AI for Marketing in 2026: What To Use, What To Skip, and What To Watch Next

AI is moving faster than any technology shift marketing has lived through. If you're a leader who feels like every peer has it figured out, don't panic. The majority of leaders and teams are still out there learning and experimenting, and making mistakes along the way. 

Wherever you are with AI right now is a great place to start. This page is built for every stage of understanding, whether you're already creating custom GPTS, running agents or you've never opened ChatGPT. 

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What Is AI for Marketing?

AI for marketing means using artificial intelligence to improve both how your marketing team works and what your marketing produces. Most people only see one half of the picture: blogs drafted in seconds, AI-generated graphics, and chatbots on your website.

While helpful, there's much more to successfully using AI in your marketing department. 

Some of the highest-value uses of AI are internal and invisible to your customers, like a notetaker that captures every sales call, a system that organizes your shared drives, or the documentation of processes no one has written down.

A team that embraces AI has the potential to not only produce better customer-facing work, but also enable employees to optimize their personal processes, so they can spend more time on valuable tasks and less on the mundane.  

Used well, AI helps marketing teams:

• Run more efficiently through better internal processes. 

• Create better, more personalized content at a higher volume.
• Understand buyers by finding patterns in your own data.
• Repurpose existing content across new formats with less effort.

Where you start depends on where your team is today.

But it's not about picking the right tool. It's about building AI literacy to improve your digital marketing strategy. That means getting comfortable enough with one platform to recognize when it's right, when it's wrong, and where it's confidently making things up.

Harvard instructor Christina Inge has called AI, “both a challenge and an opportunity for those in marketing.” And we think the challenge is well worth the opportunity, indeed.

How AI Has Changed the Way Buyers Discover and Decide

Like every major shift before, buyers are choosing what’s fastest and easiest. To stay visible, your content strategy must adapt to how buyers now ask questions and how AI delivers results.

The Shift in How People Search

Search isn't being replaced. It's evolving. ChatGPT now has 900 million weekly active users, and AI search accounts for roughly 56% of global search volume. At the same time, 95% of Americans still use traditional search engines. Buyers are layering, not switching. 

Traditional Google search doesn't even look the same. AI Overviews appear on almost half of all searches as of early 2026, and on mobile, they take up nearly half of the screen. Many searches now produce an answer before a buyer sees a blue link. 

Buyers are searching across more platforms and getting answers faster. Your job is to make sure that your company is part of those answers. 

What Modern Search Optimization Actually Looks Like

"SEO" used to mean optimizing for Google's ranking algorithm. The discipline has widened. You'll now see three acronyms used together:

• SEO (Search Engine Optimization): ranking in search engine results pages
• AEO (Answer Engine Optimization): showing up in direct-answer features like AI Overviews and voice search
• GEO (Generative Engine Optimization): being cited inside generative AI responses from ChatGPT, Gemini, Claude, and Perplexity

*You'll see that AEO and GEO are often used interchangeably, but often mean the same thing: showing up wherever AI is making suggestions. 

Most of what works for one works for the others, but the old keyword-stuffed, search-engine-first approach is dead. What replaces it is content that is genuinely helpful, structured cleanly, and demonstrably credible.

Both Google and AI search systems weigh E-E-A-T heavily when deciding what to cite or rank.

What is E-E-A-T? 

E-E-A-T stands for Experience, Expertise, Authoritativeness, and Trustworthiness. Google made it a core part of its quality rating guidelines, and AI search systems still valuable those as signals for content worth citing. 

In practice, that means four things:

• Write the way buyers actually ask. Headers, FAQs, and section titles should mirror real questions, not stuffed keywords. Buyers are talking to LLMs in full sentences, and that's bleeding into how they search Google, too.

• Structure for both humans and machines. Clean headers, bullets, FAQs, schema markup, summaries, and current dates and facts. These signals help AI parse your content and make pages easier for humans to read.

• Show real experience. Real authors with real bios. Cited sources. First-person examples and original data. Generic, anonymous content does not signal trust.

• Be helpful first, self-promotional second. A bullet list of impressive things about your company is not the same as helping a buyer decide. Both AI and readers can tell the difference.

