Since late last year I’ve been gradually sharing some developing ideas I’m calling my bets for 2024. To start to share this thinking here, this is another in a series of x24 posts. Expanding on topics from my 2022 book, Multiplied.
Documenting some of my most recent talk materials, the rest of this post introduces five connected themes:
- More powerful tools
- Reshaping delivery
- Supported by institutional change
- Needing different types of leadership
- And with increased agility
More powerful tools
In the past year there’s been a lot of interest in large language models (LLMs) and generative AI. By now, most people I talk to have had the chance to experiment with some form of AI like ChatGPT.
My bet here is that where technology is becoming more powerful and accessible (a multiplier), this will put more emphasis on what we do with it and how we use it. We now need to make stronger connections between the technology and the human capacities of our systems, organisations and services.
What’s clear is that AI is becoming increasingly powerful. It will continue to change how our we work and how we use services. It’s already a factor in starting to shift expectations people have for digital experiences and it’s also opening up the potential we have to meet user needs in new ways.
There’s now the question of how organisations start to realise this potential. Like how to make the best use of LLM-powered chatbot interfaces. We’re already seeing experiments, like this GOV.UK example shared earlier this week. This is the first experiment I’ve seen shared like this by UK Government teams. One aspect of this type of LLM-powered chatbot interface to highlight is the dependency on content design. Specifically, how the quality of information and information governance is going to determine how useful, accurate, and clear a conversational AI might be in meeting user needs.
Looking beyond how users’ access information and services there’s an even bigger opportunity in how LLMs and generative AI will transform the workplace – how we transform casework and the types of knowledge work that support service and policy delivery. However, I think we need to be deliberate with how we approach this particular use of generative AI, especially with the goals of automation and efficiency. I’ve been referencing a book that’s given me a useful perspective here – Impromtu: Amplifying Our Humanity Through AI by Reid Hoffman*.
*As an aside, what’s interesting about this book is it’s co-written with GPT-4. It documents a number of direct conversations with the AI to illustrate and explore key points.
Rather than AI replacing human labour and human agency, Impromtu emphasises its potential to amplify our human abilities and human flourishing. This is the multiplier. The potential to combine the power of technology with human agency:
“…But when human users treat GPT-4 as a co-pilot or a collaborative partner, it becomes far more powerful. You compound GPT-4’s computational generatively, efficiency, synthetic powers, and capacity to scale with human creativity, human judgement, and human guidance”Reid Hoffman (2023)
In the context of how we design and run public services, this is the question of how we best support human work and decision making.
In Multiplied, I introduced the idea of inside-out transformation:
“True service transformation through technology now needs to happen inside our organisations. This is how we transform what happens backstage – the building blocks of how organisations work, how services are delivered, and how outcomes can be improved. It’s only by transforming the relationship organisations have with technology that they can reimagine and deliver services in new ways.”
The potential of inside out transformation is now even greater with generative AI. By continuing to bring this focus we have even more possibilities to meaningfully transform how we work. But the challenge is to recognise our role in shaping what are ultimately human systems meeting human needs. For organisations that succeed, my bet is that the need for good design has an equally important role here alongside how we work with technology. We need to continue to bring curiosity, creativity and integrity into this type of work. It’s the need for AI and tech-based solutions to start with, and work, in real human contexts and scenarios. It’s about the need for deep integration of technology within organisations so it can best meet human needs. Design and research will continue to be essential in bringing and maintaining the type of user centred approaches we will need.
My bet here is that, increasingly, more is possible when it comes to designing, testing and scaling digital solutions. Having more powerful tools at our disposal will change how we work and how fast.
In Multiplied, I looked at examples of how public and third sector teams were able to respond during the Covid-19 pandemic. This was where organisations were able to stand up new solutions and services in record time. These teams benefitted from a history of investment in government design systems, shared standards and open sourced solutions. Most of all, they found the ability to meet urgent needs more quickly through collaboration.
Along with how we might maintain this type of urgency, technology is reshaping what is possible. We can now build things faster, and there are plenty of examples of how AI can write code and build working software. In Multiplied, I talked about the potential and use cases for using low code and no-code solutions for building working software. These types of tools are becoming more powerful combined with generative AI.
There are also examples of generative AI being used to automate what could be described as creative work. For example, I’ve seen designers using image generation to create high quality storyboards and illustrations. This is already shifting how teams can approach creating useful artefacts to support service delivery.
Most importantly, my biggest bet here is that success is dependent on what we build from. For the public sector, the good news is we have over a decade of investment in the GOV.UK and NHS.UK design systems. These are world leading in how patterns have been designed, tested and maintained. With changes to how we build, continued investment in these systems will be key to maintaining and ensuring good design in service delivery. Also, if and when service delivery becomes more automated, then designers and researchers will be able to focus more on the underlying design systems and components being used by AI.
Inevitably, there’s already a generative AI proof of concept that can prototype form pages based on prompts using the GOV.UK Design system (created by Kuba Bartwicki). There’s lots to work through here, but we’re seeing the possibilities of how this might change the speed and accessibility of interaction design for services. If you’re a designer in government I’m sure you’re already thinking about what this means for your work. But the need remains for good prompts, or how we’re able to ask the right questions of our tools.
All of this means that how we design and build will continue to evolve.
In Multiplied, I focused on the idea of configurability, or “how teams use, and are able to adapt technologies, capabilities and processes.”
I still believe configurability is a key mindset for digital transformation. This is a kind of design engineering mindset. It’s where we need to understand platform models as well as focusing more on reuse of patterns and common components. It’s this that will enable us to configure future services and systems to deliver the best possible user experiences and outcomes. I also think this is how services start to become more adaptable. Specifically in how AI and data might shape and configure individual user journeys and experiences to best meet user needs in new ways.
