A CMO's Guide to Responsible & Results-Driven AI

How to start a pilot, scale out the program, and achieve ROI


Welcome, marketing leaders, to our guide on how to embrace the power of generative AI within your team.

As artificial intelligence continues to revolutionize various sectors, marketing and the professionals that practice it stand to gain immensely from these technological advancements.

This guide aims to demystify the concept of generative AI and its potential to transform marketing strategies across all its functions and role levels. By harnessing the capabilities of generative AI, marketers of all levels can unlock unprecedented opportunities for creativity, on-brand content personalization, team management, evolved metric analysis, & customer engagement.

Whether you're a curious AI novice looking to explore what’s possible or a seasoned, tech-savvy experimenter looking to stay ahead of the curve, this guide will provide valuable insights and practical strategies for leveraging generative AI in responsible ways.

We cover a lot in this guide: responsible adoption standards, risks, how to run a pilot program, common AI definitions, limitations, and use cases by team and industry. We'll also give you a roadmap to develop an AI council that can help your team or your entire company adopt AI in a controlled and productive way.

Let's begin.

Section 01

Planning & running an AI pilot

Adopting AI in your organization should begin with a pilot phase. Running a pilot program can give you a sense of how the technology can be used to drive business goals, increase revenue, and create a better workflow for your teams in a controlled way. Here’s how to set one up.

1. Set guardrails at a company & team level

You should set guardrails and standards around how, when, why, and where AI is going to be used before you start experimenting with it. Set guardrails at the company level and others at the team level.

Company-level guardrails should include policies for security and technology adoption. For example, you can say that every AI tool within the business must have SOC2 compliance and clear data protection processes in order to be used. This will reduce the number of tools available to you but set a baseline for security that the whole company can follow.

At the marketing team level, you'll want to set guardrails about the ways team members get approved to use generative AI. For example, you may be comfortable with them using AI to repackage human-made content, but not comfortable using it for performance evaluations.

Read more about this in the section: Founding an AI council and establishing guardrails for responsible use.

2. Build a dedicated team to run the pilot’s operations

Create a cross-functional team of experts to spearhead the pilot’s operations and make sure it runs smoothly from inception to completion — aka a tiger team. The members of this team should be well-versed in different aspects of the organization's operations. They’ll be responsible for setting the pilot’s objectives and KPIs, laying out the pilot step-by-step, orchestrating “on the ground” operations of putting the tool in use, and measuring the outcomes according to the KPIs.

By leveraging their diverse expertise, the AI tiger team can swiftly troubleshoot issues as they arise and ensure the project stays on track. And if all goes well, they can effectively scale the implementation across the organization based on the insights and lessons learned from the pilot phase.

3. Choose your lead use cases

AI can be used in a myriad of ways. But to understand its impact, it helps to start by focusing on one to two use cases, maximum. This can be turning ebooks into multi-channel campaigns or optimizing blog posts for search.

Choose something that’s currently causing friction or slowing your team down and focus the technology on alleviating that problem. Run A/B tests on outcomes that use AI and those that don’t and monitor differences in efficiency and quality. Narrowing the pilot’s focus offers some initial perspective on both its impact and how well it's received by your team before the technology is rolled out everywhere.

4. Find the right AI copilot for the job

We encourage using a copilot or AI platform that is purpose-built for marketing rather than using a single LLM or a more general chat tool on its own. Investing in a platform that your entire team can use as a collaborative back-bone will help you set a great foundation for scaling AI out of the pilot phase and into widespread adoption.

As you can expect, our bias here is toward Jasper because we built it specifically for enterprise marketing needs. Past content creation, we prioritize features like team collaboration tools, a company knowledge hub for outputs that reflect your brand and product intel, and analytics and insights that can help you measure the impact of your team’s efforts.

5. Run your pilot for no more than 1-2 months

The goal of a pilot program should be to test a hypothesis then prove or disprove it. Creating a schedule for reviewing those results and assessing next steps can help ensure you get clear outcomes and get the most out of this learning opportunity.