Trust Signals Beyond Your Content

Your articles and landing pages are not the only things AI looks at. When AI systems choose which businesses to recommend among similar options, they evaluate your full online footprint.

This is sometimes called your reputation graph: the connected web of every place your business shows up online, including review sites, social profiles, directories, news mentions, and third-party citations.

If three businesses offer the same service at similar quality, AI recommends the one whose information is consistent and credible everywhere it appears.

A few specific things that factor in:

• NAP consistency. Your name, address, and phone number need to match across your website, Google Business Profile, review sites, and directories. An old phone number on a forgotten Yelp listing or a 2012 Twitter account with the wrong website hurts you.

• Active review presence. Recent reviews on the platforms relevant to your industry. Quantity, recency, and how you respond all factor in.

• Third-party validation. Press mentions, podcast appearances, guest articles, partner pages. These reinforce that other credible sources recognize your business.

• Internal consistency. Messaging, claims, and key facts should match across your own properties. Conflicting info between your homepage and your About page is noise that AI notices.

This is closer to digital PR than traditional SEO, and it deserves a real audit. 

In the past, SEO was about ranking. Now, it’s about being summarized, suggested, and recommended. That’s the shift.

AI is the new gatekeeper. If AI skips over you, your buyer never sees you. In this case, you’re not just buried on page two; you’re not even part of the answer. 

Where to Start: Building AI Literacy

The fastest way to catch up with AI is to start by building your AI literacy. Tools will change, but the skill of using AI well won't. 

The single most important shift for any marketing team: use AI as a collaborator, not a generator. A generator hands you a finished product. A collaborator helps you think, draft, edit, and refine.

Teams that treat AI as a generator end up with generic outputs that lack the things that make your content truly you. Teams that treat AI as a collaborator end up with sharper work and a smarter team.

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Get Comfortable with 1-2 Platforms

Don't try to evaluate every AI tool at once. Pick one platform and use it for a week on subjects you already know really well.

Ask it about your industry, your customers, and your work. Notice where it's right, where it's vague, and where it starts making things up. Push it further with deeper questions and watch how responses and results change. 

You'll start to notice patterns and hallucinations that are signs of a poor AI-generated response. When it's wrong about something you know to be right, you'll start trusting your judgment on future projects and how to make sure the results are valid. 

This is how you scale without sounding like everyone else. Moving beyond more content to creating better content, at a faster pace, all with trust built in.

If you want help using generative AI for content creation, check out our resource: How to Create Human Content While Using AI. It will give you a practical workflow on how to treat AI like a collaborator, anchor drafts in your own stories, then do a human pass to protect tone and POV.

Supporting Your Marketing Team with AI

The bigger gains usually come from improving processes first: AI notetakers, file organization, documented workflows, and internal knowledge bases. These are invisible to customers but make every customer-facing piece of work better.

AI works better when you have foundations in place: a defined ICP, a documented brand voice, and clear brand guidelines.

Without those, AI-generated work will be inconsistent, no matter how good the tool is.

Become Editors, Engineers, and Iterators

We have to shift our mindset around AI to be successful and adapt to the constant change of the industry. That starts with putting ourselves in three roles with AI: 

• Editors: Treat every AI output as a draft. Revise for accuracy, voice, and nuance until it sounds like you. Give feedback the same way you would a co-worker to improve each iteration. 

• Engineers: AI tools are allowing people with little to no coding experience to build amazing things. Build custom prompts, reusable templates, custom GPTs, tools, and Claude projects and skills. 
• Iterators: Test, watch, and improve. Pressure-test outputs over time, log what fails, and refine.  

The teams most willing have low ego and a tolerance for mistakes. They're willing to try something and try again if it doesn't work. Stay curious. 

To get our list of the best AI tools for marketing, check out our resource: Top 14 AI Tools for Content Creation in 2026.

How to Use AI: Written, Video, and Website Content

For marketing teams, there are three places where AI is going to make the most impact: written content, video content, and your website. Here's how to get the most out of AI when working on these types of projects. 

