What every enterprise needs to know about AI governance and onboarding

2025 has seen a shift in the way organizations are treating AI. 

Artificial intelligence is no longer seen as a new technology to experiment with. Today, business leaders want to see a clear ROI on the budget they’re allocating to AI tools and training.

The reality? Gartner has estimated at least 30% of generative AI projects will be abandoned after proof of concept by the end of 2025 due to poor data quality, inadequate risk controls, escalating costs, or unclear business value. 

What’s more, 39% of marketers don’t know how to use generative AI safely, according to research by Salesforce. 

Which all suggests far more marketers than we’d like to admit are executing AI strategies that are going to fail to drive growth—and could even be leaving their organizations liable to regulatory fines or lawsuits. 

An AI governance and onboarding strategy will ensure the AI tools and training you incorporate into your marketing efforts drive a serious ROI. It will also help you navigate compliance risks, prevent data leaks, and avoid the brand damage an AI blunder would cause. 

In this article, we’ll walk you through the key frameworks for responsibly onboarding AI across your marketing organization. 

We’ll also explain why your business needs an AI governance council—and what yours should look like.

What is AI governance?

AI governance is the set of policies and procedures a business puts in place to make sure artificial intelligence is being used safely and ethically across its organization.

Effective AI governance not only ensures the way a business is using AI marketing tools complies with external regulations like GDPR and the AI Act. It also sets guardrails in place that address artificial intelligence’s flaws and limitations.

For example, a UNESCO study revealed the most popular large language models that power most AI tools—including GPT-3.5 and GPT-2 by OpenAI, and Llama 2 by META, and Google’s Gemini—have a tendency to produce content that perpetuates racial and gender stereotypes and exhibit homophobic attitudes.

Plus, even today’s most advanced AI models hallucinate. The AI company Vectara maintains a Hallucination Leaderboard that ranks how often leading LLM models produce an output that includes misinformation. It currently shows that today’s most accurate model—Google’s Gemini-2.0-Flash-001—still hallucinates 0.7% of the time. Some models even hallucinate over 25% of the time.

That means even the most accurate Large Language Model on the market hallucinates in nearly one in a hundred outputs. 

AI can also produce privacy concerns. For example, the Italian city Trento was fined by its government for breaking data protection rules in the way it handled citizen data using AI in street surveillance projects. The data collected wasn’t sufficiently anonymized, and also incorrectly shared with third parties.

Governance of AI needs to formally address these inherent flaws of artificial intelligence to mitigate the risk of using AI tools while maintaining the benefits they can bring to your marketing operations. 

Define the purpose with use-case-driven onboarding

As a marketer, there’s a real pressure to be using AI-driven processes to do better work, faster—whether it’s from within your organization, your own fear of missing out, or a combination of the two.

And so the vast majority of businesses are starting to bake AI tools into their marketing operations. In fact, according to research performed by Epsilon Marketing, 94% of businesses are using some form of artificial intelligence to prepare or execute their marketing.

However, according to a Salesforce survey, 43% of marketers say they don’t know how to get the most value out of gen AI. 

The first step? Picking tools that actually serve your team’s needs—and quickly moving on from those that don’t.

Which is why a key part of any AI governance strategy is formalizing a process for choosing and onboarding AI tools into your organization. This will help make sure you’re adopting tools that solve clear business problems, not just chasing the latest and shiniest thing.

Effective AI adoption starts with assessing your team’s current priorities, its current skill gaps, its current toolstack, and the resources available to it—then strategically picking tools that plug the gaps. 

For example, if digital advertising is set to play a big part in the coming quarter’s campaigns, you might prioritize onboarding AI tools that will help you maximize your ROI on your ad spend. Or if your team is short on analytics talent, AI can help fill that gap by highlighting trends in your GA4 and CRM data. 

Of course, getting the best AI tools for the job into your teammates’s hands is only the first step in an AI onboarding process. The next is training them on how to get the most from those tools—and how to avoid the most common AI pitfalls. 

