// EDITOR ’ S QUESTION ?
FARHAN CHOUDHARY , PRINCIPAL ANALYST , GARTNER
T he adoption of Machine Learning in the enterprise is being catalysed by Digital Transformation , the need for democratisation and the urgency of industrialisation . According to our recent research , 48 % of respondents to the 2022 Gartner CIO and Technology Executive Survey have already deployed or plan to deploy AI / Machine Learning in the next 12 months .
The ongoing Digital Transformation requires better and faster but also ethical decisionmaking , enabled by advances in decision intelligence and AI governance .
One of the most prominent reasons why we ’ re seeing an increasing enterprise adoption of Machine Learning is the desire to bring the power of Machine Learning to a widening audience – the democratisation of data science and Machine Learning ( DSML ), lowering the barrier to entry which is enabled by technical advances in automation and augmentation .
In addition , companies require shorter time to value and broader use and scalability of DSML , which are being enabled by advances in XOps and multi-cloud .
To assess where Machine Learning can be applied in the enterprise , the CIO and IT team first need to determine the ‘ what ’ of the problem statement , for example , ‘ what ’ business KPIs does the organisation want to be impacted through the work in Machine Learning , and second , the ‘ how ’ of the problem statement , i . e ., how will the organisation accomplish this task .
That said , Machine Learning can be applied across many parts of the business , some applications or opportunities could be low hanging fruits , some could be money-pits or some cutting edge . A thorough and systematic assessment of opportunities should be conducted before determining ‘ where ’ Machine Learning can be applied by enterprise IT and where a democratised approach can be followed . This should be a top-down approach . Let ’ s assume we ’ re in retail business and we want to leverage Machine Learning while working in collaboration with enterprise IT to generate tangible business value .
The first order of business is to conduct an assessment on business value we expect the project to generate or KPIs that it would impact , and the feasibility of using Machine Learning in the enterprise . Say our priorities are revenue growth , and we want to use Machine Learning to impact the volume of sales ; then , this could be done through use of Machine Learning in products and services , sales and marketing or in customer service ( these are our separate lines of businesses that can leverage Machine Learning ).
In addition , there are opportunities in sales and marketing , R & D , corporate legal , human capital management , customer service , IT operations , software development and testing , and many other areas where Machine Learning can be applied .
THE ADOPTION OF MACHINE LEARNING IN THE ENTERPRISE IS BEING CATALYSED BY DIGITAL TRANSFORMATION , THE NEED FOR DEMOCRATISATION AND THE URGENCY OF INDUSTRIALISATION .
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