Imagine an AI-powered assistant that will do everything for you, allowing no effort to be required of you. This sounds perfect, right? However, studies and opinion tweets show that when AI have zero human input, users often lose interest.
The paradox of artificial intelligence (AI) product design is this: reducing the level of friction is vital, but removing effort altogether leads to a loss of engagement by users.
As AI continues to dominate the digital landscape, businesses are also trying to design user-centred AI products to ensure smooth interaction. The real question is: What if a small amount of effort is all that is required to have sustained user engagement? This article will explore why user effort plays an important role in AI product design and how it can convert passive users into active participants.
Understanding User Effort in AI Product Design
User effort is the effort required to physically or mentally interact with an AI product. This effort can be as simple as fine-tuning a chatbot’s response or actively curating a recommendation system. While effortless experiences are normally expected to be the goal of user interaction, it is once user effort is introduced that engagement and satisfaction can be increased, as well as longer-term retention improved.
Examples of User Input in AI-Based Products
- Chatbots rely on the user to input a corresponding conversation context before outputting a response (such as ChatGPT, or Gemini prompts).
- Recommendation systems can be used so that users can rate, like or dislike suggestions (e.g. Spotify and Netflix).
- AI writing tools encourage users to alter and amend text as opposed to generating the content that is desired immediately.
Why is user effort very important?
- Creates a sense of ownership: users value what they contribute.
- Enhances personalization: more user input results in more tailored AI interactions being provided.
- Increases engagement and retention of users: a small amount of effort will result in users becoming more invested in the experience.
The Psychology Behind User Effort
Cognitive load theory: The Balancing Act.
Cognitive load theory states that when there is too much effort exerted by users, this overload will overwhelm them, while when the effort is too little, users will lack engagement. The survival of an AI product in design requires finding the optimal balance and checking that challenges contribute to engagement for users so as not to get them exhausted.
How Effort Influences Motivation & Engagement
People generally become motivated when they are required to choose and make decisions themselves or when some point needs to be resolved, for example. This is often aligned with:
- The IKEA Effect: When users of AI-generated results value the end results as higher in quality if they played an active role in the results being created.
- Self-determination theory: postulates that people are more motivated when they have a sense of being in control and autonomy.
Reward Mechanisms: Measuring Continued Performance Levels
Effort should neither need to feel like a burden nor an undesirable experience that should be endured, as it should be seen as a rewarding process of success. Successful products properly integrate the following features:
- Gamification: These are points, badges and leaderboards for progress (e.g., Duolingo).
- Feedback Loops: This involves real-time responses that are immediately made to reinforce engagement (e.g., Grammarly).
- Personalized Rewards: These are AI-suggested improvements based on the user’s effort.
Designing AI Products with User Effort in Mind
User effort should not be an afterthought in the design of AI products; it should be a fundamental design principle. The aim is to produce AI experiences which actively involve the user and avoid passive consumption of content. Here are some methods which AI products can be designed with user effort to include in the design:
1. User-Centric Design Principles
A well-designed AI product needs to take into consideration how users communicate and interact with technology and what amount of effort is needed to perform activities that require a level of engagement. Some of the important key design principles are:
- Interactive Learning: AI is meant to allow users to adjust and tailor their responses rather than providing a single solution that is fit for all situations.
- Guided AI Assistance: Instead of fully automating tasks, the AI should provide recommendations that guide users throughout the task while maintaining engagement with the user.
- Customizable features: are made available to users who can modify their settings and personalize their interactions. This makes the overall experience more meaningful for the user.
2. Balancing Simplicity and Complexity
A common issue in AI design is getting an understanding of how much effort is appropriate or excessive. Sticking to the process of finding a balance between simplicity and complexity is essential:
- Ensure there is no excessive friction: if an AI system is too complex then users may become frustrated and abandon the user experience.
- Prevent disengagement: When users’ interactions are too easy and carefree, the users may stop being interested rather rapidly.
- Introduce progressive difficulty: AI should adapt to the user’s ability to play, improving the engagement of the user and making for a more dynamic and rewarding experience.
3. Implementing an Iterative Design Process
AI products should evolve as user interactions arise; the action needs to be:
- Testing different levels of user effort to see which one attracts engagement from the users.
- Obtaining user feedback and modifying the effort which is required to complete a task.
- Ensuring usability testing refines the balance of automation and user involvement.
The most effective AI products view user effort as a variable to be optimised to find ways to eliminate it as opposed to something that needs to be removed. By carefully taking into account how much user effort is involved in the various stages that are required, businesses can produce AI solutions that are sustainable, engaging and rewarding.
Case Studies: Successful AI Products with Effective User Effort
Case Study 1: Lens Protocol – Empowering Users in Decentralized Social Media
Lens Protocol is a decentralized social media platform where users actively shape their experience. Instead of an algorithm dictating their feed, Lens allows users to:
- Choose what content to engage with based on on-chain activity.
- Own their social media interactions via NFTs, requiring active participation.
- Customize their experience through modular components, making them feel in control.
By requiring users to engage with the ecosystem directly, Lens fosters deeper commitment and long-term retention.
Case Study 2: Uniswap – Interactive Decision-Making in DeFi
Uniswap, a leading decentralized exchange (DEX), exemplifies how AI-driven automation can still involve user effort. Unlike centralized exchanges where order books match trades, Uniswap requires users to:
- Manually select liquidity pools and assess risks.
- Make strategic decisions based on AI-driven market insights.
- Engage with governance proposals, reinforcing their sense of ownership.
These case studies ensure that users remain actively involved, increasing their attachment to the platform and promoting continuous usage.
Strategies for increasing User Effort in AI product Design
- Gamification: Making Effort Enjoyable
Incorporating game-like elements such as challenges, achievements and progress tracking encourages users to stay engaged with the platforms. Platforms such as StepN have effectively used gamification to reward the user effort in Web 3 fitness tracking.
- Personalization: Personalizing AI to User Delivery/Input
AI systems that evolve based on user input make users feel valued and appreciated. Much like Web3 wallets such as MetaMask which allow for the settings options to be customized in addition to the assets being managed, users also need to engage with the wallet to be able to connect to different dApps and optimize the experience that they have.
- User Feedback Loops: Continuous Improvement
Real-time feedback mechanisms allow users to see the outcomes of their interactions with the AI software, thus enabling them to understand what they can do better in their financial strategies. The Web3 platforms like Aave provide the information needed relating to borrowing and lending, encouraging them to revise their financial decisions.
Conclusion
While AI aims to reduce user experience effects, purely achieving this simplification reduces the level of engagement, which can cause a drop in user engagement. Successful AI product design extracts the right level of interaction, which allows the users to interact and customize themselves and be actively engaged in the product. In the Web3 world issues of security, decentralisation and utilization of the user’s ownership require a level of interaction and effort with the users.
The best AI products will not just work for users but work with them. Whether it is customizing a decentralized social profile with Lens Protocol, setting up integers on Aave managing the positions of Dexterity, or making informed trades on Uniswap the effort put in by the users will create a deeper bond of engagement and therefore license themselves to engage with the technology.
Companies that embrace the involvement of users rather than attempting to eradicate it as much as possible will produce more engaging, satisfying and enduring experiences in both the Web2 and Web3 worlds.
Read also: Merging Web2 and Web3: Revolutionizing E-commerce Through Decentralized Innovation