For example, AI can spot patterns in data, but it can’t interpret them. This means it will be able to spot that a fashion retailer sees a huge spike in traffic for Christmas trees in December, but it won’t know why. So, an AI tool might highlight that as a strategic opportunity based on your GA4 data, not knowing that demand will go back down to zero in January.

You should also define the metrics you’re going to use to measure whether an AI tool is benefitting your business. For example, if you’re implementing an AI-powered customer service chatbot, you might track metrics like:

• Average response time
• First-contact resolution rate
• Customer satisfaction scores
• Percentage of tickets that are resolved without human intervention
• Engagement and job satisfaction levels within your customer service team

Defining what success looks like before you implement the tool will help you prevent your judgment from being clouded by “shiny object syndrome.” Instead, you’ll be able to make an objective judgement on whether or not you’re seeing a positive ROI on the tool.  

Put governance first by establishing AI risk guardrails

Letting individual departments make AI decisions in isolation—without any centralized strategy or accountability—is a recipe for compliance headaches, security risks, and wasted budget. Which means marketers should not be creating their own AI governance strategy. Instead, they should be working with a centralized AI governance council made up of representatives from every department, from legal and security to marketing and HR.

Why? Because AI impacts every corner of your business. Your marketing team needs to understand AI bias risks when they’re using tools for audience segmentation. Your HR department needs to know compliance requirements when they’re screening resumes with AI. Your procurement team needs to evaluate vendor security practices when they’re buying AI tools. And if any of these areas go wrong it could cause a PR disaster that marketing needs to clean up.

Once you’ve established your governance council it’s time to make sure they actually understand what they’re overseeing. 

Your initial training should get them up to speed with what the AI landscape looks like today, including:

• How artificial intelligence actually works
• The risks associated with using it
• Common use cases across different departments
• The current AI regulations
• Any sector-specific compliance requirements that apply to your business

And given how fast the industry is changing, artificial intelligence isn’t an area where your business can afford to rest on its laurels. The technology itself is advancing so quickly that new risks and opportunities are surfacing every quarter. Plus, new compliance requirements are emerging at the state, federal, and international level.

Organizations should therefore run regular workshops that update their AI governance council on the latest changes to the artificial intelligence landscape. They should also consider assigning someone on the governance team to monitor regulatory developments and flag any new laws that could impact the organization’s AI strategy.

Your AI governance council’s main responsibility is to set clear policies that mitigate the risks that come with using artificial intelligence. 

Key focus areas include:

Data input and output

According to a Gartner report, poor data quality costs organizations an average of $12.9 million each year. And AI will only amplify that problem without the right guardrails in place.

First, organizations need to establish processes that make sure they’re only feeding their AI tools appropriate data.

That includes:

• Taking steps to minimize or eliminate bias within their training data
• Anonymizing all Personally Identifiable Information (PII)
• Ensuring they’re not violating the intellectual property rights of others

They also need to carefully review the output of their AI tools at every stage to catch misinformation before it can cause any damage. For example, a landing page that targets a pain point that AI hallucinated when analyzing the transcripts of your customer calls isn’t likely to drive many conversions.

Human-in-the-loop checkpoints

An important thing your AI governance guidelines need to establish is when—and where—you need to place “human-in-the-loop” checkpoints. Organizations should map this out based on how likely AI is to malfunction—and how much damage it could cause if it did.

Take product recommendations on an ecommerce website. If AI recommends the wrong sneakers to a customer, the worst-case scenario is they don’t buy anything or they return the product. While organizations should track the overall performance of the web traffic their product overviews drive and watch for unusual patterns, there’s clearly no need to review them on an individual basis. 

On the other end of the spectrum there’s outsourcing the management of your Google Ads campaigns to AI. You want the efficiency gains of automated bidding and budget allocation, but you can’t afford to let the AI blow your entire quarterly marketing budget on irrelevant keywords.

