Customers have mixed feelings about artificial intelligence (AI). For example, many people don’t trust self-driving vehicles but are happy to engage with chatbots.
Most customers understand the benefits of AI.
They like that it can enhance their shopping experiences, provide personalized customer service, and help them find solutions to their problems much faster.
Despite the positives, many have concerns that AI may cross the line, becoming too invasive and a threat to their privacy.
Businesses need to address these concerns if they want customers to trust their brand.
In this article, we will explore the complexities of consumer trust in AI, the concerns contributing to consumer skepticism, and how businesses can allay customers’ fears.
Why Customer Trust Matters
According to Qualtrics, only 29% of customers trust organizations to use AI responsibly. Misuse of personal data is also consumers’ top concern when companies use AI to automate interactions.
Meanwhile, Deloitte’s 2025 Connected Customer Survey revealed that 47% of respondents experienced at least one kind of security failure in the past year, and 27% reported two or more.
That’s a startling statistic, one that reduces trust in technology.
And trust is fragile; hard to acquire but easy to lose. And once lost, it can be hard to regain.
Why does customer trust matter?
Trust is a key to customer retention and loyalty. Customers who trust your brand won’t hesitate to do business with you again.
They’re also more likely to sing your praises to others, which could bring new customers to your door without you lifting a finger to attract them.
Imagine how much that could save you in marketing and advertising costs.
When it comes to AI, customers’ distrust lies in how companies implement AI in marketing and business processes, how they’re using data, and how AI may lead to errors or expose them to risks.
A lot of distrust of AI is due to a lack of information. If you can demonstrate that you use AI to benefit customers rather than harm them, it can ease their fears.
Biggest Concerns Customers Have Around Artificial Intelligence
Customers worry about the implications of AI. According to a survey by YouGov.com in 2025, 54% of Americans approach AI cautiously, while:
- 47% are concerned
- 44% are skeptical
- 22% are scared.
What’s driving customers to be cautious, concerned, sceptical, and scared of AI?
1. Distrust in The Accuracy of AI
AI technology relies on data and algorithms to function. While a computer can process data much more efficiently than humans can, it can return wrong or inaccurate results if the data is incorrect or biased.
This is particularly evident in generative AI but also applies to predictive analytics, fraud detection, and even medical diagnoses.
Another example is recommendation engines that continuously suggest irrelevant products, creating a poor customer experience.
When AI gets it wrong, it can destroy customers’ trust in its capabilities.
To improve accuracy and minimize biases, you must continuously fine-tune your AI models with ongoing training and updates from a broad spectrum of data. That’s a process increasingly supported by AI coding agents.
2. The Lack of Human Involvement
Customers may feel okay about using an AI chatbot for simple questions, but that doesn’t mean they want human support removed from the experience.
Per a Zendesk and YouGov survey, 84% of respondents believe human interaction should always remain an option. That says a lot about what customers expect from AI: speed and convenience, but not a dead end.
For this reason, companies should avoid using AI as a wall between the customer and the support team.
AI can handle basic queries, route requests, and help people find answers faster. But for complex, sensitive, or urgent issues, customers still need an easy way to reach a human agent.
Striking a balance between AI and human support can help meet customers’ needs and gain their trust.
3. Privacy and Security Concerns
Customers want to know what happens to their information. If a company uses AI, that concern gets even stronger because people may not know what data the tool collects, how it uses it, or who can access it later.
A data breach is the clearest risk. But trust can also break in smaller ways.
For example, when customers feel tracked without a real explanation, pushed into vague consent, or left with no clear way to manage their preferences.
The best move is to be clear before collecting anything. Ask for consent, explain what the data is for, and keep the language simple enough for a normal person to understand.
If your team needs a better way to manage consent, reviewing Axeptio alternatives can help compare options for opt-ins, disclosures, and privacy preferences. But the tool only helps if the policy behind it is clear.
4. Distrust in The Veracity of Information
One of the drawbacks of AI has been its use in creating disinformation and scams.
AI is making it hard to differentiate between true and false information, images, and videos. This has caused many people to lose trust in AI.
Younger consumers, aged between 25 and 44, express more trust in AI, and because they’ve grown up with digital technology, may be sharper at spotting fake content.
In contrast, older consumers, aged 55 and 65+, are less trusting and may have greater difficulty distinguishing between real and fake AI-generated content or scams.
How To Build Your Customers’ Trust in AI
AI has become an integral part of modern business operations, enhancing efficiency, streamlining workflows, and improving customer experiences.
However, despite its many advantages, some customers remain wary of AI.
Businesses that proactively address this can shift their perception from negative to positive.
Here are five strategies to build your customers’ trust in AI.
1. Develop an organizational AI strategy
Using AI without a clear strategy can create the exact problems customers worry about: unclear data use, biased outputs, weak security, and decisions no one can explain.
Your AI strategy should set the rules before the tools start making decisions. That means defining where AI can be used, what data it can access, who reviews its outputs, and what happens when something goes wrong.
