Designing AI Solutions - Emerging Patterns in AI Experience Design

Introduction#

Large Language Models and AI agents are reshaping how we interact with technology. We're increasingly collaborating with AI in dynamic, conversational environments. In this blog, we explore six emerging patterns in AI Experience Design (AI XD).

We've all experienced disappointing interactions with AI, or know people who “hate chatbots", often because the ‘vanilla' experience isnt enough to make it feel useful, valuable or joyful. Very often, the disappointment we feel during the interaction has less to do with the capabilities of the model and more to do with the experience; the interaction between a person (or people), an AI agent and a desired outcome. Its a significant barrier to adoption, especially in an enterprise setting where the design of interactions between humans can contribute to overwhelm, ignore real-time context or even disregard user intent and behaviour entirely.

From this perspective, we've set out the benefits each pattern offers, the challenges we face from a design perspective, and some practical guidance on how to leverage it. These patterns aim to be agnostic of interaction types or modality, offering opportunities within external, consumer facing experiences or internal AI solutions to support knowledge workers in making better decisions.

We want to share the emerging patterns we're seeing - from both building and playing with many generative AI solutions - to help design better human-AI relationships.

Why do we feel these emerging design patterns are important?#

There are hallmarks of our interactions with technology today, both as knowledge workers and consumers, that make getting the things we want to do more difficult than they should be. They limit our productivity, effectiveness and value.

For example, knowledge workers and consumers alike are overwhelmed by the sheer volume of information, choices, and tools we have. This constant barrage of messages, often contradictory, prevents us from making clear decisions or achieving the focus we need. In fact, studies show that employees spend up to 40% of their workday just searching for the information they need [McKinsey, Reference. ] This information overload hinders productivity, but also happiness.

Additionally, we all face a limited capacity for attention. Even if we could find it, we simply can't process or analyse all of the knowledge required to make informed decisions. We can only focus on a small subset of available data at any given time, often clinging to existing beliefs or ideas, which reduce our ability to consider alternative solutions or outlying patterns.

Then we have the tools themselves, which often fail to recognise these limitations. Most technology is designed for “averages" —one-size-fits-all solutions that don't adapt to individual contexts. Whether its the software we use for work or consumer-facing apps, they don't account for our specific needs, constraints or the unique circumstances in which were working. As a result, technology ends up being inefficient or counterproductive. In this sense, generative AI offers a unique opportunity to enable more adaptive, personal interactions.

Finally, there is an inherent misery in many work environments. Increasing volumes of ‘busywork', the likelihood of needing to be ‘always-on' and the need to repetitively perform non-value-adding tasks contributes to disengagement and chronic stress. While generative AI and AI agents hold the potential to remove this misery by reducing or even eliminating these types of experience, there is a mistrust of AIs ability to improve work overall. People often resist the idea of “AI taking over, perhaps down to a fear over displacement, but also because we feel a sense of ownership over our own work - bull**it work included.

The application of AI alone isnt a solution. Instead of improving experiences, poorly integrated AI creates new tasks and anxieties, especially for knowledge workers. These types of challenge create the need for human-AI interactions that are not only efficient, but aligned with our cognitive capabilities, individual circumstances and the constraints were under as people. The six patterns set out below represent emerging approaches to designing interactions that acknowledges people, fostering a more adaptive, transparent, and meaningful relationship with AI.

Six emerging design patterns for human-AI interaction#

The relationship between emerging design patterns and principles to guide effective, human-centred AI experiences

• Curation: AI as an Intelligent Menu - AI curates and organises information, reducing overwhelm by helping us navigate vast datasets.
• Giving Feedback: AI as a Thinking Partner - AI provides real-time, thoughtprovoking feedback that challenges assumptions and offers alternative perspectives to improve work.
• Situational Awareness: AI that Understands Here and Now - AI adapts to real-time context, making interactions more relevant by continuously processing environmental and user need
• Continuous Discovery: Turning Every Interaction into User Research - AI uses every user interaction to uncover insights and refine its understanding, ensuring ongoing learning and improvement.
• Amplification: AI Augmentation of User Input for Better Outcomes - AI enhances user input behind the scenes to produce better results while preserving the users original intent.
• Showing the Working Out: Transparent AI Reasoning - AI makes its decisionmaking process visible and useful, fostering trust and enabling better, more informed decision-making by users.

These six patterns highlight the fact that designing effective AI solutions is about much more than user interface. They are about shaping and coordinating the behaviour of both people and AI agents. Ultimately, we are aiming to facilitate the overlaps between decisions, humans and AI agents within complex systems of knowledge. These patterns allow us to improve interactions between people and AI systems by:

1. Prioritising two-way knowledge exchange: enabling a dialogue where both AI and humans contribute ideas, context, and insights. This will be the key to developing more capable AI systems, equipped with expert knowledge and intuition that isnt codified today.
2. Creating experiences that adapt and change across circumstances: AI tools and applications can become obsolete or irrelevant very quickly. AI systems that have situational awareness, self-learn, and adapt to changing context and the unique circumstances of a user will remain far more useful overtime.
3. Improving trust and confidence: transparent, collaborative interactions lower barriers to usage, drive higher engagement and enhances both human and AI decision-making. Design helps people remain confident in the systems decisions and outputs overtime.

