Addressing AI concerns to build consumer trust

By Mike Nash | March 19, 2025

Reading time: 4 minutes

Addressing Ai Concerns To Build Consumer Trust — Addressing AI concerns to build consumer trust.

Abstract

There an overwhelming number of AI-based tools and services being released but consumers are still not fully on board. Some consumers don’t trust AI and question the real value of it. To encourage AI adoption, brands and businesses must intentionally embed AI tools into their products in a way that allows consumers to see immediate effects.

Research Topics:: Artificial Intelligence / AI | Consumer Research | CX/UX-Customer/User Experience
Industry/Market Focus:: Consumers | Information Technology (IT) | Research Industry
Content Type: Research Industry Voices

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How to encourage AI adoption

Editor’s note: Mike Nash is the president of KS&R. This is an edited version of an article that originally appeared under the title “How to Build Trust in AI-Enhanced Products & Solutions.”

Over the past 18 months, nearly every company has been touting some newly enhanced product, solution or experience built on AI. However, KS&R’s recent research, based on a survey conducted with 500 U.S. consumers in October 2024, indicates that actual consumers may not share the same enthusiasm. Our findings reveal that while enterprises eagerly embrace AI, many consumers remain wary and concerned about trusting these advancements. In fact, 22% of consumers cite trust as a primary concern regarding AI, indicating a significant trust gap that, if unaddressed, could hinder adoption. So, how can enterprises build consumer trust in their AI-enhanced offerings?

How to build consumer trust in AI

Through analysis of consumer concerns and sentiment, we believe that businesses must prioritize these three elements – relevance, clarity and openness – to strengthen consumer trust in AI. Here’s a closer look at each of these recommendations, along with actionable insights and examples for enterprise leaders.

1. Relevance: Invest in the proper use case

Insight: Just because AI can be added to a product doesn’t necessarily mean it should be. Consumers are quick to recognize when AI feels more like a marketing tool than a useful enhancement. When AI is implemented with a clear, consumer-centered purpose, however, it has the potential to offer real benefits that build trust.

Example in action: A major banking app that uses AI to help users manage their budgets and forecast upcoming expenses serves as a strong example of a relevant AI use case. Rather than being a flashy feature, this functionality directly supports users’ financial wellness. In contrast, some “smart” devices that use AI primarily for tracking without providing meaningful consumer value often feel more intrusive than helpful.

Steps to take

Evaluate AI’s consumer relevance: Does it address a real consumer need? Does it provide tangible value?
Ensure that privacy considerations are built into the AI design, respecting consumer data preferences and rights.
Test and gather consumer feedback before a full rollout to gauge reactions and refine the application.

Key takeaway: Only implement AI if it directly enhances the consumer experience in a meaningful, valuable way.

2. Clarity: Engage in storytelling that emphasizes consumer benefit

Insight: There’s an education gap in the public’s understanding of AI. Many consumers don’t yet see how it can positively impact their lives, so businesses need to make AI benefits clear and relatable.

Example in action: AI embedded in fitness apps offers customized workout plans based on health data, a clear benefit that consumers can easily understand and appreciate. Companies that use relatable, real-life examples bridge the education gap and help consumers envision the positive effects of AI.

Steps to take

Use relatable scenarios in marketing and user guides to showcase AI’s consumer benefits.
Focus on clear messaging about both the capabilities and limitations of AI to mitigate consumer fears.
Apply the purpose-process-promise framework:
- Purpose: Explain why AI is in the product and how it benefits users.
- Process: Describe how AI works in simple, accessible language.
- Promise: Reassure consumers of data security, privacy and AI’s limitations.

Key takeaway: Use relatable storytelling to demonstrate AI’s value and bridge the gap between technology and consumer understanding.

3. Openness: Prioritize transparency

Insight: Transparency is essential for building trust, particularly as consumers become more aware of data privacy issues and ethical concerns around AI. Being open about AI’s role, data usage and ethical safeguards can significantly improve trust.

Example in action: Some tech companies have introduced AI user guides that explain how their AI works, what data it uses and how that data is protected. These guides help consumers understand AI’s purpose and process, reinforcing trust through openness.

Steps to take

Conduct regular internal and external audits to review AI systems, focusing on privacy, fairness and accuracy.
Publish findings from these audits and make them accessible to consumers to demonstrate a commitment to ethical AI.
Communicate data-handling practices clearly, explaining how data is collected, used and protected.
Actively work to mitigate biases in AI through diverse data sets and bias audits, then share these efforts openly with consumers.

Key takeaway: Transparency builds confidence. Regularly share how AI works, what data it uses and the steps taken to ensure ethical practices.

As enterprises push the boundaries of AI innovation, they must prioritize fostering trust as a key pillar of success. Companies that proactively address these trust concerns will not only drive adoption but also position themselves as leaders in a rapidly advancing, AI-driven world.