Dangers of AI: Risks and How to Manage Them

AI tools are transforming ecommerce, speeding up operations and creating more personalized shopping experiences. And they’ve become widespread: 75% of Shopify store owners use AI tools, according to a 2025 survey.* But in addition to the benefits they bring, they can introduce risk.

Careless implementation, exposed data, biased outputs, inaccurate content, and weak oversight can harm your ecommerce business. Bad actors can use AI tools for data theft, AI bias can discriminate against customers and workers, and nascent AI technology can present an alignment problem with your business.

This guide covers 10 AI risks ecommerce businesses face and five ways to manage them.

What is artificial intelligence?

Artificial intelligence (AI) is the branch of computer science dedicated to making machines emulate human intelligence. 

For most of history, machines have been passive tools controlled by humans. While an AI tool can’t “think” the way we do, it simulates thought processes. 

AI decision-making processes involve compiling and analyzing data, considering historical precedents, and choosing a path based on predicted success—closely resembling the process humans undertake when making choices.

Rapid advancements in AI mean machines are now involved in many sectors of the economy with little to no human intervention. For ecommerce brands, in particular:

• Generative AI tools like Shopify Sidekick are powered by large language models (LLM) that can generate text which you might use to write product descriptions or marketing copy. 
• Pricing tools like Shopify Smart Pricing can suggest markdowns and let you A/B test price changes before a full sitewide rollout.
• AI-powered export controls can oversee the export of goods, software, and services internationally.

10 dangers of AI in ecommerce

1. Data privacy and third-party data sharing
2. Cybersecurity, fraud, and phishing
3. Technical failures and hallucinated outputs
4. High implementation and maintenance costs
5. IP and copyright risk
6. Misinformation and inaccurate AI-generated content
7. Bias and discrimination
8. Job displacement and over-automation
9. Lack of transparency and accountability
10. Environmental impact

The AI risk in ecommerce may not rise to the level of other industries. But using ecommerce AI still has potential consequences: governance and oversight challenges, data access, output review, and accountability can pose a bigger risk than the technology itself.

Here are a few areas where AI safety is key:

1. Data privacy and third-party data sharing

Ecommerce brands have access to customer data including names, addresses, purchase histories, and financial information. AI systems often use this data to personalize experiences, but it’s your legal responsibility to keep this information safe. 

Customers are also concerned about the data AI collects. In a March 2025 YouGov survey, 53% of US adults were very concerned about AI causing erosion or violation of personal privacy. Gartner found security threats are the top barrier for AI implementation amongst high-maturity organizations.

To safeguard information you’re using for AI: 

• Avoid unauthorized AI tools. Shadow AI was a factor in 20% of all data breaches, per IBM. This happens when employees use unauthorized internet-based AI tools.
• Implement access controls. The same IBM report found 97% of organizations that reported an AI-related security accident lacked proper access controls. 
• Turn off data retention. OpenAI, Claude, and Microsoft Copilot may use your data to train their model unless you opt out.
• Disclose how you use data for AI. Relyance’s 2025 report found 84% of customers would abandon companies that aren’t transparent in how they use their data for AI. Just over three-quarters would switch to brands that are. 

2. Cybersecurity, fraud, and phishing

Artificial intelligence can clone human voices and make branded content appear like it’s authentic. Fraudsters use both tactics to scam customers and businesses. 

The IC3 received more than 22,000 complaints reporting AI-related information in 2025, with losses exceeding $893 million. It found generative AI tools were a culprit. Fraudsters clone a business’s tone of voice in official-sounding emails—sometimes cloning an employee’s voice to call for money. This type of AI fraud cost businesses more than $30 million last year.

The challenge is that AI can make fraud attempts harder to spot. One 2025 study found 76% of the general population was concerned about the use of AI to create more convincing phishing scams. This figure was higher amongst baby boomers and Gen X.

That said, AI can support fraud detection and review when implemented appropriately. Shopify Protect, for example, was trained on more than 10 billion transactions. It spots patterns in risky orders to block fraudulent transactions before they happen.

3. Technical failures and hallucinated outputs

Like all machines, AI systems are prone to technical glitches and failures. 

Take AI chatbots, for example, which might retrieve inaccurate information if the store policies it’s pulling from aren’t up to date. This impacts customer loyalty: when AI information conflicts with a brand’s messaging, just 29% trust the brand.

Because some AI models use unsupervised learning, false information can be baked into the model if it’s not flagged by a human. HAI’s 2026 study found that AI hallucination rates across models range from 22% to 94%. 