Written Content

Most teams start here, but they can also make the most mistakes here. Just because you CAN churn out ten articles a day doesn't mean you should. For success with written content creation with AI:

• Start with real source material. Interview a subject matter expert, transcribe the conversation, and feed the transcript into Claude or ChatGPT. Original input produces better original outputs. Asking AI to be the expert will only produce generic content that could be found anywhere online.
• Generate interview questions, outlines, and first drafts, not finished articles. AI gets you from blank page to draft. The human editing gets you ready to publish.
• Use AI to improve your prompts over time. The fastest way to get better at AI-assisted writing is by figuring out how to push AI to generate better results. Ask AI what it needs to provide the best answer. 

Video Content

The tools and capabilities around video production and AI are getting better faster and faster. You can use ChatGPT, Claude, or Gemini to help you write interview questions for a video, draft the outline for long-form video concepts, and create scripts, but a word of caution. 

If you don't give your AI tool a deep understanding of your voice or the voice of your subject, the script will fall flat because it won't sound like you. It will sound like AI. 

That's why a foundational understanding of what works inside AI platforms is essential to maintain your brand and use AI to scale production.

Website Content

Your website is where AI moves from a content creation tool to a tool your buyers can interact with daily. 

• AI-powered chatbots: High-quality conversations and real support. 
• Interactive self-service tools: Allow visitors to compare options, pricing, and more before talking to sales. 
• Out-of-the-box AI features in your CRM/CMS: Personalization, content recommendations, and search are now native in many platforms. 

Some teams "vibe code" their own tools with AI assistants. Useful when it works, but painful when no one on your team understands it, or you're not successfully collecting the data you need to support the sales team. 

Why Self-Service Tools Matter More Than Ever

Today's buyers want to research, price, and shortlist on their own terms before talking to anyone. Self-service tools meet them there. They demonstrate transparency, give buyers the information they need to make a decision, and pre-qualify the people who eventually reach out.

The companies still hiding pricing, gating basic information, or forcing every question through sales are losing buyers to competitors who don't. AI makes these experiences easier to build, smarter, and more personalized than ever before.

To learn more about self-service tools, check out our resource: The Business Case for Self-Service Tools: What to Build and Why. It shows how turning your site into a buyer self-service hub speeds decisions and lifts lead quality. It will help you pick the first tool to launch, govern it well, and prove the ROI.

What to Use: The Latest Updates in AI Tools and Technology

The AI tools landscape moves fast. This section is refreshed regularly to reflect what's actually working for marketing teams right now. (Last updated: May 2026)

The Major AI Platforms at a Glance

Four general-purpose AI platforms cover most marketing work today. None of them does everything the best. Experiment with a couple to figure out what works best for you and occasionally test your outputs against another tool to make sure you're getting the best results. 

Claude (Anthropic)

Strongest for: Thinking partners, long-form writing, and structured analysis. 

Platform features worth knowing: 

• Projects: These give Claude a persistent context for ongoing work, whether that's creating social media content, client information, or a campaign strategy.
• Skills: These are reusable folders of instructions that Claude can call on automatically. You can find pre-built skills inside of Claude or use Claude to create skills that match your workflow.
• Co-work:  One of Claude's three main interfaces. Stronger than Claude Chat alone. Co-work lets Claude work alongside you on your computer with files, browsers, and apps. 
• Connectors: These plug Claude into Google Drive, Gmail, HubSpot, Salesforce, Slack, and more.
•  Artifacts: These generate working code, charts, and visual designs inside the chat.
•  Claude Code: This extends the platform into engineering workflows.

ChatGPT (OpenAI)

Strongest for: Everyday writing, brainstorming, and image generation 

Platform features worth knowing: 

• Custom GPTs let you create reusable assistants with their own instructions and files.
• Images 2.0 handles text rendering and batch generation well enough to produce real marketing assets.
• ChatGPT also has its own equivalent of Claude Co-work (agent mode) and deep Microsoft integrations.

Gemini (Google)

Strongest when: You live in Google Workspace

Best features: 

• Built into Gmail, Docs, Drive, and Meet with no copy-paste between apps.
• Gems are Gemini's version of custom GPTs.
• Nano Banana 2 is now widely regarded as the leading image-generation model.

Perplexity

Strongest for: Research and live web search

Strengths: 

• Cites every source and has the highest brand citation rate among AI search engines.
• Less useful as a general writing assistant.
• Great as a fact-check layer or starting point for time-sensitive research.

