AI is everywhere. From DIY brand ads, AI influencers, and even Bigfoot vlogs.
Yet when teams try to make it all work, it’s rarely that smooth. AI tools don’t always integrate with legacy systems. Data lives in too many places to be useful. And the “automation” that’s supposed to save time sometimes ends up creating more manual work.
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I’ve seen most teams hit the same AI challenges: data gaps, integration issues, and creative control. In this post, we’ll explore the 10 biggest challenges marketers face when implementing AI in 2026, how they show up in real teams, and how to navigate them effectively.
I’ll also highlight expert insight from industry leaders so you can learn how to tackle these challenges and harness the power of AI in marketing.
Table of Contents
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The 10 Biggest Challenges When Implementing AI
Along with the benefits of using AI in marketing outlined in the image below, the challenges of implementing AI in marketing are vast.
This includes challenges related to the AI systems, processes, team buy-in, and more. We’re working through the most significant challenges and the data that justifies these day-to-day challenges. You’re not the only one feeling the struggle.
Here are 10 common challenges when it comes to AI in marketing.
1. Hampering Creativity
When I first started using AI in our creative process, I was convinced it would spark better ideas. AI tools that could generate headlines in seconds surely could give me tons to work with, right?
Well, yes and no. AI-generated concepts are fast and structured, but if you don’t give enough context or tweak the final output, everything will start sounding the same.
Automation should be assisting, instead of leading your entire process. Emotion still starts with humans. Now, I use AI only after I have a direction, not before. It helps me expand possibilities and not define them.
2. Undermining Competency
Our 2025 State of Marketing report found that while 82% of marketers use AI for content creation, even the best AI models can’t replicate feeling.
They can mimic empathy, but they don’t experience it. I’ve seen brands post AI-written content that checks every SEO box but leaves no impression on readers.
The issue is the dependency on AI tools. When we let AI dictate tone or structure without layering our lived experience, content doesn’t resonate with our target audience, or really help anyone.
Pro tip: Treat AI as an assistant. Keep your perspective at the center so the content still sounds like it was written by someone who gets it. For instance, when I was writing about AI tools for Excel earlier this year, I tested every single tool on the list. That helped me target pain points other marketers might be having and share my recommendation on what worked, what didn‘t, and where I’d spend my budget.
3. Inaccurate or Overconfident Information
AI doesn’t lie intentionally, but it does hallucinate confidently. I’ve had drafts where the stats looked believable. The phrasing was authoritative, and yet the facts were completely off.
One inaccurate claim can quietly erode a brand’s credibility. For instance, Anthropic submitted a court filing containing a fabricated citation generated by its AI system in a high-profile case. Well, yes, Anthropic is an AI-first company, but they should have really refrained from using it in the court of law.
Several law firms have been sanctioned for submitting legal briefs that relied on AI-generated “cases” that did not exist, and there have been at least 95 such incidents in the U.S.
AI is brilliant at assembling information but still needs human intervention to verify all claims.
Pro tip: Build fact-checking into your workflow. I’ve learned to treat AI output as a starting draft. Before publishing, every number, quote, and claim gets verified by a human. It’s a small step that saves a lot of damage later.
4. Bias and Representation Gaps
I recently tested a bunch of AI image generator tools for an article. My prompt to Gemini was, “A group of marketing professionals collaborating in an office for a global campaign.” Here’s what I got back:
While the image was great, every face looked the same: young, urban, Western, and mostly white. Maybe it was a Gemini problem, I thought. So, I turned to ChatGPT.
ChatGPT output:
The skin tones were more varied with ChatGPT, but every face still looked similar, symmetrical, glossy, and model-like.
That’s when I realized the bias wasn’t in my prompt. It was what the model had learned to assume. If we don’t define what real representation looks like, the AI will decide for us. And it often gets it wrong.
AI bias is real. These systems reflect the limits of their training data, and that bias shows up in the outputs we use. A 2025 study found that image-generation tools reinforced narrow beauty standards, overrepresented Western features, and underrepresented diverse body types.
