# Insight Is Not Enough: The Next Chapter of Design Research — Dr Asma Qureshi — session 2026-08-26T05:00:00.000Z → 2026-08-26T05:30:00.000Z _444 transcript lines · 90 slides_ ## Transcript welcome. Last three talks of the day, and coming up first, doctor Asma Kharesi, Yeah. Awesome. Got that right. I do check but you never know. So, he's gonna talk to us about how design research is shift strategic strategic capability and what that looks like in practice. Please join me in work Thanks, Steve, for almost pronouncing my name correctly. Because it's Asma, if you want to say accurately, then out of 10, But it's never asthma, which happens to me a lot. Alright. So, it's already 03:00, you have heard of many good presentations Thank you everyone for sharing your insights. It's very very useful. I'm going to be talking about a framework on insights of the function in the future. That does not mean that I'm a clairvoyant or any psychic. Who can just, you know, put her hand on a globe and say what's it's going to look like. This is based on my experience of working with a number of organizations. So a heads up and a disclaimer, it is not a rocket science. It's just that I've sort of collated and consolidated every learning from people into a framework that I see. Some of the organizations will already be implementing that. And some of them will be on the journey, and someone will really have to start their journey, really. But it still doesn't matter. It's just like as long as we are ready to see research as a practice. So the argument what I'm going to present today is the insights still matter insights still matters, but insights on its own is no longer enough. The future of design research is more traceable, more governed, more continuous, and more connected to decisions. And this is not basically a gloomy message for our professional or a discipline or as professionals. It's a sign that the discipline has become important enough to be asked harder questions, The opportunity now is to build the kind of research practice that can answer those questions confidently. Okay. Let me start with a question which that sounds pretty familiar. So Somewhere inside an organization, a number from a research is being quoted. It might be in a board paper, a case study, a funding deck, or a presentation you have never seen. Someone says, our research shows that a customer struggles with this. And the number travels the, presentation, the quote travels and the recommendation travels. But then months later, if someone asks you, where did that come from? Not which slide, not which document, not which project. What is the evidence of that number that is being quoted? So the question is small, but it changes the whole standard for research. The future of research begins with the trail that survives the presentation. And talking of trails, a lot of Abrahamic religions, a lot of prophecies that people are still practicing after five thousand years, The confidence in the prophecies often comes from what's the trail of evidence of whatever Jesus, Moses, or Mohammed said at some time. Whether we want to believe it or not, but this is still the religious scholars of the day, this is how they go about it. Now I wanna ask you this question and how common is this. That you've seen a finding from a work reappeared months later, slightly detached from its original context. Have ever anyone asked you to trace a number because of its source, or was it harder for you to even find out? Or source turned out to be something like that. Final, Really, really final? Please see this one. And, I mean, this is usually funny, and I'm guilty of doing that. Where I've written on the folder, please use this one because this is the latest one. But it's a serious problem underneath. Our evidence often travels further than our documentation. When research says inside the project team, that may be survivable. But when it when it moves to executive decisions, regulated services, AI systems, or public accountability, it becomes less survivable. So here's the truth that we're going to be talking, and this is my framework, that I'm going to present or shift that I'm, talking about in, research as a practice. First is insight is becoming evidence. The memorable finding still matters, but it has to be traceable. Second, AI is moving from a tool we use into an environment within we operate in. It affects it affects our participation, like Vienne talked about, an analysis, and our evidence is produced. Third, the research projects are becoming learning infrastructure. The future is less about individual studies and more about continuous systems of organizational learning. And fourth, the findings are becoming decision led and outcome led. The question is no longer, what did we learn? It's also about what changed because we learned it. So talking about the first shift, insights to evidence, this is not a rejection of insight. Many of us built our careers on the ability to notice what others missed, turn ambiguity into sins, and help organizations see customers and communities more clearly. But the environment around research has changed. The more infrared sorry. This slide is just too bright in my eyes. I'm sorry for that. This one, can you just move slightly? Sorry for that. But the environment around the research has changed. The more influence research has more it is expected to which withstand scrutiny. A finding that persuades Rome is useful. A finding that can be reconstructed later is much more powerful. So the question for the next chapter of design research is, neither it's whether we can produce compelling insights We certainly can. The question is whether those insights can survive challenge, reuse, decision making, and the project team when the project team has specifically moved on. The simplest way to think about it is, on the left hand side is a clear inside. Clear, memorable, and easy to quote. 