With the ever growing importance of AI , machine learning and data management to successful organisational and business performance, productivity and profitability, and its corollary growing adoption by organisations, enterprise sand business of all shapes and sizes, we would suggest that two major issues are becoming increasingly common.
The first is that too many organisations are being led by the nose with respect to the latest trends, technologies and tools. Rather than taking the time to understand in detail what their bespoke requirements are and maturing into systems in a phased and smarter, intelligent manner, they are too often, in a combination of panic, pressure and enthusiasm, succumb to procuring sophisticated and complex technologies, tools and systems which they can fully do not fully use and understand. Given that ICT projects are one of the biggest sources of organisational PM failure, this is especially worrying.
Secondly, there is a shortage of AI, ML and data knowledge, expertise, awareness and understanding across organisational levels, with the talent that does lead the way too often forming minor empires and silos, in the manner that BMS and ISO used to twenty or more years ago.
As part of a high-performance culture, organisations need to encourage the awareness, understanding and training of AI, machine learning and data management skills across all organisational levels.
It is of crucial strategic, transformational and operational organisational and business insight, intelligence and planning that AI, ML and related data technologies are understood and deployed so as to help assure and underwrite performance, productivity, profitability and high-performance cultures.
Whilst the latest evidence suggests increasingly organisational cultures are embracing the need for AI and data technologies, the biggest problems are a lack of talent, knowledge, expertise, awareness and understanding.
Interestingly, the research suggest that the biggest issues lie in what two broad areas. The first is awareness and understanding of what constitutes high impact, high quality and high value intelligence and data and how to gather it. There is also, in our experience, an over reliance upon quantitative rather that qualitative and ethnographic based intelligence and data.
The second broad area is a lack of ‘technical’ talent, in areas such as ML modelling, data science and data engineering. These are highly skilled and specialist areas, but interestingly the research suggest that there is a lack of understanding of business use. We have noticed that internationally, there is a significant lack of understanding of AI, ML and data technologies generally, their deployment and benefits amongst leadership and management personnel.
The research also shows, encouragingly, that amongst those organisations with ‘mature’ AI, ML and data technologies deployment, which were only about 25% of respondents, there was an understanding of the importance of building and developing bespoke solutions using platforms and systems such as scikit-learn and TensorFlow using supervised learning and deep learning methodologies and focused on structured data acquisition.
Perhaps unsurprisingly the retail sector had the highest number of ‘mature’ respondents. Worryingly, the education and training sector has the lowest percentage of ‘mature’ users.
Organisations, enterprises and businesses of all shapes and sizes must be encouraged to proactively develop Al, ML and data technology awareness, understanding, skills and expertise across all organisational levels.