Work Done Right : a systems thinking guide to Digital transformation with Matt Kleiman

“Don’t be fooled by shiny technology… have a look at your business pain points and what problems you need to solve first”

Matt and I delve into the world of driving sustainable digital transformation with all its pitfalls and iterative loops. We unwrap the journey of digital transformation in organisations – which is inevitably fraught with challenges – from enacting organisational change to managing career risks and adapting to the rapid evolution of emerging technologies. Organisational stamina is however one of the biggest challenges we face – not giving up at the first success or failure, but organisations are like people – always looking for a quick fix.

We delve into how taking a systems thinking lens can be transformative, especially coupled with the revolutionary potential of generative AI and Large Language Models (LLMs) in industries like construction, which have historically been skeptical of technological advancements due to past disappointments. Generative AI and LLMs, despite the challenges exemplified by Google’s struggles with bias, are lauded for their capacity to revolutionise data management and processing. They promise a future where complex data is not just managed but harnessed to drive decisions, optimize processes, and ultimately, catalyze growth. – leaving time for the more complex human elements to be top of mind. For technology implementation to be successful, it must be rooted in continuous progress, systemic analysis, and the dismantling of operational silos through collaboration and empathy.

Matt shares his insights from his career to date, and the model he developed of how to successfully implement digital transformation – work done right !

The main insights you’ll get from this episode are :

–       Work Done Right is a collection of lessons learned from various industries with common themes of how best to achieve or not to achieve digital transformation.

–       Society needs infrastructure but is not good at providing it on time and on budget; we must improve processes using technology to help project leaders get it right first time.

–       The Work Done Right methodology is about process, culture and systems thinking – we must view projects holistically as interconnected wholes rather than in silos.

–       Within the system, we must define the quality we want and the systems we need to achieve it but work quality requires a speak up culture, akin to speaking up about health and safety for the greater good.

–       Human error can cause problems but there are rarely systems in place for errors to happen, i.e. people do not speak up about quality/process failures – tech and engineering are very knowledgeable but fail to take account of human factors that are part of the processes/system.

–       Translatability of ways of working from one industry to another is very beneficial, e.g. energy companies approaching other industries that have a good track record for safety of operations in hazardous environments, e.g. aviation.

–       Systems engineering and systems thinking can be used to ‘engineer out’ value risk. Any large organisation naturally builds up silos over time due to specialisation and bureaucracy but derisking is important as doing things differently entails risk.

–       There are competing elements of culture and technology at play in the explore-exploit scenario – change is often initiated for the sake of it without recognising the good reasons why systems are put in place.

–       ‘Splashy technology syndrome’ describes situations in which people desire digital transformation but are distracted by the current tech hype cycle, e.g. crypto, IoT, AI, etc. – FOMO takes over in the rush to use new tech, but any disappointment in the result reinforces the conservative bias.

–       GenAI can be transformational but should not be used for long-term business decisions. There is a widespread data problem in that most data is not used, but LLMs can make sense of messy data, and using 60% of data instead of 10% equates to a huge competitive advantage.

–       Long-term, there will always a place for humans – human decision-making and experience are irreplaceable, but success will depend on using gen AI and LLMs to improve our decision-making.

–       The OODA (observe, orient, decide, act) loop designed by the military can be applied to any competitive endeavour, can be incentivised and is iterative (build, measure, learn) – it aligns incentives with successful implementation and offers organisations the opportunity to develop a learning mindset through repetition.

–       Organisational stamina is the biggest challenge we face – not giving up at the first success or failure, but organisations are like people – looking for a quick fix.

–       Organisations must determine failure points and rectify them there and then before progressing, with no blame game and no catastrophising – identify why a business objective is not being reached and deploy the OODA loop repeatedly to move forward.

Find out more about Matt and his work here :

Enjoyed the show?

It means a lot to me and to the guests. If you enjoyed listening then please do take a second to rate the show on iTunes.  Every podcaster will tell you that iTunes reviews drive listeners to our shows so please let me know what you thought and make sure you subscribe using your favourite player using the links below.

Browse through all podcast episodes Audio Stream

Suzie Lewis

Discover fresh perspectives and research insights

Sign Up to receive our latest news and transformation insight direct to your inbox!

TransformForValue takes your privacy seriously. We may process your personal information for carefully considered, specific purposes which enable us to enhance our services and benefit our customers. Please note that by subscribing now you may from time to time receive other emails from about events or other activities that we think might interest you.