64% struggle to understand eligibility. That's the line everyone remembers, and it is also used and referenced. And this is how it goes. On the right side is the trail behind the insight. Who participated? How they were recruited, which questions were asked, how the analysis was done, whether AI assisted the process and which decision the finding informed. The insights tell people what we learned. The trail tells when we can trust that. For a long time, design research became very good at producing the thing on the left. The future also asks us to become just as good as preserving things on the right side as well. The life cycle of an insight that we've just and, you know, I talked a little briefly, about the familiarity of an insight when it's lost. It starts beautifully. You might have experienced a lot of time There's an insight based presentation, great insight. You would have heard of that. Great work. Well done. And then it goes into executive deck. Board paper. Months later, someone asked where the number came from, and a small archaeological expedition begins. And we call that a research archaeology. Research old x we search Slack threads, repository tags, half remembered project names. Someone says, I think it was in the version before that. So the humor because what we're talking about here is so real, and we can recognize that in the organization, maybe making decisions from the evidence that no longer exists. And that is why traceability is not an admin It is part of the evidence quality. Now, it has also consequences. In the Australian financial services design and distribution obligations, have made the quality of customer evidence more visible. ASIC has reported more than 80 stop orders since the obligation began, And one detail is especially important for researchers. Flawed customer questionnaire. And they were identified as a for several interim stop orders. That's a very practical warning. A research institute can also become an operational risk. The lesson is not that every piece of design research is suddenly a regulatory document, The lesson is that in some sectors, especially where products affect people's money, eligibility, access of vulnerability, the quality traceability of research evidence become more much more important, and more pronounced. A poorly designed question is not always a poor question. Sometimes it's a weak link in the decision system. So this basically leads to an evidence led up. And this is, you know, sort of a framework, sort of a mini letter I suggest. At the first rung, research is episodic, and this is the most common form of research we see. A study happens, insights are delivered, and the project ends. That's where often, you know, basic regular, day to day design research looks like. The second round research becomes more repeatable. There are consistent protocols. Repositories, and reusable patterns. At the third rung, it becomes more auditable, and that's where I think the research ops become more, you know, come into play. We can reconstruct providence consent analysis evidence, and use of decision lengths. At the fourth rung, it becomes contestable. People affected by decisions can understand questions and seek review. Now this is not a sort of a maturity risk that every organization has to achieve that. Not every study needs to sit at rung four. A marketing concept test and a hardship eligibility journey do not carry the same consequences. The real leadership question is, what does this evidence need to survive Once we know that, we can design the right level of rigor. The second shift from my framework is about AI. Not surprising. Many conversations about AI and then research begin in the wrong place. They ask whether AI will replace researchers, That's a fear everyone is carrying here. Or whether research research should use AI tools. They're not unimportant questions, but they're too narrow. The bigger change is AI is becoming part of the research environment. It is in the tools we use, it is in the systems we study, It is increasingly in the way participants express themselves. So the future question is not simply, can I use AI to move faster, It is, how do I know what kind of evidence I now have when AI may be present at multiple points in the chain or in the user experience? Recent Australian digital inclusion index figures shows that AI no longer is a niche behavior, especially high among students and younger adults. Before research, this matters all because AI is now sitting sits quietly inside the organization. Think about the open ended comments or verbatims as we say, in an unmoderated or in a survey or in an unmoderated diary study or asynchronous task at the end of a day long. A participant may use AI to turn rough thoughts into a polished language. They're not trying to mislead us. They're simply using an everyday tool, but it changes what we are analyzing. Are we reading the participants' lived experience or a model's cleanup version of it? That does not mean we throw that data away. It means we acknowledge a new form of bias and a document how we manage it. So looking at this document here, or having a conversation about a very old research problem, our research has five words. AI polishes them. A survey platform summarizes a response A researcher uses AI to cluster the themes. An executive uses AI to summarize. Everything all the way from top to bottom AI led. Congratulations. We have built a very very efficient game of telephone. Again, the problem is now that AI exists in the chain. The problem is when nobody knows when it enters the chain, what it changed, and who remained accountable for the interpretation. Research has always, involved mediation. We summarize, interpret, and translate. AI simply makes the mediation faster, less visible, and easier to forget. That's why the future is not AI free research, it is AI aware research. So let me ask another practical question, and I would see now because I've listened to presentation and I loved it, so I'm referring to you again and again. Does your organization have a written AI boundary? Not a general policy that says people should use AI response specific boundary that says what is acceptable what is risky, what must be documented, and where AI must never substitute for human participation or judgment. Some teams have written this