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
Why is AI struggling to discover new drugs?
A generation of start-ups have failed to live up to the hype. Executives are now betting that more powerful tools will crack the complexities of human biology
Customized multi-agentic AI workflows made simple – Capgemini
Create AI agents and orchestrate workflows with Capgemini’s no-code self-service platform for multi-agent automation.
Agentic AI and multi-agent AI systems making autonomous, intelligent decisions for organizations are set to transform business for years to come
Cognizant CEO Kumar: Agentic AI will create a reinforcing flywheel of ROI | Constellation Research Inc.
Cognizant CEO Ravi Kumar said agentic AI use cases can create a self-funding flywheel for enterprises starting with the software development cycle and moving through other processes. Speaking at the Goldman Sachs Communacopia + Technology Conference, Kumar laid out a framework for AI agents across multiple vectors. He added that Cognizant is seeing benefits now that 30% of its
Cognizant CEO Ravi Kumar said agentic AI use cases can create a self-funding flywheel for enterprises starting with the software development cycle and moving through other processes.
Speaking at the Goldman Sachs Communacopia + Technology Conference, Kumar laid out a framework for AI agents across multiple vectors. He added that Cognizant is seeing benefits now that 30% of its code is written by machines, up from 18% just a quarter ago.
How To Upgrade Your MCP Server With Context Engineering – The New Stack
Context engineering anchors AI agents to ground truth. So here’s how to upgrade an MCP server to help agents find legit and sourced patterns.
via: https://blog.jonudell.net/
The Case for Using Small Language Models
Small Language Models (SLMs) are redefining enterprise AI by offering faster, more efficient, and cost-effective solutions compared to Large Language Models (LLMs). Their compact design enables deployment on edge devices, allowing real-time decision-making without cloud dependency—ideal for applications like autonomous vehicles, voice assistants, and wearable tech. SLMs consume less energy and require fewer resources, making them more sustainable and accessible for widespread use. Their ability to be fine-tuned for specific domains enhances accuracy and reduces irrelevant outputs, especially in industries like healthcare, finance, and agriculture. They also offer greater control, privacy, and transparency, supporting secure data processing and regulatory compliance. SLMs integrate easily into existing systems, enabling agile development and rapid prototyping without major infrastructure changes. Organizations should align model size with task complexity and explore SLMs for localized, privacy-sensitive, and scalable AI solutions. With their adaptability and efficiency, SLMs are positioned to drive practical, responsible innovation across industries.
What’s the ROI of our AI? Here’s a Template
It’s not that hard to do a solid ROI analysis of the AI you’ve deployed.
All you need are lots of data on AI usage and the suite of key performance indicators (KPIs) you’re interested in, combined with the analytic tools of the Credibility Revolution.
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