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Digitisation and transformation of the workplace – How to make it work

Written by Vernon Cheung

February 27, 2024

Digitisation and transformation of the workplace – How to make it work

By Raine St. Claire

Digital transformations are fundamentally reshaping workplace dynamics, and this transformation will continue in the years ahead. Driven by AI, MLOps, hyper automation, and integration tools, these innovations boost efficiency and productivity but pose challenges requiring staff reskilling. Understanding these changes is essential for a competitive edge in the evolving workforce.

What are these digital innovations and how are they impacting the workplace?

One unmistakable trend gaining momentum in recent years, poised for further growth, is the increased adoption of AI, especially MLOps. While many are familiar with the concept of AI, its vast potential across industries is often underestimated.

What Is MLOps and why is it significant? 

MLOps, or Machine Learning Operations, is a branch of AI that focuses on the practical application of AI. Surprisingly, these technologies find applications in diverse domains.

Which industries leverage this technology? 

The profound impact of MLOps extends well beyond the conventional tech-centric sectors. Diverse commercial domains across banking, finance, retail, e-commerce, logistics, and healthcare, have harnessed advanced AI and MLOps tools to optimise their data, aligning it seamlessly with their products and services.

Moreover, MLOps has transcended expectations by finding applications in unconventional sectors:

Agriculture
In agriculture, technology has ushered in a new era, revolutionising the way farmers manage their crops and maximise yields.

Energy
Within the renewable energy sector, cutting-edge predictive models are employed to predict energy production from sources like solar panels and wind turbines with exceptional accuracy.

Entertainment
MLOps has redefined content creation and personalisation in the entertainment industry. Streaming platforms, for instance, leverage MLOps to dissect user preferences and viewing habits, offering tailor-made recommendations that captivate and engage viewers.

What does it do? 

MLOps automates functions that would typically require manual intervention, streamlining processes, enhancing efficiency, and reducing the risk of human error. The challenge lies in organising and restructuring the abundance of data into digestible formats using MLOps.

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Hyperautomation

This buzzword is all about intensifying the automation process, making operations even more efficient, productive, and cost-effective. As a subcomponent of MLOps, hyperautomation encompasses various processes, including machine learning and deep learning; a process that employs neural networks with multiple layers to emulate cognitive processes, finding applications in self-driving cars, virtual assistants, visual recognition, and natural language processing.

  • Integration tools

These combine data from disparate sources, employing “extract, transform, and load” processes to filter accurate information from potentially erroneous data.

  • Task/Process mining tool

These applications illuminate user interactions with a company’s software, evaluating task efficiency and suggesting improvements while process mining tools delve into broader processes, optimising overall efficiency.

Digital twinning: Real-world simulation

Digital twinning, an emerging trend, involves creating a digital replica of a product to assess its real-world functionality. Physical objects are equipped with sensors linked to a central system, enabling real-time tracking and analysis.

Digital twins span four categories: parts, products, systems, and processes, finding applications in manufacturing, healthcare, and city simulations for energy usage and traffic optimisation.

Smart offices and cloud utilisation

Two existing concepts gaining traction are smart offices and cloud-based data storage. Offices are becoming smarter with light and door sensors, digital booking for conference rooms, smart desks, and video monitoring.

Digital transformation challenges and strategies

Understanding the challenges that employees encounter during global digital transformation initiatives is crucial. Equally important is identifying the strategies that organisations can employ to empower their workforce with the essential knowledge, skills, and collaborative mindset required for successful integrations and improved operational capabilities.

Complexity in machine learning

Invest in talent by recruiting or upskilling employees with machine learning and AI expertise. Start small by beginning with manageable projects before tackling intricate tasks. Leverage platforms to explore AI/ML platforms for simplified model deployment.

Ongoing innovations

As the world undergoes rapid digitisation, scientists and engineers are vigorously driving the evolution of work for the future. According to a report from Emergen Research, the digital transformation market is projected to grow by over 20% by 2030.

These developments are not merely enhancing operational efficiency and productivity; they are also ushering in fresh challenges that require employee upskilling and the implementation of well-thought-out strategies for effective execution.

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