down. Some have a shared understanding. But nothing findable. Many are still relying on individual judgment. The problem with the individual judgment is it does not scale. It does not survive staff turnover, procurement, scrutiny, or executive decisions. So a written boundary is not bureaucracy. It's a simple way to protect trust in the evidence. So a, an AI bounded AI practice gives us three categories. And, again, a small sort of a categorization or grouping to understand. There are users where AI can assist, like very easy and simplest to use, transcription support, translation support, repository reveal or first pass summaries, provided the source remains traceable. But even translation and, yes, I still use, AI, but it could be really funny with some of the languages which are not, spoke outside the European or the English speaking world. Really funny and hilarious Then there are uses that require governance. For example, coding support. Clustering, prompt based analysis, sense making at scale. These may be useful, but the process needs documentation and human accountability. And then there were users where we should never normalize AI, and that is synthetic personas, replacing real participants, AI generated quotes presented at evidence or AI only discovery supporting decisions that affect people's lives. I tell everybody I'm glad I did a PhD when AI did not So I wrote those 100,000 words by you know, through a lot of hard work, blood and sweat. So the point is not to ban the tool. The point is to draw the boundary before the boundary is tested. A team that can explain its practice will feel will be much better positioned than a team that says we are careful. Now the third shift, part of the framework is from projects to learning infrastructure. For many organizations, research still operates as a sequence of projects. A team has a question, a study is commissioned, findings are delivered, and then everybody moves to the next priority. The model will not disappear. But it will always exist, and projects will still exist. Excuse me. But the future of design research cannot only be queue of individual studies, The issues that we are researching in 2026 are too continuous. Trust, access, vulnerability, digital exclusion or inclusion for that matter, AI harm, service performance, long term behavior change, Those issues do not fit neatly into quarterly project cycles. So research has to become something more durable. A system for continuous learning not just a service that responds to requests. This is a shift from a calendar to a system. As I suggest. A calendar study tells us what's happening in this quarter. It is useful but is not enough. A learning system asks different questions. What signals are we watching continuously? What evidence would I tell to our tell us to stop? Scale, or redesign something completely? What are we monitoring after launch? What are the equity, trust, and harm signals? This is especially important because many of the important research questions do not end at launch. A service can test well and still fail particularly over time. An air feature can perform acceptably in evaluation and still create harm in the use. The annual plan tells us what we intend to do A learning system tells us when the organization is committed to or noticing. And this is not a European or a Silicon Valley concept. We can see versions of it in Australia as well. Monash University Living Labs program involved more than 18,000 participants or stakeholders in 2023 to 2024. With more than 100 partners, more hun one more than 100 researchers, and more than 1,000 students. Now this is not a one off workshop. It's an infrastructure for learning across complex systems. And I'm not suggesting every organization to adapt Monash model. The point is the operating principle, ongoing participation, experimentation, and learning becoming part of how the institution works. For design research, this is a future signal. The durable value is not only the method It's a system that allows learning to continue after an individual project completes or ends. In Australia, research infrastructure has to meet two conditions. And I'm so glad, Bhavan and Chanel talked about their presentation where they did almost like a hybrid or more of in person research. In Australia, it has to be hybrid by design. Continuous panel recruited only through an o online portal can look efficient and well governed while still missing people who do not have reliable connectivity, private devices, sufficient data, confidence, and digital systems. When I first arrived to Australia, I couldn't believe the in Australia, Internet is slower than my home country, which is considered a third world country. So and it was obviously, it has evolved over the years. I've been here for fourteen, fifteen years. But if you look at the regional areas in Australia, there's still a big struggle of technology. So it's basically we have to design systems where culturally neutral, Research involving first nations people must take indigenous data sovereignty seriously, including authority to control benefit sharing and culturally legitimate governance. AI makes this more urgent because data and insights can now be reused, remixed, and summarized at speed. Australian research structure infrastructure cannot simply be digital first and then inclusive later. Inclusion and cultural governance have to be part of the architecture from the beginning. This is a provenance first workflow, I suggest, It begins with the intent. What decision is this research informing? What risk exist and what who what may be affected? Then the participation, who is included, may be excluded, and why are they excluded? What support is needed? What consent governs the cap, work? Then cap an analysis, how the work was collected, how it was interpreted, and where did AI automation play a role. Then evidence packaging. How do we show not only the finding, but also the uncertainty, alternatives, and the limits of the insights that are being produced. And then finally, the decision link, What choice did this evidence come from and also the