explores how our obsession with being busy affects how we work—and how we live.”

Step 3: Develop body paragraphs

The body of your draft is where you expand on your main points. Each paragraph should center on one idea, introduced by a topic sentence. From there, use evidence, examples, or explanations to support your point.

Here’s a simple structure to follow for each paragraph:

• Topic sentence: What is the point?
• Support: What facts, logic, or examples prove it?
• Analysis: Why does this matter?
• Transition: How does this connect to the next idea?

Example:

Step 4: Push through the roadblocks

This is where many writers get stuck. Maybe you’re not sure how to connect your ideas, or everything you’ve written suddenly feels off. That’s completely normal.

The key is to keep moving forward. Don’t aim for perfect—just aim to finish. If you feel stuck, try these strategies:

• Use placeholders: Drop in quick notes like “[insert example here]” or “[transition needed]” so you don’t lose momentum.
• Set small goals: Try a word count target (like 200 words) or a time-based sprint (15–25 minutes).
• Don’t reread or edit: Resist the urge to fix things as you go. Trust that revision will come later.
• Write through doubt: Even if your ideas feel clunky or uncertain, getting them down is better than getting them “right.”

Step 5: Write your conclusion

When you’re ready to wrap up, revisit your thesis and summarize the key points you’ve made. A good conclusion reinforces your argument without simply repeating everything you’ve said.

You might also leave the reader with a final thought, call to action, or question that encourages further reflection. Whatever approach you take, aim to end on a strong, thoughtful note.

[tip] Focus on clearly tying your ideas together and leaving the reader with something to think about—you can refine the tone or wording during revision. [/tip]

Tips for writing an effective rough draft

Here are a few tips to help make your drafting process smoother:

• Write now, edit later: Resist the urge to fix typos or rephrase every sentence. Save that energy for revision.
• Be okay with messy: The draft doesn’t need to look good. It just needs to exist.
• Use notes: Jot down reminders about sources, quotes, or ideas to follow up on.
• Take breaks: Step away when you feel stuck—rest can spark new insights.
• Track your progress: Celebrate small wins, like finishing a paragraph or hitting a word count goal.
• Have a trusted peer review it: Share your rough draft with someone who can offer constructive feedback. Fresh eyes can help you spot gaps, unclear sections, or ideas worth expanding.

What to avoid

• Overediting early on: Resist the urge to perfect sentences during the rough draft phase.
• Ignoring structure: Stick to your outline to maintain organization.
• Neglecting transitions: Ensure each paragraph connects smoothly to the next.​
• Forgetting your audience: Keep your reader in mind as you write—your tone, examples, and focus should all serve their needs.
• Skipping placeholders: Skipping a detail to keep your momentum is a great practice. But don’t forget to come back and expand on those items.
• Writing without breaks: Drafting takes focus. Step away when you need to so you can recharge and come back with a fresh perspective.

How AI is changing the drafting process

Drafting used to mean wrestling with a blank page, slowly building ideas sentence by sentence. Today, AI writing tools can jump-start that process by generating full drafts, outlines, or even starter sentences based on your input. The benefit? You get past the blank page faster and can focus on shaping your message.

But it’s still your job to revise, refine, and make it your own—AI is a starting point, not the finish line. It might miss nuance, include generic phrasing, or reflect surface-level understanding of your topic. That’s why treating AI as a starting point, not a shortcut to a final draft, is essential. The best results come when you use AI to generate ideas and rough content, then revise it with your voice, insights, and purpose in mind.

That’s where Grammarly’s AI comes in—it’s built to support writers through every stage of the writing process, especially the messy middle of writing a rough draft. And because Grammarly works where you write, you can work on your draft without breaking focus or switching between tools.

Here’s how Grammarly supports your drafting process:

• Jump-start your rough draft: Use Grammarly’s AI prompts to turn an idea or outline into a starting draft. Whether you need a hook, paragraph, or full response, Grammarly helps you generate content faster.

• Stay in flow as you write: Grammarly offers suggestions for clarity and tone in real time, helping you revise rough sentences without breaking your stride.

• Write without fear of unintentional copying: Grammarly’s built-in plagiarism checker helps you avoid accidental borrowing and maintain originality.

Make your rough draft writing less rough

A rough draft isn’t the final version of your writing—it’s the beginning of it. By getting your ideas down without pressure, you create the foundation for stronger, clearer, more compelling work. With these five manageable steps and a willingness to write imperfectly, you’ll break through the hesitation and into the heart of the writing process.

Rough draft FAQs

What are the three components of a rough draft?

A rough draft typically includes an introduction, body paragraphs, and a conclusion. The introduction sets up your topic and thesis, the body develops your ideas with support and examples, and the conclusion wraps everything up.

Does a rough draft have to be perfect?

Not at all. In fact, it shouldn’t be. A rough draft is meant to be messy—it’s a space to explore ideas, test structure, and see what works. You’ll revise and improve it later, so focus on getting your thoughts down rather than making every word flawless.

How long does it take to write a rough draft?

It depends on the length and complexity of the piece, but many people can draft a short essay (500–800 words) in 1–2 hours. Longer pieces may take several sessions. Setting a timer or writing in short sprints can help you stay focused and make steady progress.

How long should a rough draft be?

A rough draft should be about the same length as your final piece. If you’re aiming for a 1,000-word essay, your draft should be close to that—maybe slightly over, since you’ll likely cut or tighten during revision. It’s better to have too much content than not enough.

Does a rough draft need citations?

Yes, if you’re using outside sources, it’s a good idea to include at least rough citations. They don’t have to be perfectly formatted, but noting where your information comes from helps you avoid accidental plagiarism and makes the final citation process easier.

The post How to Write an Effective Rough Draft: Practical Tips for Every Writer appeared first on Grammarly Blog.

