Representing the DAIRDUX Alliance, I had the opportunity to deliver a key note presentation at the Predictive Maintenance Analytics Summit in Stockholm in May 2019.
My key reflection from the event was a quite uniform picture delivered by partners, academic leaders and corporates, on what the journey is all about to operationalize and industrialize data and AI approaches in our core operations to put data/AI in production. This is my attempt to put words on what was discussed as core considerations YOUR journey must address for your organisation to become Data and AI Ready (DAIR). I.e on tactical level the journey can be done in many ways. But the core fundaments must be addressed:
The new core organization and operations is a moving target. Therefore focus must be on creating an operationalization and adoption engine. Giving your organization the approach and muscle to continuously industrialize new Data and AI innovation into the core operation and business model.
The successful innovation, industrialization and adoption engine is by nature:
- Intersectional. The new core is creating value and is sitting in the intersect between your domain capability and Data/AI Capability.
- Perpetual. Adoption of Data and AI in core operations and your business model is a continuous evolution, not a project.
- Balanced and lifecycle oriented. Operational value is a multiplier requiring balanced investment covering key facets, in the right order of relevance of a industrialization life cycle.
Working as an enterprise (or Alliance) towards the same holistic picture is essential but difficult.
Key ingredients to be agile and create operational value on use case level and ALSO drive enterprise scaling and synergies include:
- Addressing the enterprise “DAIR DILEMMA”. Creating a learning organization by design.
- New Core Literacy and Culture
- Executive Immersion – Data & AI Ready Leadership.
The Data/ AI ready journey is too complex, it covers too much, and it is too big to address it all as a transformation project/ program. BUT oversimplification or agile anarchy does not work.
Learning how to “black box” topics so you have control over them, know they are there, but then consciously decide how to deal with topics just in time is key to avoid slowing everything else down.
The real effort is on operational and tactical level = Operational team level for each use case / core process.
No more power points. All the conceptual ppt slides, Transformation roadmaps, or What is said and done in the executive management teams does not really matter as much and does not deliver production value. In the end it is very much about execution on operational team level. Their ways of working, and that the steering and organisation and mandate is enabling and empowering them to get the work done. Too much investment and time is spent on the wrong things. Instead of just getting on with the work. Investing in what matters. To infuse Data and AI into core operations has more similarity with pouring milk into coffee than, the management consultant’s traditional view of defining the target state, current as is, do the gap analysis and then a transformation project in between. To be Data and AI Ready. is about start pouring, the sooner the better. You can not control how the milk will mix with the coffee. but you can control the pouring. And as you pour you learn by doing. Which beans you prefer. Spilling less milk, getting the right strength of your latte, serving it with a heart sprinkled on top.
Learn by doing, peer-to-peer sharing and co-creation is the most powerful and direct route to adoption.
To become Data & AI Ready is all about learn by doing. To infuse data and AI capabilities is about getting the team of domain experts and data and AI experts to work together to learn from each other on a daily basis. To together figure what works for the team. Creating a common lingo and culture. When you are starting out you feel very alone if you are the only one with a new skill in an old team. Sharing and working with peers will give you the energy and inspiration to press on. On the job learn by doing and Peer-to-Peer sharing and learning is proven as the strongest form of adult learning. Learn on the job instead, solving your real work. Winning time rather than spending time in training to then go home and do the job and figure out how it will really work.
The best joke of the conference was someone acknowledging that “We have more pilots than Lufthansa. We need to reset our approach to create an engine to industrialize new ideas and put them into production.”
In the Dairdux Alliance we call our open source ways of working and better practices to support Data and AI industrialization and adoption for….. AIRPLANE….For just this reason. We have all these pilots. We need to put them in an AIRPLANE to make them fly…..
We would very much want your input and feedback what should be added as essential for your DAIR Industrialization Engine. Please contribute/comment/ share what YOU think are essential characteristics and ingredients of AIRPLANE.