We recommend running the pilot for one month then performing a formal post-mortem assessment to review the results. If more time is needed, adding a second month with more tightened criteria for your desired results could also be useful. Running the pilot for too long introduces more room for error, which could dilute the learnings from the pilot.

Section 02

Moving from piloting AI to full adoption

The majority of marketing teams have started experimenting with generative AI in some capacity. But few have made it past the pilot phase to responsibly integrate it as a key part of their overall operations and strategy.

A significant hurdle here is integrating AI into existing systems and workflows. The transition from pre-AI operations to post- can disrupt long-established processes and necessitate retraining, which require time and cultural shifts. It can help to think about full adoption of AI in your marketing team as a learning curve with three phases: individual acceleration, team acceleration, and business acceleration.

Most companies start by equipping individual members of their team to use AI in their workflow. For example, enabling a content marketer to use AI in writing blogs. In this phase, you may see individual efficiency gains scattered throughout your team. Individual efficiency gains are nice, but not the full extent of what AI can do for your company.

A graph showcasing where users can be on the AI Adoption curve

How do you move from individual efficiency to team efficiency gains?
It begins with aligning your full team on a single copilot and infusing that tool with your brand voice, style guidelines, company, and product knowledge. From here, your copilot becomes the backbone of your team ensuring alignment in message, tone, and style. In this phase, team members can work cross-functionally on a campaign with the AI underpinning their outputs.

By collaborating via a copilot like this, you move from individual efficiency gains to a better cross-team workflow, potentially reducing the time involved in content review cycles and cross-team collaboration.

Getting to the final phase, accelerating business results, requires bringing analytics and automated optimization of your content into the mix. Do this by leveraging AI to spot patterns in the performance of your team's marketing content and campaigns, and facilitating the rapid optimization of your outputs to produce better outcomes.

Section 03

Getting to AI ROI

Much like the adoption path, you'll want to think about measuring ROI along a continuum. Most companies start by measuring time-savings and a reduction of cross-team friction. Almost immediately you'll be able to translate those time savings into key performance indicators for the marketing team. For example, if time savings enable you to get a campaign to market two months earlier, you'll have two additional months of leads, sign-ups, purchases or conversions.

As a next stage of evaluation, you can look at performance improvements garnered through AI optimization. For example, analytics platform Amplitude noted that three weeks after starting to use Jasper, their content rose to the first page of search engine results. Companies using AI to recognize and surface patterns in performance can credit future performance increases to that investment.

Getting to ROI is an important step in the adoption process of a copilot for your company and should be incorporated into your tech stack decisions and adoption milestones.

Section 04

Limitations & risks of generative AI

Like any piece of technology, generative AI is not perfect. Here are three of AI’s biggest flaws that you should be aware of before adopting it across your marketing team or your company.


AI systems learn from the data they’re fed. Since that data is often made, collected, and/or organized by humans who sometimes have biases, LLM training data may unintentionally have biases. Many language models have filters to reduce the risk of bias or harmful outputs popping up, but filters aren’t enough. It’s a marketing team's responsibility to ensure that they review content for potential biases and that all their work is inclusive and accessible.

Inaccuracies or hallucinations

Inaccuracies or hallucinations (when the generative AI platform makes things up that are untrue, seemingly out of nowhere) can occur in AI-generated content due to a number of factors. For example, the training data used for the AI model may not be comprehensive enough, leading to gaps in knowledge and potential errors in output. AI models can also struggle with understanding context and nuance, resulting in inaccurate or irrelevant responses. Another factor is the limitations of current AI technology, which may not be advanced enough to fully understand complex human language and generate completely accurate outputs.

Overall, it’s important for marketing teams to carefully review and fact-check all their AI-generated content before sharing it with audiences to ensure its factuality. Failing to do so can impact your brand’s reputation with your audience. See our 9 tips for AI content editing later in the ebook for insight on how to approach this.

Data privacy

Make sure that you are using an AI copilot that has SOC2 compliance and meets other high security standards for how it handles data. Jasper passes SOC2 and GPDR compliance and we offer single sign-on, or SSO, for password management. We also don’t train underlying LLMs on any information submitted to Jasper to protect our customers' IP and data. Additionally, inputs to Jasper are never used to train underlying LLMs and we have a dedicated security team to ensure our systems stay trustworthy.

