We need to rein-in AI adoption with Sustainability, Scalability and Security aforethought

Other key findings include:

1. Sustainability: Responsible use of Generative AI

   The energy demands of using generative AI (GenAI) present a significant challenge for organizations prioritizing sustainability. Training large language models consumes electricity equivalent to powering hundreds of homes annually.

   As organizations increase adoption of GenAI, balancing the technology’s benefits with environmental responsibility is critical. To do this, organizations need to rethink infrastructure and energy management.

   AI workloads behave differently from traditional traffic, often causing unexpected power surges that strain networks. Forward-thinking tech leaders need to adopt practices that maximize efficiency while minimizing environmental impact and aligning with customers’ environmental, social, and governance goals to ensure GenAI’s transformative potential does not come at the expense of the planet.

   Tech leaders will need to optimize their infrastructure by accurately scaling network switches based on expected levels of traffic and adopting distributed architectures that can reduce energy usage by up to a quarter compared to centralized systems. Partnering with sustainable cloud providers and leveraging real-time energy monitoring tools are also effective strategies to minimize environmental impact.

2. Scalability: Right-sizing is the way to go

   We are seeing many businesses incorporating anything-as-a-service solutions into digital transformation to manage growing data demands while improving energy efficiency. These solutions can be scaled up or down as needed, without the hefty upfront costs of building infrastructure. Such flexibility and cost-effectiveness make the model particularly appealing in today’s dynamic business environment.

   The key enabler of this approach is right-sizing: organizations pay only for the resources they actually use. This goes a long way in terms of streamlining operations and fostering agility and sustainability.

   As data demands continue to rise, combining right-sizing with as-a-service models offers organizations an efficient and adaptable pathway to digital transformation while keeping sustainability at the forefront.

3. Security: Future-proofing operations through better data management

   With the growing need for data and real-time analysis, the ability to move data seamlessly and securely to where it is needed will be crucial. This includes moving data from the edge to the cloud, and from one cloud to another. As such, it is important that organizations invest in key infrastructure elements for better data management:

   • Dense multi- and hybrid-cloud connectivity that ensures multiple paths for seamless workload shifts during cloud service disruptions to sustain operations and comply with regulatory requirements
   • An integrated safe and secure ecosystem that is deployable instantly without the need for technician visits or manual connections
   • On-demand flexible connectivity that adapts to and meets various requirements and regulations
   • An easy-to-use application capable of running multiple services simultaneously without needing dedicated ports or access for each service

   With these elements, organizations will be able to improve their decision-making quality and speed through greater data availability and usage. This translates into stronger business outcomes and improved key performance indices across operational domains.