Work 2.0: Reimagining Work Design
How do we make progress in shaping the Human-AI workplace?
In one of our recent polls 73% of respondents voted for ‘reimagining work design’ as a key priority. The comments recognised the importance of upskilling and leading with empathy but highlighted that a more fundamental shift was needed in redesigning work and processes from the ground up around human-AI collaboration.
According to the World Economic Forum [11], digital work design is now central to organisational agility, with flexible scalable models replacing rigid hierarchies. Finding new ways to organize work - both within firms and at their fringes - has become a central factor in economic success or failure. As the focal point of organizations shifts alongside the global economy from an emphasis on products to information, a shift is also occurring from linear to exponential organizations. Organizations now need to be ambidextrous, in the sense of providing structure, culture, and processes for older, established work and products, while at the same time they provide new and different work designs for the technology-product-market combinations of tomorrow.
The challenge of the transition requires reimagining the fundamentals of work, from questioning the quality and completeness of data, to re-thinking how we release capability, all the way to our workforce practices that may enable or restrict creativity, curiosity, and ultimately innovation.
In this issue we will explore how you get started in reimagining work design by highlighting all the different critical dimensions that need to be considered.
Data, Data, Data! ... I can’t make bricks without clay
You may have recognised this expression from Sir Arthur Conan Doyle’s Sherlock Holmes emphasising the importance of data in drawing any conclusions.
The expression “garbage in, garbage out” is often used to highlight data quality issues that constrain performance. Like me, you may have experienced situations where data integrity and architecture were holding back progress in moving to new ways of working underpinned by updated processes and systems.
I would argue that data is the most important and defining requirement for success in the human-AI workplace, because data is the fuel for AI. Without quality and complete data that can be used to train these models, or map how work gets done it is impossible to reimagine the way in which work can get done in a way that will release value. AI brings to life the value that is resident in your data, as has been highlighted at a recent MIT conference on Digital Technology and Strategy [3].
The Josh Bersin Company has highlighted the importance of creating a Work Operating System – a real time map of how work gets done, and how it moves across teams, roles, and platforms regardless of titles and roles. People don’t do roles, they do work. You can then use that to focus on where you can automate, reinvent and unlock value.
Re-thinking “slack”
The idea of operating with slack may be counterintuitive, but according to operational research in an environment with variability the optimal strategy is to operate with slack otherwise this will inevitably lead to slow downs and poor performance [1].
The idea of operating with slack should be considered alongside freeing up employee capacity. It’s that fundamental question of how should employees be spending their time? As GenAI and other advanced technologies are poised to take over many transactional tasks that can free up worker capacity, this will create space for work that requires critical thinking, innovation, collaboration, and meaningful human interaction.
The real benefits of slack will likely emerge when leaders are able to let go of the belief that emerged during the Industrial Revolution that their job as leaders is to maximize worker utilization. Some organizations have had success over the past few decades in working to shift this mindset and recognize the value of slack in work design. Examples such as Google’s well-known 80/20 policy, where employees spend 80% of their time on core projects, with 20% (or one day per week) on unplanned company-related innovation activities that interest them personally. This has reportedly been responsible for half of Google’s global projects, including Gmail, GoogleNews, and AdSense.
Releasing capacity should also lead to benefits for employees. In Deloitte’s 2025 survey, employees listed flexibility, well-being, time off, and learning new skills—all of which are facilitated when organizations create slack—among the top 10 motivations that drive them to perform at a high level [2]. Reclaimed capacity can benefit both employees and organizations: One does not need to come at the expense of the other [8].
What are we designing for?
As organisations develop new work designs for the technology-product-market combinations of tomorrow, we should go beyond efficiency, reallocating tasks where people have a comparative advantage. Use AI not only to save time on routine work but also to open up new possibilities, such as tackling complex problems or generating fresh ideas.
As AI helps call centre agents truly solve customer problems, or releases software engineers to focus on the more creative aspects of their jobs, or product designers to be innovators and sense-makers deciding what makes sense to design, morally, emotionally, or through intrinsic motivation, the “cognitive burden moves from human to AI” [3]. Josh Bersin describes this in his AI maturity model, moving from personal productivity to task automation, to workflow reengineering, to autonomous agents.
Applying the ‘what humans do best’ principle will ensure that human capacity and capability remain at the centre of ‘reworking work’. It’s about co-creating work with employees, empowering them to identify non-essential tasks that can release capacity and replace that with tasks that help achieve important goals and outcomes. Simple tools informed by workforce data, like job canvases—visual tools that map out and define the key responsibilities, skills, impact, and value of a particular role—can expand worker and leader visibility into that role. Nevertheless, according to Deloitte’s 2025 Human Capital Trends report [2], only about a third of workers feel empowered to provide feedback about how to make their work more valuable.
Where do we start?
In a future with limited resources, we need technology to help solve problems. And for that we need leaders who recognise that human and digital roles will be frequently reimagined. To lead in this world people must be trained not just to perform but to pivot. Google's Career certificate programme recognises the importance of constantly reskilling, and offers online, on-demand training focused on helping people change careers and make pivots.
Deconstructing work and reimagining work design isn’t about implementing a project plan, but adopting a “taste and learn” approach, starting small, bringing people into the process, and building into the cultural values of the organisation. This will ensure readiness on the talent side, which is key, as without talent engagement you will not be able to maximise and release value.
Efficiency is important but what can get overlooked in organisations is where value is created and focusing attention on those value-adding activities. Let’s not forget that Britain’s rowing team won gold in the Sydney Olympics by almost obsessively aligning everything they did with a single goal ‘Will it make the boat go faster?’
True transformation only happens when systems are built to continuously evolve. This is a reality that we would need to get used to…
Resources
David Simchi-Levi, Michael A. Trick (2011) Introduction to “Little's Law as Viewed on Its 50th Anniversary”. Operations Research 59(3):535-535.
Deloitte. When work gets in the way of work: Reclaiming organizational capacity. Human Capital Trends, 2025.
Stackpole, B. (Nov 2024) How to redesign work for the age of AI. MIT Management.
Murray, S. (Oct, 2025) How artificial intelligence impacts the US labour market. MIT Management.
Marone, M. (July 25, 2025) The Fluid Future of Work: Rethinking Roles in the Age of Intelligent Machines. Harvard Business Impact.
McKinsey & Company. Development in the Future of Work. McKinsey Learning Lab, 2025.
Menaka Hampole, Dimitris Papanikolaou, Lawrence D.W. Schmidt, and Bryan Seegmiller Artificial Intelligence and the Labor Market NBER Working Paper No. 33509 February 2025, Revised September 2025.
Parker, S.K., Trezise, M., Thomas, C.S. (2025). Healthier Work in the Age of AI: An Application of the SMART Work Design Model. In: van Niekerk, A., Harry, N., Coetzee, M. (eds) Unlocking Sustainable Wellbeing in the Digital Age. Human Well-Being Research and Policy Making. Springer, Cham. https://doi.org/10.1007/978-3-031-87616-5_2
Verganti, R., Vendraminelli, L., Iansiti, M. Design in the Age of Artificial Intelligence. Harvard Business School, 2020. Working Paper 20-091.
Work Design in the Age of AI: The Role of Task Characteristics, Job Identity, and Disclosure of Use. Academy of Management Proceedings, 2025.
World Economic Forum. Future of Work Strategic Intelligence Briefing. WEF, 2023.