UX Roundup: Workflow Redesign in Games Studios | Living Without AI | Learning With AI | Job-Specific Streaming Media | Integrating Voice & Pointing

Summary: Case study of redesigning workflows for AI in computer game studios | Can you live 4 days without AI? | AI increases learning when used as a tutor and hurts when doing the exercises for the students | A streaming media platform for your job | DeepMind demos integration between voice input and pointing

UX Roundup for May 18, 2026 (GPT-Images-2)

Gaming Proves It: Task-Level AI Is a Dead End

A new Wharton research paper confirms what I’ve been arguing: bolting AI onto existing workflows produces negligible organizational impact. The real gains come from redesigning how work gets done.

Zimran Ahmed studied 20 computer game studios from AAA behemoths to scrappy indie teams and found a pattern that maps almost perfectly onto the framework I laid out in my analysis of redesigning workflows for AI. Studios that treated AI as a task-level productivity tool saw individual benefits that never aggregated into business results. Studios designed around AI from the start saw cycle times collapse by 4–20x.

The gaming industry is the ideal test case because it has used AI longer than most sectors. If any industry should have an easy time adopting generative AI, it’s this one. But organizational barriers were identical to those in every other industry: tacit knowledge, siloed teams, and human resistance to change.

Copy-and-Paste AI Is Local Search by Another Name

Ahmed describes the universal first step: give employees access to ChatGPT Enterprise and let them experiment. He calls this “Copy-and-Paste AI” where the user puts context into a chat window, gets a result, and pastes it back into their work. Every studio started here. It required no organizational change. And it produced no organizational impact.

Task-level use of copy–paste AI wins a few points, while the big prize of complete workflow redesign for AI flies away. (NotebookLM)

This is precisely the local search bias I discussed based on the earlier INSEAD/Harvard data. When you hand people a tool and say, “Be more productive,” they optimize the task immediately in front of them. A product manager drafts better emails. A designer generates placeholder art. Useful, but irrelevant to firm-level throughput. In the INSEAD/Harvard experiment, the control group did exactly this, and generated only half the revenue of firms that redesigned whole workflows for AI.

When you focus on your own local problem, you don’t know what you’re missing in the larger context. (NotebookLM)

The Mapping Problem Lives in Tacit Knowledge

Ahmed’s central insight about why workflow automation stalled is devastating in its simplicity: multi-person workflows run on unwritten rules, and nobody can automate what nobody has documented. The true process differs from the SOPs. Before AI could replace handoffs, someone had to extract, reconcile, and codify institutional memory — and employees had every incentive not to cooperate, because tacit knowledge is job security.

Unwritten rules (tacit knowledge) must be made explicit for AI workflows to work. This cannot happen if each group hoards its private knowledge. (NotebookLM)

This is the mapping problem. The INSEAD researchers demonstrated that the binding constraint on AI profitability is not technical access or coding skill. It is the cognitive and organizational challenge of discovering where AI creates value across the production system. Ahmed’s interviews supply the human texture behind that abstract finding: artists holding crisis meetings, art directors pledging to “support this, but then protest in other places.”

Boundary-Crossing Is Workflow Redesign in Miniature

The most actionable finding is what Ahmed calls “reaching across boundaries.” Individuals used AI to complete tasks that previously required other teams. A product manager wrote database queries. A finance controller automated data extraction. An engineer generated art assets. These people already possessed the domain knowledge; they just lacked the technical execution skills that AI could supply.

Crossing boundaries led to greater workflow improvements than when employees optimized a local problem. (NotebookLM)

This is bottom-up workflow redesign. Each boundary-crossing instance eliminated a handoff, which is exactly what I recommended: remove handoffs, parallelize variants, move humans to exceptions. The product manager who reduced query failure rates from 80% to 5% by iteratively documenting his tacit knowledge into a markdown file was solving the mapping problem in real time, building what Ahmed calls “Read/Write AI”: a compounding knowledge system.

AI-Native Studios Are Company Redesign Made Real

The paper’s most striking data comes from AI-Native studios built on AI from the founding. Teams of five generalists replaced departments of specialists. A vertical slice that would have taken four months took four weeks. Thirty icons were produced in one day instead of weeks. Ahmed calls this “pipeline collapse.” I call it the inevitable consequence of designing the firm around AI rather than inserting AI into the firm.

A small team of AI-empowered generalists replaced a large number of AI-less specialists in one of these case studies. (NotebookLM)

These studios embody every principle from the INSEAD experiment: they eliminated human glue, parallelized exploration, moved humans to judgment and evaluation, and built living documentation that AI agents could reference and update. They achieved what the treated startups in the field experiment achieved: dramatically more output with dramatically less capital and the same headcount.