“AI has human-like reasoning and decision-making skills, but it’s important to remember that at its core it is still an algorithm,” says Alex Pilon, senior developer at Shopify and AI advocate. “It can produce reasoning and decisions that look perfectly reasonable on the surface yet are actually wrong in a specific context.” 

To mitigate these AI dangers:

• Have a backup plan in place for any crucial systems that incorporate AI—particularly in the early stages of an AI rollout. 
• Include a human in the loop. Have them review any AI-generated output against your brand’s voice, policies, and product data.
• Test AI tools against real-world use cases, instead of relying solely on vendor performance claims. 

4. High implementation and maintenance costs

The high cost of available AI products and services was the third-biggest obstacle to AI adoption, according to a 2025 Statista report.

Tropic’s research supports this: It found small businesses spent 50% more on AI than last year, while spending from midmarket and enterprise brands has grown 58%. Uber, for example, reportedly spent its entire 2026 AI budget in just four months.

Calculate the total cost of ownership before committing to new AI software. That includes fees associated with:

• Implementation 
• Integration 
• Training (both staff and the AI model itself)
• Governance 
• Ongoing monitoring 
• Output review

Start small with a handful of AI tools before you expand, and take advantage of AI features built into the software you’re already paying for. Sidekick, for example, is included in every Shopify plan. It gives access to an AI assistant that’s already familiar with your store’s data with no third-party tooling required.

5. IP and copyright risk

OpenAI reportedly used 9.7 trillion words for its GPT-4 model, including publicly sourced data from the internet. 

However, US law considers work automatically copyrighted when it’s posted in a “tangible medium of expression” (including the internet). This means an AI model’s output could infringe on someone else’s intellectual property (IP).

Take steps to limit risk when using AI-generated content. That might mean:

• Reviewing tool terms before you decide to use AI’s output
• Establishing approval workflows, like checking for plagiarism 
• Consulting a lawyer to get commercial use approval for AI-generated content

6. Misinformation and inaccurate AI-generated content

Content generation is the most common ecommerce AI use case for Shopify store owners, at 69%, according to a 2025 survey.* But like many new technologies, AI could pose serious threats to societal infrastructure.

AI risks for ecommerce brands include:

• Inaccurate AI-generated content. This is problematic for AI-generated product claims, reviews, or marketing content: 75% of consumers form negative opinions about a brand if they encounter incomplete or inaccurate product details online.
• Deepfakes and impersonation. AI can rapidly generate fake news articles or deep fakes, making it seem like figures have said things they haven’t. YouGov found 58% of Americans are very concerned about misleading video and audio deepfakes.

Always verify AI-generated content before you publish it. 

“Starting with AI for the first time, I would say interact with it as a ’thought partner’—just ask it some questions about something, about anything you’re doing,” says Alex. “Take things with a grain of salt as you build your intuition for how it works and what its capabilities are.”

7. Bias and discrimination

Whether they power AI search engines or chatbots, AI algorithms are a reflection of their training data. The International AI Safety 2025 report found general purpose AI models can amplify social and political bias based on age, race, gender, culture, disability, and political opinion.

Bias becomes particularly problematic if you use AI in functions like human resources or customer relations. For example, Reuters reports that more than 80% of employers in the US use AI in the hiring process, yet one AI-powered HR software has been accused of excluding applicants for discriminatory reasons.

Data scientists must give careful attention to algorithm design and the data sources they use to train the system. The AI Safety report lists poor training data and system design choices as two sources of AI bias. And while it found researchers have made progress toward addressing bias, it requires ongoing testing, monitoring, and governance rather than one-time model adjustments.

8. Job displacement and over-automation

Half of Americans now worry that the rise of AI could put them or someone else in their household out of work. 

Despite those concerns, there’s a difference between task automation and full job replacement. Employees are learning to work alongside AI assistants: A 2026 study published by Ipsos found 51% of employed AI users use AI at least partly for work purposes. Fifteen percent say they’ve taken on new responsibilities enabled by AI.

The skills employers look for are also changing. PwC’s 2026 AI Jobs Barometer found tasks added to AI-exposed roles are 2.5 times more likely to rely on soft skills like creativity, judgement, and empathy.

Mikey Moran, CEO of Private Label, explains how he handles job-related concerns from his team in a Shopify Masters interview.

“I say ‘No it’s not going to replace you—it’s going to make you twice as valuable because of all the stuff that you’re going to be able to get done in a day, so then I can probably pay you more once it starts working.’” 