AI For Written Content

Most written content work happens inside one of the major LLMs. The right pick comes down to format and how you prefer to work. 

AI for Research and Knowledge Work

When the job is making sense of a lot of information, reach for tools built for that, not general LLMs. 

AI for Image and Graphic Generation

Image generation has improved fast in 2026. A few tools cover most marketing needs. 

AI for Video Creation and Repurposing

Video is the highest AI use case for most marketing teams in 2026. 

AI Notetakers and Meeting Tools

Often, the most underrated AI investment a marketing team can make. 

The best tools for the job will continue to change as AI improves and the market shifts. Build that AI literacy so you have a sound foundation for finding the tools that are the right choice for your organization. 

What to Skip: AI Moves That Break Trust and Waste Time

Some trends look exciting on the surface, but quietly hurt performance. Here are the biggest ones to avoid in 2026.

6 AI Trends in Marketing To Avoid in 2026 (and Why They Break Buyer Trust)

Some AI tools are here today, gone tonight. Others simply create more noise, confuse you and your audience, and make everyone feel a little less human along the way. AI can be amazing, but only when used with intention.

Here’s what to skip in 2026:

1. Using AI to crank out useless content at scale. 

Just because AI can produce 50 blog posts in a day doesn’t mean it should. Flooding your site or social networks with bland, repetitive, or low-effort content hurts your credibility more than it helps. More content does not equal better. Better content is better. If your content sounds like AI wrote it, your audience scrolls right past or leaves and goes to your competitor. 

2. Automating every touchpoint without human oversight.

When every email, chat, or website interaction feels stiff or robotic, buyers disconnect. Automation is powerful, but overdoing it, without human quality control, turns your customer journey into a vending machine: impersonal, mechanical, and very easy to walk away from. People still want to feel like people, not like another number or another transaction. 

3. Prioritizing AI “tricks” for SEO over genuinely helpful content. 

Trying to game SEO with keyword-stuffed AI content or “SEO hacks” might get you a short-term lift in numbers, but it won’t build long-term trust. AI search tools like Perplexity and Google SGE are rewarding actual answers and clarity, not fluffy, bland, repeated content. Write for people first, and the algorithms will follow. 

4. Ignoring humans during the review process.

AI is a great starting point, but “prompting and publishing” without a second set of eyes is risky. Human editors bring specifics that AI cannot: empathy, nuance, and buyer insights based on experience. If you skip this step, you risk tone-deaf messaging, errors, or content that simply doesn’t resonate. In other words, your AI struggles to “read the room”. Humans, especially during the review process, read the room right. 

5. Trying to personalize with dirty data.

Your AI is only as smart as the data feeding it. Using outdated, incomplete, or mismatched data to drive personalization leads to awkward and off-base experiences that break trust, and break it quickly. Guessing someone’s intent, or worse, their name or job title, and getting it wrong is a quick way to lose them. One poor experience like this, is then multiplied by the number of people who are then told about it. 

6. Chasing every shiny AI trend with zero strategy.

New AI tools launch daily. It is tempting to immediately jump on the next “game-changer” before the competition does. But adopting tech without a clear use case is a monumental time-suck. Before you plug anything into your organization’s (or your personal) workflow, ask: What problem is this solving? 

If there’s no clear answer, move on. 

Let’s take a closer look at the measurable red flags that show up when automation runs on autopilot, or you're missing a strategy that helps AI create meaningful content. 

5 Measurable Signs the AI is Hurting Your Marketing

These 5 signs aren’t based on guesswork or gut instinct; they are observable data patterns and performance drops that indicate AI is undermining, not enhancing, your marketing efforts.

1. Automated Sequences Are Ignored

• The Evidence: Your follow-up emails or DMs are going out on time, but open rates are declining, reply rates are minimal, and unsubscribes are rising. 
• What it Means: Automation is running, but recipients are disengaging. Timing, frequency, or content relevance may be misaligned with buyer readiness. 
• The Action to Take: Segment and test behavioral triggers. Compare automated vs. manual engagement performance. Adjust cadence and/or messaging type based on engagement heatmaps. 