Some parts can be handled internally. Other parts may need outside expertise, especially if your AI systems touch sensitive customer data, regulated industries, or multiple markets with different privacy laws.
A strong AI strategy should cover:
- Industry best practices and ethical guidelines for AI development and deployment.
- Compliance with relevant privacy laws, including the European Union’s GDPR data protection regulations if you have a global customer base.
- Internal policies and processes that ensure accuracy, accountability, and ethical standards are maintained.
- Ongoing employee training so AI tools are used effectively and responsibly.
- Regular reviews and audits of AI processes, with updates as your organization and customer needs shift.
The point is not to turn every company into an AI compliance expert. Instead, it’s to make sure someone owns the risks before customers are asked to trust the system.
2. Share your AI strategy with your customers
Transparency is a key pillar to building trust in AI. Customers want to know how AI is being used and how it impacts them.
- Make it clear to customers when they are interacting with AI rather than a human.
- Provide clarity on AI-driven decisions, especially in areas where customers may feel at risk, like finance and healthcare.
- Explain the security protocols that keep data secure and uphold ethical standards.
- Reassure customers that a human is always accountable for AI-driven decisions and that they have access to human intervention when necessary.
3. Obtain customer consent to collect and share data
As Deloitte’s 2025 survey also notes, 82% of users said AI technology could be misused, up from 74% in 2024.
That concern makes consent even more important. Customers need to know when data is being collected, what it will be used for, and what they are agreeing to before they share personal information.
To make that clear from the start:
- Notify customers when they’re about to engage in AI platforms where data is collected.
- Use simple language to explain AI processes so customers understand what they are agreeing to.
- Display AI disclosures prominently on website banners and notifications before customers engage with AI chatbots, or expand on them in your FAQs section.
4. Don’t neglect the human factor
Customers still value human interaction, especially when dealing with complex or sensitive issues.
Striking the right balance between AI and human support creates a more robust support structure that customers will feel more comfortable with.
Build a hybrid customer service model that includes human and AI-enabled support.
- Use AI to assist human agents rather than replace them. For example, self-service options like AI chatbots, knowledge bases, and virtual assistants can help customers find answers quickly without human intervention. This could allow agents to manage and resolve more complex queries faster.
- Always offer customers the option to speak with a human customer service agent when needed. Build processes into chatbots and virtual assistants that allow a seamless transfer to a human agent when required.
5. Adjust trust-building messaging by audience age
Not every customer needs the same explanation to feel comfortable with AI.
As mentioned earlier, younger consumers tend to trust AI more than older consumers. But that does not mean younger customers ignore privacy, accuracy, or security. It just means each group may need a different kind of reassurance.
Younger customers may focus more on speed, control, and convenience. Older customers may want clearer answers about privacy, scams, human support, and what happens if the AI gets something wrong.
Make the basics easy to find:
- What the AI tool does.
- What data it uses.
- How customers can reach a human.
- What safeguards help prevent errors, scams, or misuse.
Vague AI claims do not help much here. People are more likely to trust AI when they understand why it is being used, how it affects them, and who is responsible if something feels off.
How to Vet an AI Implementation Partner You Can Trust
Building trust in AI takes more than picking a tool. A lot depends on how that tool is set up, what data it uses, how errors are handled, and who takes responsibility when something goes wrong.
That work has a lot of moving parts. Bias checks, consent flows, staff training, and AI policies usually need input from people who have done it before.
If you bring in an outside partner, apply the same standard your customers would apply to your brand: do not trust the pitch too quickly.
So, before hiring an AI implementation partner, look for practical proof:
- Real examples of AI projects they have worked on.
- A clear explanation of how they handle sensitive data.
- A process for checking errors, bias, and risky outputs.
- Human review built into the workflow.
- Reporting your team can understand without needing a translator.
- Client references or outcomes you can verify.
If they only talk about faster workflows, smarter automation, or better customer experiences, push for more detail. You need to know how they handle the parts customers never see, but still feel when something breaks.
If you are comparing agencies or consultants for AI implementation work, GrowthFolks gives you a cleaner way to filter the options. The platform looks at fit, track record, and outcomes confirmed by both the client and agency, so you don’t have to rely only on polished case studies or self-reported wins.
For AI projects, that kind of proof matters. A partner helping you build customer-facing AI should be easy to question, check, and verify.
Wrapping Up
Consumers are not against AI. Most understand why companies use it and how it can make the customer experience faster, easier, or more useful.
The concern starts when AI feels unclear. People want to know what happens to their data when they are interacting with AI, what the system can and cannot do, and how to reach a real person if something feels off.
Building trust means making those answers easy to find. So, be clear about your AI practices, ask for permission before collecting or sharing customer data, and put privacy and security rules in place before problems show up.
AI can improve the customer experience; that’s a fact. But trust still depends on how responsibly you use it and how accountable your company stays after launch.
