The past half-decade has been incredibly challenging for the medical fraternity, necessitating extensive health and safety restrictions; applications driven by Artificial Intelligence (AI) and Machine Learning (ML) played a critical role in saving lives and fostering resilience. It seems we are only adapting to living and working through a pandemic now. At the same time, companies worldwide have made significant investments in AI and ML for better business outcomes and to advance the innovative purpose of their organizations through data, finding novel ways to tap into the ‘new oil’ of the digital economy.

Problem-solving for global transformation

AI involves developing intelligent machines that can perform tasks that usually require human input and intelligence. ML, on the other hand, is a category of AI that uses statistical techniques to grant computer systems the ability to learn from data without being explicitly programmed, for example, by progressively improving performance on a specific task. 90% of the world’s data has been created in the last two years alone, and with a staggering 2.5 quintillion bytes of data being created every 24 hours, this volume of data is expected to double every two years. This explosive growth in data and research has heightened the focus on ML, which powers the cultivation of a massive ecosystem of technologies, frameworks, and libraries. This means that when technologists need to solve a problem, they have many ML technologies and frameworks.

Advancing human functionality

The spheres of AI/ML development can be categorized into three functionalities: sensing, comprehending, and responding. Sensing involves functionality like document analysis and computer vision; comprehending extends to natural language processing and probabilistic decision-making, while responding involves the creation of a complete contextual learning agent and functionality such as disease incidence prediction. The algorithmic capability in ML is fuelling change at an unimaginable pace, particularly in the fields of:

AI & Vaccine Development: Previously, it took years, if not decades, to develop a new vaccine. But just three months after the first reported cases, we saw COVID-19 vaccines undergoing human tests. This record speed of vaccine development was partly due to AI models that helped researchers analyze vast amounts of data about the coronavirus. This application of AI in vaccine development will revolutionize how drug discovery, vaccine decisions, and precision medicines are handled in the future.

Automation: ML technology is helping businesses achieve cost efficiency through precision by automating many manual tasks. Organizations can reduce excessive human involvement in repetitive tasks while assigning human resources to more essential business activities, such as customer service.

Industry-wide impact: AI/ML touches our daily lives in many ways, helping to better our healthcare systems, improve retail management, and bring new functionality to fields such as cybersecurity and IoT, to name a few.

Elevating the potential of data usage

The value of analyzed data lies in enabling and facilitating the smooth functioning of everything, from the government to modern business. With analyzed data, it is possible to gain visibility of availability, demand, supply chains, trends, and more, all of which are the key ingredients in any business case, and without analyzed data, business growth would halt. ML can give companies the power to extract precise, forward-looking intelligence from their data efficiently. This means that not only can ML assist with greater process automation, productivity, and compliance, but it can also help businesses create predictive forecasts and sales figures to optimize costs and pricing, assess risks, segment customers, and so much more.

Growing opportunities

ML forms the basis of the mechanisms through which AI can be implemented, using predictive analysis to enhance the machine’s human understanding and ability to decipher data.

As a mix of methodologies, practices, and tools for continuous software delivery, DevOps has become popular due to the agility, simplicity, and faster resolution it enables. Proven to speed up operational processes and support cross-functional teams, work can happen more effectively with less friction and enhanced productivity. What happens when we bring ML and AI together? It’s called AIOps, and it makes streamlining and integrating data to identify problems much more straightforward. Predictive analytics, service analytics, and automated workflows combine better end-user experiences and even faster production cycles by disrupting operational silos, making conditions more favorable for innovation.

Intelligent operations

Use cases for AIOps include the ability to perform in-depth log analysis to predict problem areas and recommend changes to the development teams, along with alerting mechanisms that can avoid mistakes and system failures. Development efficiency is greatly enhanced because AIOps enables data collection related to sprints, user stories, the development velocity for each developer, and bugs raised/fixed. ML algorithms can perform analyses per developer or team, taking care of reporting requirements on sprints or releases, all of which add up to higher efficiency, lower rates of failure, and less stress. AIOps is also instrumental in AI-automated testing, contributing to enhanced software quality, helping to manage and monitor infrastructure, and moving from proactive traditional monitoring tools to predictive, using AI and ML algorithms.

To effectively leverage ML, organizations need to embed AI methodology in their end-to-end business model to elevate the human capacities for learning, perception, and interaction into the core of an organization’s data strategy. This needs to happen at a level of complexity that will ultimately supersede our abilities. To this end, organizations must engage and work with data from all business perspectives to understand and test suitable ML algorithms and frameworks. In the not-so-distant future, ML will become core to every business.

However, today, from a quick-win perspective, it is possible for retail, e-commerce, and consumer analytics to apply ML to forecast demand, optimize prices, provide customer recommendations, and detect and prevent fraud. ML is already used for credit scoring, risk analysis, trading exchange forecasting, and fraud detection in the banking and finance industry. The opportunities for leveraging these emerging technologies will only continue growing and prolifer as AI and ML platforms are open for new algorithms and past learning to support real-world use cases and scenarios.

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