This might require setting clear parameters—maximum daily spend limits, prohibited keywords, target performance thresholds—and then having marketing team members monitor the campaigns daily. If the AI starts making decisions outside those parameters or performance drops below acceptable levels, humans will spot them fast and can take back control immediately.

Transparency and auditability of AI-generated outputs

If your AI tools start producing unexpected results then you need to be able to trace the problem back to its source. Was it a data collection issue? An AI hallucination? A problem with how the data was labeled or cleaned?

This isn’t just bureaucratic paperwork—it’s your insurance policy against AI failures. These records should document data quality from origin through every transformation, identify sources of errors, track updates and changes, and clearly attribute data to its original sources.

Think of it this way: if your AI system makes a decision that costs your business money or exposes you to legal liability, your data provenance records are what will help you understand what went wrong and prove to policymakers and regulators that you were following proper data governance and AI governance procedures.

Go beyond the demo to thoroughly vet your AI vendors

According to research by MIT, 55% of all AI failures come from third-party tools.

To avoid suffering from one of those failures, organizations need to evaluate and onboard AI vendors beyond surface-level demos and promises.

While there isn’t an easy way to mitigate the risks posed by these tools, organizations should focus on:

Transparency

Does the vendor clearly explain how their AI works and what data it uses? You’ll want vendors who provide documentation and insight into their models. This is crucial for building trust and adhering to regulatory requirements—for example, banks need explanations for automated decisions to comply with fair lending laws.

Avoid black-box solutions. Look for vendors that:

• Provide detailed documentation about their models
• Explain what data inputs drive decisions
• Can walk you through their training processes

This isn’t just about compliance—although that’s obviously a concern here. Remember: the majority of AI failures come from third-party tools. If an AI tool starts producing unexpected results you need to be able to quickly course correct yourself to keep any damage caused by a failure to a minimum.

Data privacy and security

If your organization operates in the EU, you’re legally responsible for ensuring that any data processing you engage in—including that performed by third-party AI vendors—complies with GDPR regulations.

That means that if an AI vendor you’ve contracted mishandles data then you can face legal, regulatory, and financial consequences—even if the fault lies entirely with the vendor. From the EU’s perspective, a failure to conduct due diligence on their AI vendors and ensure appropriate data protection agreements are in place can actually be seen as negligence on the part of the organization.

So, a crucial part of the onboarding process for any AI tool is getting a clear understanding how the companies behind them handle data privacy and security. You should treat their approach to data handling as an extension as your own. If it doesn’t meet your organization’s standards you should avoid using their tools.

Regulatory compliance

If your AI content generation tool creates content that infringes on someone’s trademark or copyright, “our vendor said the training data was clean” won’t protect you from a lawsuit.

Look for vendors that can explain how they’re complying with all the laws and regulations they need to. And be sure to ask which developments to AI regulations they’re keeping track of—and how they’re already addressing those in their product roadmap.

The vendors worth working with will support your compliance efforts by providing documentation, audit trails, and expert consultation. The ones to avoid will treat compliance as your problem to solve after you’ve already implemented their technology.

Cross-functional onboarding is not just a MOPS problem

Smart organizations are building AI governance councils that involve every department—not just the ones using it directly. That means bringing IT, compliance, legal, HR, and data teams to the table to shape your AI policies.

The ones that aren’t? They’re at risk of creating chaotic patchwork of disconnected tools, compliance nightmares, and security vulnerabilities that could have been avoided with proper cross-functional planning from day one.

Here’s why each function matters for your AI marketing success:

• IT teams need to evaluate security risks, assess infrastructure requirements, and ensure any AI tools integrate properly with your existing tech stack. 
• Compliance teams understand the regulatory landscape and can help marketers navigate requirements like GDPR, CCPA, and industry-specific regulations. 
• Legal departments can review vendor contracts, establish guidelines for AI-generated content that won’t land your company in hot water, and help you navigate the evolving intellectual property landscape around AI.
• HR teams can help ensure your team has the skills needed to work effectively with AI tools—and understands how to mitigate the risks associated with them.
• Data teams understand your organization’s data architecture and can ensure AI tools have access to the right information—while maintaining proper governance and quality standards.

