The Capacity Media 2024 predictions roundup: the AI edition

Adding to predictions across other areas like cybersecurity and the state of the digital divide, here is the Capacity roundup of the connectivity industry’s AI forecasts for 2024.

Angus Ward, CEO at Beyond Now

At large, CSPs are looking towards AI to support reductions in operating costs. But CSPs that really aim to thrive won’t let the hope behind AI disappear in an efficiency drive alone. The critical juncture lies in understanding that while operational efficiency is imperative, the potential for AI to usher in transformative changes should not be overlooked.

The ’30%’ of CSPs that we expect to have clear strategic direction on how to grow their business will emerge as true pioneers in leveraging AI. Supported by the right business models, these CSPs will harness AI not just for efficiency gains but to unlock new revenue streams, harnessing its transformative power to drive innovation and profitability.

Liz Centoni, Executive Vice President Chief Strategy Officer & GM, Applications, Cisco

Adoption of AI is a once-in-a-generation technology shift and it is sitting at the intersection of innovation and trust. Yet, 76% of organizations don’t have comprehensive AI policies in place. There is mostly general agreement that we need regulations/policy and industry self-policing and governance to mitigate the risks from GenAI. However, we need to get more nuanced, for example, in areas like IP infringement, where bits of existing works of original art are scraped to generate new digital art. This area needs regulation.

We must also ensure that consumers have access to and control over their data in the spirit of the recent EU Data Act. With the rising importance of AI systems, available public data will soon hit a ceiling and high-quality language data will likely be exhausted before 2026. Organizations need to shift to private and/or synthetic data which opens the possibility for unintended access and usage.

There is plenty that organizations can do on their own. Leaders must commit to transparency and trustworthiness around the development, use, and outcomes of AI systems. For instance, in reliability, addressing false content and unanticipated outcomes should be driven by organizations with RAI assessments, robust training of LLMs to reduce the chance of hallucinations, sentiment analysis and output shaping. In 2024, we will see companies of every size and sector formally outline how responsible AI governance guides internal development, application, and use of AI. Until tech companies can credibly show they are trustworthy, you can anticipate governments creating more policies.

Steven Carlini, Vice President of Innovation and Data Center at Schneider Electric

The impact of AI is far greater than just infrastructure or management. There has been a growing realisation that moving resources closer to where they are needed is a sound approach to many of today’s digital challenges. Compute power, data processing, and analysis and now AI are being moved to the edge. Distributed IT or Edge computing has been implemented in sectors such as retail and finance, and manufacturing will increasingly deploy edge computing to enable increasing use of industrial internet of things (IIoT), as well as automation and more. The next 12 months or so will be when everyone starts talking about the need for edge AI. This AI at the edge will support not just optimisation of infrastructure and operations, it will also be key in supporting enterprise applications.

Liz Centoni, Executive Vice President Chief Strategy Officer & GM, Applications, Cisco

Sustainable energy plays a vital role in addressing climate change. Selecting smaller AI models with fewer layers and filters specific to use cases, companies will begin to reduce energy consumption costs compared to general systems. These dedicated systems are trained on smaller, highly accurate data sets and efficiently accomplish specific tasks. In contrast, deep learning models use vast amounts of data.

The fast-emerging category of energy networking, which combines the capabilities of software-defined networking and an electric power system made up of direct-current micro grids, will also contribute to energy efficiency. Applying networking to power and connecting it with data, energy networking offers comprehensive visibility and benchmarking of existing emissions and an access point for optimizing power usage, distribution, transmission, and storage. Energy networking will also help organizations measure energy usage and emissions more accurately, automate many functions across IT, smart buildings, and IoT sensors, and unlock inefficient and unused energy. With embedded energy management capabilities, the network will become a control plane for measuring, monitoring, and managing consumption.

Sarah LaSelva 6G, Director of 6G at Keysight

Unlike other sectors, the wireless industry will take a more measured approach to integrating AI. Operators will focus on thoroughly training the machine learning models on diverse data sets, quantifying the impact, and putting in place a new test methodology. As AI adoption matures, it will transform the wireless industry over the next decade, unleashing new capabilities such as improved beam management and smart spectrum sharing.

Candid Wuest, VP Cyber Protection Research, Acronis

With the significant rise of AI in the past year, there's been equally increasing security risks. We've seen a spike in FBI reports regarding the creation of deep fakes through generative AI. Cybercriminals have been exploiting deep fakes with the intention to cause serious consequences through misinformation, such as a public crisis, family extortion, or severe stock disruptions. It is probable that this will happen more often as the technology becomes better understood, especially with financial incentives. Some cybercriminals may also start using AI in creative ways to extract sensitive information. Phishing has become the "prime child" of generative AI, and I predict that these risks will still pose a threat without intervention. In 2024, I believe we will see a large number of new regulations surrounding AI.

Steve Leeper, VP Product Marketing, Datadobi

The trajectory of storage technology is poised for a significant shift as the year 2024 approaches, with declining flash prices driving a broad-scale transition towards all-flash object storage systems. This shift is expected to result in superior system performance, catering adeptly to the voracious data appetites and rapid access demands of AI-driven operations. As flash storage becomes more financially accessible, its integration into object storage infrastructures is likely to become the norm, offering the swift performance that traditional HDD-based object storage and scalability that NAS systems lack. This evolution will be particularly beneficial for handling the large datasets integral to AI workloads, which necessitate rapid throughput and scalability.