AI will amplify traditional product adoption barriers

We are not very good at predicting how well new products will be adopted. Sometimes we underestimate because we cannot understand potential. 82 years ago, IBM’s Thomas Watson famously underestimated adoption saying “I think there is a world market for maybe, five computers”. In contrast, Google overestimated the adoption of Google Glass in 2013, failing to gain the predicted mainstream usage.

As excited as we may be about AI-powered customer service, agentic shopping, conversational search and automation, we risk overlooking the adoption challenge in our rush to make something new. Discovery activities and design thinking can help us de-risk what we build, and increase the likelihood of success. However as AI allows us to build faster (and discard faster), building quickly in the wrong direction is not a desirable outcome. We’re entering a period of innovation and making the right investment in the right thing remains critical.

Build it and they will come

We all fall in love with our own ideas, and it’s tempting to simply go ahead and build it, even if there is no guarantee of adoption. However we quickly come up against these barriers:

Add AI into the mix and we see how the barriers increase

Awareness and understanding

People may not know:

• That your product or service exists.
• How it works.
• Why it’s relevant to them.
• How to find or access it.

AI amplifier: Outside tech circles, many people may not know about what AI can do or why it matters. Use cases aren’t immediately obvious, and the language can feel too technical. We may have a positioning problem – “It’s not for me”.

Trust and credibility

• New brands must earn trust and established brands must maintain it.
• People may doubt reliability, safety, or quality without social proof.
• Reduced access to human customer service agents can undermine trust.

AI amplifier: Automation saves time, but can obscure how decisions are made or what data is being used. This may undermine trust. Questions around data usage, bias in datasets and algorithms, accuracy, and communication channels may also decrease trust.

Value exchange

• The price or time investment feels too high for the perceived benefit.
• Benefits are unclear, intangible, or delayed.
• Novel ideas need to have a ‘wow’ factor to gain traction

AI amplifier: Users may wonder what they’re getting in return for their data. If AI feels like cost-cutting, or a reduction in headcount or service, it may negatively affect trust and adoption. Self-service or automation must deliver clear, user-centered benefits.

Habit and ecosystem

• People are creatures of habit. Switching routine costs time, effort and familiarity, potentially outweighing perceived benefits.
• People may resist products that don’t fit into existing systems, devices, cultures and lifestyles.

AI amplifier: This is about risk. Users may have various fears around wasting money, a product not working or taking too long to learn. Yet we also know that fear can drive new behaviour – fear of missing out. Attention spans are lower than previously, and your chance to impress users is limited.

Complexity and change fatigue

• Products or services that are too different or hard to understand can deter adoption.
• Overwhelming onboarding or a steep learning curve may do the same.
• People can feel overloaded by new products and technologies.

AI amplifier: AI-powered experiences may not be immediately intuitive to use. The desire to reject old interfaces and proven frameworks may hinder users and remove conventions that were well understood. If the new way feels too hard or alien, adoption will be slower as people take longer to adapt to it.

Accessibility and ease of use

• Limited availability and poor inclusivity can hinder use.
• Doesn’t align with values, lifestyles and social norms, or doesn’t match the environment.
• Challenges for users with additional needs as inclusive design is an afterthought.

AI amplifier: Are you creating this for you and people like you, or for everyone? Tech teams are at risk of building in a bubble. The lack of diversity and representation increases the risk of creating products for early adopters that fail to cater for mainstream audiences.

And we can’t forget timing

Timing is everything. In 2006, a colleague pitched the idea to replace the paper Thompson directory with a website to connect customers with local tradespeople. The idea didn’t gain traction and funding wasn’t forthcoming. Seven years later, websites like check-a-trade and mybuilder.com emerged, proving the idea was sound, but the world just wasn’t ready for it in 2006.

At Equal Experts, we’re helping clients scan horizons, track trends and identify the right time and place to invest, because even great ideas can fail if launched too soon.

What this means for you

If you’re building an AI solution, ask yourself:

1. How will you make it relevant to non-technical users?
2. How will you maintain trust and transparency when AI ‘wizardry’ is happening behind the scenes?
3. What’s in it for your users? Is the value exchange fair and balanced, and is the timing right?
4. Can you build on existing processes, systems, behaviours and habits?
5. Are you solving a big enough problem for users to overcome the learning curve?
6. Is your solution inclusive for everyone who needs to use it?

With AI products and experiences on our horizon, the chance of successful adoption will be greatly enhanced if we plan activities alongside the product development to address these adoption barriers. The things we create only matter if they reach, and resonate with, the people they’re made for.

About the author

Liz Leakey is the Global Head of Design and Product at Equal Experts, with over 20 years of experience leading creative digital teams to deliver outstanding user experiences. She has worked with organisations including the BBC, Pret a Manger, Sky Betting & Gaming, the Department for Work and Pensions, The Very Group, and the John Lewis Partnership. Liz helps teams adopt a user-centred approach to building products and services – designing with empathy to create digital experiences that genuinely improve people’s lives. Passionate about enabling and empowering teams, Liz fosters collaboration and champions diverse thinking to drive innovation and solve complex problems together.