Intelligent CIO Middle East Issue 71 | Page 34

EDITOR S QUESTION
STEPHEN GILL , ACADEMIC HEAD OF THE SCHOOL OF
MATHEMATICAL AND COMPUTER SCIENCES , HERIOT-WATT
UNIVERSITY DUBAI

The use of Machine Learning tools is possible now due to the large accumulation of data used to train the tools and the very fast speed of modern computers to process data quickly . Machine Learning for enterprise use is growing rapidly and its application ranges from enhancing customer experience to creating new products . Machine Learning has permeated almost all areas of modern businesses . Enterprises are increasingly adopting Machine Learning to tackle complex business challenges and reap value in the form of improved productivity , reduce cost of operations , perform more accurate financial forecasting , analyse customer needs better and reduce repetitive tasks for workers .

The industrial sectors that are best suited for Machine Learning deployments are healthcare , automotive , energy management , retail and customer service .
Such sectors are already leveraging Machine Learning that helps them in the prediction of poor and erroneous behaviour , optimisation of production processes and comprehensive analysis of the market in order to respond to customer demands more accurately . In the automotive sector , for instance , Machine Learning is helping massively in carrying out predictive analysis of component durability and in the rapid recognition of anomalies and defects , as well as in the optimisation of the supply chain .
Machine Learning is equally critical for the successful running of smart grids as it helps in carrying out realtime analysis of energy consumption patterns which in turn is necessary for ensuring that the supply of electricity matches the demand . Smart grids ultimately save economies billions of dollars in energy costs , thanks to Machine Learning .
To remain relevant and competitive , a CIO must adopt two positions within their organisation : guardians of infrastructure and digital catalysts of business value . As Machine Learning and AI continue to transform businesses across a myriad of sectors , organisations are gradually starting to see their huge potential .
However , it is not possible that all stakeholders are able to visualise the full potential of AI and Machine Learning . As with any initiative , stakeholder support is key for its eventual success and that is why so many CIOs focus on creating solid , evidence-based business cases for the technology investments that they want the management to approve .
While quantitative-empirical communications could be influential for other IT colleagues , CIOs also need to address non-IT stakeholders ( especially members of senior management ). They can do so by telling persuasive stories that illustrate the impact that the investment in emerging technologies such as Machine Learning will have on multiplying business value , especially on profits and revenue .
Being a good storyteller and a salesperson might not come naturally to a CIO who has risen through the IT and engineering ranks . Hence , it is important for them to develop such communication skills from their counterparts in sales and marketing , in order to gain the management buy-in and support needed to deploy their digital initiatives .
One of the most common Machine Learning challenges that enterprises face is the availability of data . Proper access to raw data is very important as training Machine Learning algorithms requires huge amounts of data . Insufficient data makes it harder to train the models properly which makes the implementation of Machine Learning even harder . In addition to proper collection of data , enterprises also need to model and process the data to fit the algorithms that they will be using . Another major challenge faced with Machine Learning implementation is data security . Distinguishing between sensitive and insensitive data is crucial for implementing Machine Learning correctly and efficiently .
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