Summary: Moving Beyond the Bot#

The design of AI experiences is not trivial, but there are battle tested approaches to navigating the challenges. At a fundamental level, similar principles of understanding users and designing great experiences apply, but there are new factors that we have to account for. We're now designing for AI with agency, as well as for people. Great AI experiences that focus on meaningful interactions between people and AI systems - experiences that curate information effectively, give useful feedback, learn and adapt transparently - can reduce overload, improve decision-making, and build trust.

These patterns help overcome common hurdles to adoption, making AI genuinely helpful, adaptable, and more valuable for everyone involved.

If you would like to dive deeper, the guide below provides more detail on each pattern, including the different ways we can leverage the pattern as part of a broader AI experience.

1. Curation: AI as an intelligent menu#

What it is: Instead of users manually sifting through endless search results or documents, AI agents can curate information from vast datasets and present the most relevant insights. In essence, the AI becomes a librarian, organising knowledge to fit the user's context.

Why it's useful: Put simply, reducing information overload. Consider that the average knowledge worker spends 1.8 hours per day searching for information, contributing to burnout for nearly half of employees - [McKinsey, Reference]. AI driven curation tackles this by indexing, finding, and combining information from a vast array of sources, often helping a person to find information, options or insights they might not have found independently, through traditional search.

The challenge: Moving from a traditional GUI-based search to an agent-assisted approach requires a shift in user expectations. People are accustomed to search engines showing a list of links they can manually verify or buttons they can press to filter a data set. An AI curator, by contrast, synthesises answers from sources the user may not even know exist. This capability comes at the cost of users perceiving less direct control over filtering and must trust the agents choices. We need to help users adapt to this new paradigm by blending AI-driven curation and collaborative design with more familiar mechanisms of searching for and finding information.

How OpenAI utilise simple, familiar interaction patterns to enable structured and unstructured discovery of information

Useful approaches to AI curation:

2. Giving Feedback: AI as a thinking partner#

What it is: A lot is written today about the delegation of typically human work to AI. We talk less about the remarkable ability of agents to act as real-time collaborators. Rather than just following instructions or producing content, an agent can actively contribute to tasks by asking thought-provoking questions, challenging assumptions, or offering alternative perspectives. This turns AI into a thinking partner that can help the user improve their own work in the moment.

Why it's useful: We all benefit from in-the-moment feedback, we just don't often get it. Our thinking benefits from both dissent and advocacy, helping us to quickly identify coherent patterns before we become too attached to our ideas. We can use AI agents effectively to challenge our thinking, generate ideas or suggest alternative interpretations of our data, none of which replaces human judgment. In fact, weve found that that humans often respond extremely well to this type of agentic feedback. AI agents are surprisingly effective in generating counterarguments, to expose gaps in reasoning or reveal alternative perspectives.

The challenge: For AI feedback to be effective, it must be intuitive and actionable – otherwise it risks becoming a distraction. Users don't welcome a meddling AI that interrupts with irrelevant suggestions, or one that nitpicks every minor detail. Striking the right balance is tough: the AI should interject at the right moments and with the right tone. If its too passive, opportunities for improvement are lost; too aggressive, and users feel second-guessed by a machine. The challenge lies in how to surface the AIs feedback: should it appear as hints, as an optional review, or as a two-way dialogue? Getting the interaction right will determine whether users embrace the AI as a partner or simply see Clippy 2.0.

Examples of ways AI can nudge, feedback and make suggestions

Useful approaches to making AI feedback intuitive and actionable:

3. Situational Awareness: AI that understands here and now#

What it is: Situational awareness refers to an agents ability to perceive and interpret contextual information in real time, and adjust its behaviour accordingly. Rather than operating in the past, in a vacuum or based on predetermined inputs, AI agents can continually take in and data about the environment, the users state, and other real-world factors. This could mean an AI that adapts a user interface based on user need in-the-moment, or an agent that understands the current context of a market or industry in order to increase relevance.

Why it's useful: The real world is not static – conditions change from moment to moment. AI systems with situational awareness can be far more effective and helpful because they account for the here and now. In the context of AI agents, situational awareness is the key to both personalisation and usefulness. When an AI grasps context, interactions feel significantly more natural and relevant. The more backstory and real-time context an AI has, the more grounded and useful its outputs can be.

The challenge: Building AI with genuine situational awareness is easier said than done. There are obvious challenges around data access and integration – which can be technically complex and difficult to do.