AI is fundamentally changing the way we communicate at work. It’s not just a technological upgrade or another productivity tool; it presents a fundamental shift in how businesses and employees operate. The learning curve to achieve enterprise-wide AI adoption might seem steep, but it is achievable. 

Every business is starting at a different place with AI. Those slower to adopt likely feel behind, especially if they are still avoiding the technology altogether. Early adopters might be ahead of the curve and the competition now; however, with an emerging technology like AI, the curve continues to move. We are all at the beginning of a long-term shift that takes proactive planning, incremental adjusting, and the occasional pivot to achieve true transformation and see real results.

No matter where you are in your journey, if you want to achieve enterprise-wide adoption, the best place to begin is with AI literacy. Read on to get the roadmap to equip your workforce with the resources to achieve AI literacy—and prepare your business to enter the next stage of AI adoption.

5 actionable steps to achieve enterprise-wide AI adoption

Business leaders looking to get ahead with the power of AI must take a strategic and comprehensive approach to achieving enterprise-wide adoption. Here’s a roadmap to help you steer your organization through the intricacies of adopting and integrating AI effectively.

1. Gain buy-in from leadership and employees

The journey to full AI adoption begins with gaining buy-in, not just from the top executives at your company but also from employees who will be expected to use AI in their daily work. Start by demystifying AI, explaining the basics of AI usage, and showcasing the benefits for everyone involved. Here are a few actionable next steps to take:

• Assign an AI business driver or tiger team to manage the research, strategy, and implementation across your company. 
• Educate your leadership through a series of workshops in which you bring in experts to share the benefits, challenges, and strategic importance of AI to transform business communication. You’ll need their approval before investing in new technology or implementing any new policies.
• Engage and prepare employees through interactive sessions, such as town halls and Q&As. In these sessions, you should not only outline the impact that AI will have on the business but also the benefits that it will bring to their specific roles. It’s crucial for employees to understand how they’ll be able to leverage AI in their daily tasks to make their work more efficient and their communication more effective.
• Showcase early wins that you achieve in pilot programs where key individuals or teams experiment with AI tools. These successes will boost confidence among employees and leadership alike.

2. Provide training and education to improve AI literacy

Next, make AI education a top priority. AI literacy is a foundational skill for every employee to focus on. Here is how you can equip your workforce with the resources they need to use AI systems responsibly, effectively, and with the desired outcomes:

• Launch AI onboarding programs that provide an overview of AI technologies, touching on everything from basic concepts and best practices to risks and security considerations. 
• Align AI education with how people work to address usage and literacy gaps between levels and teams. Create customized training sessions that are practical, hands-on, and tailored to every function within your organization. They should focus on the specific AI tools and use cases that they will use in their role on a daily basis.
• Promote continuous learning for early-career and senior-level workers alike by regularly updating training materials to reflect the latest AI advancements and insights. This will ensure that your team remains on the cutting edge and you are constantly investing in upskilling your workforce.
• Offer a learning stipend for employees who are interested in further improving their AI literacy and fluency. This could be a portion of your company’s learning and development budget dedicated to providing external courses, workshops, and books to employees.

3. Invest in the right AI tools

Every function within your organization likely has countless options for AI tools. Over the past year, we’ve seen hundreds of point-solution startups pop up across industries. It’s a complicated landscape that gets more crowded by the day. Here’s how you can break through the noise and choose the right tools for your business:

• Identify AI capabilities in your current tech stack, looking for tools that your employees already use. Rather than investing in more tools, look for AI technology that works with your existing tech stack to create ease around AI adoption and everyday usage.
• Carefully select vendors based on criteria such as ubiquity, scalability, ease of integration, customer support, and robust data security. Look for AI technology that is easily embedded into employees’ existing workflows and communication channels.
• Invest in AI tools that can be customized to your brand guidelines and that tailor results to your organization’s context, tone, clarity, and fluency. This would create more consistent and effective communication across the entire company.
• Prioritize security and privacy by monitoring how your employees use AI technologies and watching for risky or unusual inputs and outputs. Security, data privacy, and protection of company intellectual property are top AI concerns for business leaders. Choose a secure and reputable AI provider to ensure the protection of sensitive company data.

4. Create acceptable usage guidelines and policies

With great power comes great responsibility. As you adopt powerful AI tools, it’s critical to guide their use with clear policies. After all, if you don’t have control over the AI systems your employees are using, how can you protect your data, your people, and your brand from the most common risks? Start with these steps:

• Develop an ethical framework for AI that addresses key issues such as data privacy, security, and bias. This framework should align with your brand’s values and compliance requirements.
• Draft clear usage policies that define acceptable and unacceptable uses of AI in your business operations. This will help prevent misuse and guide employees in making ethical decisions.
• Stay up to date on AI regulations to ensure your policies remain compliant with both local and international laws. Regular reviews and updates to your policies are necessary as regulations evolve.
• Prioritize standardization across your enterprise to ensure that all employees are using the same tools under the same guidelines. This will help combat the uncertainty that can arise from differences in AI use and proficiency. 

5. Build an enterprise-wide AI culture

Finally, nurturing a culture that embraces innovation and continuous learning is vital for achieving sustainable AI success. Here’s how you can create a culture that supports your business transformation:

• Encourage employees to share effective prompts, best practices, and lessons learned with one another so everyone learns to use AI tools responsibly and celebrates wins together.
• Identify AI champions who experiment with new AI features and technologies. Think of these people as incubators for new ideas and inspiration for AI-driven innovation and skill sets.
• Incentivize innovation by offering rewards for teams or individuals who come up with new ways to leverage AI to improve communication. Recognition can go a long way in motivating employees to think creatively.
• Embrace AI for the long term by investing in continuous education and new technologies. Regularly seek feedback from employees to learn how to improve your operations and business communication with AI.