The Lesson Is Universal

Even though this was a study of computer game studios, the lessons generalize. Every industry has tacit knowledge trapped in specialist silos, handoffs that consume weeks, and employees who optimize locally because they've never seen the whole system mapped. Ahmed’s conclusion matches mine: don’t ask “what can we automate?” Ask “how would we build this if we designed around AI from day one?”

The studios that asked the second question didn’t just make games faster. They made games that weren’t possible before. That is the difference between task optimization and workflow redesign, and it is worth a fortune for the business.

We can all learn important lessons about redesigning workflows for AI from this case study of games studios doing so. (NotebookLM)

(Hat tip to Ethan Mollick for alerting me to this paper from his department.)

As I have mentioned many times, the credibility of research findings increases when the same (or roughly the same) results emerge from different research projects conducted by different research groups, using different methodologies and studying different domains. Here, we have a French research group and an American one agreeing, even though one used a randomized controlled quantitative study and the other used a qualitative interview-based method, and one studied incubator startups and the other targeted computer game studios. I believed the first study because it was big and thorough, but I believe it even more now.

Deprivation Study: Living for 4 Days Without AI

Could you go without AI? Could you do it for 4 whole days?

In user experience, we typically measure what happens when people use a system. We run usability tests and calculate task success rates. However, some of the most profound behavioral insights come from the exact opposite approach: taking the system away entirely.

In user research, this is called a deprivation study. You rarely understand a technology’s true impact until it breaks. When a system functions perfectly, it becomes invisible. To truly see how deeply a tool has embedded itself into human behavior, we force users to stop using it and observe the fallout.

Participants in a Korean deprivation study were asked to do just that. Eunseo Oh and colleagues from the Korea Advanced Institute of Science and Technology (KAIST) conducted a small qualitative study where 10 users volunteered to refrain from AI use for 4 days. Whenever they felt the urge to use AI, they logged their context, emotional state, and workarounds into a diary, followed later by in-depth interviews.

Deprivation studies are a standard user research method to investigate the role of a technology in users’ lives. (GPT-Images-2)

AI vs. Smartphone Deprivation

Deprivation studies are a highly revealing qualitative method. A decade ago, classic deprivation studies of smartphone use yielded striking results. When researchers confiscated users’ smartphones, the immediate result was acute anxiety, “phantom vibrations,” and intense FOMO (Fear Of Missing Out). Eventually, this withdrawal led users to reclaim their present-moment awareness and reflect on their mindless scrolling.

AI deprivation tells a fundamentally different story. While taking away a smartphone forces users to confront their emotional dependencies, taking away AI forces knowledge workers to confront their cognitive dependencies. Losing a smartphone causes emotional withdrawal; losing AI causes severe workflow paralysis and a crisis of professional competence.

The Pain of Withdrawal: New Usability Baselines

The study’s main finding is that AI is no longer perceived as optional software; it has become implicit work infrastructure, much like electricity. When this infrastructure was removed, the researchers observed profound disruptions:

• Traditional Search is Now an “Excessive” Burden: Without ChatGPT, users had to revert to traditional search engines like Google. Participants reported that synthesizing, reformulating, and iterating search keywords felt unexpectedly cumbersome. AI has permanently raised expectations: having to translate human intent into machine-readable keywords and sift through links is now viewed as an unacceptable cognitive load.

Traditional search felt cumbersome without the ability to get a simple, synthesized answer from AI. (GPT-Images-2)

• The Social Cost of Asking Humans: Bereft of AI, users had to ask human colleagues for help. Curiously, they hated it. Accustomed to the infinite patience of a chatbot, participants perceived asking humans for assistance as a heavy social burden. One cited a fear of being viewed as a “finger prince/princess,” Korean slang for someone too lazy to look up basic information.

Without AI, people felt they were imposing on other humans when asking them questions. (GPT-Image-2)

• Lowered Standards and Task Avoidance: Generative AI provides massive productivity gains. But without AI assistance to polish writing or catch bugs, participants actually lowered their standards. Rather than expending manual effort to achieve excellence, they accepted mediocre outcomes to save time. Furthermore, they simply abandoned tasks that felt too daunting without an AI copilot.

Users lowered their standards without AI to help them refine their work. (GPT-Images-2)

The Silver Lining: Reclaiming Professional Value

Despite the friction, the deprivation period yielded a profound silver lining. By forcing users to do the heavy lifting themselves, the study revealed the hidden costs of AI dependency.

