Intended audience: leaders looking to win at the long game in a world that changes faster than many can adapt.
Note that AI was used to research workforce trends and latest statistics but the qualitative examples, analysis, and conclusions are my own.
I had an interesting conversation the other day about the future of creative work. The young man challenges his LinkedIn audience to think what would make someone stand out if everyone has the same access to the same AI tools. There were many comments praising human qualities and human creativity. However, this discourse pointed at a deeply flawed way we think about the nature of work, AI, creativity, and being human in an increasingly automated world.
It’s not “we have access to the same AI tools…” It’s “we have access to the same tools.” Just tools. Not AI. Full stop. This thought is older than AI. What made someone a resilient, creative, enthusiastic professional hasn’t changed. I’m worried that by chasing the next big thing many leaders forget, or set aside, something that we knew for a long time already.
Trillions for tools, pennies for people.
Don’t take my word for it. Here are some hard stats to support my thesis:
74% report increased IT & Software investment yet many still rely on manual, disconnected processes that create false sense of control until real-world demands expose the gaps [1][2]
Department of Education shows that British employers reduced their investment in staff training by £6bn in 2024, compared to 2022 levels [3] yet UK business investment in AI is due to raise by 40% [4]
While the industry celebrates AI’s potential to “unleash creativity” the reality on the ground is one of increasing “technostress” and “change exhaustion” [5]
It used to be that businesses invested in people. Now businesses invest in AI. The declines in training expenditure is particularly alarming given the pace of technological change and raising complexity of work. The result is a workforce that is being asked to do more with complex tools but with less institutional support for learning how to use them safely and strategically.
This led to the raise of “Shadow AI”, where employees, eager to be productive but left untrained, use unapproved AI tools at work. In the UK, 68% report that staff use shadow AI tools, which already led to data or IP exposure in 44% of businesses [6]. This phenomenon is the ultimate evidence of my hypothesis that businesses are forgetting to invest in people.
Signals that businesses are investing in humans again
There are tentative signals out there that people investment is on the rebound.
McKinsey Forward programme has a strong focus on adaptability, effective communication, relationship building, problem-solving, and digital and AI essentials. Notice how digital and AI essentials are at the bottom of the list.
Deloitte US released a report called “In a new era of work, winning organisations will build the human advantage,” that outlines the importance of building adaptability, resilience, and creativity. Those human skills. It’s funny that this report is dated 4 March and I’m writing this blog 10 days later. Great minds think alike!
IBM SkillsBuild is committed to training 2 million people in AI by the end of 2026. The program focuses on “essential workplace skills”, critical thinking, and integrity.
Lloyds Banking Group surveyed 1,200 UK companies and found that more firms are planning to invest in their staff (35%) than in AI technology itself (33%) in 2026.
Why is this important?
As a future designer I’m paid to think about long horizons. As a human-centred AI advocate I’m loving the shift towards investing in humans. As a service designer I want to design services that will support me in my old age. As a design leader sometimes tasked with building design capability and design thinking withing organisations, I sometimes want to scream:
“What AI? What adoption roadmap? Sweep your house first! You have managers who don’t know how to be managers! You have people that lack basic skills to do their jobs properly! Train them first, then we can talk about AI.”
It’s important because in a world that changes every five minutes, in a world where everyone has the same tools, the true differentiator will be human creativity.
Disclaimer: Human-centred AI is a huge field that combines ethics, sutainable development, technology, some pretty advanced computer science, and more. We’ll be talking about the design side of things. AI was used to conduct research for this piece and find sources and facts. All words are my own.
I’ve seen some terrible implementations of AI in firms. I mean truly, truly awful. Imagine using a proprietary AI—a simple GPT wrapper with custom instructions—but for some reason the business decided to build its own front end for it. Now imagine that same firm blocking access to the usual AI tools like Gemini, OpenAI, Claude, Perplexity, and everything in between. Now you’re stuck with an inferior model (Gemini 2.5 Flash, for example) that lives in a terrible proprietary interface.
You can access the chat history but you can’t search it. There is a saved prompt library to save time but you can’t make your own to suit your needs. There is also something called “agents”, which are like Gemni Gems or OpenAI custom GPTs, but again you can’t make your own. The proprietary AI constantly cuts off long responses and you have to re-run the prompt by specifying “continue from bullet point #3,” which costs tokens and your own valuable time.