When your brand says it serves “all customers” but your visuals show only one type of person, people notice, even if silently. As marketers, we choose whether that bias becomes part of our message.
5. Data Privacy and Security Risks
AI tools depend on massive datasets. But what’s less discussed is how much of our own data they quietly absorb. From campaign analytics to internal reports, the lines blur fast.
I recently came across an IBM report that shows that organizations with high levels of ungoverned or “shadow AI” use faced breach costs $670,000 higher on average than those with better AI governance. Even major AI players are vulnerable — a breach at OpenAI caused internal design data to be stolen.
Pro tip: Collaborate with legal and compliance teams from day one. Before connecting any tool, I audit what data it accesses, where it’s stored, and who owns it. Convenience is never worth a privacy breach.
6. Over-Dependence on Automation
I’ve seen what happens when automation goes too far. Some brands automate everything: social posts, ad variants, campaign reports. It can look great at face value, but it often lacks depth. Smart consumers can spot this from a mile away, which can cause them to not take your brand seriously.
Over-dependence on automation makes teams fast but blind. That’s the real risk. Efficiency means nothing if no one’s paying attention.
You need to include human review points in every workflow. Before anything goes live, someone checks it. Simple, but it works.
7. Integration and Workflow Gaps
AI tools promise seamless integration, but marketers know how often that’s a myth. One platform exports data in a different format, another doesn’t sync analytics, and soon you’re juggling five dashboards.
That’s one reason I like HubSpot’s setup so much. It pulls directly from our CRM, so the information stays consistent across campaigns, reports, and follow-ups. When everything connects properly, people spend less time fixing data and more time acting on it.
Pro tip: Map your process. Before adopting any AI tool, define exactly where it enters the workflow and who owns its output. The smoother your system, the better the results.
8. Unclear ROI and Value Measurement
Proving AI’s ROI is still tricky. It saves time, but how much of that translates into business results? According to HubSpot’s 2025 State of Marketing Report, 61% of marketers say measuring AI’s business impact is their biggest barrier to scaling it.
Unilever, for instance, has built an in-house tool to create product descriptions and automate visual content for brands. However, they still have concerns around data privacy, bias, and intellectual property, which limit how far they can scale and measure impact.
AI can scale your work, but without clear metrics, you won’t know what it’s scaling.
Pro tip: Set goals before implementation, time saved, engagement uplift, or cost reduction. Every AI experiment I run now has a baseline metric. Otherwise, it’s just guesswork with good branding.
9. Training and Time Investment
According to McKinsey’s 2025 AI Adoption Survey, teams that invest in structured AI training are 2.3x more likely to see positive ROI within the first year.
When I first introduced AI to my content team, I completely underestimated the learning curve. A few people jumped in confidently, while others froze at the prompt line. We spent weeks just trying to get everyone comfortable.
So I tried something different, weekly “AI hours.” I didn’t set any deadlines or expectations. I was just exploring. Within a couple of months, AI marketing automation was a part of our regular days. People stopped fearing the tools because they finally understood them.
Pro tip: Build training time into your rollout plan. AI saves time later, but it costs time early. Give your team the space to learn before expecting results.
10. Keeping Up With New Trends and Tech
I’ll be honest, keeping up with AI feels like a full-time job. Every week, there’s something new: Veo-3 producing realistic videos with synced audio and Grok Imagine rolling out AI characters.
It’s exciting, but also overwhelming. I’ve learned to slow down. I now test one trend at a time and double down only when it adds real value.
Pro tip: Pick one emerging trend, test it with purpose, and move on if it doesn’t deliver.
The State of Artificial Intelligence in 2025
New research into how marketers are using AI and key insights into the future of marketing.
- Marketing AI Tools
- Practical Tips
- Trends and Statistics
- And More!
Download Free
All fields are required.
11 Tips for Implementing AI
Okay, now we know what some of the biggest challenges are. Let’s find out how to overcome them in a way that helps you reach your marketing objectives while you reap all the AI benefits you’ve heard so much about.
Here are 11 tips to help you do just that.
1. Enforce AI policies.
Dan Robinson, head of marketing and ecommerce at Instantprint, recommends that businesses implement and enforce AI policies to aid smooth AI implementation.