You should also bring all these teams into the fold when it comes to your onboarding process for AI-powered tools. Just as you wouldn’t deploy a new CRM or marketing automation platform without an IT security review and legal contract approval, AI tools should go through the same rigorous vetting process. The difference is that AI tools often carry additional risks around data privacy, bias, and regulatory compliance, so should be put under even higher levels of scrutiny. 

Consider adapting your existing SaaS onboarding workflows to include AI-specific checkpoints. This might include bias testing, data lineage mapping, and regulatory impact assessments—all completed by the most relevant team before MOPS gets the green light to start baking the tool into its workflows.

According to a report from Deloitte, organizations with mature AI governance frameworks see a 28% increase in staff using AI solutions and nearly 5% higher revenue growth. So while an onboarding process this intensive might sound like it’s going to put a handbrake on your AI marketing strategy, it will actually be key to it delivering a strong ROI in the long run.

Documentation, auditing & change management

Thoroughly documenting your department’s AI processes isn’t the most glamorous project you’ll ever work on. But it might be one of the most important.

Just ask Ross Levinsohn, the former CEO of The Arena Group—the publisher of Sports Illustrated. Levinsohn was fired after an investigation by Futurism discovered Sports Illustrated was publishing articles by fake, AI-generated writers. 

In a statement, a spokesperson for The Arena Group claimed the articles in question were “licensed content from an external, third-party company, AdVon Commerce.” But the damage had been done, and Levinsohn was ultimately asked to step down in the wake of the scandal.

If it had a clearer picture of how AI was being used within its organization, The Arena Group might have been able to catch this before it became one of the biggest marketing blunders we’ve seen in the age of AI.

With that in mind, be sure to document your AI use cases from day one. 

Record:

• What each AI tool is being used for
• Who’s using it
• What business outcomes you’re expecting

You should also map out your data flows so you understand exactly what information is feeding into your AI systems and what the outputs are being used for. Track model versions religiously, too—that seemingly minor update from your vendor could be the reason your content quality suddenly dropped.

Most importantly, document your human checkpoints:

• Which AI outputs get reviewed by humans before publication? 
• Who’s responsible for fact-checking AI-generated claims? 
• When does someone need to step in and override AI recommendations? 

Fail to hold stakeholders accountable for fulfilling these roles and you could find yourself following in The Arena Group’s footsteps with your own PR disaster.

You should also schedule regular audits of your AI workflows to check they’re still performing like they should. At first glance, your ad optimization AI might be delivering a great ROI. But a formal audit of the ads it’s running might reveal that it’s making inflated claims about the benefits of your product. 

It’s important to thoroughly stress test all your AI marketing tools here. Ask your customer service chatbot about a policy you changed recently. Does it give an incorrect answer based on outdated information? Then it’s time to update your chatbot’s training data.

Auditing the marketing collateral AI has been used to create is a lot easier—and therefore a lot more likely to get done—if you build AI usage tracking directly into your marketing operations stack. That way you won’t have to rely on team members to remember to log what they used AI for.

For example, you could tag AI-assisted content in your CMS so you can track which articles were written with AI help versus purely human-created content. And you can add fields to your CRM to note when AI tools influenced lead scoring or customer communications. 

This will let you clearly see which AI tools are driving results—and which are potentially performing worse than their human-only counterparts. It will also make it easy to quickly pinpoint the AI-assisted assets when you come to audit them (or if you ever need to remove them for any reason).

Ethics, bias & brand risk management

Left unchecked, AI algorithms can learn problematic patterns from historical data that leads them to discriminate based on race, gender, and sexual orientation. Just ask Amazon. The tech giant built an experimental hiring tool that used artificial intelligence to rate how suitable candidates were for the job they’d applied for. But research by Jeffrey Dastin revealed that the tool “taught itself that male candidates were preferable”—penalizing resumes that included the word “women’s” (as in “captain of the women’s soccer team”).

