Magazine Button
Experts discuss the rise of Machine Learning adoption in the Middle East

Experts discuss the rise of Machine Learning adoption in the Middle East

Industry ExpertMiddle EastSoftwareTop Stories

To make decisions more quickly and accurately, enterprises are increasingly turning to Machine Learning, arguably today’s most practical application of Artificial Intelligence (AI). Machine Learning is a type of AI that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine Learning algorithms use historical data as input to predict new output values. Industry pundits share insights why Machine Learning has been made a central part of business operations. 

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. And 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. 

Farhan Choudhary, Principal Analyst, Gartner 

The 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 on-going Digital Transformation requires better and faster but also ethical decision making, 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. 

Walid Issa, Senior Manager, Pre-sales and Solutions Engineering – Middle East Region, NetApp  

Organisations are increasingly adopting AI, Machine Learning, and deep Learning (DL) to support their critical business needs. Their AI developers train computers to learn and to solve problems in a manner that is similar to or even superior to, humans. Machine Learning is enabling them to analyse large amounts of data to unearth previously unknown business insights. It is also helping them to interact directly with customers by using natural language processing and also giving them the opportunity to Automate various business processes and functions. This will help organisations to solve real-world problems, deliver innovative products and services very fast and most importantly, give them the edge in an increasingly competitive marketplace. 

Now, AI and Machine Learning have moved beyond the realm of concept into real-world application, representing the great opportunity to stay competitive, drive growth, and cut costs. 

AI and Machine Learning are well suited in different verticals such as manufacturing, healthcare, telecom, public sector, retail, finance and automatise. If I select healthcare as an example, Artificial Intelligence is transforming healthcare in ways we never thought possible. And it really is all about data. Using data, AI and Machine Learning can help healthcare professionals make more informed, accurate, and proactive assessments and diagnoses. The ability to analyse data in real time enables healthcare professionals to improve the quality of life for patients and ultimately save lives. This will enable proactive diagnoses using smarter healthcare tools, help physicians find the right data faster and keep patients and healthcare organisations safe from cyber criminals and attacks. 

For CIOs and IT leaders, best practices would be to build a data fabric; a unified data management environment that spans across their edge devices, data centres, and one or more public clouds, if they have or plan to go to the cloud, so AI data can be ingested, collected, stored, and protected no matter where it resides. Only then they can optimally train AI, drive Machine Learning, and empower the DL algorithms necessary to bring AI projects to life. Our NetApp AI solutions can help them remove bottlenecks at the Edge, core, and cloud to enable more efficient data collection, accelerated AI workloads, and smoother cloud integration. Our unified data management solutions will support seamless, cost-effective data movement across their hybrid multi-cloud environment. And our world-class partner ecosystem provides full technical integrations with AI leaders, channel partners and systems integrators, software and hardware providers, and cloud partners to put together smart, powerful, trusted AI solutions that help them achieve their business goals. 

Ramprakash Ramamoorthy, Director, AI Research, ManageEngine 

Since the onset of the pandemic, the first touchpoint for many businesses has been digital. Organisations must remain digitally competitive to stay afloat, and they achieve this by implementing newer technologies like Machine Learning. Another factor is the ongoing AI summer, during which there have been a lot of investments in AI and other associated technologies, which in turn has increased the adoption of Machine Learning across the globe. 

Because Machine Learning enables enterprise software to move from process automation to decision automation, using Machine Learning involves rewriting current, traditionally deterministic processes and workflows to make them probabilistic.  

For instance, a traditional anomaly system uses the bell curve to identify anomalies, whereas a Machine Learning-powered anomaly system identifies anomalies along with the probability of an outage occurring. CIOs have to drive these changes and incentivise teams to use and integrate new technologies like Machine Learning into their everyday workflows by citing the impact they could have on business growth. 

Machine Learning has impacted almost every field given the growth of digitisation across domains, and IT is no exception. AIOps is a trending topic in enterprise IT right now. Vendors currently deploy AI across service delivery, operations monitoring, security, and endpoint management. The list of AI use cases includes anomaly detection, forecasting, outage prediction, root cause analysis, chatbots, malware and ransomware detection, phishing detection, and agent prediction. 

The right Machine Learning system should work with the enterprise’s available data quantum. In the consumer space, people talk about terabytes of data. But in IT, data sets can be much smaller, sometimes just a few hundred rows. Choosing an accurate Machine Learning model for the amount of data available is key. 

The model must also work in the organisation’s preferred deployment modes. Typically, ML models are restricted to cloud deployments, but in IT, an anti-ransomware model needs to be deployed at the Edge. 

It is also crucial that the vendor employs security and privacy best practices and that the models remain bias-free when deployed. 

ManageEngine is very optimistic about how Machine Learning could change the way we work in the near future, and we are continually investing in the technology. Ensuring the data is bias-free, implementing an explainable model with integrated systems that work with multiple deployment modes, and better identifying the right use cases will improve the ROI from ML deployments. 

Click below to share this article

Browse our latest issue

Intelligent CXO

View Magazine Archive