When it comes to human-AI interactions, theres a fine line between helpful context-awareness and annoying overreach. Weve found that users deeply appreciate an AI agent that “gets it" but feel annoyed or uncomfortable if the AI requires too much information from them or is perceived as too persistent. Maintaining situational models can be challenging – contexts can change rapidly, and human expertise and intuition can be invaluable. Ultimately, this means that AI needs a user to help it continuously maintain its “world model" in order to be useful.

Simple ways we can help users to collaborate with AI agents to develop situational awareness

Useful approaches to helping AI adapt to a situation:

4. Continuous Discovery: turning every interaction into an opportunity for user research#

What it is: Experience design and product development often treats user research and discovery as a phase – something you do at the start of a project or at periodic intervals. Continuous discovery is the idea that understanding user behaviour, needs and preferences is an ongoing process, and AI can help turn every user interaction into an opportunity to learn. Each conversation, click, or piece of feedback can be analysed to uncover insights, even those that users don't explicitly articulate. In essence, the AI is always learning, helping to refine a adapt to the problem space in real-time.

Why it's useful: It can be extremely difficult to capture feedback from experts, practitioners or general users in relation to how effective, coherent or usable an AI output actually is. Whats valuable, is allowing agents to help us gather reactions, feedback and intuition in-context; in a meaningful situation, rather than aiming to do so removed from the experience of work.

The challenge: Capturing feedback can be difficult. We often capture aspects of a user interaction that sets out what happened, or what the user liked or disliked. Getting to why, continuously over time, is more of a challenge. It can feel like were asking a lot of people to commit their thought process to memory and provide feedback to shape the systems future capabilities. This is particularly important in tightly bounded AI systems, where a free-form, conversational exchange or iteration with an AI agent is neither desirable or possible.

AI systems can continuously learn from users choices, iterations and conversations

Useful approaches to embed continuous discovery with AI

5. Amplification: invisible augmentation of user input for better outcomes#

What it is: Amplification refers to the adaptation of a users input, prompt or instruction to produce a better result – all while preserving the users original intent. In other words, the AI does extra behind-the-scenes thinking beyond the users explicit ask. For example, ‘shadow prompting involves the AI agent reformulating or enriching a users query invisibly to get a more useful answer. The user provides a spark and the agent takes that (often) minimal input and adapts where it beleives a better outcome can be achieved.

Why it's useful: Users often (quite reasonably) give minimal or high-level instructions, but the optimal answer might require a more invovled set of steps. AI agents can bridge that gap without requiring the user to spell everything out. For instance, if you ask an AI, “brainstorm marketing ideas for my new coffee product", a straightforward single-thread answer might give you a fairly uninspiring list. An amplified approach could have AI agents run multiple, increasingly ambitious brainstorms, ultimately combining the best ideas. The end result is richer and more diverse than a single-pass response.

The challenge: The flip side of doing things behind the scenes is the risk of losing users sense of ownership or transparency (which is why the next pattern relating to transparency is so important). If an AI modifies a users request without telling them, does it risk doing something the user is disconnect to or didnt want? There's a trade-off between helpful amplification and unwanted manipulation. If amplification is implemented poorly, it could actually distort the users intent. From a design perspective, figuring out how to let users guide the amplification (if they want to) is tricky.

Utilising (sometimes) invisible assistance to drive better outcomes

Useful approaches to amplify user inputs:

6. Showing the Working Out: transparent AI Reasoning and thought process#

What it is: This pattern is all about making AI agents' thought processes visible. Where we design interactions to “show working in a way that is meaningful and conducive to effective human decision making. This often means providing explanations for why an AI model or agent gave a certain answer or providing citations backing its response. Chain-of-thought reasoning popularised asking a model to show the intermediate steps it took to arrive at an outcome. We're finding that “showing the working out can be most effective when its incremental and explorable; meaning users can dial the level of detail up or down as they see fit.

Why it's useful: Trust is clearly a cornerstone of widespread AI adoption, particularly in an enterprise setting. If people cant understand or verify what an AI is doing, theyll be hesitant to use it for anything important. Transparency also aids repeatability and consistency. When “working out" is visible and explorable, it's easier to diagnose issues, provide timely course correction or produce repeatable approaches that can be replayed over time. Transparent agents help users make better decisions.

The challenge: Explaining AI decisions is hard and theres a challenge in presenting this information. For example, too much transparency can overwhelm users. If every answer comes with a dump of probabilities or a long chain-ofthought narrative, people can tune out. We also need to remember that transparency alone doesn't guarantee the truth. So, we need to tackle a number of challenges; the quality of reasoning, the clarity of its presentation, and allowing reasoning, citations and sources to become part of a dialogue.

Finding ways to surface AI agents thought process, decisions and progress to users is vital in the context of trust and confidence

Useful approaches to design for transparent, trustworthy AI outputs: