TECHNOLOGY
Yet , as with any technological advancement , the marriage of cloud and AI brings its own set of challenges . “ The challenge for many companies is how to scale up ,” says Paul Cardno , Global Digital Automation & Innovation Senior Manager at 3M .
Balancing Gen AI ’ s ambitions with its limitations At the heart of the scaling challenge lies the sheer computational power required to run Gen AI models , particularly LLMs . As deployments scale , so do the associated costs .
“ Complex deployment and ongoing consumption mean that these models are expensive to develop ,” Dr Chris Hillman , Data Science Senior Director International at Teradata notes .
Take for instance a Gen AI-powered customer service chatbot . Initially deployed for a single product line , a company could gradually extend the chatbot ’ s capabilities across its entire catalogue , necessitating a significant increase in computational resources and knowledge base . As the system handles a growing volume of concurrent interactions , the computational power needed will increase and , therefore , the costs .
This cost factor is pushing businesses to carefully evaluate their Gen AI strategies , balancing the potential benefits against the substantial resource requirements .
“ It is important to understand the full implications across all dimensions ; from publicity to infrastructure to return on
SOME OF THE TOP ENTERPRISE USE CASES OF GEN AI ARE
• Code generation , documentation , and quality assurance
• Product development and management
• Content creation and marketing
• Customer support automation
• Data analytics
investment ( ROI ) to what is done and needs to be improved for use cases like this to be a reality ,” explains Dr Chris .
Such complex deployments and ongoing consumption mean that these models are expensive to develop . Moreover , the cloud ’ s pay-as-you-go model , while offering flexibility , can lead to unexpected costs if AI workloads are not carefully managed .
The growing cloud costs as deployments scale may mean that in the rush to implement Gen AI across their offerings , organisations should really examine what value this implementation in this specific instance is bringing .
“ Cost will continue to be an issue and the ability to accurately assess the ROI will be vital if Gen AI projects are to be deployed in production ,” Dr Chris warns . As models become more complex and data-hungry , organisations will need to get a balance between leveraging the cloud ’ s capabilities and optimising resource utilisation . aimagazine . com 171