What a difference a year makes.
For several years in succession, the annual WEF confab in Davos was in thrall to crypto, with its evangelists taking center stage to tout their revolution. But this year’s World Economic Forum saw the emergence of a new gospel. From roundtables and press releases to the signage on the Promenade, the message was loud and clear: The future is AI.
What does that future look like — and how do we get there? That, in large part, is what Davos 2024 was about.
The shift in focus to AI was not a total surprise, reflecting as it did the rapid rise in AI investments and interest over the past year — epitomised by the ascent of ChatGPT and other generative AI applications in 2023. As these new tools seized the public imagination, they sparked enthusiasm about AI’s promise, along with realism about the need to ensure that its social effects remain positive.
The key to reaching a positive outcome, of course, is to take a strategic view of AI, and then take the tactical steps needed to bring about the AI future we want. What those steps should be depends on who you ask — but beneath the torrent of AI pronouncements that flooded this year’s WEF, several key themes were clear.
AI and trust
The overarching theme that pervaded every AI discussion was the need to build trust — in the technologies, the data sets, the players, the path forward, and the outcomes we can expect from the coming AI era.
While individual players had their own takes on practical approaches, there was a clear consensus that trust is essential to the success of AI. That also means digital inclusion — to close the digital divide and present AI solutions that bring everyone forward in a trusted, transparent fashion.
AI and data
Most companies promoting AI at Davos were focused on data and how to combine it with AI to achieve a range of functional outcomes. In many instances, these outcomes extended far beyond the simple use cases we have grown used to over the course of the past year, in which quick “insights” are plucked from the data to light the way forward for marketers, product teams, or financial planners.
Instead, we’re talking about the sort of deep analysis of complex data that drives comprehensive decision-making — from high-level strategy to the smallest tactical details.
AI and security
A crucial and pervasive topic at Davos was how, in this Digital+ Economy, AI can make your data, your transactions, and your working environment more secure. From keeping financial and healthcare information safe to countering breaches, viruses, and ransomware, there was an intense focus on how AI and intelligent automation can transform the cybersecurity landscape — notably in the growing area of AI-based threat prediction.
AI and productivity
Unsurprisingly, the prospect of using AI to boost day-to-day productivity in both professional and personal life — particularly in the case of Gen AI — was a major focus of the AI solutions presented at Davos. The role of AI in task automation, predictive analytics, and workflow optimization got heavy emphasis — as did the recognition that implementing AI for productivity requires careful planning, integration with existing workflows, ongoing monitoring, and taking ethical considerations and employee privacy into account.
AI and industry
Based on discussions at Davos, you can expect domain-specific use of AI in industrial production to surge — in everything from automotive manufacturing to health care services, where AI in diagnostics and treatment was a much-discussed topic.
Beyond low-hanging fruit such as quality control and process optimisation (where sensor and control system data can be combined powerfully with AI) are topics like predictive maintenance (monitoring equipment health, for example), supply chain optimisation, and robotic process automation (RPA).
In the smart manufacturing realm, IoT integration enables AI to analyse data from sensors and connected devices; AI also can create “digital twins” — digital replicas of physical manufacturing processes to test and optimise before changes are made. And that just scratches the surface of the solutions discussed.
AI and sustainability
A major topic was the crucial role AI can play in promoting sustainability. For example, AI can optimise energy distribution in smart grids, analysing sensor and smart meter data to predict demand, manage fluctuations, and improve efficiency.
It can forecast weather patterns to optimise use of renewable energy sources, process vast amounts of climate data to improve climate modeling and help predict and respond to natural disasters. It can analyse data from sensors, satellites, and drones to optimise agricultural practices and monitor air and water quality. And AI can track and optimise the entire lifecycle of products, from production to disposal — promoting a circular economy by minimising waste.
Generative AI
Use cases for GenAI extend far beyond simple text and image generation — though some will require controls to ensure positive social outcomes. Video generation, voice synthesis, and production of music, art, and design are rich but controversial applications of GenAI.
Drug discovery, game development, fashion design, and architecture are all hot prospects for Gen AI, as are areas like financial modeling and biomedical imaging. Generative models can also help in anomaly detection, recommendation systems, and translation.
