Forrester releases a new framework to help organisations optimise AI costs in its latest report, “AI Cost Optimisation: The Why, What, And How”.
Forrester VP, principal analyst Michele Goetz and report author said: “As generative AI embeds itself into every layer of enterprise operations, organisations must grapple with escalating costs amidst increasing demand. Forrester’s framework equips leaders with the tools to optimise spend, manage risks, and strategically align their AI cost models with their business objectives.”
Key findings
The report found that operationalising AI at scale requires investments across infrastructure, data and model management, continuous monitoring, maintenance, and updates. Making informed decisions entails understanding the total cost of ownership.
Moreover, managing AI costs involves optimising AI cost levers, including direct levers (models, data, infrastructure) and operational levers (governance, business transformation, training).
The scope and scale of data is the most significant lever of model cost, making it essential to optimise data sources, quality, and transfer to control costs.
There are also costs hidden within AI operations, including governance, business transformation, and training, that organisations must account for for the success and sustainability of AI initiatives.
Driving AI innovation
“In Asia Pacific, organisations are navigating unique opportunities and challenges in adopting generative and agentic AI,” said Charlie Dai, VP and principal analyst at Forrester. “To unlock the full potential of AI while managing costs effectively, leaders must prioritise local market dynamics, such as data sovereignty, regulatory compliance, and talent development.”
“By aligning these factors with strategic, cost-conscious investments, APAC enterprises can drive innovation while ensuring long-term operational effectiveness and business resilience,” Dai added.