Frustrating, isn’t it? Shouldn’t happen in 2025, right? Well, you’d be surprised how often this happens.
We could dig into the phenomenon. Why do companies insist on building shitty AIs wrapped in shitty UI? Fears about security, reputational risks, business objectives, all that isn’t that important for this article. It just happens and it sucks. Let’s try and fix it.
I can’t believe I’m saying this in 2025 but the secret sauce is involving your users at every step. Welcome back to 1982. Blade Runner is in the cinemas, anthropomorphising the humanity’s fear of advanced AI. IBM, already a computing giant, is democratising technology by putting a computer on every desk. Michael Cooley writes a short book called “Architect or Bee? (The Human Technology Relationship)” in which he coins the term “Human-centred Design” for the first time.
The idea was simple: the technology should be designed to enhance human capabilities, creativity, and problem solving, rather than simply replacing human effort or reducing complex tasks to simple, repetitive actions. The idea endured for 43 years and is now central to the emerging field of Human-Centred AI.
Our personal boulder of Sisyphus
So why then do we keep banging our heads against constant lack of investment, lack of resources, lack of buy-in? I think of Design and I think back to the countless LinkedIn posts and Reddit threads bemoaning the challenges we face in getting buy-in. Design is routinely deprioritised, understaffed, underresourced, and underappreciated. Designers go about their lives championing, advocating, getting alignment, getting buy-in and getting shut out of big strategy meetings.
The reason is as cynical as it is simple. The lack of buy-in is due to the lack of tangible benefit. Human-Centred Design is still seen as a naive, idealistic philosophy, rather than valuable part of the process. McKinsey can write a thousand reports on the business value of design, no one will listen.
Framework for Human-Centred AI
I’m not re-inventing the wheel here. You might’ve seen variations of this framework everywhere. Double-diamond, tripple-diamond, Alpha-Beta-Live, Imagine-Deliver-Run… the list goes on. The point is that I’m proposing specific methods and activities within each “diamond” that are unique to designing AI products. Read on.
Discover
1. Solve an actual problem
Do. The. Research. Talk to your users. See what their problems actually are. Not doing so is akin to a general sending in his army without reconnaissance. Companies just slap AI on anything these days. That inevitably fails unless you solve an actual problem worth solving with AI.
2. Pick ambitious use cases
AI can do a lot of useful stuff. It can summarise, pull documents, generate content. From talking to your users you’ll have a good idea of what they need. Prioritise the use cases and pick an ambitious one. No point in solving small problems, right?
3. Define KPIs and benchmarks
I like a good old fashioned planning workshop. There is a lot of data out there. Just sit down and do it, however long it takes. A day, two days, a week. It doesn’t matter. If NASA engineers can meticulously plan out 10,000 single points of failure and plan contingencies for each one — so can you map out some KPIs and benchmarks. This is key. The more specific and granular, the better. For example, “we want to improve efficiency of annual reports” is terrible. “We want to reduce the time it takes to produce an annual report from 40 hours to 4 hours, 95% accurate pre- and post- human audit” is much better.
Design
1. Design with users and iterate often
This is also not new. Brainstorm, test, iterate. We’ve been doing this for 40+ years. I usually start with rough concepts, progress to low fidelity prototype (little more than interactive powerpoints), and only then—as the ambiguity reduces with each iteration—move on to fully clickable or functional prototypes.
2. Measure KPIs early
Measure whatever you can measure with prototypes. There are tools to tell you how successful a usability test was: System Usability Scale, my own AI Experience Score (AES), UX-Lite, and many more. The key message is to measure qualitatively to begin with. Forget automated AI benchmarks, we’re not building new models here.
Build
1. Start small
Build quick spikes, functional prototypes, show them to users, get feedback. Start small and scale up. 5 users, 10 users, 100, 1000… and so on. Here’s a cautionary tale I’ve seen across multiple large organisations: teams work in isolation on some proprietary AI tools, teams release their creation to the wider company, the AI tools performs poorly, consultants are brought in to figure out why and create an adoption strategy that’s already build on shaky foundations. This expensive mistake would’ve been avoided completely with a robust ‘test and iterate’ cycle or a closed Beta to gather feedback.