monitoring? What happened after the decision? What would trigger us to do the research again? The research conversation may look familiar. What changes is what survives after the study. And a fourth shift that we are going to be talking from the framework is the findings delivered to decisions and outcomes. I'm hearing more and more, and I read a lot on LinkedIn, and there's so much of conversation around decision linking to decisions and outcomes. And this is most important for the senior stakeholders, senior leaders, because it connects research to value. A finding delivered is not same as a decision improved. A beautiful tech is not same as a reduced risk. And a strong code is not same as better access, greater trust, or less harm for the happens after the readout. This does not mean pretending research alone can fix all the organization outcomes or problems. It means being much clearer about the decisions, research informed, the assumptions it took, the risk it surfaced, and the outcome should be monitored. In other words, research needs to be more accountable without becoming too simplistic. This is one of the simplest changes with the biggest implications. Most repositories capture the start study title, method, date, and the research lead. But the missing field is often which this decision did this inform. Imagine opening a study record and seeing not only, seeing something like heart hardship on, onboarding interviews. But also the decision it informs, the product owner, the risk to viewed in a follow-up signal to monitor. That changes from a library of outputs into a memory system for decision. It also changes the conversation with executives. Instead of saying, here's what we found, research can say, has the decision this Okay. I'll skip this one. Now I want to bring the four ships back to the room. And I wanna ask you a question, which one's happening in your organization? So first is, one is evidence. People ask How many for traceability? One, two, three. Okay. And then the second one is AI is changing the method or simple. How many times a conversation is? How much of AI has been utilized in the process of synthesis or insights writing? Okay. Third is the infrastructure. Research is moving beyond projects into continuous learning. Are you seeing this change that we're not talking about projects? At Atlassian. Yeah. I was aware of that. And then fourth is the impact. Stakeholders want clear links between research and outcomes. Okay. This is a point of the framework we are talking about It gives leaders a way to name what's already changing rather than treating every pressure as a separate, you know, problem. Once we can name the chef, we can decide what needs to be built next. So here's my forecast for 2026 to 2031. Again, not a psychic or not a clear one, and summarizing everything that we have learned, The job title may still be researcher, research lead, strategic, design strategist, insights director, but the deliverable audience and the shelf life will change. Evidence will move from persuasive readouts to decision grade evidence that can be reconstructed later. AI will move from ad hoc use to documented, governed, and bounded practice. Infrastructure will move from project by project to continuous learning systems, An impact will move from satisfaction with findings to evidence of decisions change, reduce risk, and outcomes monitor. And this is a future I see for design research. Less like a presentation factory, more like a a decision capability. And I see that as a more powerful and serious role. And I seriously believe we are still going to be well employed with at least for the next foreseeable future. So don't worry. Okay. And the good news is, okay. The most important thing that I wanna share here God. Sorry. I just need water. So with the framework that I'm suggesting, the very good news is that it does not require a five year transformation project. There are some of the changes that can be made very easily starting next week. So I would suggest four simple moves. The first one is for evidence. Add one field to the repository. With which research decision did it inform and where it came from. For AI, publish the boundary where AI can assess. Where it needs governance, and where it must not substitute for real evidence. Will take a little longer than a week or so, but it will still give you a good clarity or headspace what needs to be done. For infrastructure, for infrastructure, choose one signal to monitor, after the launch. Instead of trading research as a finished ad delivery, and also for impact link one study to one decision. One risk or one outcome. Now this is going to be the tricky one because often this going to be other conversations and, you know, just pinpointing and highlighting one decision which one decision. But the moment we start writing and that's the beauty of write, writing. I shared this with Anya, and I read it on LinkedIn, and I loved it that think like an engineer, share like a poet. And I feel there's some bit of poetic or poet in all of the for all of us as researchers. We express we wanna do the storytelling in a way that is people connected with that. So if we just start working on that in terms of the documentation and start coming up with what is one decision, what is the one thing that we want to connect our outcomes that will start to make sense. So all these small operating changes create new habits. New habits and obviously create new expectations, and new expectations are how disciplines mature So the future of design research begins in the very practical places. And this is where I want to leave the argument, Insight is not enough, not because insight has become less valuable, but because the world around has become more consequential. The future of design research is evidence, judgment, infrastructure, and impact. And the responsibility comes with a simple expectation, show you're working, stay close to decisions, and measure what changes. And that is it. And I finish early on. Thank you. Question Yes. Sorry. Where do you see See, something that I've personally experienced, and I've got