Not all tools have these high standards, so make sure you set security standards for the use of AI technology (see below) to protect your data.

Section 05

Founding an AI
council & establishing guardrails for responsible use

The ultimate impact of AI technology is measured by ROI just as much as it’s defined by how safely and responsibly it’s used. Establishing a dedicated AI council to address some of the technology’s biggest benefits and concerns (like the limitations we just mentioned) is a great first step to ensuring efficient and safe use. We’ll outline how to get an AI council started and some of the major guardrails the team should consider establishing before diving into company-wide AI adoption.

Creating a council

First, you need to assemble the team that makes up the council. Ask around and find volunteers at your company who were early adopters, experimentalists, and thinkers who ponder AI’s ethical questions. Try to ensure there's representation from different parts of the organization and different role levels. Consider getting a formal executive sponsor who can help align leadership and offer additional resources. Also, engage your legal team from the start; it’s way easier to navigate legal waters when they're on your side from the beginning.

With your council assembled, start asking the big questions:

  • Is this a journey your company is ready to embark on?
  • What’s the company's collective stance on using AI tools like Jasper or others?
  • What boundaries do you want to set?

Understanding these facets will help you draft your mission.

That mission can be dependent on your business and your goals, but having an example might help. Jessica Hreha, head of global networking and network security marketing program at VMware, founded the AI council at the cloud computing company. Regarding VMware's AI council mission, Hreha said:

“Our focus is educating & empowering marketers. It wasn’t just about ensuring that marketing is leading internally and that VMware is leading externally, but that we're offering an opportunity for our individual marketers to upskill and take advantage of this transformational opportunity.”

Headshot, Jessica Hreha

Jessica Hreha

Head of Global Integrated Campaigns Strategy
VMWare logo

Now, congratulations are in order — you've just built your AI council! But remember, this is only the beginning of your AI journey. You'll need to continually reassess, learn, and adapt. Things like newsletters, AI summits, and workshops on use cases can be a great way to keep everyone engaged and aligned on AI's place in the business.

From this point, you can start diving into how to align on the best guardrails for responsible use across some of the following key areas:

Security standards

Research from Salesforce shows that more than half of generative AI adopters use unapproved tools at work. This is a big problem because not every AI tool treats data security the same way. As part of adopting AI, you'll want to set some company-wide standards for safely using the technology that are acceptable for use inside your company and have mechanisms to enforce those standards.

For example, you should set a requirement that all AI technology you use must have SOC2 compliance and data privacy protocols. You should ensure that your data and IP are not being used to train the underlying  model. You should look to regional requirements like EU laws and California Data protection laws to ensure your technology is set up well to meet individual regional requirements as well.

Standards of use

AI can be used in countless ways, but that doesn't mean using AI is the right decision for all things. As a company, especially in the piloting phases of AI, set and communicate standards for which AI use-cases are approved and which types of work should not leverage AI. You may, for example, decide that you’re comfortable using AI to remix existing content into new formats, but not to write something brand new.

Alternatively, you could decide that you’re ok using AI for marketing but not for internal performance evaluations. Have the use-cases discussion and set some guardrails for your company.

A transparency statement

AI is still new in terms of its public awareness; we’re collectively still getting used to it being everywhere. So it’s safe to assume that many people now are not only asking but investigating the idea of “Did AI make this?” when they see content. And that isn’t a bad thing.

We recommend including a transparency statement on your website on your use of AI in marketing materials. Deliberate transparency on AI use is a key part of how an enterprise business or marketing team can foster and maintain trust with their audience. Curious customers are sure to appreciate it as AI becomes more prevalent.

This transparency not only demystifies AI but also reflects your commitment to ethical practices. By openly sharing your AI utilization, you are essentially inviting your audience into a conversation, making them feel included and valued. Furthermore, this openness can boost your brand's credibility and foster stronger relationships with your audience. Remember, trust is the foundation of any successful business relationship, and transparency is the most direct route to gaining and maintaining that trust.

