'Intelligence on Tap': Reimagining Work in the Intelligence Age
Intelligence on Tap’ is how Microsoft’s 2025 Work Trend Index report characterises the new ‘reality’ we are entering. It distinguishes between three phases: Human with Assistant, Human-Agent teams, and Human-led and Agent-operated. Building systems that are “AI-operated but human-led” [9] is where machine intelligence is blended with human judgement. This is not a linear progression, and the pace with which firms will progress to this new organisational blueprint remains so far unclear.
I have joined numerous discussions over the last year on AI’s anticipated benefits, and consequences, alongside words of caution. I have observed a lack of consensus, perhaps unsurprisingly, regarding the role and capabilities of AI technologies. Approaches vary from adopting AI as another tool, to adopting it as a ‘cybernetic teammate,’ [6] to even those focused on creating the first $1bn solely AI company!
In reflecting on the spectrum of approaches for AI adoption, there is an opportunity to delve deeper into how these technologies are already transforming our organisational, leadership, and work models with potential benefits but also (unintended) consequences for business, the economy and society.
Work Chart vs Org chart
Until now companies have been built around domain expertise, siloed in functions like finance, marketing, and engineering. Hierarchies and roles have been constructed to organise work in teams with similar domain expertise, and a clear control structure.
But with the ability to tap into ‘expertise on demand’ we are potentially moving to a dynamic, outcome-driven model where teams form around goals, not functions. As we move from hierarchies to expertise, knowledge work is being transformed, challenging the way work is organised, and teams are formed, but also the underlying nature of work.
In their pioneering book ‘The Future of the Professions,’ Daniel and Richard Susskind focused on the distinction between whole jobs vs. tasks and processes [10]. This helps to identify the tasks or activities for which AI may be best suited, and those which humans should be doing.
Research in AI adoption is in its early days, but a Harvard study has recently demonstrated that AI’s role can transcend that of a mere tool or facilitator, to an active participant in collaborative processes at the workplace. As organisations continue to integrate AI technologies, understanding these dynamics will be crucial for organisational theory and practice. The superior results produced by AI-augmented teams, alongside the ability of AI-enabled individuals to perform at levels comparable to teams [6], would impact decisions around the shape and size of teams, or whether and when teams are needed at all.
In a world where knowledge and expertise extend beyond functional boundaries, and do not necessarily originate inside the organisation, several questions arise that researchers are already asking: how does AI integration affect the development of expertise over time and does the ability to learn beyond one’s functional boundaries lead to genuine expertise development or is it primarily facilitating access to existing knowledge [3], [6]. The answers to these questions will have significant implications for the way work is organised, as well as the ability to retain people and skills. Offering growth and development to employees has long been a differentiator and an attraction tool; however, this may become less relevant and insufficient.
Are you a brave leader?
As agents are being integrated into teams, the ability to get the best out of human-agent teams becomes a key leadership skill. Leaders would need to understand where AI can perform better, which tasks can and should be delegated to AI, and how agents can be managed to amplify their impact.
82% of leaders expect to use agents to meet the demand for more workforce capacity in the next 12-18 months, according to Microsoft’s Work Trend Index report.
On the human side, as AI unlocks expertise, it will require bravery from leaders to trust people to grow beyond their traditional roles. Upskilling and reskilling are therefore critical for most forward-looking leaders: 47% list upskilling as a top workforce strategy for the next 12-18 months, while 35% of managers are considering hiring AI trainers to guide employee adoption [9].
Human in the loop?
In this ‘new reality’, employees will need to adopt a thought partner mindset and build related skills: learning to iterate with AI, knowing when to delegate to AI, prompting with context and intent, refining outputs instead of accepting first drafts, spotting weak reasoning or gaps, and knowing when to push back or steer the conversation or plan. Also given AI’s ability to break down silos, there is value in training employees to think more broadly, across functional boundaries.
All this sounds promising, however, given the unpredictable pace of AI adoption it is unclear how much longer there will be paid work for humans to do. The idea that we could now be freed of the drudgery that weighs us down, is very appealing – after all humans were not meant to just answer emails all day. Nevertheless, as we are being led to believe, we are currently on a trajectory of artificial intelligence, defined as ‘artificial general intelligence’ (or ‘AGI’) and ‘superintelligence’ at which point if machines can do everything, what will remain for humans to do?[1],[2],[5].
Economists have already begun exploring the implications of AI that is significantly more capable than it is today. In a recent article Daniel Susskind argues that in fact even in a world where AI can perform most tasks more productively than human beings, there will still be tasks for humans to do. This would be due to either ‘preference limits,’ where humans prefer an un-automated process, ‘general equilibrium limits,’ where a human still has a comparative advantage over AI in performing certain tasks, or ‘moral limits,’ where a human is required to exercise moral judgement [5].
Where next?
Irrespective of the pace of AI adoption we are reminded that the amount of computational resources (or, in short, “compute”) used to train the most cutting-edge AI systems has doubled every six months over the past decade [8]. What AI models can do today was unthinkable just a few years ago and can (already does) deliver significant productivity gains.
Therefore, reimagining work does and should continue to involve the use of AI technologies in a way that can benefit human society. But we should remain humble…technology has surprised us before and we should expect it can do so again.
Resources
Acemoglu, Daron, and Restrepo, Pascual. 2018. The race between man and machine: Implications of technology for growth, factor shares, and employment. American Economic Review 108(6), 1488–1542.
Acemoglu, Daron, David Autor, Jonathon Hazell, Pascual Restrepo. 2022. ‘Artificial Intelligence and Jobs: Evidence from Online Vacancies,’ Journal of Labor Economics. 40:S1.
Anthony, C., B. A. Bechky, and A. L. Fayard, ““Collaborating” with AI: Taking a system view to explore the future of work,” Organization Science, 2023.
Berwick, I. (2025, May 21). What makes a positive difference to the way that staff feel about work? https://on.ft.com/3Sgoh5E
Daniel Susskind, What Will Remain for People to Do?, 25-08 Knight First Amend. Inst. (Apr. 7, 2025), https://knightcolumbia.org/content/what-will-remain-for-people-to-do [https://perma.cc/K8LG-B4UA]
Dell'Acqua, Fabrizio and Ayoubi, Charles and Lifshitz-Assaf, Hila and Sadun, Raffaella and Mollick, Ethan R. and Mollick, Lilach and Han, Yi and Goldman, Jeff and Nair, Hari and Taub, Stew and Lakhani, Karim R., The Cybernetic Teammate: A Field Experiment on Generative AI Reshaping Teamwork and Expertise (March 28, 2025). Harvard Business School Strategy Unit Working Paper No. 25-043, Harvard Business School Technology & Operations Mgt. Unit Working Paper No. 25-043, Harvard Business Working Paper No. No. 25-043, The Wharton School Research Paper , Available at SSRN: https://ssrn.com/abstract=5188231 or http://dx.doi.org/10.2139/ssrn.5188231
Financial Times. Europe Best Employer’s report. https://www.ft.com/bestemployers#
Korinek, Anton. 2023. ‘Scenario Planning for an A(G)I Future,’ IMF Finance & Development Magazine, Dec.
Microsoft. 2025 Work Trend Index Annual Report.
Susskind, Daniel and Richard Susskind. 2015. The Future of the Professions.