2. Personalization Errors Are Showing Up in Customer Feedback or Metrics.  

• The Evidence: You’re seeing support tickets, unsubscribes, or complaints related to incorrect names, irrelevant product suggestions, or mismatched messaging. 
• What it Means: Your AI is using faulty or outdated data for personalization, resulting in visible customer-facing errors. 
• The Action to Take: Audit CRM health. Check property accuracy, sync timing, and tag logic. Implement QA rules that flag anomalies before messages go out. 

3. Marketing Metrics Look Strong, But Sales or Pipeline Doesn’t Reflect It.

• The Evidence: You’re seeing high email send counts, chatbot sessions, and blog posts published, but MQL quality is declining, lead-to-customer conversion is flat, or pipeline contribution is slipping. 
• What it Means: You’re scaling activity, not outcomes. The AI-driven volume isn’t translated into revenue impact. 
• The Action to Take: Align marketing KPIs with revenue targets. Review lead scoring logic, attribution reports, and sales feedback. Optimize for influence, not just activity. 

What to Watch: AI Shifts Every Marketer Should Prepare For 

The tools are changing, the platforms are shifting, and the marketers who stay ahead won't be the ones who adopt the most AI. They'll be the ones who understand what's worth watching and why it matters.

AI Agents: What They Are and Why They're Changing How People Buy

AI agents don't just answer questions. They complete tasks. Give an agent a goal, and it will autonomously work through the steps to get there: browsing websites, filling out forms, requesting quotes, booking appointments, and making purchases.

This is different from the AI most people are familiar with. It acts on your behalf.

For businesses, two things are worth preparing for.

Agentic buyers are customers whose AI agents are doing the vendor legwork for them. That agent isn't browsing casually. It's looking for specific information to complete a task, and if your site doesn't surface it clearly, your business may not make the list.

Building an agent-friendly website means making sure the information an agent needs is easy to find and act on. Pricing, services, process, credentials. If an agent is trying to request a quote on someone's behalf and your site makes that difficult, it moves on. The businesses that win in an agentic world will be the ones whose digital presence is built to be legible and actionable, not just to humans, but to the systems working on their behalf.

AI Employees: What That Means for Your Team

Can AI really replace your employees? Let's talk about what an "AI employee" actually looks like. 

An AI employee is typically a trained bot or a configured set of automations built on top of an existing AI platform. The quality varies enormously, and users should be cautious before handing everything over to an automation.

Vet these tools the way you'd vet a human hire. A detailed job description, rigorous testing, and real onboarding. You need to understand what the automation does, how it makes decisions, and where it can fail before you deploy it.

The better framing is supplementation, not replacement. AI employees work best filling gaps your team doesn't have capacity for, like basic data analysis or reporting. But a human still needs to deploy these tools, understand what they're producing, and catch the errors automated systems make with confidence.

Ads Inside AI Platforms: How to Decide When to Invest

ChatGPT, Perplexity, and other AI platforms are beginning to roll out advertising. The formats are still evolving, and performance data is limited, which is exactly why now is the time to pay attention.

The questions worth asking as this develops: Are your buyers actually on these platforms? What do sponsored placements look like inside an AI response, and do users trust them? What would it cost to test, and how would you measure it?

You don't need to be an early adopter. You do need to understand the landscape well enough to make a smart decision when the time comes.

Hyper-Personalization: It's Already Here, and It's Expanding

You already experience this every day. Netflix surfaces the show most likely to keep you watching. Spotify generates a playlist built around your specific listening patterns, not a general genre.

The underlying capability powering those experiences is the same one that will soon power a home services company recommending the right solution for your specific situation, or a B2B platform showing you the use case most relevant to your business.

For marketers, this means generic messaging has a shrinking shelf life. Businesses that think in terms of specific buyer contexts and segments will be better positioned as personalization becomes standard. And the upside is real: reaching fewer people who are more likely to buy is a better outcome than reaching more people who aren't.

AI Makes Strategy More Essential, Not Less

AI has made it easy to produce content. That's also the problem.

When anyone can generate a polished 1,500-word article in minutes, the output itself isn't the differentiator. What separates forgettable AI content from content that builds trust and drives decisions is the strategy behind it. What are you trying to say? Who specifically are you saying it to? What makes your perspective worth reading?

