Digital Business Transformation

How to influence and co-ordinate agile, distributed business transformation

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

SELECTED BLOG POSTS

Kimi K2 and the Quiet Shift in Enterprise AI | by Dinand Tinholt | Nov, 2025 | Medium

The immediate consequence is practical. Enterprises can deploy K2 inside their own cloud or on-premises environments, keeping data under existing security and compliance frameworks. Because of its quantization-aware training, the model can run on older GPUs and more modest clusters. That reduces the barrier to experimenting with advanced reasoning systems. For companies under pressure to define a sovereign AI strategy, an open model of this caliber offers a credible path toward control without dependence on external vendors.

Nearly a third of companies plan to replace HR with AI | HRD Asia

Employers are entering 2026 with plans to replace employees with artificial intelligence – and even HR professionals will not be safe from these plans.

A new survey from AI Resume Builder revealed that 30% of companies plan to replace employees with AI in 2026, after 21% said they replaced specific roles with AI this year.

Nearly half (49%) of employers with plans to replace staff said that between 10% and 45% of their current workforce will be replaced by AI. 

AI Agents & Skills, Plus What Games Can Teach us About Adoption

AI Agents & Skills, Plus What Games Can Teach us About Adoption

Enterprise AI Adoption Signs of Life There are some promising signals emerging around enterprise AI adoption and its impact on companies who are using it. Last month’s Accountable Acceleration: Gen AI Fast-Tracks Into The Enterprise report by Wharton Human-AI Research...

The Amnesia Problem: Why Neural Networks Can’t Learn Like Humans | rewire.it | rewire.it Blog

Imagine a musician who spent ten years mastering classical piano, then decided to learn jazz. On the first day of jazz lessons, something terrible happens: the musician forgets how to play Bach. Completely. By week two, muscle memory from thousands of hours of practice has vanished. This isn’t incompetence. It’s amnesia.

This phenomenon has a name in machine learning: catastrophic forgetting. And it’s not a flaw in specific architectures or training procedures. It’s fundamental to how neural networks learn.

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