Embarking on the road to enterprise-wide AI adoption is no small feat—it requires a thoughtful and strategic approach. By securing buy-in, providing targeted training, establishing clear guidelines, investing in the right tools, and fostering a culture of innovation, you can position your organization not only to adapt tothe AI-driven future but to thrive.

Implementing AI enterprise-wide

The journey may be complex, but the potential rewards for your organization and its people are immense. Embrace the journey with openness and enthusiasm, and watch as AI transforms your business operations.

Want to learn more about AI literacy, the stages of AI adoption, and the roadblocks to improving communication across your enterprise? Download the ebook The New Language of Business: How an AI Literate Workforce Is the New Competitive Advantage.

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Over the past year, artificial intelligence (AI) has been dominating headlines in business, technology, and academics (to name a few). Of course, it’s mainly generative artificial intelligence (gen AI) that people are talking about when they refer to the latest AI tools. But it’s important to break through the buzzy headlines to understand the massive opportunity that this transformative technology presents. 

Business communication is one of the biggest areas for disruption. In the US alone, gen AI has the potential to save up to $1.6 trillion annually in productivity if all workers were using it for communication. However, every organization is in a different stage of generative AI adoption. Some companies are avoiding the new technology, resisting change from their traditional working methods. Many have a piecemeal approach to AI adoption, experimenting with different tools across a few select teams. And few, if any, have reached enterprise-wide adoption, using gen AI to its maximum potential to transform their business.

In this blog, we’re covering the five phases of generative AI adoption in business—and uncovering the gaps that might be preventing you from reaching the next level. 

The AI adoption framework

AI has quickly emerged as a key driver and accelerator in business transformation. Whether you’re using it to gain new insights, increase content production, automate tedious and manual work, or enhance overall communication quality, it’s likely that your business and employees have seen some of the benefits of this new technology.

However, as AI technologies evolve, it can be challenging to keep up and understand how to leverage these tools most effectively. This is where the AI-adoption framework comes in. Use this as a way to assess where your organization is currently, identify areas for improvement, and navigate the complex landscape of AI so you can transform your business.

The 5 Stages of AI Adoption

• Becoming aware. The first stage is simply being aware of AI technology. At this stage, people within your organization have an early interest in AI and may be researching different tools to build an understanding of their capabilities and different use cases for your business. The focus of this stage is on garnering interest in AI, exploring its potential for business communication, and finding out how to use it to add value for employees.
• Experimenting. The second stage is all about experimentation with AI tools. In this phase of adoption, AI literacy is likely limited, with only a few select people or teams actively using AI in their day-to-day work. In the experimentation stage, there is no formal AI strategy in place, and it’s likely that each team within your organization is experimenting with a different tool. The focus here is on exploring the potential of AI, building skills and expertise to improve communication, and identifying areas where AI is adding value to the business.
• Optimizing. In the third stage of AI adoption, businesses are focused on optimization. Graduating from the experimentation phase means applying all of the lessons learned into repeatable processes. You should use that to define an AI strategy and implement tools for your employees to use regularly. In this stage, it’s important to improve the AI literacy of your entire workforce, not just certain individuals or teams. AI should become integrated into key business processes, and your focus here should be on achieving measurable improvements in productivity, communication effectiveness, and business performance.
• Standardizing. The fourth stage is all about ensuring standardized usage of AI across the business. This involves investing in proper technology enterprise-wide, creating a culture of innovation, and encouraging the responsible use of AI tools to drive the business forward. In this stage, AI is integrated into every area where employees communicate. The focus here is on empowering every employee to communicate with more effectiveness, clarity, and context rather than just adding more noise. At this stage, you’re driving maximum efficiency across teams, scaling automation and content creation, and creating a competitive advantage due to gains in productivity and creativity. 
• Transforming. The final stage of enterprise-wide AI adoption is when businesses truly transform and gain a competitive edge. At this level, your business is using AI to completely transform its operations and employees’ communication. Your business is likely seen as a leader in your industry, recognized for driving innovation and disruption. The focus here is on maximizing the benefits you see in employee productivity, business communication, customer satisfaction, and the bottom line.

Understanding where your business is in its AI adoption journey is key if you want to adapt and win in today’s competitive market. This framework should help you understand your current stage of adoption. But how do you know what’s holding you back from reaching the next stage?

AI adoption gaps: What’s holding you back from reaching the next stage

Having an understanding of where your business currently sits is a solid first step toward business transformation. But it’s what you do with that knowledge that really matters. Enter AI-adoption gaps. These are the key blockers that companies must overcome to reach enterprise-wide AI adoption.

Stage: Becoming aware

• The zero-to-one gap. If your business is stuck in the awareness stage, it’s likely that you have a lack of buy-in across the organization to try out AI technology. You could be missing key buy-in from leadership, whose approval you need before bringing in new technology. It could also be because the company is stuck in a state of fear of messing up, so they’re avoiding getting started altogether. Or there could be a lack of buy-in from employees who prefer to avoid new technology and use more traditional methods to communicate. 
• How to bridge the gap: To enter the next phase of experimentation, you should focus on communicating the benefits of AI for individuals and the business so that both employees and leadership are keen to try it out. Start small and simple. You don’t need your long-term AI strategy fully developed from the get-go. Choose one problem, like improving one aspect of one team’s communication effectiveness, and see how AI can solve it.

Stage: Experimenting

• The literacy gap. The majority of companies today are in the experimentation stage of AI adoption. It’s possible that you have a few individuals who use AI regularly for communication, but your workforce’s overall AI literacy is holding you back from reaching the next phase. Someone who is literate with AI has a fundamental understanding of the tools and their capabilities, is comfortable using them regularly for some communication tasks, and is starting to see personal benefits—but has room to improve to realize its full potential. 
• How to bridge the gap: The key to bridging this gap and entering the next stage of optimization is investing in proper training and policies. It’s crucial to ensure that your entire workforce not only feels confident using AI for their roles’ specific use cases but also knows your organization’s guidelines and policies around AI usage to enhance business communication. 