That's why frameworks like Endless Customers matter more now, not less. A clear methodology for what you create, why you create it, and how it connects to the buying journey means your AI outputs have purpose. Without that foundation, you're not accelerating your marketing. You're accelerating the noise.

Original Research and Experience: The Differentiator AI Can't Replicate

There's one thing AI cannot generate: your experience.

Your client stories, your proprietary data, the patterns you've observed, and the industry-disrupting things you've learned that don't exist anywhere else. That's the raw material that elevates content from accurate and generic to worth trusting.

This is the heart of E-E-A-T: Experience, Expertise, Authoritativeness, and Trustworthiness, which has been a long-standing standard for quality content by Google. And it's still relevant today.

Content that references real clients, real outcomes, and real data from your own work demonstrates something generative AI never can.

Lean on your internal experts. Use their language. Pull in real examples from your clients' work. That's what positions you as a source your buyers, and the AI systems influencing their decisions, will keep coming back to.

How To Make An AI Strategy Work Inside Your Organization

It's tempting to throw all the latest AI tools at your team, but it's important to take a strategic approach that balances moderation, independent thinking, and experimentation so that you encourage and empower your team, not intimidate them.

Build AI Literacy First

Before you implement anything, your team needs a baseline understanding of how AI works. Not deep technical knowledge, just enough familiarity with the terminology, the process, and the logic of working with these tools that they feel confident when something new lands in front of them.

The goal is curiosity. Give your team space to experiment with no ego and no expectation of perfection. Let them learn what good outputs look like versus bad ones, how to iterate on a prompt, and how to push a tool further.

When the market shifts and a better tool emerges- and it will- a team with that foundation will adapt. 

Don't force people into tools they aren't ready for. Encourage them to be open, be curious, and build confidence at their own pace.

Establish AI Safety Guidelines

Before anyone on your team puts AI to work, your organization needs clear standards for how it's used responsibly.

That means defining what data is safe to input into AI platforms and what isn't. Proprietary client information, financial data, and internal strategy documents, those require careful consideration before they go anywhere near a third-party tool.

It also means setting expectations around AI-generated content and imagery. If AI can generate images using the likeness of your employees or produce content under someone's name, your team deserves to know that upfront.

No one should be caught off guard by something they didn't approve. Clear policies around what's acceptable protect your people and your brand.

Consider your industry's standards too. Regulated industries carry additional obligations, and staying ahead of those expectations is far easier than catching up after something goes wrong. 

Pick the Right First Project

The best place to start with AI isn't wherever leadership thinks it's most needed. It's wherever your team actually wants relief.

Ask your people to look at their own workflows and identify the tasks they find most repetitive, most time-consuming, or most draining. Those are the right candidates for AI. When someone uses AI to take something they dislike off their plate, they immediately feel the value, and that builds momentum.

The goal is to free your team up for the work that requires real judgment, creativity, and human connection, the work where they find fulfillment. AI should be clearing the path to that work, not replacing it.

Why Coaching Makes the Difference

AI tools don't come with a built-in strategy. That's the part that coaching provides.

Working with a coach means having someone who can look at your organization, identify where AI can genuinely move the needle, and help you avoid the traps that waste time and budget. Not every AI opportunity is worth pursuing, and knowing the difference requires perspective that most teams don't have when they're just getting started.

This is where coaching from IMPACT comes in. The Endless Customers framework gives your AI adoption a direction. It connects what you're building with AI to a clear purpose, your buyers' questions, your team's expertise, and your business goals. Without that strategic foundation, even the best tools produce outputs that don't add up to anything.

Is Your Marketing Ready For The Next 5 Years?

The real gap in AI Marketing is mindset, structure, and readiness. Downloading the latest app won’t fix that. What matters is whether your team knows how to use AI to earn buyer trust, scale your strategy, and drive measurable revenue.

That’s exactly what we do with the Endless Customers Coaching & Training Program. We work side by side with your sales and marketing teams to build the skills, systems, and confidence they need to thrive in the age of AI.

Ready to see where you stand? Take our free AI Marketing Readiness Quiz. You’ll get a clear score and the exact steps to take next — whether that means small, quick wins or a full coaching plan to set your business up for lasting success.