The solutions he’s integrated into Instantprint’s AI implementations include team guidelines and a code of conduct.
“Employees must adhere to the guidelines we’ve set out. We nurture an environment of trust, but also provide our team with the rules and regulations they need to be aware of to use these tools effectively and safely. Our ‘AI Code of Conduct’ is set out by each platform we use, with dos and don’ts for each tool,” Robinson says.
Robinson notes that the policy is designed to be collaborative.
“Making policies a shared effort means that we’re more likely to have rules that will work for our team, developed by our team, with the exception of legal and ethical frameworks as a standard,” Robinson says.
What I like: Robinson accepts the importance of AI policies that are legal and ethical, but doesn’t stop the team from adding their thoughts. With a collaborative effort to develop policies, you’re more likely to get buy-in from team members.
2. Start with low-risk AI implementations.
Rosella Dello Ioio, head of content at Enate, says, “Businesses should be clearly defining the data they can and can’t share with public and private AI models. Consider hiring a Chief AI Officer to take the lead on security and governance within the business.
“Once the rules around these challenges have been clearly established, begin rolling out GenAI in your marketing department by identifying all the people whose job involves creating (writing, designing, and building) and let them find the best AI co-pilot for their tasks.
“Creative roles such as Copywriting and Graphic Design are relatively low-risk in terms of sensitive data as opposed to a CRM Manager who wants to use GenAI to analyze customer feedback and complaints.
“Test and procure low-risk tools to support these creative individuals in boosting productivity and slashing the time spent on mundane tasks while ensuring governance protocols are adhered to.”
What I like: It’s justified for marketers to be concerned about data and analysis by AI tools, but Dello Ioio has found a solution that allows marketing leaders to start implementing AI in a way that feels manageable and safe.
Sometimes, the first step is the most challenging, and once leaders get rolling with AI, they may be inspired to try more.
Look at HubSpot’s AI content assistant for low-risk AI experimentation. It’s free to demo, and you can write content, create emails, landing pages, and more.
3. Leverage AI alongside your existing tech stack.
She says, “When used effectively in combination with other tools and skills of our respective teams, the door is wide open for possibilities. I would encourage folks to look at AI as an assistive tool in their digital toolbox.
“Those who will see the best results and realize the most benefits of AI, in my opinion, will be those who view this tech through a Venn diagram lens.
“Leveraging this tech along with your existing tech stack in addition to your skilled team, where these overlap, is where we will find success.”
What I like: Bowden’s tip could help marketing leaders close the gap on challenges that hamper creativity. If AI is used in addition to your skilled team, then there’s everything to gain through collaboration.
Consider taking the best of AI and the best of that all-important human touch and find the areas where they can best support each other.
4. Talk to your team.
Communication is, of course, everything! When it comes to AI, marketing leaders can eliminate a lot of AI challenges with team buy-in and communication.
Jessica Packard, head of marketing at Timmero, found that some reassurance allowed her team to see AI for what it is: a marketing tool that can aid their workflow.
Packard says, “Initially, my team of copywriters was apprehensive about how AI could potentially replace their work in the organization.”
She continues, “The fear was understandable, but it was important to reassure them that the AI tools are still underdeveloped and they cannot create copy that successfully engages readers on an emotional level as humans do.
“It’s also important to show them how leveraging AI can be beneficial to their work, from brainstorming content ideas to help writing creative titles and meta descriptions.”
Montse Cano, international SEO and digital marketing consultant at Montserrat Cano, shared similar tips for overcoming AI challenges. She follows a set of questions and uses AI in a way that helps teams.
She shares her process, “We identify what needs we have in our team that we could meet by using AI. Is it code generation, text content ideas, or images? Then, assess current resources to test and validate outputs, i.e., do we need any training, hire someone else.”
Johannes Larsson, founder and CEO at Johannes Larsson, stresses the importance of communication with an onus on regular comms and empowerment.
Larsson says, “We regularly communicate with our team about the benefits of AI and how it can empower them rather than threaten them. We also aim to provide training and resources to help them develop new skills and expand their knowledge in areas where AI is involved.”
