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

Bringing AI into the world of DevOps – Jyoti Bansai explains how

Harness and optimizing apps – its not so much about the code, it’s about everything else around the code.

The move to develop ‘Super-Opt’ tools is gathering pace, and the scope for where they can be applied is also growing. Super-Opt, or tools for advanced levels of application optimization, can have a number of targets, but all have that key underlying aspiration, to allow the business using them to get the best value out of the software tools at their disposal.

Which AI Use Cases Should We Focus On?

When we stop lumping all AI into one bucket, we can identify the different use cases worth focusing on (hint: they’re not the ones you hear about the most).
A truly impressive amount of energy is currently being devoted to AI in the workplace. But this enthusiasm will soon be derailed unless we move beyond a very lazy lumping of everything into one AI category.

Hybrid working has benefits over fully in-person working — the evidence mounts

The COVID-19 pandemic upended lives around the world. And for one group of mainly higher-skilled, higher-earning office-based workers in sectors such as computing, marketing, accounting and finance, it meant an almost-overnight revolution in working practices. Working from home, previously regarded as an oddity or a perk, became the norm for millions of people.

Now, many employers want to roll back the home-working revolution amid fears that remote working harms productivity, creativity and collaboration. In some cases, they are mandating that employees return to fully in-person working, often to strident protests from their employees.

Enterprises are preparing to build their own LLMs – why that’s a smart move | ZDNET

Enterprise LLMs can be built on publicly available, foundational models. That’s a good start.

Are enterprises ready to build and maintain their own internal large language models (LLMs)? 

Artificial intelligence — especially the generative variety — has captivated the unbridled interest of tech professionals and executives alike. Consider this: Despite all the endless talk of budget cuts for cloud services and infrastructure in recent times, the money faucet has opened wide for AI funding. But is it flowing outward to outside services, or inward to resident talent and resources?  

From Chatbots To Business Allies: Enterprise Knowledge AssistantsC

As we’ve seen, the journey from simple chatbots to complex EKAs has been fast-paced and transformative.

The evolution of artificial intelligence (AI) in business is continuous. What started as simple chatbots providing basic customer service has now grown into enterprise knowledge assistants (EKAs) that act as strategic allies. These modern AI-powered tools are designed to change how we search for information, provide customer support, manage tasks and do business.

As we look to the future, it becomes clear that EKAs are becoming the trend in the corporate world.

The Rise of AI Agent Infrastructure

An explosion of GenAI apps is plain to see, with applications for productivity, development, cloud infrastructure management, media consumption, and even healthcare revenue cycle management. That explosion is made possible by rapidly improving models and the underlying platform infrastructure our industry has built over the past 24 months, which simplified hosting, fine-tuning, data loading, and memory — and made it easier to build apps. As a result, the eyes of many founders and investors have turned north to the top of the stack, where we can finally start to put our most advanced technologies to work for end-users.

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