2. Continue to measure KPIs
Continue measuring. Perhaps now we can use AES quantitatively, include analytics. The more data you gather the better. To expand on the pattern I described above: an AI tool is released, adoption and engagement are poor, no one knows why. Consultants are brought in but no usage data or user feedback is available. Consultants are forced to run expensive research to understand why.
Scale
1. Bake in feedback mechanisms
Analytics feedback is great but it won’t tell you why people are doing what they are doing, or what exactly they are frustrated about. We had a discussion about this with a client just a few weeks ago. They saw high drop-off at a certain part of their journey. It was a form field on a certain page during onboarding. The drop-off spiked there and no one knew why. During interviews it turned out that that particular question had nothing to do with it. The culprit was the form field below, which was unusable and unaccessible. Analytics data will only tell you so much, user research is the thing that will help you pin-point the problem.
2. Measure ROI
This is where we get to the meat of the matter. Hopefully you’ve been collecting data and measuring progress this entire time. Now it’s time for that presentation back to the board. Let’s talk more about that in the next section.
Measuring ROI
I love the Google HEART framework. It ties subjective terms like “happiness” with objective measures, like “satisfaction scores.” It also allows you to track the impact of design on the bottom line. You can read more about the Google HEART framework here.
I’ll give you an example. We were building a new product. I called in a workshop. In it we had representatives from design, product, technology, analytics, and data science. We went through the exercises and design ended up being responsible for “Happiness.” We measured it by taking our overall satisfaction score, which we measured with usability studies and contextual surveys on site. I aggregated a whole bunch of various usability metrics into an overall Happiness score, which was displayed on the Google Analytics dashboards for the whole business to see. The data science team then used this aggregated score to measure its impact on a) downstream KPIs (e.g., CLTV) and b) the gross revenue. For the first time we could improve something on the site and see that, for example, it resulted in a 2% uplift in revenue. This was huge. The principle is the same for AI. Plan the metrics in advance and find a way to tie them to the bottom line. Suddenly all ROI calculations become a lot easier.
Conclusion
If you want to succeed in this AI race we’re seeing, then a Human-Centred approach is what’s going to get you there. Start with a vision, speak to actual users, map out the whole process, test and iterate often, plan KPIs in advance and measure them relentlessly throughout. Let’s avoid a future where companies pour money into crappy AI that benefits no one.
tl;dr: Human centred AI (or HCAI) is a paradigm, similar to human centred design, that contains methods, tools, and principles to ensure AI is designed with humans at its heart, that it’s usable, accessible, safe, and trustworthy. It’s aimed to give designers, researchers, technologists, developers, entrepreneurs a framework to achieve these goals.
Note: This article is about design and written by a designer. I don’t pretend to know anything about actually developing AI models or even AI products. I do know how to make great products though.
Why is Human Centred AI important?
Let’s start with a short story. Your company decides to build a proprietary GPT wrapper that’s secure and trained on your company data. They go ahead and build it and for some unexplained reason no one is using it. It’s down on all metrics and people still insist on using OpenAI or Google variants on their phones rather than engage with the tool your company spent millions developing from scratch.
What’s going on?
Eventually someone somewhere decided to do some user engagement and find out. Turns out that people hate it. You can’t edit your prompt, you can’t stop the system from generating its answer mid-flight, and the output it does produce is riddled with errors and is probably a bit racist. Most infuriatingly, it doesn’t even solve the user need for fast, reliable answers about your company policy.
Oh no! Should we have engaged with users from the start?
Let’s look at another side of the spectrum. Human-centred designers and researchers come on scene early. They run workshops with stakeholders and users to reach consensus on what the system should do, find all the current frustrations people are experiencing, and actually it turns out that no one has any issues with getting accurate answers quickly. The real problem is in the minutiae high-effort tasks, like doing desk research or consolidating multiple reports into one summary.
The scope is tweaked, some money is saved, and the team proceeds to build a focused, single purpose chatbot that does desk research and writes research summaries.
The team puts in clear measures that show the product is moderately successful and saves people a bit of time every day. Everyone is happy.
What the research is saying
In our previous research we spoke to several individuals around Manchester area to understand their usage of generative AI at home. The main theme was that people use it for small and low-risk tasks. Finding recipes, writing short, fun poems in another language, writing a complaint email, that kind of stuff.