Below is an example of an AI disclosure. Feel free to take a look at our ethics page for even more inspiration.

Example transparency statement:

“We use AI to assist in some content development at our company. To ensure transparency, accountability, quality and privacy, we adhere to internal AI usage standards. These standards help us safeguard against biases, maintain data security, and uphold our commitment to ethical marketing practices. One of these standards is that AI should be used to assist in content creation, not fully automate it. We ensure that every piece of content we develop is shaped and reviewed by people who have an understanding of our audience and AI’s limitations.”

Require human involvement & oversight

No one wants to read content that sounds robotic and stilted, especially from a brand trying to market its services. So while AI has the potential to streamline processes and increase efficiency, marketing teams need to maintain a balance between automation and human oversight to make content feel more, well, human.

Solidify your editorial process to ensure that humans are always at the helm, overseeing and editing, when it comes to everything from idea origination to publishing AI-assisted content. Setting tight standards and reviewing every AI output ensures your content is accurate and unbiased. It also ensures that your content sounds natural and provides real value to your audience.

At Jasper, we don’t publish anything that’s been wholesale created by AI — there’s always a human in the loop and thorough editing; we recommend that our customers follow the same guidelines.

We actually developed a list of key tips to keep in mind when editing AI content. Check out the list on the next page, but be sure to read the full article to get even more context on how to spot AI content and why these editing and discernment skills are vital today.

Section 06

Definitions of key AI terms

Since generative AI exploded in popularity, keeping up with the terminology can feel like learning a new language. What is the difference between an LLM and a RAG? How does multi-modal differ from multi-model? Here are some common terms you may come across as you learn more about AI and its capabilities.

Generative AI

Generative artificial intelligence, often referred to as generative adversarial networks or GANs, is a specialized branch of artificial intelligence. Its primary goal is the creation of intelligent machines that possess the capability to generate new pieces of art or writing, perform speech recognition, make decisions, and translate languages. This form of AI operates by learning from extensive volumes of text and data, enabling it to produce meaningful insights, strategies, and predictions. In the context of content creation, generative AI accelerates content creation processes in many ways for marketing teams.


A copilot is an artificial intelligence platform specifically tuned to a user or company. Core to a copilot’s features are memory, personalization and a tailored collection of skills. Users train the copilot by securely uploading or connecting data and company intelligence into it. After learning from a company’s core details, the copilot can tailor its outputs and prescribe strategies informed by those memories. The user can then tap into any number of skills to access that tailored intelligence.

Jasper is a marketing copilot, so it’s specifically trained on the past performance, brand standards, and company intelligence of the marketing team using it. All that training works both upfront and in the background to assist marketers as they work to create higher performing content that’s reflective of their company and its brand.

Large language model (LLM)

A large language model, or an LLM, is a kind of artificial intelligence that can learn from large amounts of text and data (and by large, we mean hundreds of billions of data points in some cases) to generate meaningful insights, strategies and predictions. Examples of large language models are OpenAI’s GPT-4 and Anthropic’s Claude 2.

Jasper's Art output capabilities make it a multimodal tool


Multimodality refers to the ability of a copilot or AI tools to generate outputs in multiple formats, like words, code, images, animations, and audio. A tool only needs to produce outputs in two or more formats to be considered multimodal, but the more formats the better.


Interoperability, also referred to as multi-model, within a copilot is the capacity to operate across a number of large language models regardless of whether they’re open or closed, large or small, text or visual. Interoperability is a positive thing for enterprises because having many LLMs at your disposal leads to greater reliability, flexibility, and diversity of strengths.

Other common LLM-related terms

  1. Context stuffing:
    a type of step-by-step prompt improvement where you run an initial prompt, take the best parts of its output, and add it to the original prompt to provide even better outputs, repeating as necessary. 

  2. Model training:
    the process of giving a large language model large amounts of training data points to learn from.

  3. Open-source:
    something that is publicly available. An open source LLM would be source data that anyone can access publicly, such as Meta’s Llama2 model.

  4. Parameters:
    LLM parameters, which can number in the billions, shape an AI model's behavior by influencing its comprehension, generation, and contextualization of language. Adjusting these settings controls the quality, diversity, and creativity of the generated texts.

  5. Prompt injection:
    the process of updating the output of a language model through prompting. Used in the context of cybersecurity, this process involves using carefully crafted prompts to make an LLM ignore its training and break the rules/bounds of what it’s allowed to say.

  6. Grounding:
    infusing large language models with use-case specific information, like one’s own data, to enhance the quality, accuracy, and relevance of outputs. It tailors the vast, yet limited, knowledge of an LLM to specific scenarios. Retrieval-augmented generation is a common way LLMs are grounded (see #13 for more insight.)

  7. Fine-tuning:
    the process of training an existing LLM on new data or to deliver outputs associated with specific tasks or topics.

  8. Function calling:
    a way of getting structured outputs from an LLM by providing specific layouts that the LLM can stick to.

  9. Inference optimization:
    a way of compressing information in order to improve LLM performance and speed.

  10. Langchain:
    a framework that helps simplify the creation of applications that use LLMs. This is also known as a language model integration framework

  11. LoRA (Low-rank adaptation): 

    Instead of fine-tuning an entire LLM with potentially billions of parameters — which can be very expensive and time-intensive — LoRA fine-tunes only a small part of it and can reduce trainable parameter size by a factor of 10,000 in some cases. This method is cheaper, faster, requires smaller GPUs and gives the same performance as fine-tuning a full model.

  12. P-tuning:
    Also known as “prompt tuning,” this involves using a small, easily trainable model prior to using an LLM. By doing this, the smaller model can complete small tasks, resulting in better outputs and more efficiency like time savings.
  13. Retrieval-augmented generation (RAG):
    This technique, used to ground an LLM, pulls in data from an outside source to improve the AI’s accuracy and reliability.
  14. Reinforcement learning from human feedback (RLHF):
    improving a model based on human feedback. For example, if a user gives an AI-generated output a “thumbs ups” and the model uses that feedback to improve, that is an example of RLHF.

  15. Direct preference optimization (DPO):
    A newer version of RLHF — it’s more stable and removes the need for a reward model. This is good for when you need a custom reward model other than user preference.
Section 07

Use cases for AI by general skill


By responding to your initial idea and expanding on it, AI can assist you in the ideation process. Give your copilot an idea and ask for variations on that theme. Dig deeper by asking for a counter-argument or new way of looking at that idea. Prompt the platform to think of ways to turn that idea into a blog post, infographic, video, Instagram post, or some other medium.


Some AI copilots, like Jasper, are connected to the internet through integrations with Google and other ways of infusing more recent data. Ask your generative AI tool to cite sources or research a particular topic to help you gather information quickly. But (!) remember: Not all AI responses, even in platforms that have connections to data sources, can be trusted. Leverage the access to more recent information so you have up-to-date intelligence, but always verify the outputs.


Use AI to summarize large volumes of content. For example, you can use Jasper to consume a transcript from a customer interview and surface the key themes. Or condense a lengthy, highly complex, and technical blog post into a series of easy-to-digest bullet points written in your preferred style.

Expand your ideas

Turn a single bullet point in a campaign brief into an outline for a blog post. Tell AI to find a common thread across a collection of thoughts you conjured during a brainstorming session. AI’s capabilities as a thought partner are vast and sometimes only limited by our imaginations.


AI can take a full page of research bullet points on a topic and structure them into a cohesive, narrative-driven outline for a blog post. Once the post is written, AI can help you structure it better for search engines, add schema markup to appear in Google snippets, and more.


One of the best uses of generative AI is getting more value out of existing content by remixing it for different formats and audiences. Turn a marquee blog post into an end-to-end campaign or a successful webinar into an email series. Input a pdf, for example, and get an output of a campaign’s worth of new content.


Most AI tools are trained on many of the most wide-spread languages. So the technology can be used to adapt content to different regions and translate into different dialects. Just be sure to thoroughly review those translations so you don’t say “steak holder” instead of “stakeholder” as you introduce yourself to a new market.