I’ve been talking about the importance of service patterns for a number of years now and this is a key part of this thinking. Last year DWP shared this important work in the health policy transformation space and I know of a number of other government departments wanting, or starting, to invest in service pattern work.
Most importantly, service patterns are a way of maintaining user centred approaches within any automation of design decisions – especially through how technology might make and automates choices about user experience, user flows and interactions.
Supported by institutional change
My next bet is on the continued need for much deeper change within our institutions. It’s only with an ambitious level of reform, within how our organisations and systems work, that we’ll realise the potential of digital transformation driven by powerful new technologies.
The big risk here is that we will use technology and AI to accommodate institutional complexity rather than address it. This is the risk of technology as a sticking plaster. Again, with the goals of automation and efficiency, this is especially the case with any temptation to add AI solutions to our legacy solutions, processes and capabilities.
This is still a question of what we build from. I’ve already seen AI solutions where the right answer should have been to fix the underlying issues of poor design, or even bad content.
How we simplify, codify and make the components of our policy, systems and organisations more accessible will be key to future use of LLMs and technology. It’s what will enable how we reconfigure and transform our organisations and the systems they are part of.
We can recognise the cycles of complexity that are hard to break. Over time many policies and their delivery implementation continually evolves to become more complex, along with supporting business processes and capabilities, including technology solutions. So everything incrementally costs more in time and resources to deliver an outcome. And we don’t necessarily deliver the best outcome.
10 years ago, while working on my first ever government service, I wrote about the example of reversing mistrust, linked to how complexity increases unless challenged. The principle remains the same here. We have to be prepared to work through the reasoning around how policy decisions are determined and how this translates into delivery complexity. This includes how we optimise and design for what needs to happen in order to deliver the best possible outcomes.
This type of change can only be achieved through the hard work of consensus building. My bet is that we need to focus more of our time and effort here. This might even be time and effort that we can refocus because of how digital roles, timelines and tools are beginning to change. This is definitely where we should refocus more of the time and effort of designers and researchers. Thinking about how they might collaborate more widely, and in more strategic ways away from product and digital teams. What’s clear is that deeper collaboration is needed. Something that’s already starting to happen in many places across digital, policy and operational teams. And also something I observed as crucial to how more change was possible, and more quickly, as part of our collective pandemic responses across the public sector.
With different types of leadership
My next bet is about the type of leadership we continue to need to support digital transformation, alongside the deeper institutional change this requires.
Firstly, we need leaders who understand technology. As I explained in Multiplied:
“The importance of this can’t be underestimated. Many of today’s leaders have not grown up with technology and internet era services in the same way that our future leaders will have. But they must still be able to understand technology, including its opportunities and constraints. Only by having a grasp of what technology can and can’t do, and how opportunities are changing, can they steer their organisations and teams effectively, both now and in the future.”
We also need leaders who will enable their organisations to adapt and work in new ways. With significant problems to solve, like the financial challenges faced by UK local government in 2024, leadership approaches must ensure the sustainability of services and local support. In Multiplied, I talked about the idea and importance of hyper-local approaches. My bet here is that we’ll continue to see more locally-aligned approaches. This means that leaders will have to interact and work in new ways with the places and communities they’re part of. If properly understood, this is also an area that emerging and new technologies have the potential to support.
And with increased agility
My final bet is about how organisations respond to the opportunities of digital transformation in 2024. It’s the challenge of how they learn and adapt through experimentation and learning.
As described in Multiplied:
“We are still at the beginning of exploring the potential uses of technology to create new types of services and operational models in the public sector. But more impact is possible as long as we’re ready to question how we organise, connect with and reach people in society. This type of work will require a willingness for more rapid experimentation, and an openness to use the technology we already have in new ways.”
I’ve written some separate reflections about the importance of starting things. What I describe as having optionality in digital transformation. This need for experimentation is about the willingness and spaces to learn by doing.
My bet is that we’ll see more organisations who are willing to commission experimental teams. Teams that are capable of building rapid proof of concepts (with new tools) and prototyping new approaches to policy and services. These teams are needed to build confidence in further investment if we intend to see more radical change.
Where I’ve already seen progress, it’s where organisations have succeeded in setting up dedicated service or innovation lab spaces and projects. This brings together how we configure, build rapidly, but still take user centred approaches. It’s something that needs far more investment and is also another good use of design and research time over aspects of building services that may become more automated in the future.
All bets are on
Despite the unpredictable nature of our work there are some clear patterns here. The important thing is that we find ways to start, to experiment, and to see where new technologies and ways of working might collectively lead us.
I’ve been closing talks with this quote included in Multiplied. It’s taken from a WHO press conference two weeks before the UK went into Covid-19 lockdown in March 2020 (you can watch a video of this here).
“If you need to be right before you move, you will never win. Perfection is the enemy of the good… Speed trumps perfection. And the problem in society we have at the moment, is that everyone is afraid of making a mistake… But the greatest error is not to move.”Dr Michael J Ryan (World Health Organisation)
I still believe we collectively need to find more of this mindset. It’s the sense of urgency I described earlier. When providing any type of service and support I think we should be able to find enough urgently in the size, scale and complexity of our work in order for us to bring more ambition and increased expectations.
I hope there are some useful ideas for your own work here. My biggest bet is that digital transformation remains central to how we transform our organisations and services in 2024 and beyond.
Feedback is always welcome if you want to get in touch.
This is my blog where I’ve been writing for 18 years. You can follow all of my posts by subscribing to this RSS feed. You can also find me on Bluesky, less frequently now on X (formally Twitter), and on LinkedIn.