In other, “real” academic research HCAI is all about developing models that don’t kill us all or turn the world into a dystopian Matrix-esque nightmare. There are things there we can adapt for our purposes.
There is a lot of academic writing on the topic out there. I checked. Most of it deals with the development of AI, but some of it is useful in the design context.
Ben Shneiderman wrote Human Centred AI in 2020 and an updated edition in 2022. It described the need for involving humans in the AI development process. However, it also showed several frameworks for AI-Human interaction. Like this one:
It shows the level of automation and control over various objects. When designing products, it’s worth keeping in mind features that go into the “Excessive” buckets. That GPT wrapper I described earlier and that you couldn’t stop? Excessive automation.
In another paper, Xu Wei proposes a methodological framework that’s rather complicated and geared towards developers, but it proposes several principles, that I adapted in our work at Deloitte.
Human centred AI principles
Principle 1: Usable
The AI must be usable. Duh. You ever used an AI tool that was a pain to use? I had this experience with many an “internal proprietary AI tool” across industries and clients. You want to upload a document? Click on this button. Then that button. Then wait. Then click to “Add file to chat”. Disgusting.
Methods to consider
Usability testing
Accessibility testing
Co-design sessions
Principle 2: Useful
You ever used an AI tool and thought, “why is this here?” Remember the failed WhatsApp chatbot? I asked it, “why are you here?” and it told me it’s there to answer questions and be a buddy. How is it different to clicking on an app in your phone then? No different. What’s the USP? It’s in your WhatsApp. We are obliged to design tools that actually answer a need. A product without a need is just an expensive failure.
Methods to consider
Visioning workshop
Process mapping
Service blueprinting
User journey mapping
Prioritisation frameworks (ICE, MoSCoW)
Decision trees
Principle 3: Trustworthy
How do you know you can trust the chat’s output? We know generative AI can be biased and error prone. There are ways we can increase this trust. There is a neat list of AI patterns out there on the Shape of AI website. Here are some of my favourite that I’ve used in the past.
Patterns to consider
Caveat (AI responses can be inaccurate)
Consent (Do you consent us using this data for training?)
Watermarks (AI generated)
Showing your work (not on Shape of AI but Perplexity and GPT5 are doing this now)
Principle 4: Scalable
Scalable can relate both to the techy under-the-hood but also to the product itself. A product that requires constant monitoring and is prone to errors is not scalable. A product that fits seamlessly into users everyday workflows is. A workflow is predictable, debuggable, and scalable. A loose cannon agent that makes decision on its own is none of these things.
Patterns to consider
Workflow mapping
Business process mapping
Service blueprinting
User journey mapping
Principle 5: Responsible
Oh ethics, my favourite topic. How do we make sure the AI isn’t bad? How do you make it ethically sound but also use it in a safe and ethical way? There are lots of issues there. The black box problem, the biases, the errors, the AI psychosis, people using AI in unethical ways to fudge experiments with synthetic data… the list goes on and on.
Mitigation strategies to consider
Human review of training data
Publish guidelines and policies for safe AI use
Human review of outputs
Principle 6: Empowering
Closely related to it being useful, this goes one step beyond. What’s the point in creating an expensive tool that doesn’t only help people but also empowers them to be better? It’s a debate between augmentation and automation. Automation makes a task easy, augmentation allows people to do things they never thought possible. To do this you really need to understand human needs and find ways to create a symbiotic relationship.
Methods to consider
In depth interviews
Co-creation workshops
Service blueprinting
Principle 7: Controllable
My pet hate. Just add that “Stop” button in! It goes beyond that of course. How do we create an AI that keeps the user in the driving seat? That doesn’t do anything unexpected but also has enough automation to be useful? It’s a fine balance and goes back to Ben Schneidermann’s automation vs. control diagram above. There is a beautiful blog that goes into a lot of detail on this topic here.
Various toggles to control variables in an output (e.g., MidJourney has a detail toggle)
Stop button (obv!)
Conclusion
Human centred AI is the next evolution of human centred design. It’s not a paradigm shift, per se, but more of an update for the age of AI. The tools are generally similar: talk to users. If you’d like to dive into the topic further here’s a list of reading materials that are worth your consideration:
