AI Product Design: Benefits, Risks, and Sharing Best Practices from Our Experience
Using AI for product design has been covered extensively in this blog. Now, let’s broaden our focus to include designing and building AI products and services. Why is this important?
According to IBM, 77% of companies have integrated or plan to integrate AI into their products and processes. AI is actively used across domains, including e-commerce, FinTech, and healthcare.
If you’re thinking of rolling out an AI-powered solution, this article will serve as a great introduction to AI product design.
But what are the benefits of AI solutions in the first place?
Benefits of AI Solutions
The AI boom shows no signs of slowing down. From 2024 to 2030, the AI market is set to expand by 36.6% yearly. AI startups are proliferating, and finding an established tech company without an AI offering is quite a challenge. Giants like Microsoft, Meta, and NVIDIA are all aboard.
In other words, designing AI products is key to staying competitive in today’s market.
However, jumping onto the AI bandwagon just because it’s trendy isn’t a good enough reason to start development. Here are some solid motivations for designing an AI product.
Enhanced Data Collection and Processing
AI can process vast amounts of data far beyond human capability, extracting valuable insights. More importantly, AI systems can analyze this information in real time, enabling immediate decision-making.
Case in point, PayPal uses ML to identify potentially fraudulent transactions almost instantly. Thanks to real-time analysis, the payment service provider can prevent fraud more effectively, protecting both the company and its users.
Personalization and Customization
Customers welcome personalized, targeted experiences. In a recent IBM survey, 52% of respondents said they want stores to send them information, ads and offers that match their preferences.
AI delivers just that. Platforms like Netflix, Amazon, and Spotify use AI to analyze user behavior and provide personalized content and product suggestions. What’s more, AI can modify software interfaces based on user preferences and behavior, making apps even more intuitive and user-friendly.
AI integration is no longer a differentiator — it’s a baseline expectation.
Your competitors either already have it or are actively working on it. Users will choose solutions that help them achieve their goals faster and more easily. AI-driven personalization creates experiences tailored just for them — and that’s exactly what users expect today.
Automation and Process Optimization
AI can automate repetitive and mundane tasks, freeing up humans for more strategic or creative activities.
For instance, AI-powered customer service chatbots can handle basic requests, lifting excess workload off human support agents
Improved Usability
AI can enhance UX in multiple ways. Personalizing user experience and optimizing processes are only the tip of the iceberg.
AI-powered NLP, for example, enables more natural and efficient human-computer interactions through voice commands and chat interfaces. This makes software more accessible, particularly for those struggling with traditional interfaces.
Creativity Opportunities
AI still feels like magic to many people, and you can easily reflect this in the UI design of your AI product.
For example, you could present your app’s AI functionalities as a superhero character. This would serve a double purpose, making the design memorable and helping users distinguish between AI and non-AI features.
Potential Pitfalls in AI Product Design
Any new project carries risk, but AI projects have particularly high failure rates. Some estimates suggest that a staggering 80% of AI products never succeed.
How can you avoid such a disappointing outcome? Consider these main pitfalls before starting implementation.
Mistrust Around AI
Your audience might be reluctant to use your AI solution because they simply don’t trust AI. The Gallup study reveals that as many as 40% of Americans believe that AI does more harm than good.
Despite the buzz around AI, users aren’t always thrilled about it. Many AI products are far from perfect and can deliver inaccurate or biased results. Moreover, AI algorithms are often complex and opaque, making it hard for users to understand how decisions are made, which leads to mistrust.
Previous Negative Experience
Potential users may have had negative experiences with AI solutions and might be reluctant to give them another chance.
For example, as few as 30% of shoppers who have ever tried a virtual assistant are content with their experience, and 20% don’t even want to repeat it.
Technical Feasibility
Designing AI products is resource-heavy. The biggest investment is training the model to ensure it’s accurate and bias-free. AI systems need substantial computational power and high-quality data for training. Gathering, cleaning, and labeling this data is also resource-intensive.
Data Security Risks
When it comes to designing AI products, collecting, storing, and using large amounts of training data presents a major challenge: securing this data throughout the process.
No wonder data security concerns are one of the top factors driving users away from AI products. Pew Research Center recently found that 53% of respondents believe that AI harms rather than helps them keep their personal information private.
AI-as-a-Service Implications
AI-as-a-Service solutions can save a lot of effort since you don’t need to build and train your model from scratch.
However, relying on external AI providers means sharing all your data with a third party, which greatly increases data security risks. It also threatens system stability, as your AI features depend on an external API.
Challenges of Building UI/UX Design for AI Products
AI is a complex technology, and your job is to make it easy for users to understand. Your design should clearly show how the AI behind your solution works.
For this to happen, your designers must understand AI themselves, which means they’ll need to work closely with the project engineers. Facilitating this level of teamwork might be tricky.
Sure, designing AI products isn’t always easy. But still, how can you make the most of AI despite its complexities, risks, and potential users’ mistrust? Building the right process is the key.