AI can easily help you adapt any piece of content to different audiences. Finalize an asset for an executive audience then, in one click, refashion it for an audience of highly-technical individual contributors who enjoy How I Met Your Mother or Bad Bunny. Get as specific with your audience as possible and don’t be afraid to use AI to repurpose content for any and all your personas.


Use AI to create a common backbone for work developed across your organization. Build a robust knowledge base on key company and brand information that’s part of the central nervous system of your AI tool. Then your team’s content will be automatically infused with the correct standards and details of your business. Ask AI to rewrite/update existing content with newer details added to your knowledge base and style guide to correct outdated product information and other inconsistencies.

Copilots like Jasper also allow you to manage your team’s content. See the status of all the content in production across your team’s campaigns and streamline the review process with AI summaries of comments.


By using AI with analytics, you can get insights on both the patterns in content that’s performed successfully and the errors in content that has performed poorly. Essentially, you can turn your analytics into a story of how your content is performing rather than a series of charts and numerical data you have to make sense of. Take a look at Jasper's Analytics & Insights features to see this in action.

Use cases for AI by marketing team function

Chief marketing officer

As a CMO, you might not be developing traditional marketing content on a daily basis anymore, but you are creating daily content in one way or another. With strategic planning, important presentations, and team performance management on your plate, AI is here to make your job easier.

  • Strategic planning: 

    AI can analyze large amounts of data to identify trends and patterns which can inform strategic decisions. It can predict customer behavior, market movements, and the effectiveness of various strategies, aiding in the creation of more targeted and effective marketing plans.

  • Content generation:
    With AI, you can automatically generate content, saving your team valuable time and resources. It can be an assistant to help you create impactful presentations for the other leaders in the business or for the board.

  • Performance analysis: 

    AI tools can provide deep insights into your marketing performance, allowing you to tweak and optimize your strategies based on real-time data. This can lead to better ROI, more effective marketing campaigns, and understanding the ins and outs of your team's content struggles.

Product marketing

Product launches:
In product marketing, everything starts with a core positioning document. Upload or connect that positioning document and any research you've done to your AI copilot and ask it to spin up the first draft of a product launch campaign. Then tag in members of your team to help refine it for publishing. This AI-enabled process will help get your product announcement to market faster while keeping all your launch materials closely tied to that core positioning document.

Adapt to different positioning:
Maybe you need to adapt your product marketing positioning to suit different audiences but you want to make sure that the core positioning is consistent throughout. Use AI to adapt your positioning for different buyers, users, and markets while keeping the product details and identity whole.

Sales enablement

Competitive positioning:

Store competitive research in your marketing copilot and tap into that with a prompt anytime you get a question about comparing your company or product to a competitor's. 

Quick one-pagers: 

As much as we love our sales teams, their requests for one-pagers can seem endless. Using your marketing copilot, store key information on your products, features, and audiences to accelerate the development of these one-pages. Or better yet, enable the sales team to use the copilot to create their own whenever they need them. 

Objection handling: 

Use AI to practice handling objections that your sales team gets on calls. Ask your copilot for options for responses and blend it with your own company knowledge to produce persuasive sound bites.

Synthesizing demo feedback: 

Use AI to read and synthesize key points from your demo transcripts to organize buyer feedback and improve your sales process.

Content marketing

Content marketing is often the team you think of first when it comes to generative AI. The technology can help in a huge number of ways: outlining blog posts, breaking through writer's block, writing meta descriptions, research, writing introductions (the hardest part of any blog,) summarizing content, and a lot more.

Video production

Tools like Wistia and Descript can help you leverage AI in the video editing process by optimizing sound and visual balance, or by creating quick cuts for promo on social media. Emerging text-to-video tools like RunwayML can also be useful to develop quick background videos or concept boards.

Performance marketing or advertising

Create variations:
Performance marketing is about testing and optimization. Use AI to turn one ad into a dozen variations. Your team can then quickly review and decide which ads to launch.

Adapt to different ad platforms:
There are seemingly endless channels to advertise on these days with more growing every year. Email, banner ads, print, every social media platform, even the YouTube ads we all try to skip after 5 seconds all have a unique advertising style. Use AI copilots to create ad copy that follow best practices for each platform, including appropriate tone and length.

