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Beyond the hype: How to build AI products that actually add value
Reading time 10 mins
Key Points
- AI theatre is real – up to 80% of users don’t engage with the AI features added to products and services.
- This results in poor returns for businesses investing in AI development to drive impact and customer value.
- Build AI products that avoid the gimmick trap by adopting an AI-native product design approach from the outset.
- Start with user intent, not AI novelty, and centre the product design strategy around experiences that couldn’t exist without intelligent systems.
- Common missteps include designing in isolation, expecting users to adopt new habits, and neglecting feedback loops.
- To design useful AI products: Focus on solving pain points, making features seamless, and keeping trust and control in the user’s hands.
- Collaborate with industry experts to ensure that your product aligns AI features with user intent, is designed for natural interaction, and treats AI as a means, not the end.
Don’t fall into the AI gimmick trap! We can take your vision for AI-powered products from start to finish – on time, on budget, and in line with user expectations.
Ben Mazur
Managing Director
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AI is everywhere—from electric toothbrushes and phone cameras, to virtual assistants and tech-enhanced sports shoes. Companies across industries invest heavily to build AI products that showcase innovation, improve user experience, and stay ahead of competitors. Yet, an estimated 70–80% of users rarely or never use these AI features, resulting in a poor return on investment for businesses relying on AI to boost customer value or business impact. While many AI-powered tools feel more like theatre than substance, the challenge lies not in adding AI for AI’s sake, but in building products that customers will use – and keep using.
At Ignitec®, we believe flashy isn’t always functional. Our approach starts with understanding the end-user’s pain points and designing intelligent, practical, and elegant solutions that deliver lasting value. If you’re looking to build useful AI products that customers will use—rather than adding costly features that sit idle—schedule a free and confidential consultation with one of our experts. We’d love to help you create something that delivers real impact and avoids the gimmick trap!
What is ‘AI Theatre’ in UX?
AI theatre refers to the growing trend of launching flashy, AI-enhanced features that appear innovative but fail to deliver real value or offer meaningful solutions. They’re often:
- Bolted onto products as a last-minute selling point
- Disconnected from core user journeys
- Hard to discover, learn, or adopt
- Used once, then forgotten
Think of virtual assistants that live in a sidebar but don’t integrate with your workflow, or auto-suggestions that rarely get clicked. These aren’t value-adds—they’re distractions.
The result? High development costs, low usage rates, and frustrated users who feel like they’re beta-testing marketing hype.
Added to that are growing consumer concerns about AI’s environmental impact, prompting calls for greater transparency, accountability, and sustainable practices in developing and deploying AI technologies. For companies prioritising sustainability and showcasing their green credentials, embedding AI features that serve no real purpose sends mixed messages—and risks alienating a values-driven customer base.
What is AI-Native product design?
In contrast to AI theatre, AI-native product design takes a different approach. Instead of layering AI as an afterthought or an add-on, it reimagines the user experience from the ground up with AI as a core enabler.
AI-native products:
- Wouldn’t exist without AI, or would function significantly differently without it
- Centre the user’s intent, with AI helping them move faster, smarter, or more intuitively
- Rely on feedback loops, allowing AI to learn from behaviour and improve over time
- Disappear into the UX, making intelligence feel natural, not forced
AI-Native vs. AI Theatre: A quick comparison
| AI Theatre | AI-Native Product Design | |
| Purpose | Show off innovation | Deliver value through intelligence |
| Integration | Added late in the process | Baked into core user journeys |
| User Experience | Often confusing or redundant | Seamless and intuitive |
| Adoption | Low after initial novelty | Sustained because it fits user needs |
| Examples | Voice assistants that sit unused in sidebars | Recommendation engines that evolve with use |
Real-World Examples
- TikTok’s “For You” feed is a good example of AI-native design. It’s not just a feature—it is the experience. The feed constantly adapts to your viewing behaviour, delivering a hyper-personalised, continuously engaging stream that would be challenging to create manually.
- Notion AI enhances a core workflow—writing and structuring notes—without pulling users out of context. The suggestions feel helpful and natural, and users remain in control. Compare that with Clippy (the infamous Microsoft Office assistant), whose generic interruptions embodied early AI theatre.