When participants were forced to construct and trace their own reasoning from scratch, they reported a much clearer, sustained understanding of their work. More importantly, they reported a renewed sense of ownership. Work produced with AI often feels alienated and “not my own.” Completing tasks manually fostered a sense of pride and professional integrity that AI-delegated work had stripped away.

People felt a renewed sense of ownership in the slower work they produced without AI. (GPT-Images-2)

Finally, because manual work takes much longer, participants were forced to rigorously prioritize. Without AI, users had to carefully evaluate which tasks were actually core to their professional identity, filtering out the busywork.

The conclusion of this research is stark: AI use is now socially inescapable. For UX professionals, the central challenge is no longer whether to use AI, but designing systems that allow users to leverage AI without sacrificing their professional agency.

AI Increases Learning When Used as a Tutor, But Hurts When Doing Exercises for the Students

By now, this is an old insight, but it’s always nice when new research studies from new scientists confirm what we already know: this vastly increases our confidence in the results.

AI in education can help or hurt the students’ learning, depending on how it’s used. Without understanding this basic fact, you will get confused by the many studies that are sometimes favorable for educational AI and sometimes have negative findings.

The basic finding is that if the students use AI to do their work for them, they don’t learn. Students may feel good about finishing assignments and exercises quickly, but the goal of student “work” is not to produce the deliverable. The old saying “the journey is the reward” is the point of student assignments: the student learns by working through the problem, not by getting the solution handed down by AI (or by a human helper).

On the other hand, learning is improved when students use AI as a tutor, to guide them through the material, to explain tough concepts tirelessly in many ways, and to provide hints when they are stuck on a problem.

A new paper by Hamsa Bastani and colleagues from the Wharton School shows this once again. In this case, the researchers studied almost 1,000 Turkish students taking mathematics courses in the ninth, tents, and eleventh grades. Three study conditions: (1) A control group with no AI. (2) Full AI, which they inevitably used to solve the math problems for them. (3) Tutoring AI, which provided step by step hints instead of direct answers to problems.

When doing practice problems, students in both the AI conditions did much better than the control group. But in an exam without access to AI, the students in the full AI group did substantially worse than the control group, indicating that they had learned less, not more, from the more efficient completion of the exercises when AI did most of the work for them. There was no statistically significant difference in this exam between the students using the tutoring AI and the control group, indicating that the problem for the “Full AI” students was not AI as such, but rather the way they used it.

Other studies have also found increased learning on final exams for students with tutoring AI, but even if that was not measured in this study, simply the ability to proceed faster through the curriculum is a benefit in itself.

AI can help or hinder learning, depending on how it’s used. (GPT-Images-2)

Streaming Media Platform for Your Job

What if there were a streaming media platform for your job? How would your home screen look? Here are some examples, drawn with GPT-Images-2.

Are there any of these videos you want me to make? Let me know in the comments.

Put That There, For Real

DeepMind has released a short demo video showing the integration of voice input with mouse-based pointing on the computer screen. This is a very natural interaction technique for certain operations on your computer. For example, “make THIS bigger.”

I am immediately reminded of Richard Bolt’s classic Put-That-There research system from the MIT Media Lab in 1980. The system featured a large projection display showing simple colored geometric symbols and labels that represented objects in a scene. Bolt’s innovation was the explicit treatment of speech and gesture as a single, coordinated communicative act rather than as separate input modes. The name of the system was how you used it: You would speak an action (e.g., PUT), point to an object, and point to where it should be moved.

46 years later, DeepMind’s new demo does roughly the same, but with a much larger set of operations, and allows users to use their own computers and use their own data rather than having to sit in a specially equipped projection room.

Compared to the classic WIMP-driven graphical user interface, where commands and objects required separate interactions to drive an action, the parallel specification of what should be done (through the voice channel) and where that should be done (through mouse pointing) streamlines the interaction by unifying the two in time. This is roughly similar to the way the PenPoint system for the GO pen-driven tablet from 1992 allowed users to unify objects and commands into a single gesture. For example, you could make a word bold by writing a “B” on top of that word. (The “B” gesture specified the command, and where it was written specified the operand.)

Exciting to see continued progress on these pioneering interaction techniques, driven by today’s AI capability to broaden the scope, and thus the utility, of unifying operator and operand specification.

History repeats itself, but better this time around. (GPT-Images-2)

Running Through Time

Just for fun: a short AI video of running through time (Instagram, 40 secs.), where I asked Seedance to generate based on a minimum of direction from me. I think it did quite well.

In general, I prefer to direct my videos more intensely so that they reflect my detailed vision. However, as AI gets better, it’s likely that creative vision will have to uplevel and stop obsessing over the details, which AI models will fill in better than humans could do.