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
Patterns and problems in multiagent systems Anthropic
Models are improving and AI agents are taking on more tasks in shared codebases, markets, and other social systems. As a result, an increase in real-world interactions between agents is imminent. We’ve already begun studying this, but still have a lot of uncertainty regarding what this looks like at scale. The trajectory is easy to imagine and hard to slow: current institutions are designed by and for people, resting on assumptions about the sufficiency of oversight at human speed. Some institutions will become human-AI hybrids; others where agents outcompete on speed or cost will become agent-only. The volume of agent-agent interaction could plausibly exceed that of human-human and human-agent interactions before the world understands the conditions for making such interactions go well.
A Six-Step Framework for Auditing Enterprise AI Agents | HackerNoon
he pattern repeats across large companies. A year of enthusiasm produced agents everywhere. One team built an agent to summarize tickets. Another built a near-identical one. A third wired an agent into a workflow, shipped it, and moved on, and now nobody owns it. Each agent calls a model, so each one has a bill and a data path, and most of them were never inventoried.
This is shadow IT with a meter running. The unmanaged spreadsheets and rogue apps of the last decade did not bill you per use. Agents do. Every redundant agent is a recurring token cost and a governance exposure, and the more of them you have, the more of both you carry.
Qwen 3.8 27B is excellent, but it defaults to wildly overthinking things
Friday’s big release was Qwen 3.8 27B, an Apache 2 licensed 27B parameter vision-capable LLM from Alibaba’s Qwen research lab. I’ve been looking forward to this one: 27B is an excellent size for running a model on a reasonably specced laptop, and its predecessor Qwen 3.6 27B was impressive.
Fish Food 700: Thinking (and working) in loops
In early June Peter Steinberger, the developer behind OpenClaw, posted (on X, but I’m not linking to X) something which seems to have changed a lot of influential people’s approaches to working with AI. ‘Here’s your monthly reminder’, he said, ‘that you shouldn’t be prompting coding agents anymore. You should be designing loops that prompt your agents’.
🔮 The market misread Google’s AI exodus
Why Jeff Dean’s departure tells us the AI investment cycle is earlier than we think
His Start-Up’s Goal: A.I. That Is Trainable and Not Controlled by a Big Company – The New York Times
A co-founder of xAI is going all in on building open-source artificial intelligence so that “anyone can control and shape A.I. for their own needs.”
Weekly inspiration & insights:
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