- Spotify’s Discover Weekly is another strong example. It uses collaborative filtering and user behaviour data to surface fresh, relevant music weekly. It doesn’t demand attention but builds trust and loyalty as it works.
- Nike’s strategic use of machine learning and GenAI dramatically improved the customer experience, optimised the supply chain, reduced waste, and fueled innovation.
Contrast these with:
- Chatbot sidebars tacked onto e-commerce sites that can’t answer basic questions.
- Auto-caption tools that require manual fixing every time.
- “AI photo enhancements” that over-process your face by default.
- Meta reportedly paid up to $5 million to use celebrity likenesses for their AI Personas chatbot feature, but it failed to gain traction with users, and the plug was pulled after just one year.
What causes AI products to fail?
AI products that failed to gain traction with users, and ultimately wasted companies’ time, labour, and money, have similar traits in common:
1. They’re solutions in search of problems
Teams often start with “how can we use AI?” rather than “how can we solve this problem better?” This leads to features that feel tacked-on instead of intentional.
2. They demand new user behaviours
If your feature requires people to learn new tools or alter deeply ingrained habits without a clear benefit, adoption will be slow or non-existent.
3. They lack feedback loops
Useful AI products learn from user behaviour and evolve. Static “smart” tools that don’t improve quickly become obsolete.
4. They prioritise wow-factor over UX
Just because something can be automated doesn’t mean it should be—especially if it adds complexity or cognitive load to the experience.
How to build AI products that win
To avoid the common traps of AI product strategy mistakes, here’s what to focus on when building AI native products:
1. Solve a real user pain point
Start with qualitative and quantitative user insights. What are users struggling with? Can AI meaningfully reduce that friction or enhance the outcome? For example, Grammarly uses AI to improve clarity in writing—a task users already want help with. It fits naturally into existing workflows without requiring new habits.
2. Make it invisible (when possible)
Great AI doesn’t scream, “Look at me!” It quietly enhances the experience. For example, Google Maps uses AI to optimise real-time routes based on traffic conditions. Users don’t need to understand how it works—they just get there faster.
3. Design for explainability and control
Users are more likely to trust and adopt AI tools when they understand why a suggestion was made and when they can override it. For example, you can provide clear cues like “Suggested by AI”, or offer “Undo” and ‘Disable AI’ options.
4. Minimise onboarding and friction
If your AI feature needs a tutorial, you might be overcomplicating it. Seamless, contextual guidance beats one-time onboarding every time.
5. Think in terms of enhancement, not replacement
AI should extend what users can do (like a co-pilot), not make them feel replaced or confused (like an autopilot).
The cost of getting it wrong is significant. Building flashy AI features that no one uses isn’t just a UX issue—it’s a business problem which can lead to:
- Wasted engineering resources
- Increased customer support costs
- Lower retention and engagement
- Brand fatigue occurs when users feel let down by overpromises
- Backlash from climate-conscious consumers
Are you ready to design products with a lasting impact?
To build AI products that genuinely add value, we must shift the question from “How can we use AI in our product?” to “How can AI augment what users already want to do?”
That means aligning AI features with user intent, designing for natural interaction, and treating AI as a means, not the end. If you’re beginning your journey in designing AI-powered products and want to ensure a solid return on your investment, we’re here to support you. Our expert team and comprehensive in-house facilities, combined with your innovative vision, will take you from start to finish—on time, on budget, and aligned with market expectations.
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FAQ’s
What does it mean to build AI products?
To build AI products means designing tools or services where artificial intelligence is integrated to improve performance, personalisation, or automation. These products can range from chatbots and recommendation engines to smart home devices or enterprise software. The key is ensuring AI enhances the user experience rather than complicating it.
Why do most AI features go unused?
Many AI features are added without solving a clear user problem, making them feel like gimmicks. If users don’t see immediate value or the feature disrupts their workflow, they will likely ignore it. Poor onboarding and low discoverability also contribute to low adoption.
How do you build AI products that users will actually use?