askable team here, I did some sort of a trial project with ask about team, where we made comparisons of human led interviews versus AI moderated interviews, and we saw differences. So for example, if we want to go really deeper and to something, obviously humanly inter, you know, moderated interviews had a lot more depth and richness. When we wanted to look at something I want I don't wanna call it quick and dirty, but something that we want to scale, and I think sorry, Rebecca. I missed your presentation this morning, but I'm assuming you would have touched on that. So if you want to do something at scale, let's say, you wanna test something with thousand participants and you don't have time, you can't interview thousand participants in a way. That's where AI moderated interviews will come in. But my, and this is my personal opinion, if we wanna do that, it has to be a balance. ## Slides ### 00:00:08 [Light blue circle on a white background] ### 00:00:31 # Insight Is Not Enough ## The Future of Design Research Dr Asma Qureshi Research & Insights Principal · A2 Online [Four circular icons: a link, a robot, a grid of nine squares, and a target] ### 00:00:54 # Insight Is Not Enough ## The Future of Design Research Dr Asma Qureshi Research & Insights Principal - A2 Online Design Research 2026 - Sydney - 26 August 2026 - 3:00 PM ### 00:01:08 # Insight Is Not Enough ## The Future of Design Research A2 ONLINE ### Dr Asma Qureshi Research & Insights Principal - A2 Online Design Research 2026 - Sydney - 26 August 2026 - 3:00 PM [Four circular icons: a link, a robot, a product grid, and a target] ### 00:01:30 # Insight Is Not Enough ## The Future of Design Research Dr Asma Qureshi Research & Insights Principal - A2 Online Design Research 2026 - Sydney - 26 August 2026 - 3:00 PM [Four circular icons: a link symbol, a robot head, a product/data icon, and a target symbol] ### 00:01:54 # Insight Is Not Enough ## The Future of Design Research ### Dr Asma Qureshi Research & Insights Principal - A2 Online Design Research 2026, Sydney, 26 August 2026, 3:00 PM [Four circular icons: a link, a robot, a product, and a target] ### 00:02:09 # Insight Is Not Enough ## The Future of Design Research Dr Asma Qureshi Research & Insights Principal - A2 Online Design Research 2026 - Sydney - 26 August 2026 - 3:00 PM [Four circular icons: a chain link, a robot, a grid of four squares, and a target] ### 00:02:31 ### START WITH THE QUESTION # Where did that number come from? - Not the slide. - Not the repository. - The evidence. > The future of research begins when the trail survives the presentation. [Flow diagram showing a progression from Board Paper to Slide, then Finding, and finally Raw Evidence, with Raw Evidence highlighted] ### 00:02:52 ### START WITH THE QUESTION # Where did that number come from? - Not the slide. - Not the repository. - The evidence. The future of research begins when the trail survives the presentation. [Flowchart showing a progression from "BOARD PAPER" to "SLIDE" to "FINDING" to "RAW EVIDENCE"] ### 00:03:08 # DESIGN RESEARCH ## Has this happened to you? QUICK SHOW OF HANDS - A finding reappears six months later. - Nobody can trace the source. - The source is `FINAL_v7_REALLY_FINAL.pptx` Our evidence often travels further than our documentation. Hands up for any one of the three. [Three cards, each with an icon: a refresh arrow, a magnifying glass, and a document icon.] ### 00:03:13 # DESIGN RESEARCH ## Has this happened to you? QUICK SHOW OF HANDS - A finding reappears six months later. - Nobody can trace the source. - The source is `FINAL_v7_REALLY_FINAL.pptx` Our evidence often travels further than our documentation. Hands up for any one of the three. [Three cards, each with an icon: a refresh arrow, a magnifying glass, and a document icon.] ### 00:03:37 # DESIGN RESEARCH ## QUICK SHOW OF HANDS ### Has this happened to you? - A finding reappears six months later. - Nobody can trace the source. - The source is `FINAL_v7_REALLY_FINAL.pptx` Our evidence often travels further than our documentation. ### 00:03:59 # Has this happened to you? - A finding reappears six months later. - Nobody can trace the source. - The source is `FINAL_v7_REALLY_FINAL.pptx` Our evidence often travels further than our documentation. Hands up for any one of the three. [Three boxes, each with an icon and a short text description: a refresh icon, a magnifying glass icon, and a document icon.] ### 00:04:23 # DESIGN RESEARCH ## Has this happened to you? - A finding reappears six months later. - Nobody can trace the source. - The source is FINAL_v7_REALLY_FINAL.pptx Our evidence often travels further than our documentation. ### 00:04:45 ### FOUR SHIFTS TO REMEMBER ## The future of design research, in four moves - **01** Insight -> **Evidence** - **02 ### 00:05:08 FOUR SHIFTS TO REMEMBER ## The future of design research, in four moves - **01** From INSIGHT to **EVIDENCE** - **02** From AI TOOL ### 00:05:09 FOUR SHIFTS TO REMEMBER ## The future of design research, in four moves - **01** From INSIGHT to **EVIDENCE** - **02** From AI TOOL ### 00:05:34 # DESIGN RESEARCH ## 01 SHIFT ONE - Insight → Evidence The future of design research [Blue chain link icon] ### 00:05:56 # DESIGN RESEARCH ## 01 SHIFT ONE Insight → **Evidence** ### The future of design research [Large blue chain link icon] ### 00:06:08 # DESIGN RESEARCH ## 01 SHIFT ONE ### Insight → Evidence The future of design research [Blue chain link icon] ### 00:06:31 # THE PROBLEM, STATED SIMPLY ## The insight is visible. The trail makes it defensible. - **THE INSIGHT** > "64% struggled to understand eligibility." ### 00:06:57 # DESIGN RESEARCH ## THE PROBLEM, STATED SIMPLY ### The insight is visible. The trail makes it defensible. - **THE INSIGHT** - "64% struggled ### 00:07:23 # The lifecycle of an insight - **1. RESEARCH READOUT** "Great insight." - **2. APPLAUSE** "Very useful." - **3 ### 00:07:48 # The lifecycle of an insight A FAMILIAR LIFECYCLE 1. **RESEARCH READOUT** "Great insight." 2. **APPLAUSE** ### 00:08:08 # The lifecycle of an insight 1. **RESEARCH READOUT** * "Great insight." 