Intelligent CXO Issue 19 | Page 20


As organisations emerge from the lockdown restrictions that were imposed on businesses because of the COVID-19 pandemic , Machine Learning has taken centre stage because it gives enterprises a view of trends in customer behaviour and business operational patterns , as well as supports the development of new products .

Many of today ' s leading multinational companies , such as Facebook , Google and Uber , have made Machine Learning a central part of their operations . Machine Learning has become a significant competitive differentiator for many companies across the Middle East and Africa ( MEA ).
According to research firm Gartner , the adoption of Machine Learning in the enterprise is being catalysed by Digital Transformation , the need for democratisation and the urgency of industrialisation .
The firm says 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 . Gartner said that the on-going Digital Transformation requires better and faster but also ethical decision making , enabled by advances in decision intelligence and AI governance .
Gartner said one of the most prominent reasons why the IT industry is 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 .
Farhan Choudhary , Principal Analyst , Gartner , said 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 .
Choudhary 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 .
He said 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 ),” he said .
Choudhary pointed out that 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 .


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