What I like: It might seem simple to suggest communication, but it’s easily forgotten when you’re all busy at work. Cano, Packard, and Larsson have recognized the why behind team challenges and recommend how you can solve this challenge through communication, reassurance, and future training.
5. Test AI with your team.
In line with the importance of talking to your team, Kevin Miller, co-founder and CEO of GRO, encourages marketers to improve their workflow efficiency with AI.
He started with a solid goal, improving his team’s efficiency by 400%, and worked with his team to document AI success.
Miller shares his story, “We experimented with ChatGPT earlier this year to improve writing efficiency for long- and short-form content creation.
“For our clients, we want to produce the highest-quality work possible to help them grow their domain authority and online traffic, so automation was a natural strategy to pursue that goal. That being said, it’s not a one-stop-shop tool."
He continues, “Aiming to improve workflow efficiency by 400% by leveraging AI tools, we asked writers to adapt their workflows and give feedback on how well ChatGPT helped improve their writing and deliverability.
“Although we did not hit those marks because of many natural obstacles and limitations of the software, we increased workflow efficiency by 200% through content templates and research assistance.
“ChatGPT is fantastic for content generation and assessment, but can’t do the work alone. It is still a part of many of our writers’ workflows to use as they see fit, and I am confident that it will continue to grow in capacity and use.”
What I like: Miller and his team have experimented with ChatGPT and actively found that AI can improve team efficiency. I especially like the level of involvement Miller’s team had in AI experimentation.
With his team reporting on their AI feedback, I think Miller was more likely to get buy-in from team members.
6. Find the areas where AI is most effective.
Using AI doesn't have to be an all-or-nothing scenario. You can find the opportunities or tasks that AI is most capable of solving; then you can do the rest.
Sara Cooper, director of web strategy and SEO at SimPRO, said that her team found AI most beneficial at generating headlines or sections of copy.
Cooper says, “One of the biggest challenges has been feeding AI the right directives to get the output we are looking for and learning not to ask for too much from AI upfront.
“For example, as the team has started to leverage AI to generate more content across our website, it’s clear that it’s most effective when supporting the personalization of headlines or sections of copy rather than generating whole landing pages from scratch.”
Jessica Ruane, senior integrated marketing manager at Openprise, echoes Cooper. Ruane says, “A big challenge that surrounds AI is effectively utilizing it in content marketing. Companies are definitely using AI to varying degrees during the content creation process.
“Some may be fully writing content with the use of AI, while others are utilizing it for research and inspiration. The best way to implement AI in content is — slowly. Focus on the ‘voice’ that you’d like to use, and experiment with rewriting phrases to get started.”
What I like: Ruane and Cooper agree that there’s a place for AI in content writing. Equally, they’re both seeing the role of AI vary based on what marketers find more useful. Consider using AI for different tasks and find what works for you and your team. AI doesn’t have to write everything.
7. Experiment carefully.
Sofia Inga Tyson, SEO content editor at Juro, resolves the AI challenge of quality and brand with careful experimentation and full disclosure to key stakeholders.
Tyson says, “There are certainly concerns about the use of AI in content strategies diluting the quality and overall authority of the website.
“Businesses are often keen to experiment with AI to scale their content production, but content writers are naturally fearful that AI-generated content at scale will have a detrimental impact on the performance of existing, expertly crafted content.
“I think it’s really important to manage stakeholder expectations in this regard and ensure that these risks are disclosed to other decision-makers in the business that might be encouraging this approach for aggressive growth.
“I also think it’s important to be cautious about AI-generated content because the true impact won’t be felt immediately. It could be months or even years before the content is evaluated negatively based on the quality or use of AI.
“Any experiments should be just that — careful, closely monitored, and kept at a scale that means it can be reversed if needed. I think this approach will bring a lot of content writers comfort as it demonstrates that you’re approaching the use of AI with caution, not carelessly jeopardizing the online presence you have already.”