2. **APPLAUSE** * "Very useful." ### 00:08:10 # The lifecycle of an insight 1. **RESEARCH READOUT** * "Great insight." 2. **APPLAUSE** * "Very useful." ### 00:08:34 # UX AUSTRALIA | WEB DIRECTIONS DESIGN RESEARCH ## THIS ALREADY HAS CONSEQUENCES ### Research quality can become operational risk. - **80+** DDO stop orders reported by ASIC ### 00:09:00 ### THIS ALREADY HAS CONSEQUENCES # Research quality can become operational risk. - **80+** DDO stop orders reported by ASIC since the obligations took effect in late ### 00:09:07 ### THIS ALREADY HAS CONSEQUENCES # Research quality can become operational risk. - **80+** DDO stop orders reported by ASIC since the obligations took effect in late ### 00:09:31 # UX AUSTRALIA | WEB DIRECTIONS ## DESIGN RESEARCH ### FRAMEWORK 01: 01 EVIDENCE # The Evidence Ladder Not a maturity race. A way to know what your ### 00:09:55 # The Evidence Ladder Not a maturity race. A way to know what your evidence must survive. 1. **EPISODIC**: Persuasive stories 2. **REPEATABLE**: Consistent protocols 3. **AUDITABLE**: Provenance + decision logs 4. **CONTESTABLE**: Explain + review Most teams are here Regulated contexts are pulling here The question is not "How do we get to rung four?" It is "What does this decision require?" [Diagram illustrating a four-level "Evidence Ladder" with each level described by a number, keyword, and explanation] ### 00:10:08 # DESIGN RESEARCH ## UX AUSTRALIA | WEB DIRECTIONS ### FRAMEWORK 01 # The Evidence Ladder Not a maturity race. A way to know what your evidence must survive. ### 00:10:33 # DESIGN RESEARCH ## FRAMEWORK 01: 01 EVIDENCE ### The Evidence Ladder Not a maturity race. A way to know what your evidence must survive. - ### 00:10:58 ## SHIFT TWO AI as a tool → **AI as the environment** The future of design research [Blue robot icon] ### 00:11:24 # 02 SHIFT TWO AI as a tool → **AI as the environment** ### The future of design research [Blue robot icon] ### 00:11:48 ### 02 AI # THE SAMPLE HAS CHANGED ## AI is now inside your sample, not only your workflow. - All Australians: 46% - Aged ### 00:12:08 # AI is now inside your sample, not only your workflow. ## THE SAMPLE HAS CHANGED - All Australians: 46% - Aged 18–34: ### 00:12:11 # AI is now inside your sample, not only your workflow. ## THE SAMPLE HAS CHANGED - All Australians: 46% - Aged 18–34: ### 00:12:35 # DESIGN RESEARCH ## The future is not AI-free research. It is AI-aware research. ### A SMALL 2026 PROBLEM - **PARTICIPANT** ### 00:13:00 ## A SMALL 2026 PROBLEM # The future is not AI-free research. It is AI-aware research. **PARTICIPANT** - 5 rough words ### 00:13:07 ## A SMALL 2026 PROBLEM # The future is not AI-free research. It is AI-aware research. **PARTICIPANT** - 5 rough words ### 00:13:31 # Does your research team have a written AI boundary? - **YES** - Written, findable, used - **SORT OF** - Guidance lives in people's heads - **NOT YET** - "We are careful" is the policy A documented boundary is stronger than "we use judgement." ### 00:13:53 ## QUICK SHOW OF HANDS ### Does your research team have a written AI boundary? - **YES** - Written, findable, used - **SORT OF** ### 00:14:09 ## Does your research team have a written AI boundary? - **YES** Written, findable, used - **SORT OF** Guidance lives in people's heads - **NOT YET** "We are careful" is the policy A documented boundary is stronger than "we use judgement." ### 00:14:30 # The future is bounded use, not blanket use. ## ASSIST ### **Low-friction uses** - Transcription support - Translation support - Repository retrieval - Draft summaries ### 00:14:54 # The future is bounded use, not blanket use. ## A BOUNDED AI PRACTICE ### ASSIST **Low-friction uses** - Transcription support - Translation support - Repository retrieval - Draft summaries ### GOVERN **Human judgement stays visible** - AI-assisted coding - Synthesis / clustering - AI moderators - Synthetic data for exploration ### DO NOT SUBSTITUTE **Never present as lived evidence** - Invented quotes - Synthetic personas replacing participants - AI-only evidence for consequential decisions Proposed operating boundary — adapt to risk, sector and organisational policy. Framework: Dr Asma Qureshi / A2 Online, 2026 Dr Asma Qureshi A2 Online Design Research 2026 ### 00:15:08 # The future is bounded use, not blanket use. ## ASSIST ### Low-friction uses - Transcription support - Translation support - Repository retrieval - Draft summaries ## GOVERN ### Human judgement stays visible - AI-assisted coding - Synthesis / clustering - AI moderators - Synthetic data for exploration ## DO NOT SUBSTITUTE ### Never present as lived evidence - Invented quotes - Synthetic personas replacing participants - AI-only evidence for consequential decisions Proposed operating boundary — adapt to risk, sector and organisational policy. Framework: Dr Asma Qureshi / A2 Online, 2026 [Three boxes, color-coded green, yellow, and red, with a checkmark, warning sign, and 'X' icon respectively, categorize AI uses into 'Assist', 'Govern', and 'Do Not Substitute'] ### 00:15:31 # The future is bounded use, not blanket use. ## A BOUNDED AI PRACTICE ### ASSIST (Low-friction uses) - Transcription support - Translation support - Repository retrieval - Draft summaries ### GOVERN (Human judgement stays visible) - AI-assisted coding - Synthesis / clustering - AI moderators - Synthetic data for exploration ### DO NOT SUBSTITUTE (Never present as lived evidence) - Invented quotes - Synthetic personas replacing participants - AI-only evidence for consequential decisions Proposed operating boundary — adapt to risk, sector and organisational policy. Framework: Dr Asma Qureshi / A2 Online, 2026 Dr Asma Qureshi A2 Online Design Research 2026 [Three colored boxes with icons: green with a checkmark for "ASSIST", yellow with a warning sign for "GOVERN", and red with an 'x' for "DO NOT SUBSTITUTE"] ### 00:15:53 # The future is bounded use, not blanket use. ## ASSIST - Low-friction uses - Transcription support - Translation support - Repository retrieval - Draft summaries ## GOVERN - Human judgement stays visible - AI-assisted coding - Synthesis / clustering - AI moderators - Synthetic data for exploration ## DO NOT