Crisis or surprise communications

Reacting quickly and getting precise language out across many channels is key to crisis communication, jumping on a trend, or generally releasing a message that wasn’t planned the day before. Use AI to store your core message about a situation and quickly turn that into email communications, social messages, soundbites, and whatever else your team needs to put out a virtual fire, post your take on a viral meme, or get a message out quickly. Sweat rags not included.

Website design

Integrate AI directly into your CMS to quickly develop content for product pages, landing pages, and more in the space where you publish. What’s easier than that? 
As an example, Jasper is integrated with Webflow in their Apps marketplace. It can also be pulled into other content management systems like Wordpress or Medium through the Jasper API or our browser extensions.


Generative AI can absolutely assist in SEO operations. These tools can identify the most important keywords and phrases from content you feed it and use them to create accurate and effective meta descriptions. Alternatively, if you have a list of keywords but don’t have the content, you can spin up blog new posts with a few solid prompts.

AI can also analyze an existing web page and automatically generate the appropriate schema markup, which boosts its ranking on searches.

Organic social media strategy

Content planning and development:
AI can help you turn a loose idea for a social media campaign into a concrete play-by-play (post-by-post?) full of copy, imagery, and relevant hashtags. Give the tool your ideas for copy and imagery based on the target platform and audience, then edit the results, and watch your engagement rise. And again, with AI analytics, you can see exactly how those posts are performing and get an AI summary of what your next move should be.

Use cases for AI by industry

white and brown house near green grass field under white clouds and blue sky during daytimeby Ronnie George


B2B companies are master content creators when it comes to lead generation. They go all-in with inbound content marketing and rock multichannel content campaigns that lead to awesome conversion points like webinars or ebook downloads (just like this one!). We've got some of the coolest use cases to get you started! 🤩

  • Assisting in ebook production
  • Turning a webinar transcript into follow-up content
  • Campaign production and coordination
  • Structuring content for search engine optimization
  • Creating ad and email variations to test
  • Sales enablement and competitive positioning


Consumer marketing relies heavily on social media, advertising and email marketing. Here are some starting use cases for these industries:

  • Importing product details and turning them into product descriptions in moments
  • Adapting a central email campaign to different audiences
  • Creating ad variations for testing
  • Pulling influencers into a central knowledge base for on-message promotions
  • Remixing content across social channels

Media & publishing

Media and publishing companies can leverage generative AI in several ways to streamline their operations and repackage their content. Here are some of the ways we see AI being used in media and publishing.

  • Assisting in promotional materials for a book launch
  • Developing headline variations for ad copy or articlesIdentifying inconsistencies in grammar usage and suggesting syntax improvements.
  • Localizing content into various languages while maintaining the tone and context
  • Repackaging core reporting into new formats for greater reach
  • Utilizing key terminology that could negatively impact your social accounts' reach

With media and publishing in particular, transparency about where and how AI was leveraged is important, and having human-led oversight and editing is absolutely crucial. As with all fields, AI should be an amplification, not a replacement of human creators. And stay tuned! We have more industry-by-industry play-by-plays on AI’s potential impact coming soon.


The future of AI in marketing awaits

As we bring this guide to a close, it's essential to remember that the future of marketing lies within the dynamic interplay between human creativity and AI's potential. Generative AI is an incredibly powerful tool that can augment our abilities, enhance efficiency, and drive innovation. However, its success is heavily reliant on the human touch — from idea inception to the final review, maintaining quality, authenticity, and connection to our audience.

Our hope is that this ebook provided you with a valuable overview of how AI can serve your various marketing needs and ushered in fresh perspectives on harnessing AI in your marketing strategy. The journey to AI adoption may be a novel one, but with careful consideration and strategic planning, it promises a future rich with potential. Remember, the goal is not full automation but collaboration between humans and machines (but humans are always in the driver’s seat.)

Welcome to the future of marketing!

Interested in giving Jasper a try?

Sign up for a demo and our AI Experts would be happy to walk you through the platform!