Process of AI Product Design
Basically, there are two ways to build an AI product. The first way is to develop it from scratch. The second one is to enhance an existing solution with AI capabilities. For example, Zendesk, a customer service tool, wasn’t originally designed with AI but has recently been upgraded with AI features.
Whichever approach you take, your process should include the following steps.
1. Define your business goals. If upgrading an existing product, use as-is and to-be maps to visualize the current state and the one after integrating AI.
2. Determine your user persona, their needs, and product journey.
3. Identify your KPIs. What can you improve in your user’s journey with the help of AI?
4. Brainstorm possible solutions to your user’s problems. Imagine having unlimited resources to consider a wide range of options and choose the best one. Embrace the problem = opportunity mindset.
5. Analyze competitors to glean insights and best practices.
6. Evaluate technical feasibility, drawing from the competitors’ solutions and other successful AI use cases.
7. Implement designs and code in small, iterative steps, allowing for a quick rollback of changes.
8. Test your product thoroughly, with a strong focus on user acceptance tests.
While this step-by-step guide offers a bird’s-eye view of the AI product design process, following it alone doesn’t guarantee success. Let’s zero in on the key ingredients of a high-performing AI solution.
Best Practices for Designing AI Products
AI product design is different from other software development projects. You need to account for the technology’s complexity, challenges in handling large amounts of data, ethical considerations, and beyond.
At Qubstudio, we have a vast portfolio of AI-powered solutions, including risk management platforms and other innovative products. Thanks to this diverse experience, we’ve developed a deep understanding of building and designing AI products. Here are our core principles.
Estimate Your Risks
AI product design comes with its own set of risks. Assessing business, customer, and technical risks is paramount to determine if the solution will be viable.
In short, it all boils down to giving honest answers to several questions. Below are the most important ones, organized by risk type:
Answering these and related questions will help you determine whether initiating the design of an AI product is feasible. To gain even more clarity, we recommend using the AI survival curve to estimate the product’s chances of success.
Use the Consideration x Context Framework
From our experience, using the Context x Consideration framework is an excellent method for defining an AI product’s market fit. This framework works like a coordinate system where:
- The X-axis signifies Consideration, or the effort required for accurate decision-making combined with the level of mistake tolerance;
- The Y-axis signifies Context, or the amount of data a model needs to make the right decision.
Combined, these two metrics will show your product’s position on the AI survival curve. If Context and Consideration are well-balanced, the product lies evenly with the curve, which means it has a high chance of success. However, if the product needs too much consideration or context, it will deviate from the curve and become infeasible.
For example, Grammarly lies evenly with the curve: it needs a moderate level of mistake tolerance (consideration) and a moderate amount of context (knowing grammar rules, not all web content).
In contrast, fully autonomous vehicles depend heavily on driving data for model training but don’t tolerate any mistakes. So, this type of AI product finds itself above the curve, indicating a lower likelihood of success.
Understand Your User Personas
Step into your user persona’s shoes when building UX design for AI products.
You might argue that this principle applies to all software products, and you’d be right — prioritizing customer needs remains the cornerstone of delivering successful products. But with AI solutions, it’s crucial to factor in technological complexity.
Your target user is rarely a data scientist, so focus on simplifying the product journey. Avoid intricate graphs and instead present data in a human-friendly, easy-to-understand format.
Build a Working User Adoption Strategy
People are still getting used to AI, so it’s important to build confidence in your users.
- Complement your fail-proof mitigation plan against legal and reputational risks with a robust communication strategy. Users should understand how your AI algorithm works and what data it uses.
- Empowering users with control over AI functions can also improve acquisition rates. Clearly communicate what AI does and doesn’t do in your product by giving AI features a distinct style in the system.
- You might also allow users to turn off AI functionality whenever they want.
- Finally, keep tabs on user feedback and regularly address their concerns.
Keep Up-to-Date with Technical Capabilities
AI isn’t magic — it’s a technology with its own set of limitations. Keep this in mind as you conceptualize your next AI product.
Follow reputable sources to stay informed about AI’s capabilities and constraints. Official resources provided by leading AI product providers, such as OpenAI, offer valuable insights into the current state of AI technology.
Another smart idea is to analyze existing solutions, including those in MVP and beta stages, and keep an eye out for AI integration updates from industry giants like Microsoft, Adobe, and Salesforce.
Conclusion
Launching an AI product is a great opportunity for any business to stand out in the market. The stakes are pretty high, though: the promising project can easily turn into a source of frustration and wasted budget due to the numerous pitfalls AI product design entails. Fortunately, sticking to best practices for AI solution development outlined in this article can help mitigate these risks.
For assistance beyond this introduction to AI product design, consider partnering with us. As a digital product design agency specializing in designing and building AI products or digital products with AI integration, we can help you develop a solution with an intuitive, compelling UI/UX design that instills trust and drives ROI.
