We can see that the C-Level is taking much more of an interest in Machine Learning than they did in Agile or DevOps.
ML has wider implications for the whole business. Rather than focussing on Customer web software development company Value the focus will shift to strategy and operations. The methods and timescales that C- Level use are different to Agile ways. They have incomplete data and longer timescales.
Companies will use ML to improve efficiency, reduce risk and increase learning if they are sapiently minded. The fall out from machine learning will most likely be a realisation that the key capability – human learning – is woefully short.
Operationally: php development company learning will be a key metric along with the current needs for better estimation and if you are lucky seeing the commercial results of your work.
Ideally I see the BAFort scale being inverted where strategy supports from below. This tone poem example represents a real world change that had to be applied to a FTSE-100 app development company website when user traffic dropped after an update in 2013. The CEO formed a core team to fix the issue.Machine Learning Project Model
The three-loop model is different to a single sprint iteration. The basic ML project model will need to be depicted as a linear process to let the board calculate using their delivery durations. They are pretty savvy and understand that need. Tech teams better be adaptable and ready to learn to play different styles. Practise will replace fail-fast.
Agile offers NO strategic competitive advantage. It is ubiquitous. It is too slow. It is not accurate enough. It does not address the business needs. Backlogs are NOT order books!
Machine Learning projects will, I think, be business led – from C-Level. Tech teams will have to respond much faster than they do today. This may result in reduced staff levels. It is not something that I want to see but am aware it may happen. I offer hedge skills to those that may be affected.