Machine learning is gaining ground in the Middle East
To make decisions faster and more accurately, African businesses are increasingly turning to machine learning, arguably today’s most convenient application of artificial intelligence (AI). How should organizations ensure the success and return on investment of machine learning deployments in their IT environments?
Machine learning is a type of AI that allows software applications to be more precise at predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values. Additionally, machine learning systems apply algorithms to data to glean information about that data without explicit programming – it’s about using the data to answer questions. As such, companies apply machine learning to a wide range of issues, from customer buying models to predictive maintenance.
According to research and consultancy firm International Data Corporation (IDC), spending on artificial intelligence (AI) systems in the Middle East and Africa (MEA) is expected to maintain its strong growth trajectory as businesses continue to grow. ” invest in projects that use the capabilities of AI software and platforms.
An IDC survey of more than 2,000 IT managers found that adoption of machine learning increased the customer and employee experience by 25% and also led to accelerated rates of innovation within the organization.
Analyst firm Gartner has estimated that by 2022, 70% of white-collar workers will interact with chatbots on a daily basis.
Motor vehicles are also able to collect complex data from their environment and interpret it to make precise and precise decisions on their own, using machine learning. IDC predicts that the number of vehicles capable of level one (driver assistance) autonomy will increase from 31.4 million units in 2019 to 54.2 million units in 2024.
Fady Richmany, Senior Director and General Manager – UAE, Dell Technologies, said that with developments underway in machine learning today, the practical uses in businesses are endless. Richmany said machine learning systems can be used to help anticipate trends and identify problems, thus playing an important role in supporting decision-making processes. “Businesses can also use machine learning for customer retention, as machine learning systems can study customer behavior and identify potential milestones in customer retention,” he said. “Additionally, they can use machine learning to facilitate market research and customer segmentation. This allows them to deliver the right products and services at the right time, while gaining valuable insight into the buying habits of specific groups of customers in order to better target their needs.
He added that companies can also increase operational efficiency by deploying machine learning to handle daily routine business tasks, thereby speeding up operations, freeing up their employees for more innovation and creating new business opportunities.
Secret Machine Learning Sauce
While vendors often claim to have a secret machine learning sauce in their products that will revolutionize a business’s operations, CIOs are urged to exercise caution when selecting the right machine learning systems and tools.
Alan Jacobson, director of data and analytics, Alteryx, said the top three considerations for selecting a machine learning system should always include usability, breadth of reach, and a results-based view.
Jacobson said a lot of the focus on machine learning has been on technology, not people and that’s where failed projects are rooted. “As technologies continue to converge, so do consumers or producers of these capabilities. Current skills gap continues to be a problem for businesses. There is a glaring lack of data scientists across the world, ”he noted.
Second, according to Jacobson, companies need to ensure that the solution chosen can help cleanse and manipulate data from all necessary sources and run across the entire technology stack in place. “The end goal should be to replace all disconnected tools with hyper-specific functions in favor of a broad-use tool,” he said. “Finally, as with any technology purchase, a results-based approach is also key to keeping things on track. Is your organization able to compare these results in advance with existing use cases, for example? The focus on long-term business impact and the direct impact on productivity are two key metrics to assess here.
Rick Rider, vice president, Applied Innovation, Infor, said infinite compute resources are the reason machine learning is now more widely accepted by the industry. Additionally, Rider said it’s actually about giving users the ability to use machine learning without building or connecting technologies for years. “Now we have platforms that allow for rapid experimentation and implementation, so effective ROI is very real with the right vendor,” he said. “Some industries tend to be more progressive, such as industrial manufacturing, distribution and more. However, even within some industries, it is more about companies that aggressively adopt new opportunities and successful technologies. These are companies that tend to constantly find new ways to pivot and expand their business, regardless of industry.
Obtain corporate membership
Stephen Gill, Academic Director, School of Mathematical and Computer Sciences, Heriot-Watt University Dubai, said that to remain relevant and competitive, an CIO must take two positions within their organization: custodians of infrastructure and digital catalysts of commercial value. Gill said that as machine learning and AI continue to transform businesses across a myriad of industries, organizations are gradually starting to see their huge potential. As with any initiative, stakeholder support is key to its eventual success and that is why so many CIOs are focused on creating strong, evidence-based business cases for the technology investments they want. that management approves, “he said. “While quantitative-empirical communications can influence other IT colleagues, CIOs should also reach out to non-IT stakeholders (especially senior management). They can do this by telling compelling stories that illustrate the impact that investing in emerging technologies such as machine learning will have on multiplying business value, especially profits and revenues.
He explained that being a good storyteller and a good salesperson may not come naturally to a CIO who has worked his way up the IT and engineering ranks, so it is important for them to develop such communication skills from from their sales and marketing counterparts, for the buy-in and management support needed to roll out their digital initiatives.
The challenges businesses face
Priyanshu Vatsha, intelligent automation and presales consultant, Proven Consult, said the technical challenges associated with machine learning systems are primarily data related. Vatsha noted that data unavailability, noisy, redundant or inadequate data make it difficult to achieve satisfactory results. “Problems also arise if the input data is biased or encrypted. Continuous validation is an additional challenge for implementing machine learning models in practice, ”he said.
Vatsha said that when it comes to non-technical challenges, building user confidence is a big challenge. “Users have to rely on them when faced with the challenge of making important decisions. Legal requirements are also often a significant challenge for a machine learning project. This concerns the protection of data privacy as well as decisions about who will be responsible for false decisions based on machine learning models.
Richmany of Dell Technologies said machine learning is still in its very early stages and even at such an early stage of development, the region is seeing it revolutionizing a range of industries, with advances in research and development in learning. machine every day. “For organizations to ensure the success and return on investment of machine learning deployments, it is important that they align them with clear, defined goals and use cases, and link them to business priorities. “Identifying and understanding whether the problems they are trying to solve could be addressed more efficiently and accurately through machine learning rather than conventional software is essential. Additionally, having experts conduct an elaborate experimentation phase of potential projects that includes everything from data collection and evaluation to basic modeling, cost and risk assessment, can help predict whether the project will be successful or not, ”he added. “This requires maintaining a corporate culture that values innovation. In the near future, we can expect quantum computing to dramatically increase the capabilities of machine learning. This will give Machine Learning the ability to create systems that simultaneously perform multistate operations. Quantum Machine Learning will have the ability to solve complex problems in a fraction of a second. “
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