SUBSTITUTE - Never present as lived evidence - Invented quotes - Synthetic personas replacing participants - AI-only evidence for consequential decisions Proposed operating boundary — adapt to risk, sector and organisational policy. Framework: Dr Asma Qureshi / A2 Online, 2026 ### 00:16:08 # A BOUNDED AI PRACTICE ## The future is bounded use, not blanket use. ### ASSIST - **Low-friction uses** - Transcription support - Translation support - Repository retrieval - Draft summaries ### GOVERN - **Human judgement stays visible** - AI-assisted coding - Synthesis / clustering - AI moderators - Synthetic data for exploration ### DO NOT SUBSTITUTE - **Never present as lived evidence** - Invented quotes - Synthetic personas replacing participants - AI-only evidence for consequential decisions Proposed operating boundary — adapt to risk, sector and organisational policy. Framework: Dr Asma Qureshi / A2 Online, 2026 [Three boxes illustrating categories for AI use: 'Assist' with a check ### 00:16:13 # DESIGN RESEARCH ## A BOUNDED AI PRACTICE ### The future is bounded use, not blanket use. **ASSIST** * **Low-friction uses** * Transcription ### 00:16:37 ### SHIFT THREE - Research projects → **Learning infrastructure** [Icon representing a network or hierarchy of connected boxes] ### 00:16:59 ### SHIFT THREE Research projects → **Learning infrastructure** The future of design research [Blue icon representing connected devices or a network] ### 00:17:23 UX AUSTRALIA | WEB DIRECTIONS # DESIGN RESEARCH FROM A CALENDAR TO A SYSTEM - 01 EVIDENCE - 02 AI - **03 INFRASTRUCTURE ### 00:17:47 ## FROM A CALENDAR TO A SYSTEM # The annual research plan is becoming a learning system. 01 EVIDENCE 02 AI **03 INFRASTRUCTURE** 04 IMPACT ### ### 00:18:09 # Continuous research already exists as infrastructure here. ## 03 Infrastructure **18,000+** stakeholders involved in Monash University living lab challenges and learning in 2023-24 - 100+ partners - 100+ researchers - 1,000+ students Not a pilot. A durable capability for learning in real contexts. [Diagram showing a network ### 00:18:33 # Continuous research already exists as infrastructure here. **18,000+** stakeholders involved in Monash University living lab challenges and learning in 2023-2 ### 00:18:57 # Australian research infrastructure cannot be digital-only or culturally neutral. ## Hybrid by Design - A continuous panel recruited only through an online portal can be internally consistent — and still miss the people the service was meant to reach. - **Remote**, **phone**, **in-person**, **assisted participation** ## Indigenous Data Sovereignty - As AI makes it easy to reuse, remix and re-analyse prior data, authority to control and benefit sharing become infrastructure requirements — not an ethics footnote. - **Authority**, **context**, **control**, **benefit** [Icon of a phone and a laptop representing hybrid design] [Icon of a fingerprint representing indigenous data sovereignty] ### 00:19:23 # Australian research infrastructure cannot be digital-only or culturally neutral. ## Hybrid by Design A continuous panel recruited only through an online portal can be internally consistent — and still miss the people the service was meant to reach. **Keywords:** Remote, phone, in-person, assisted ### 00:19:47 # Australian research infrastructure cannot be digital-only or culturally neutral. ### HYBRID BY DESIGN A continuous panel recruited only through an online portal can be internally consistent — and still miss the people the service ### 00:20:10 ## Australian research infrastructure cannot be digital-only or culturally neutral. ### Hybrid by Design A continuous panel recruited only through an online portal can be internally consistent — and still miss the people ### 00:20:35 # What changes is what survives after the study. - **1. INTENT** Decision + risk - **2. PARTICIPATION** Consent + inclusion - **3. ANALYSIS** Human judgement + AI log - **4. EVIDENCE** Source + uncertainty - **5. DECISION + MONITOR** What changed? The interview can stay human. The operating model around it becomes traceable. [Diagram showing a 5-step process: 1. Intent (with a target icon), 2. Participation (with a group of people icon), 3. Analysis (with a magnifying glass icon), 4. Evidence (with a link icon), 5. Decision + Monitor (with a target icon)] ### 00:20:39 # What changes is what survives after the study. - **1. INTENT** Decision + risk - **2. PARTICIPATION** Consent + inclusion - **3. ANALYSIS** Human judgement + AI log - **4. EVIDENCE** Source + uncertainty - **5. DECISION + MONITOR** What changed? The interview can stay human. The operating model around it becomes traceable. [Diagram showing a 5-step process: 1. Intent (with a target icon), 2. Participation (with a group of people icon), 3. Analysis (with a magnifying glass icon), 4. Evidence (with a link icon), 5. Decision + Monitor (with a target icon)] ### 00:21:03 # What changes is what survives after the study. ## FRAMEWORK 02 - **INTENT** - Decision + risk - **PARTICIPATION ### 00:21:06 # DESIGN RESEARCH ## UX AUSTRALIA | WEB DIRECTIONS ### FRAMEWORK 02 ## What changes is what survives after the study. - **1. INTENT** ### 00:21:30 ### 04 Shift Four The future of design research - Findings delivered → **Decisions + outcomes** [Blue target icon] ### 00:21:52 # 04 SHIFT FOUR ## Findings delivered → **Decisions + outcomes** The future of design research [Blue concentric circle icon, resembling a target] ### 00:22:08 # Shift Four Findings delivered → **Decisions + outcomes** [Blue target icon] ### 00:22:14 ### 04 SHIFT FOUR - Findings delivered → **Decisions + outcomes** [Blue target icon] ### 00:22:38 # The most valuable metadata may be the field almost nobody captures. - STUDY: Hardship onboarding - METHOD: 12 interviews + service-data review - DATE: June 2026 - RESEARCH LEAD: Dr Asma Qureshi - **DECISION INFORMED** - What choice changed because of this evidence? - **OUTCOME / SIGNAL** - What will tell us whether it worked? Most repositories are excellent at remembering the study — and weak at remembering the decision. ### 00:23:01 # THE MISSING FIELD ## The most valuable metadata may be the