AGILE TRANSFORMATION
There are many new ideas and organisational models for modern companies to choose from, but any one example is unlikely to be a perfect match for your needs. We analyse each model in terms of the capabilities it can create, and we help organisations choose the best techniques and patterns to achieve their goals.
We like to work with small multi-disciplinary teams to coordinate a distributed transformation process involving a much wider network of people throughout the organisation. This approach, working through influence and networks against a common set of capability goals, is a great way to spread new ways of working and make the most of all the different groups and functions that are needed for transformation to succeed.
DISTRIBUTED CHANGE
In every organisation, outside of the formal project structures, there are many more enthusiastic individuals who want to help the organisation work in a more modern, connected way. Mobilising these ‘change agents’ is a great way to create a passionate network to spread new ideas, practices and behaviours.
Enterprise social networks provide access to the wider ‘human sensor network’ of the organisation, and our method involves these people in making sense of change and helping to assess its results in close to real-time. This makes transformation everybody’s responsibility and can provide a great boost to employee engagement.
A TYPICAL PROGRAMME
An agile framework to co-ordinate work on:
- Coalition building: developing and working with change agent networks
- Org models: a toolkit of new structures, techniques and new ways of working
- Tech platforms: service-oriented approach for distributed work, sharing & connecting
- Leadership: leadership guidelines (and future leaders) for the new world of work
- Culture & practice: a conversation with the organisation about suitable methods
- Process improvement: identifying and fixing processes that block new ways of working
EXAMPLE PROJECTS
SELECTED BLOG POSTS
Shifting Work Patterns with Generative AI∗
We present evidence on how generative AI changes the work patterns of knowledge workers
using data from a 6-month-long, cross-industry, randomized field experiment. Half of the 6,000
workers in the study received access to a generative AI tool integrated into the applications they
already used for emails, document creation, and meetings. We find that access to the AI tool during
the first year of its release primarily impacted behaviors that could be changed independently and
not behaviors that required coordination to change: workers who used the tool spent 3 fewer hours,
or 25% less time on email each week (intent to treat estimate is 1.4 hours) and seemed to complete
documents moderately faster, but did not significantly change time spent in meetings.
The shift from talent intelligence to work intelligence – Fast Company
Become proactive instead of reactive by moving away from skills identification to work redesign.
While talent intelligence platforms (TIPs) serve an important purpose in identifying skills, they are inherently limited and never designed to address the fundamental question: How is work itself structured and how is it changing?
Fish Food: Episode 639 – How to innovate like Amazon
Amazon’s innovation system, the evolution of the marketing operating model, Deep Research, A2A, and snail mail newsletters
US Officials Target Nvidia and DeepSeek Amid Fears of China’s A.I. Progress – The New York Times
China’s success in artificial intelligence has the Trump administration and lawmakers weighing rules and investigations to slow Beijing’s progress in the industry.
L&D Leaders, Prepare for AI Disruption
If you found transitions like LMS to LXP, micro-learning and gamification disruptive as an L&D pro, the next chapter will prove even more transformative.
AI as Normal Technology | Knight First Amendment Institute
We articulate a vision of artificial intelligence (AI) as normal technology. To view AI as normal is not to understate its impact—even transformative, general-purpose technologies such as electricity and the internet are “normal” in our conception. But it is in contrast to both utopian and dystopian visions of the future of AI which have a common tendency to treat it akin to a separate species, a highly autonomous, potentially superintelligent entity.
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