A study by Yiran Yang also discovered that AI-generated images “consistently favor White people compared to people of color” and reinforce “social values of white supremacy and norms of white beauty.” Research by Joy Buolamwini and Timnit Gebru also discovered AI-powered facial recognition systems misclassified gender in just 1% of white men, but in up to 35% of black women—exposing the heavy bias in datasets used to train these models.

This all goes to show that the output of your AI tools is always going to be as biased as the data you train them on. For example, research into gender bias within AI models revealed their tendency to automatically assign he/him pronouns to professions like doctors and pilots and she/her pronouns to nurses and flight attendants. That kind of bias is going to be present in any AI-generated content you create. So you need guardrails in place to make sure that gets spotted before the content is published. 

Organizations should therefore establish content review protocols for all AI-generated assets designed to spot bias. Every ad, landing page, and email AI touches should go through this process to ensure you aren’t publishing content that perpetuates stereotypes or biases.

You should also formalize a process employees can use to escalate AI-related issues or errors. If a team member has to edit out language that reinforces gender stereotypes from an article or cancel a personalized email campaign that was going to recommend stereotypical products to customers based on their ethnicity, your AI governance council should know about it. That way you can work on a strategy that addresses the bias in the data your AI tools are being trained on—or even roll back your use of AI, if necessary. 

Future-proof by scaling governance as AI matures

It feels like just about every week a new ground-breaking AI tool hits the market. The industry is changing so fast that your current AI tech stack could be outdated this time next year. The most effective and responsible AI onboarding strategies therefore foster a culture of trustworthy experimentation. Your team should be empowered to test new AI tools on low-risk use cases before being used in customer-facing applications.

For example, before launching a public AI customer service chatbot, you might pilot it for internal help desk queries where mistakes won’t impact external customers. And before using AI for ad copy, you might use it to create internal communications to see how it fares there.

Each of these pilot programs should have clear success metrics and well-defined rollback plans. And a key part of each program should be documenting what works and what doesn’t, building institutional knowledge that guides future AI investments.

This approach will keep your team on the cutting edge of what AI can do without risking a public AI-related blunder. 

You should also start preparing for the regulations changing AI governance—including the EU’s AI Act, growing FTC guidance on AI marketing practices, and state-level regulations like California’s AB 2013, which requires developers to post information on their websites regarding the data used to train their AI systems. Companies that wait until these regulations are finalized will end up scrambling to retrofit compliance into AI systems that were built without proper safeguards. Build your AI strategy with them in mind and you’ll be ahead of the curve when you’re legally required to start abiding by these regulations. 

The future of AI marketing isn’t going to be an all-in-one tool. Instead, your tech stack is likely to be made up of one AI model you use for content generation, another you use for predictive analytics, and a third for ad optimization. Plus, the platforms you’re already using will have their own AI capabilities, from your CRM’s AI-powered lead scoring built in to your email platform’s send time and subject line optimization.

Effective AI governance will be about making sure those separate AI models work together seamlessly to share data and insights—but with safeguards in place that prevent a hallucination from one tool entering the training data every model is pulling from. It will also mean standardizing AI workflows so different tools can integrate easily and training teams to think in terms of AI workflows rather than individual AI tools.

AI is a team sport

To marketers, “AI governance” can sound like red tape that’s going to slow them down from executing an AI marketing strategy while their competitors get an edge on them.

But isn’t a blocker—it’s an enabler for trust, scale, and speed. And without it, you could undo years of brand building efforts with one AI-related blunder. 

It’s clear now that AI is far from a passing fad. Instead, it’s going to be a key driver of growth across every department—especially marketing. Enterprise organizations therefore need to operationalize AI like any other high-impact capability: with structure, visibility, and intention.

Of course, this all starts with picking the right AI tools. Check out our guide to choosing the right marketing AI tools for real business impact to find out how to pinpoint the tools that will have the biggest impact on your marketing efforts. For an AI solution tailored to the needs of larger organizations, explore Semrush Enterprise AIO.