What I like: Tyson’s holistic approach to AI covers brand reputation, the team, and stakeholders. She considers the desire to leverage AI with the needs of the team that uses it. I love the reassurance for all involved when experimentation is monitored carefully.
8. Monitor quality and accuracy.
Once you’re set up and using AI, you don’t want to neglect the all-important quality check.
Annika Haataja, SEO consultant, says, “As you expand your use of AI, don’t forget to monitor quality and accuracy. We all know that AI can sometimes make mistakes, which may hurt adoption if teams don’t trust the results.
“Have people review a sample of AI output to catch errors, and empower them to have faith in their own expertise in the process.”
What I like: Diligence around AI output could decline as teams become comfortable with AI usage. Haataja encourages us to stay mindful of quality and accuracy as AI adoption scales, an important reminder for all of us.
9. Refine your data sources.
Simon Brisk, director at Click Intelligence Ltd., found AI bias a challenge. Interestingly, they found better data once his team refined the data sources.
He says, “One significant challenge we’ve faced at Click Intelligence when integrating AI is ensuring data integrity. AI models are only as good as the data they’re trained on. Inaccurate or biased data can lead to misguided marketing decisions.
“For instance, while analyzing user behavior for an ecommerce client, skewed data initially suggested a preference for a specific product line. Only after refining data sources did we realize a more holistic preference trend, thereby recalibrating our marketing strategy.”
What I like: Brisk’s experience with AI bias shows the potential impact and the solution. By recalibrating data sources, Click Intelligence could recalibrate its marketing strategy based on reliable data.
10. Stay competitive.
The world of AI is developing fast. Chris Stott, account director at Anything is Possible, recommends staying ahead of AI developments.
He says, “Staying ahead of the competition is paramount. It’s essential for us to consistently deliver exceptional value. This means rigorously testing all AI software to keep us on the cutting edge and guarantee that we provide top-tier results to our clients.”
What I like: Incorporating AI into your systems can be exciting and empowering. Testing AI from a place of experimentation is a great way to explore AI capabilities with your team. You’ll soon get a feel for what supports you and what you can live without.
11. Overcome data integrity and change management.
Jessica Shee, SEO specialist at iBoysoft, shares the importance of overcoming data integrity and change management.
She says, “Change management and data integrity are two significant obstacles to implementing AI in marketing. For accurate AI-driven insights and decision-making, it is essential to ensure high-quality, pure data.
“Misaligned data can cause inaccurate forecasts and ineffective marketing campaigns. Invest in data cleansing, validation, and data integration tools to address this issue.
“Change management is an additional obstacle. Integrating AI can disrupt existing workflows and require team members to acquire new skills. Transparent communication about the benefits of AI and training team members to cultivate acceptance and proficiency are necessary for a smooth implementation.
“Working with AI experts, integrating teams in decision-making, and starting with trial projects can help integrate AI while resolving difficulties. A systematic strategy, continual training, and clear communication ensure a smoother transition and optimize AI marketing benefits."
What I like: Shee recognizes that you don’t have to do everything alone. You can bring in experts and start with trial projects to help support your team through AI implementation challenges.
What’s stopping you from implementing AI?
Implementing AI can get overwhelming. There’s always a new tool or update to try out. Each tool claims to be better than the rest, but brings with it a new set of challenges.
My advice? Be selective about which tools you add to your stack. Always add one AI tool at a time so you can fully understand the impact and the challenges that come with it. Prepare your team to handle AI through clear roles, clean data, and a process that keeps humans in control.
In the worst case, you’ve run an experiment (and isn’t that what marketing is all about?), and in the best case, you’ve got the data to support the success of your AI endeavors. Getting buy-in from your team or stakeholders will be easier when they know AI drives marketing objectives.
Plus, with the top tips from marketing leaders, you’ll start with the best possible insights to make AI and marketing an absolute success.
Editor's note: This post was originally published in November 2023 and has been updated for comprehensiveness.
The State of Artificial Intelligence in 2025
New research into how marketers are using AI and key insights into the future of marketing.
- Marketing AI Tools
- Practical Tips
- Trends and Statistics
- And More!
Download Free
All fields are required.
Artificial Intelligence
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