While hindsight is 20/20, foresight can separate success from failure. However, the technological breakthroughs of recent months mean that decision-makers have their work cut out for them - with the rapidly changing enterprise IT landscape making it difficult to plan, strategise, and execute. The following talking points could help decision-makers to see the forest from the trees.
#1: 2024 could be a watershed moment for the workforce
Human resource (HR) departments are set to add immense value to their tech stack via AI. This is not to say that AI will unseat HR professionals. Quite the opposite, because robots can't apply the level of consideration, individual reasoning and group collaboration that is needed from humans, especially in roles like HR where empathy is essential.
Rather, we'll see AI being leveraged with more frequency precisely to tap into HR's power skills - such as selecting qualified candidates, developing potential, managing complex workforce needs, retaining talent, making key decisions, and being able to put themselves in others' shoes. This will ultimately allow teams to operate more strategically, speeding up tasks like job description preparation, drafting of interview questions, improving communications, supporting payroll and benefits administration.
Also set to have a major impact on the workplace in 2024 is how employers respond to continued economic headwinds. Inevitably, this will be most felt on budgets, so expect decision-makers to carefully re-evaluate how to tangibly improve employee experience.
2024 will also see more digital natives enter the workforce, which will have immense repercussions for the future. While iPad babies are still in school, they will soon be eligible to gain employment. And this generation is eerily sophisticated and comfortable with tech. They will expect - rightly so - seamless tech experiences across their entire job cycle, from recruitment to day-to-day work, professional development and company culture. 2024 will be a pivotal year in this regard, and investing in the future will be crucial to attracting tomorrow's top talents.
#2: Real-time data and AI's turning point
As the explosive growth of generative AI carries over from 2023 into 2024, organisations need to ensure the data underpinning AI models is grounded in truth, reality, and is as fresh as possible. Leveraging this data layer that supports both transactional and real-time analytics, will be critical to timely decisions and agility in the face of mercurial market dynamics.
Ultimately, the shift towards a more data-centric approach of training LLMs, will result in a faster delivery of generative and predictive experiences, significantly improving the output of the models while reducing hallucinations.
As a natural progression in the evolution of LLMs, they now incorporate the different aspects of processing and understanding information using multiple modalities such as text, images, audio and video. We are now more familiar and comfortable with using AI, hence rather than AI as a tool that will replace jobs and people, AI will be increasingly perceived as a partner.
AI models can act as co-pilots to translate data into actionable insights such as generate best practices and recommendations. 2024 will see businesses tap into AI co-pilots for faster time to insights. This will be driven by the need for contextual interaction of AI and the data it produces. To leverage augmented data and analytics, businesses could look to embedding AI co-pilots into their products.
#3: The growth of LLMs will have far-reaching consequences.
The onward momentum of large language models (LLMs) will make it crucial to demystify AI hallucinations. Coupled with real-time contextual data, Retrieval-Augmented Generation (RAG) will mitigate these risks and improve the accuracy and value of models. Real-time data infrastructure will also accelerate the rise of hyper-personalisation, as enterprises strive to gather, process, and act on vast volumes of data instantaneously.
Another caveat is the overuse of AI tools potentially stifling innovation. Good employees will lean on AI tools to lighten their workload, but exceptional ones will use them to execute mundane tasks and focus more on value-creating work. The key is to understand AI's limitations and to exercise good judgment.
#4: AI at the Edge: A new frontier
Breakthroughs such as the convergence of AI and edge computing will continue to mature, allowing for more robust real-time analytics and decision-making at the edge. Enhanced edge AI capabilities will reduce the need for data transmission to the central locations in the cloud, ensuring faster responses and better privacy preservation. This could also pave the way to deliver AI at scale. For instance, the edge could handle model inferences, while the cloud may handle model training. Inversely, the edge may offload queries to the cloud depending on the length of a prompt and so on.
However, this will require models to strike the right balance. Models will need to be lightweight and capable of running in resource-constrained embedded devices and edge servers, while continuing to deliver results at acceptable levels of accuracy.
Ultimately, when it comes to a successful AI strategy for the year ahead, companies need to consider an edge computing strategy in tandem with the cloud. This will enable low-latency, real-time AI predictions in a cost-effective way, without compromising on data privacy and sovereignty.