field almost nobody captures. - **STUDY**: Hardship onboarding - **METHOD**: 12 interviews + service-data review - **DATE**: June 2026 - **RESEARCH LEAD**: Dr Asma Qureshi ### DECISION INFORMED What choice changed because of this evidence? ### OUTCOME / SIGNAL What will tell us whether it worked? Most repositories are excellent at remembering the study — and weak at remembering the decision. ### 00:23:24 # Which shift is already happening in your organisation? Hold up 1, 2, 3 or 4 fingers. - **1 EVIDENCE** People ask for traceability - **2 AI** AI is changing the method - **3 INFRASTRUCTURE** Research is becoming continuous - **4 IMPACT** Leaders ask what changed If you held up more than one finger, that is the point. [Icon of a chain link next to "EVIDENCE"] [Icon of a robot next to "AI"] [Icon of two overlapping squares next to "INFRASTRUCTURE"] [Icon of a target or bullseye next to "IMPACT"] ### 00:23:45 # DESIGN RESEARCH ## YOUR TURN ### Which shift is already happening in your organisation? Hold up 1, 2, 3 or 4 fingers. - **1 EVIDENCE**: People ask for traceability - **2 AI**: AI is changing the method - **3 INFRASTRUCTURE**: Research is becoming continuous - **4 IMPACT**: Leaders ask what changed If you held up more than one finger, that is the point. [Four distinct boxes, each containing a number, a category title, a short description, and an icon representing the category (chain link for Evidence, robot for AI, product box for Infrastructure, target for Impact).] ### 00:24:08 ### YOUR TURN # Which shift is already happening in your organisation? Hold up 1, 2, 3 or 4 fingers. - **1 EVIDENCE** - People ask ### 00:24:33 # DESIGN RESEARCH ## MY FORECAST FOR 2026 → 2031 ### The job title may stay the same. The deliverable, audience and shelf life will ### 00:24:57 ### MY FORECAST FOR 2026 → 2031 ## The job title may stay the same. The deliverable, audience and shelf life will not. ### 00:25:23 # MY FORECAST FOR 2026 → 2031 ## The job title may stay the same. The deliverable, audience and shelf life will not. - ** ### 00:25:47 # DESIGN RESEARCH ## FOUR MOVES FOR MONDAY ### The future starts with four small operating changes. 1. **EVIDENCE**: Add one field: "Which decision did this inform?" 2. **AI**: Publish the boundary: assist, govern, never substitute. 3. **INFRASTRUCTURE**: Choose one signal to monitor continuously after launch. 4. **IMPACT**: Define one outcome that should move if the research matters. None of these requires a transformation programme to begin. [List of four points, each with a number and an icon: 1. a network/connection icon, 2. a robot head icon, 3. an infrastructure/dashboard icon, 4. a target icon.] ### 00:26:08 ## FOUR MOVES FOR MONDAY # The future starts with four small operating changes. - **01 EVIDENCE**: Add one field: "Which decision did this inform?" - **02 AI**: Publish the boundary: assist, govern, never substitute. - **03 INFRASTRUCTURE**: Choose one signal to monitor continuously after launch. - **04 IMPACT**: Define one outcome that should move if the research matters. None of these requires a transformation programme to begin. ### 00:26:33 ### FOUR MOVES FOR MONDAY ## The future starts with four small operating changes. 1. **EVIDENCE**: Add one field: "Which decision did this inform?" 2. **AI**: Publish the boundary: assist, govern, never substitute. 3. **INFRASTRUCTURE**: Choose one signal to monitor continuously after launch. 4. **IMPACT**: Define one outcome that should move if the research matters. None of these requires a transformation programme to begin. [List item 1 has an @ symbol icon for Evidence] [List item 2 has a robot icon for AI] [List item 3 has a server rack icon for Infrastructure] [List item 4 has a target icon for Impact] ### 00:26:55 ### FOUR MOVES FOR MONDAY # The future starts with four small operating changes. - **01 EVIDENCE**: Add one field: "Which decision did this inform?" ### 00:27:08 # UX AUSTRALIA | WEB DIRECTIONS DESIGN RESEARCH ## FOUR MOVES FOR MONDAY ### The future starts with four small operating changes. - **01 EVIDENCE**: Add one field ### 00:27:32 ## FOUR MOVES FOR MONDAY # The future starts with four small operating changes. - 01 **EVIDENCE**: Add one field: "Which decision did this inform?" - 02 **AI**: Publish the boundary: assist, govern, never substitute. - 03 **INFRASTRUCTURE**: Choose one signal to monitor continuously after launch. - 04 **IMPACT**: Define one outcome that should move if the research matters. None of these requires a transformation programme to begin. ### 00:27:54 # DESIGN RESEARCH ## Insight is not enough. The future is - EVIDENCE - JUDGEMENT - INFRASTRUCTURE - IMPACT Show your working. Stay close to decisions. Measure what changes. Design research is not running out of relevance. It is gaining responsibility. ### 00:28:08 # UX AUSTRALIA | WEB DIRECTIONS ## DESIGN RESEARCH # Thank you. ## Dr Asma Qureshi Research & Insights Principal · A2 Online - Research strategy - AI governance - CX/UX - Insight operating models asma@a2online.au a2online.au [linkedin.com/in/draaq](https://www.linkedin.com/in/draaq) ## Connect on LinkedIn I would love to continue the conversation about where research is heading — and what organisations ### 00:28:32 ### Connect on LinkedIn I would love to connect for information about career insights, opportunities, and industry news. [QR code for LinkedIn profile] ### 00:28:53 # Thank you - Connect on LinkedIn - I would love to connect with you. If you have any questions about this topic or anything else related to health tech, please reach out. [QR code for connecting on LinkedIn] ### 00:29:08 ### Connect on LinkedIn I would love to continue the conversation about research in nursing, midwifery and allied health work. [QR code for a LinkedIn profile] ### 00:29:14 ## Connect on LinkedIn I would love to continue the conversation about materials in engineering to build work, opportunities and networks. [QR code for LinkedIn profile] ### 00:29:38 ### Connect on LinkedIn I would love to connect with anyone interested in these topics. My LinkedIn profile is in the QR code, or you can search for me. [QR code for connecting on LinkedIn]