Stage: Optimizing

• The technology gap. Once your workforce is upskilled and has elevated its AI literacy, it’s time to turn your attention to the technology you invest in. If you find your business stuck in the optimization phase, it’s likely because you don’t have the proper AI communication tools in place to support each function or the strategies to make the most of those tools. 
• How to bridge the gap: To enter the next stage of standardization, it’s crucial to invest in trusted, responsible, and ubiquitous AI technology. Not all AI tools are created equal. For your entire workforce to reap the benefits, you’ll need a solution that combines user-friendliness, scalability, and robust data security. The best place to start is an AI-powered communication assistant that is easily embedded into employees’ existing workflows and communication channels.

Stage: Standardizing

• The systems gap. The final gap that you must overcome to achieve enterprise-wide AI adoption is building systems that standardize AI usage and effective communication throughout your enterprise. You must ensure that everyone is invested in a culture of innovation. When you focus on building systems that support this culture, you’ll upskill your entire workforce, enable everyone to communicate more effectively, and maximize the benefits of AI.
• How to bridge the gap: To bridge the systems gap and achieve business transformation, you’ll need to double down on standardization. This means standardizing the tools you invested in (from the technology gap above) and creating standardized, role-specific training and enablement so every employee feels confident in using them effectively.

Unlocking the Potential of Generative AI

As AI continues to demonstrate its transformational capabilities on business communication for organizations across all industries, it’s critical for you to take this moment to assess where your business stands. Once you know what stage you’re in, you can prioritize building a strategy for AI adoption. In the next chapter, we’ll lay out a roadmap to get started or improve your AI capabilities to scale effective communication, drive business results, and stay ahead of the competition. 

Ready to find out what stage your business is in? Take this assessment to get started.

The post The 5 Stages of Enterprise-Wide Gen AI Adoption appeared first on Grammarly Blog.

Writing often includes references to spoken words. Examples of this are dialogue in novels, quotes in articles, and paraphrased discussions in blogs. Written speech can be categorized into two types: direct and reported speech. Both types are crucial for effective communication. Knowing when and how to use each improves your writing and communication skills. This is particularly important in narratives, journalistic writing, academic texts, and professional correspondence.

In this blog post, we’ll explain direct and reported speech, explore their differences, review common mistakes, and demonstrate their correct usage through a variety of examples.

Table of contents

What is direct speech?

What is reported (indirect) speech?

Direct vs. indirect speech: What’s the difference?

When to use direct vs. indirect speech

Rules for converting direct to indirect speech

Special cases in indirect speech

Common mistakes with direct and reported speech

Direct and reported speech FAQs

What is direct speech?

Direct speech is an exact quote in a sentence. In nonfiction writing, it’s a person’s exact words. In fiction writing, such as plays, and occasionally in creative nonfiction, such as personal essays, it’s a character’s dialogue.

Direct speech is generally used in interviews, plays, narratives, and conversations. It is also sometimes used in reported pieces. In most cases—but not plays and interview transcripts—it’s enclosed in quotation marks and paired with a reporting verb like said or asked.

With direct speech, the tense and word order the speaker used originally are retained. This means direct speech isn’t always grammatically correct. A writer captures the speaker’s authentic voice by leaving the speaker’s original words intact (or by writing dialogue that isn’t grammatically correct).

Here are a few examples of direct speech:

What is reported (indirect) speech?

Reported, or indirect, speech is written speech that reports what a person said without quoting them verbatim. It may involve rephrasing, paraphrasing, and grammatical changes.

With indirect speech, the verb tense and pronouns are adjusted to fit the narrator’s perspective. Take a look at these examples:

• Direct: “We waited all afternoon for you to arrive!” Kacie said.
• Indirect: Kacie told me they’d waited for me all afternoon.

Indirect speech does not go inside quotation marks. Instead, it’s paired with verbs and nouns or pronouns that communicate that it is speech, like “he asked,” “they told us,” and “she said.”

Alternative reporting verbs and their impact on tone and meaning

With indirect speech, you lose the original speaker’s tone and word choice to convey meaning. Usually, writers make up for this by describing indirect speech with words like:

• Insisted
• Suggested
• Shouted
• Recommended
• Implied

Consider how these verbs communicate emotion and other aspects of the speaker’s tone, like authority or urgency. Verb choice can also reflect the relationship between the speaker and the listener. Here are a few examples that illustrate this:

• Authoritative: Kyle told Javier that he should arrive early for the interview.
• Advisory: Kyle suggested that Javier arrive early for the interview.

• Subtle: When Yulia asked for my friend’s number, she implied she’d be calling him the next day.
• Assertive: When Yulia asked for my friend’s number, she said she’d be calling him the next day.

• Forceful: Enrico commanded them to stop.
• Neutral: Enrico told them to stop.

When determining whether direct or indirect speech is the better choice, think about the type of sentence you’re writing. Generally, any emotionally charged sentence, like an imperative sentence or interrogative sentence, is best written as direct speech.

Direct vs. indirect speech: What’s the difference?

There are several differences between direct and indirect speech. They include:

Structure

Direct speech reports the exact words spoken by a person, enclosed in quotation marks.

• “I’ve got the files ready,” Maha said.

Indirect speech involves paraphrasing what was said without using the speaker’s exact words.

• Maha told me the files were ready.

Use of quotation marks

Direct speech requires quotation marks around the spoken words. Indirect speech does not.

• “Hello!” he said.
• He said hello to me.

Tense usage

Direct speech nearly always preserves the quote’s original tense.

• “Aren’t we eating right now?” she asked.

Indirect speech typically involves backshifting, which is where the tense shifts backward, depending on the reporting verb. Often, this means present tense shifts to past tense.

• She asked if we were eating at that moment.

Pronouns

Direct speech keeps the pronouns used by the original speaker. Indirect speech adjusts pronouns to match the reporter’s perspective.

• “You both got accepted, right?” he asked.
• He asked if we’d both been accepted.

Tone and emphasis

Direct speech preserves its speaker’s exact tone, emphasis, and style. Indirect speech may alter the tone, often due to paraphrasing and recontextualizing the speech.

• “Don’t forget to bring your headphones!” James exclaimed. “You’ll want them, trust me!”
• James insisted that I bring my headphones.

Punctuation

When writing direct speech, include a comma before the quotations and capitalize the first word within them.

• Dahlia responded, “Dogs’ instincts are always right. Humans, not so much.”

Sentences that include indirect speech follow the same punctuation rules and structure as every other sentence.

• Dahlia affirmed that dogs have superior instincts to humans.

When to use direct vs. indirect speech

Use direct speech when the speaker’s exact wording is important, like a direct quote in a legal statement or a character’s dialogue in a story. Use indirect speech for summaries, paraphrasing, or maintaining flow in formal and academic writing.

Generally, indirect speech is more succinct and easier to fit into the overall tone of your work. However, direct speech is a clear report of what a person said, so choose it when clarity is most important.

Regional differences between direct and indirect speech

US and UK English have different conventions surrounding direct and indirect speech. Generally, UK English uses indirect speech more often, especially in conversations and news reporting. In contrast, US English typically favors direct speech in these areas because it highlights the speaker’s tone and intent.

Stay consistent, whether you opt for direct or indirect speech in your writing. Switching between them can confuse readers. In spoken English, however, it’s much easier to switch between them, and switching often enables you to highlight tone and meaning shifts.

Here’s an example:

So, we were driving to the mall, right, and she told me I needed to stop. She was like, “You need to stop now!” And I said, “We can’t stop, we’re going 80 on the highway, and there’s no safe place to do that.” But she was so insistent because, apparently, I ran over a traffic cone, and we were dragging it.

Rules for converting direct to indirect speech

Rule 1: When to change tense (backshifting)

When the reporting verb is in the past, follow these guidelines to backshift effectively:

• Present simple → Past simple
• Present continuous → Past continuous
• Present perfect → Past perfect
• Will → Would
• Can → Could

Rule 2: When not to change tense in indirect speech

Although backshifting is a common rule, it’s not always required. Maintain the speech’s tense in the following scenarios:

When the reporting verb is in the present or future tense

You don’t need to backshift if the reporting verb is in the present tense (e.g., says, tells) or future tense (e.g., will say).

• Direct: He says, “I’m too busy to come to the party.”
• Indirect: I know he will say he’s too busy to come to the party.

When the original statement expresses a universal truth, fact, or unchanging condition

You don’t need to shift the tense, even if the reporting verb is in the past.

• Direct: Buddha said, “We make the world with our thoughts.”
• Indirect: As Buddha said, we make the world with our thoughts.

Optional backshifting for stylistic reasons

Even when backshifting is grammatically allowed, it’s sometimes omitted for stylistic clarity or to maintain relevance, especially if the original quote feels current or significant.

• Direct: “You must keep trying,” the professors said.
• Indirect: Both professors said we must keep trying.

Rule 3: Pronoun adjustments

Adjust pronouns based on who is speaking to whom.

• I → he/she (or they)
• You → I/he/she/they
• We → they

Rule 4: Time/place word changes

• now → then
• today → that day
• tomorrow → the next day
• yesterday → the previous day
• here → there
• this → that

Rule 5: Converting complex sentences

Be sure to maintain a consistent tense and pronouns when you adjust multiple clauses in a sentence.

• “I will go home and then call you,” she said.
• She said that she would go home and then call me.

Rule 6: Omitting that in indirect statements

You can often omit that in informal speech or writing. However, including that is often preferred for clarity in formal writing.

• She said (that) she was tired.

Special cases in indirect speech

Yes/no questions

When writing a yes/no question in indirect speech, use if or whether.

• “Are you coming?”
• He asked if I was coming.

WH questions

With questions, keep the question word (who, what, when, where, why, how), but structure the sentence as a statement.

• “Where are you going?”
• He asked where I was going.

Commands and requests (affirmative and negative)

For commands and requests, use the reporting verb + to–infinitive. Reporting verbs include told, asked, requested, ordered, and advised. For negatives, add “not to.”

• “Sit down,” she said
• She told me to sit down.

• “Don’t be late,” he said
• He told me not to be late.

Common mistakes with direct and reported speech

Mistake 1: Tense confusion

If you forget to backshift when necessary, a sentence can become confusing.

Tip: Always check the tense of the reporting verb and backshift accordingly.

Mistake 2: Incorrect pronoun usage

Retaining original pronouns in indirect speech can be confusing and obfuscate the speaker’s identity.

Tip: Always adjust pronouns to reflect the sentence speaker’s perspective.

Mistake 3: Failing to change time/place references

Indirect speech rarely occurs at the same time as the direct speech it’s reporting. Be sure to omit words like today and here.

Tip: Always shift time and location references appropriately.

Mistake 4: Mixing direct and indirect forms

Do not mix direct and indirect speech in one phrase. Here is an example:

• She said “I love it and that she would buy it”

Tip: Stick to one reporting style per statement.

Direct and reported speech FAQs

Can you use both direct and indirect speech in one sentence?

Yes, but it should be stylistically intentional and grammatically sound.

Is backshifting always required in indirect speech?

No. If the reporting verb is in the present, or if the original statement is still true, backshifting may not be necessary.

Can indirect speech be used in storytelling?

Absolutely. Indirect speech can help maintain a formal or narrative tone and reduce repetition.

Are quotation marks ever used in indirect speech?

No. Quotation marks are only used in direct speech.

Is indirect speech more formal?

Typically, yes, indirect speech is considered to be more formal than direct speech. This is especially true in academic and professional writing.

The post Direct and Indirect (Reported) Speech: Rules and Examples appeared first on Grammarly Blog.

As AI advances, colleges and universities must rethink how they assess student work while maintaining academic integrity. At the University of Florida, Professor and Associate Provost Dr. Brian Harfe observed that traditional writing assignments in his large-enrollment general education course were becoming increasingly vulnerable to AI-generated responses. Instead of resisting the technology, he explored how it could be incorporated into coursework in a way that supported student learning. Grammarly’s Authorship functionality became a key part of his personal approach, promoting transparency in student writing while encouraging students to engage critically with AI.

The challenge: Assessing the traditional writing assignment in the era of AI

Dr. Harfe observed a growing challenge in his course: the rapid development of AI tools posed new difficulties for assessing student understanding. Standard essay tasks were at risk because AI could produce content that mimicked student responses, making it harder to assess comprehension and critical thinking accurately.

In response, Dr. Harfe redesigned his end-of-course assessment to include AI as part of the process. Rather than ban AI use, he asked students to generate an initial draft using AI. Students were then required to critique the AI’s output and revise it to reflect their own personal viewpoints and insights. This approach maintained the original student learning objectives while removing the need to police AI usage. It provided a compelling learning opportunity for students to explore the strengths and weaknesses of AI as a writing and thinking tool.

Initially, Dr. Harfe asked students to indicate which parts of their work were AI-generated versus personally authored using a manual color-coding system. However, this approach had limitations. It placed a burden on students and introduced the risk that some might inaccurately represent their own contributions. Dr. Harfe recognized the need for a more reliable and scalable solution.

The solution: Testing Grammarly Authorship in the classroom

When Dr. Harfe learned that Grammarly Authorship was coming to market, he saw an opportunity to build on his early experiments. Authorship could automate the process of distinguishing between AI-generated and student-written text, removing the friction of manual tracking while providing deeper insights into how students interact with AI-generated content. He decided to use Authorship in his course to test both its practical classroom application and its potential as a teaching tool.

Integrated workflow steps:

• Initial AI draft: Students began their assignments by prompting an AI tool to create an essay draft. This helped students structure their thinking and provided an opportunity to evaluate the alignment between the AI’s perspective and their own beliefs.
• Personal modification: Students revised the AI-generated draft to better reflect their own perspectives, adding critique, insight, and analysis. This encouraged students to move beyond the passive use of AI and engage in reflective editing.
• Automated text attribution: Authorship automatically categorized content as either AI-generated or human-written in real time. This eliminated the need for manual color coding and created a more accurate and objective record of authorship.
• Enhanced transparency: Authorship tracked how students used revision and paraphrasing tools and flagged any copy-pasted content from external sources, providing additional context around the student’s writing process.
• Real-time analytics and replay: The tool’s replay feature allowed Dr. Harfe to review the evolution of each submission—watching how students modified AI-generated text and added their own thinking. This created a more complete picture of the student’s learning journey.

Results: Reflections and observations

310 students successfully submitted final assignments using Authorship, providing a large sample of data and student behavior to review.

• Engagement: Broad participation demonstrated student willingness to use AI transparently when given the structure and tools to do so.
• Understanding: 90% of students reported that the Authorship data was easy to understand and interpret.
• Student Insights: 79% found the Authorship report and replay feature valuable in reflecting on their own writing process.

“It’s really important to show students what AI can be used for: the good, the bad, the ugly, and then let them make decisions on when and how they should use it in their future lives. Grammarly Authorship is a tool that facilitates that process for faculty in a more collaborative way with students.”

— Dr. Brian Harfe, Professor and Associate Provost, University of Florida

Lessons learned and future implications

Dr. Harfe’s approach offers a valuable example for other educators exploring how to integrate AI into teaching and assessment meaningfully. His use of Authorship was exploratory in nature, rooted in curiosity about student learning and transparency rather than tool promotion. The insights gained point to broader considerations for how institutions can adapt to a changing landscape.

Key Lessons:

• Adaptability: Teaching strategies must evolve alongside technology. Dr. Harfe’s assignment design embraced AI’s capabilities without sacrificing the course’s core learning objectives.
• Transparency and accountability: Authorship’s analytics and attribution helped reduce guesswork for students and instructors, allowing a more transparent assessment of student effort and engagement.
• Critical engagement and student agency: Structuring assignments to include AI-enabled drafting followed by student revision created space for reflective, critical engagement—giving students ownership over both process and product.

Conclusion

As AI tools continue to evolve, educators are seeking practical ways to adapt their teaching without compromising core values. Dr. Harfe’s course-level experimentation with Grammarly Authorship provides one such example. His focus remained on helping students understand how to use AI thoughtfully, transparently, and in ways that support their own learning.

Connect with Dr. Brian Harfe to learn more about his research.

The post Modernizing the Traditional Writing Assignment: Lessons from One Professor’s Approach at the University of Florida appeared first on Grammarly Blog.

Artificial intelligence (AI) has the potential to drastically transform nearly every area of your business. This isn’t news. Soon, the integration of AI will no longer be an option for businesses but a necessity. Some might find this daunting. But innovative companies and professionals have already been using AI to reshape work. 

AI stands out for its ability to transform business communication by automating and enhancing manual and time-consuming communication tasks. AI can help companies accelerate high-quality content production, gain new insights, scale personalized customer outreach, and enhance overall communication quality. AI isn’t just a technological upgrade. It’s a fundamental shift in how employees communicate and how businesses operate. It’s the new language of business.

The benefits of being fluent in this language are significant. Knowledge workers using AI say it increases their productivity, reduces stress, and lightens their workload. Leaders tout similar benefits for their business, including saved costs, faster pace of innovation, and increased quality of service.

However, your organization will only realize the benefits of AI if all of your employees are equipped to use the technology. Until everyone is empowered to “speak the same language” of AI, you’ll only see limited success—and right now, the majority (78%) of workers say they want to learn how to use AI more effectively for their jobs. 

Read on to learn the fundamentals of AI literacy across the enterprise and its importance in transforming your business. 

Understanding AI literacy gaps across the enterprise

Before we dive in, it’s important to start with the fundamentals. The (near) future workplace will be one in which every employee’s unique skill set will be augmented by artificial intelligence. While some may fear or resist this new technology, the reality is that machines will not replace knowledge workers—instead, they will enhance, automate, and make our lives easier. But this is possible only if every employee can proficiently use AI tools to capture their full potential.

Every employee at your company is likely in a different phase of AI literacy. If your company aims to continue on its path of AI adoption, it’s critical to start by ensuring your workforce has an equitable AI skillset. Here are four key AI literacy definitions that describe where someone may be in their relationship with AI at work:

• AI avoidant: They choose not to interact with AI tools at work. 
• AI familiar: They experiment with AI at work.
• AI literate: They use AI comfortably in their daily work.
• AI fluent: They are using AI in advanced ways that many others are not; also referred to as “power users.”

According to Grammarly’s annual report, The Productivity Shift: From Overwhelm to AI Empowerment, 39% of knowledge workers report using AI at work regularly. Of this group, only 13% identify themselves as AI fluent while 26% are AI literate. The largest cohort (39%) consider themselves AI familiar, meaning that they have experimented with AI but are not yet using it as a part of their daily work, while 22% still avoid AI altogether.

Regular AI usage is more common among business leaders, with 33% falling into the literate category and 30% considered fluent. Only a small percentage (9%) of leaders are avoiding AI technology completely. 

Businesses must not only address AI literacy, but also close the AI literacy and fluency gaps that exist between levels, teams, and generations.

It’s clear that AI experimentation is rampant in the workplace, particularly among younger workers. Notably, Gen Z and millennial generations have embraced AI, with over 80% having at least experimented with AI tools at work. The older generations are far more likely to resist the new technology, with 23% of Gen Xers and a staggering 46% of baby boomers avoiding it altogether.

This rampant experimentation amplifies the urgency and need for AI literacy across the enterprise. If your employees are familiar with AI tools but not using them safely or effectively, it increases the risk for your business. That’s why it is key to invest in proper training and develop formal policies to up-level the skillset of your entire workforce.

Perhaps the most noteworthy AI usage and literacy gaps exist between different teams within an organization. Knowledge workers in sales and customer experience (CX) have been more resistant to adopting AI in their roles. Meanwhile, their colleagues in IT and marketing are mostly literate with AI tools. These gaps must be addressed in order for businesses to reap the benefits of AI enterprise-wide. 

If the usage and literacy gaps go unaddressed, it can lead to inconsistencies in how your business processes are handled and can create bottlenecks in which AI-utilizing departments must wait for others to catch up. For instance, if only the IT department uses AI regularly while other departments such as sales and CX do not, your organization loses out on opportunities for enhanced productivity and innovation. While it’s expected that some teams will adopt AI more quickly, you should aim to ensure that innovation is not siloed but instead used to up-level all teams.

AI literacy vs. data literacy

While AI literacy refers to a deep understanding of AI technologies and the ability to use them effectively, data literacy is the ability to read, understand, create, and communicate data as information. It involves skills in data science, data analysis, interpretation, and critical thinking to make informed decisions. Data-literate individuals, such as data scientists or workers in fields like data analytics or computer science, can gather data, assess its quality, identify patterns, and draw meaningful conclusions. Data literacy is hard to achieve, but AI removes the need for all workers to have this skill since the technology itself has high data literacy.

The importance of understanding LLMs

One critical component of AI literacy is understanding the large language models (LLMs) that AI technology uses to actually generate text. LLMs are trained on vast amounts of data, which allows them to perform the tasks that we ask them to do. There are many different LLMs that are trained on different data sets and fine-tuned to perform certain tasks. Some LLMs may be great at natural language processing, which allows them to generate text when asked a question. Others perform better at coding tasks, and others are better suited for translation assistance. 

The foundation of an LLM is its training data. This training data could be vast amounts of public text gathered from the internet or it could be proprietary data sources. Both the volume and the quality of the data that each LLM is trained on impact how that LLM will learn. The more high-quality data, the better the LLM becomes at predicting human language patterns, generating contextual and relevant responses, and performing the specific tasks it’s been fine-tuned to perform.

Two (of many) LLM behaviors to be aware of:

• Biases: If an LLM is trained on unreliable data, such as massive amounts of text data from the internet, which is subject to societal biases, it can reflect or amplify existing prejudices found in its training data.
• Hallucinations: Receiving a seemingly perfectly crafted answer from AI models may sound ideal, but LLMs can create outputs that sound confident and reliable but are actually false or misleading.

Understanding the basics of LLMs is essential to AI literacy because effective use of AI requires you to be aware of its capabilities so that you know when to use certain LLMs for the task at hand. Responsible use of these tools also requires you to be aware of their behaviors so that you can spot potential biases and inaccuracies and actively work to avoid them.

The compounding effect of AI literacy

It’s no surprise that effective communication contributes to both individual and organizational success, but the impact is significantly amplified for those who are comfortable using AI in their workflows. According to The Productivity Shift, AI-fluent workers report significantly higher productivity (96%) and work satisfaction (96%) compared to their AI-avoidant peers (82% and 81%, respectively). The benefits extend to relationships, with 95% of AI-fluent workers reporting improvements in their interactions with colleagues and customers. These findings highlight the powerful synergy between effective communication and AI adoption in the workplace.

AI is transforming workplaces by delivering benefits far beyond improved communication. From boosting productivity (93%) and reducing workloads (91%) to enhancing creativity (91%) and work satisfaction (89%), AI empowers workers to focus on what matters most. Additionally, AI reduces performative communication (86%) and fosters softer skills like empathy (76%), creating an environment where employees can thrive. 

The C-suite recognizes that the productivity gains from effective communication and AI amplify business results. The vast majority of C-suite leaders report significant impacts on key outcomes, including increased revenue (90%), higher customer satisfaction (97%), and faster innovation (95%). These results demonstrate how improving communication with AI not only drives workplace productivity but also enhances overall business performance.

Enabling an AI-literate workforce

AI will fundamentally change the way we communicate at work. If your workforce does not invest in AI literacy programs and address these skills gaps, you will be left behind. This is not just a technological upgrade. It’s a fundamental shift in how businesses and employees operate. The learning curve might seem steep, but it is achievable. 

Now is the time for leaders to assess their progress toward enterprise-wide AI and invest in ubiquitous AI tools that their entire workforce can leverage. Regular interaction with AI applications can naturally enhance employees’ understanding and comfort with these technologies and foster a more literate workforce. These tools should be embedded into employees’ daily workflows so they can learn to use them in the most productive and effective ways.

When your workforce is comfortable with AI and capable of leveraging a suite of AI tools to drive innovation and efficiency, you will be well on your way to achieving a full business transformation.

The post The Critical Role of AI Literacy Across the Enterprise appeared first on Grammarly Blog.

As AI continues its rapid integration into the enterprise, customer experience and support teams are navigating a distinct set of challenges. While experimentation is often encouraged in other functions, CX operates under different expectations: precision, empathy, and virtually no margin for error.

In our latest Two Truths and AI fireside chat, CX and support leaders came together for a candid conversation about what’s working, what’s not, and how AI is reshaping our understanding of productivity, trust, and team enablement.

5 Takeaways for CX Leaders

1. “Productivity is about having a very focused impact with minimal friction.”

Redefining productivity in CX starts with outcomes, not activity. Customer-facing teams are often measured by how much they do: response times, ticket volume, hours logged. But real productivity comes from progress on what matters most—delivering standout customer experiences, solving root problems, and building trust. That kind of impact demands ruthless prioritization, clear alignment, and an environment where issues can be surfaced openly.

To move in that direction, leaders should examine how surface-level metrics connect to customer and business outcomes. As AI accelerates workflows, it’s essential to ensure increased speed translates to better results. Monitoring performance is key—but so is inviting open feedback from teams to uncover risks, friction points, and opportunities for deeper impact.

Read more: How Leaders Measure AI’s Success

2. “The fear of getting it wrong is real.”

CX teams are adopting AI more cautiously—and for good reason. According to Grammarly’s recent annual report, AI adoption in CX is slower than in other functions. The hesitation makes sense. Customer interactions require emotional nuance and precision—areas where AI still feels unproven. Combine that with fragmented tools, strict compliance requirements, and often nebulous AI use policies, and CX reluctance becomes rational.

To unlock adoption, CX leaders can start by reducing fear. Broad access to new AI tools is helpful, but it needs to be paired with clear guardrails. It also requires safe spaces to experiment, whether through sandbox environments or low-risk internal use cases. Lastly, you need to normalize the learning curve. Showcasing and celebrating early adopters can support here, turning AI power users into ambassadors who help upskill the larger team. 

3. “Take away the tedious so teams can focus on what matters.”

AI should elevate the human side of CX, not replace it. The most valuable AI use cases in CX don’t have to be flashy—they can be practical. Automating meeting notes, surfacing account insights, or simplifying access to customer data frees teams to focus on the work only humans can do: listening, empathizing, and building real relationships.

The goal isn’t automation for its own sake. It’s about reducing busywork so your people can show up with more presence, clarity, and creativity. And like any good tool, AI still requires human judgment, especially when the stakes are high. Teach your teams to treat AI as an assistant, not an authority.

Read more: The Global CX Communication Playbook

4. “You can’t scale what people don’t trust.”

Governance, clarity, and enablement drive adoption at scale. Trust is the foundation of every customer interaction—and it’s also the foundation for AI adoption. Without confidence in how data is handled and what the tools are doing, teams hesitate to engage. The most successful organizations aren’t just giving their teams tools; they’re also giving them confidence in the tools they use. You can build trust with enterprise-grade protections like data loss prevention and encryption, and by making your policies clear from day one. In addition, offer role-specific training that goes beyond how-to guides and speaks to real-world use cases.

Learn more: Grammarly’s Answers to Your AI Vendor Questions

5. “If AI adds complexity, it’s not helping.”

Great CX depends on simplicity, and so should your AI strategy. Customer experience work is inherently fragmented. Teams bounce between platforms for CRM, support tickets, adoption metrics, and communications. AI, if not thoughtfully deployed, can add to that chaos—especially when tools live in silos or come with steep learning curves. 

Before bringing in a new AI solution, ask: Will this reduce context switching or add to it? Can it connect to the tools we already use? Will it simplify work, or just shift it elsewhere? The best AI doesn’t just solve problems—it removes friction, connects workflows, and helps teams stay in flow. 

CX is where your brand’s promise meets reality. And in a world of rising expectations and tighter budgets, doing more with less isn’t just a goal—it’s a mandate. The opportunity before us isn’t just about automating tasks. It’s about creating the space, clarity, and confidence teams need to deliver work that truly matters.

The post Real Talk on Productivity, AI, and What’s Working in CX appeared first on Grammarly Blog.