9. Lack of transparency and accountability

AI algorithms—particularly those based on deep learning and neural networks—can be complex and opaque, making it challenging to understand how they arrive at specific decisions or recommendations.

This lack of transparency can erode trust in AI systems, particularly in ecommerce, where transparency and accountability are important for AI-powered decisions like:

• Fraud review. False positives risk canceling a legitimate order.
• Pricing. Clear logic behind suggested price drops can help prevent unnecessary markdowns while catering to the 32% of shoppers who are concerned about biased or unfair pricing with AI.
• Product recommendations. Pushy upselling tactics and misrepresenting queries are among the top three concerns shoppers have around AI.

10. Environmental impact

AI relies on machines stored in data centers to train and monitor the models. They rely on electricity to keep the machines running and water to cool them down.

One research report found Microsoft’s GPT-3 language model, for example, can evaporate 700,000 liters of water. The UNU estimates that by 2030, AI’s water usage will match the needs of 1.3 billion people and occupy land roughly twice the size of Jakarta, which is home to more than 32 million people.

This matters for ecommerce brands, as 79% of consumers continue to be concerned about environmental sustainability, yet only 20% believe a brand’s sustainability claims. 

Think about sustainable business practices alongside AI usage, like: 

• Offsetting your carbon footprint elsewhere—for example, with carbon-neutral shipping through Shopify Planet
• Switching to sustainable packaging materials 
• Opting for renewable energy sources 

How to manage AI risks in ecommerce

To make AI safer, map the tool, limit access, test outputs, document who owns the decision, and review as business needs change. Then follow these AI safety guidelines to mitigate the risks of AI:

1. Secure data access and review AI tool permissions

Ensure that all data used by your AI systems is securely stored and transmitted:

• Turn off model training and check each tool’s data retention policies 
• Choose an AI vendor that regularly issues software security patches
• Check what each AI tool has permission to access and only share the minimum required to function 
• Remove vendor or app integrations you no longer use 
• Use access controls so that when a team member works with sensitive data on a computer, it’s hidden from other users
• Document how to remove access to the AI tool if an employee leaves.

2. Test outputs for bias, accuracy, and brand fit

Use diverse and representative training data in your AI models and regularly test AI algorithms for bias. Review everything AI generates—whether that’s product descriptions, recommendations, or support ticket responses—before publishing or automating decisions. 

Remember that not every task has to have AI involvement. 

“AI can’t feel emotion,” says AC Hampton, founder of Supreme Ecom. “And the one thing you do with marketing is push out emotion. Pain points, desires, real feelings—you can’t make that up. That’s where I don’t let AI touch.” 

3. Disclose AI use where it affects customers

Be transparent with your customers if you’re using their data for AI. Their loyalty is at risk: Relyance found 84% of customers will stop using a brand’s products entirely if they aren’t clear about data usage.

For example, if your ecommerce store uses an AI-powered chatbot:

• Make it clear if they’re talking to an AI agent. Search Engine Land’s 2026 study found 80% of consumers want AI-generated content to be labeled. 
• Offer the chance to speak with a human, if required. SurveyMonkey reports 88% of shoppers think this should always be on offer. 

4. Keep people responsible for AI-assisted decisions

Combine AI with human oversight to ensure automated decisions are monitored and editable, and can be overridden when necessary. 

High-risk areas that require human review and final approval might include:

• Hiring decisions 
• Crisis communications
• Customer refunds or disputes 
• Pricing decisions 

That’s how accessories brand Ridge uses AI for business analysis. “We feed our sales data, trends, launch calendar, and forecasts into an AI model that does the complex analysis,” says Sean Frank, CEO at Ridge. “That one person reviews it, applies judgment, and makes the calls.”

5. Revisit AI tools and policies regularly

Regularly monitor your AI toolstack to review:

• The data each tool has permission to
• The quality of AI outputs
• Your team’s usage patterns
• Vendor terms, particularly around model training and data retention

A small selection of AI tools gives you fewer policies and data access controls to check. 

“There’s a total misconception that you need 50 different tools,” says Catherine Goetze, founder and CEO of Physical Phones. “We use one or two on a regular basis, and we just know how to use them really, really well.”

*Based on a 2025 survey of 500 Shopify store owners conducted in English across Australia, Canada, the United Kingdom, Ireland, New Zealand, and the United States. Respondents were established store owners with two or more years on the platform. Results reflect the experiences of this specific sample and may not be representative of all store owners.