Start by identifying a real user need and ensuring AI enhances, rather than disrupts, existing behaviours. Design with the end-user in mind, focusing on simplicity, transparency, and usefulness. Testing early and often helps ensure the AI feature adds genuine value.
What is AI theatre in product design?
AI theatre refers to adding impressive-sounding AI features that look good in demos but don’t serve a meaningful purpose for users. These features are often poorly integrated and underused. It results in wasted resources and can damage user trust.
Why is AI theatre a problem when building AI products?
AI theatre diverts attention from solving actual user problems, often leading to features that users quickly abandon. It can waste time, inflate development costs, and make the product seem less trustworthy. Worse still, it undermines the real potential of AI.
How can AI-native product design improve adoption?
AI-native design starts with the assumption that AI is core to the user experience, not an afterthought. This leads to seamless, intuitive interactions where AI quietly enhances value. Users are more likely to adopt features that feel natural and genuinely helpful.
What are the common mistakes when building AI products?
Common mistakes include adding AI for its own sake, ignoring user feedback, and failing to consider usability. Over-promising capabilities without delivering value also damages trust. Many teams overlook the cost of maintaining and evolving AI systems.
How do you align AI features with user intent?
Observe how users naturally complete tasks and identify where AI can make those tasks easier, faster, or more accurate. Don’t force users to engage in new behaviours to showcase AI. Let the technology work quietly in the background to improve their experience.
Which industries benefit most from building AI products?
Industries like healthcare, finance, retail, and mobility benefit most from AI products that personalise experiences or automate complex tasks. However, any industry can benefit if the product solves a specific need effectively. Success depends more on user alignment than on the sector itself.
Why do AI features often feel disconnected from the product?
Many AI features are added late in the development process rather than being designed into the product from the start. This makes them feel out of place or redundant. Integration must be intentional to ensure relevance and ease of use.
How do you know if your AI feature is useful?
The feature is likely useful if users repeatedly engage with it and it measurably improves their outcomes. Analytics, feedback loops, and usability testing help determine this. High abandonment rates or support tickets can signal poor fit or design.
What role does sustainability play in building AI products?
Sustainability matters because AI systems often require significant energy to develop and operate. Embedding unnecessary AI features sends mixed messages for companies that promote green values. Thoughtful design reduces environmental impact while maintaining performance.
Who should be involved when building AI products?
Cross-functional teams, including product designers, data scientists, engineers, and user researchers, should all collaborate. Each brings a different lens to solving the problem effectively. Crucially, users themselves should be involved in testing and feedback.
What is the difference between AI-native and AI-enhanced products?
AI-native products are designed with AI at their core, making them dependent on intelligent systems. On the other hand, AI-enhanced products bolt AI onto existing tools to improve them. The former feels more seamless and intuitive.
When should AI not be used in a product?
Avoid using AI when it adds complexity, invades privacy, or doesn’t improve the user experience. If a task can be completed more effectively without AI, it’s best to keep it simple. Overusing AI can reduce trust and increase support costs.
How do you ensure your AI product is ethical?
Ensure transparency in how AI works and allow users to understand or override decisions. Avoid biased data and test thoroughly across diverse user groups. Ethics should be part of the design process, not a post-launch fix.
Which AI features tend to deliver long-term value?
Features that learn from behaviour and improve over time—like recommendation engines, predictive text, and personalisation tools—tend to perform well. They feel helpful without being intrusive. Long-term value comes from relevance, not novelty.
What is the best way to test AI features in your product?
Use A/B testing to compare the AI version with a baseline and measure real impact on user behaviour. Collect both qualitative and quantitative feedback. Ongoing evaluation helps refine the feature and improve its usefulness.
How do you build trust in AI-powered products?
Trust comes from clarity, control, and consistency. Explain why AI makes suggestions, let users override it, and ensure it works reliably. Misleading claims or errors damage trust quickly.
Why is user research critical when building AI products?
Without understanding users’ goals and challenges, it’s easy to build AI features that miss the mark. Research helps teams identify opportunities for meaningful improvement and guides decisions about where AI should (or shouldn’t) be used.
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