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CX innovation has accelerated rapidly since 2020, as business and consumer expectations evolved dramatically during the Covid-19 pandemic. Now, finding the best way to engage and respond to customers has become a top business priority and a key business challenge. Not only do customers expect the highest standard, but companies are prioritising superb CX to gain the ‘edge’ over the competition
Artificial intelligence (AI) is set to become indispensable in business operations. For global enterprises, AI offers significant benefits by simplifying complexity and enabling confident decisions—when used in the right way.
Those HR recruitment teams that seamlessly integrate AI technologies will optimise their recruitment practices and will have the opportunity to better realise their commitment to diversity, equity, and inclusion (DEI) initiatives. AI algorithms can, if care is taken, ensure fair and unbiased candidate evaluation, promoting a more diverse and inclusive workforce.
AI and machine learning offer exciting innovation capabilities for businesses, from next-level predictive analytics to human-like conversational interfaces for functions such as customer service. But despite these tools’ undeniable potential many enterprises today are unprepared to fully leverage AI’s capabilities because they lack a prioritised data strategy. Bringing siloed and far-flung unstructured data repositories into a single, accessible source is one of the enterprise inhibitors of being able to utilise AI effectively.
Whilst most of the attention around artificial intelligence (AI) thus far has been on ChatGPT, it is just the tip of the iceberg. In many ways, ChatGPT shouldn’t be thought of as true AI as it is – at its heart – just generative, learned behaviour. The future of AI, in contrast, is a system that can think for itself autonomously. The really exciting stuff is yet to come. The good news is that this evolution towards a brighter future is much closer than previously predicted.
The power of AI combined with suitable use cases and a robust implementation plan can help businesses to radically reduce the time spent on manual, repetitive tasks, and allow teams to prioritise value-added work.
But in all the excitement, it’s evident that many businesses are held back by inertia, and a lack of understanding about how to actually go ahead and implement AI into their business.
In the past year or so, we have seen the emergence of many new and exciting applications for predictive AI in the energy industry to better maintain and optimise energy assets. In fact, the advances in the technology have been nothing short of rapid. The challenge, though, has been in supplying the ‘right’ data to make them effective.
It will be interesting to see what the future holds for predictive AI. Whilst there is much to admire, predictive AI is still in the emerging technologies phase and needs to overcome the challenges of scaling up. This article focuses on the benefits of the technology and what the energy sector will need to focus on over the next 12 months to ensure it doesn’t try to run before it can walk.
In an era where machine-generated media holds increasing influence over how we communicate, live, and learn, ensuring accountability will be paramount. Holistically integrating Zero Trust security spanning authentication, authorisation, data validation, process oversight and output controls will be vital to ensure such systems are safeguarded as much as possible against misuse. But what would Zero Trust Generative AI look like? Why is it required? How should it be implemented? And what are the main challenges the industry will have?
The digital revolution has disrupted nearly every industry, and manufacturing is no exception. With advancements in artificial intelligence (AI) and the Internet of Things (IoT), the manufacturing sector is undergoing a transformative shift, unlocking new possibilities, and redefining traditional processes. Before we dive deep into the topic it’s important to understand what digital manufacturing is. Digital manufacturing refers to the integration of digital technologies into the manufacturing process, encompassing everything from design and prototyping to production and distribution. AI and IoT play a crucial role in this transformation by providing manufacturers with valuable insights, automation capabilities, and real time data.