Forrester warns AI costs to surge from USD $2.3bn to USD $13.8bn
A new framework from Forrester addresses enterprise concerns over the sharply rising costs associated with scaling generative artificial intelligence (AI) initiatives.
Forrester's latest report, AI Cost Optimization: The Why, What, and How, lays out a roadmap to help organisations manage spending as generative AI outlays are forecast to increase from USD $2.3 billion in 2023 to USD $13.8 billion in 2024.
The research highlights that operationalising AI beyond initial pilot projects leads to substantial, and sometimes exponential, increases in costs. Factors such as infrastructure, model lifecycle management, governance, and operational overhead all contribute to the overall financial footprint of enterprise AI deployments.
Cost factors
One major finding outlined in the report is that the total cost of ownership for AI extends beyond direct spend on models and infrastructure. It includes significant investments required for data management, model maintenance, continuous monitoring, and updates. Forrester's cost optimisation framework identifies both direct and operational levers for cost control. Direct levers include choices around models, data, and infrastructure, while operational levers involve aspects such as governance, business transformation, and skills development.
The quality, scope, and management of data are singled out as the largest determinants of AI model costs. Efficiently optimising data sources, quality, and transfer mechanisms is crucial in managing rising expenses tied to AI deployments.
The report also notes that operational elements like governance, workforce training, and organisational transformation can introduce hidden costs, which must be proactively managed to ensure the longevity and sustainability of AI programmes.
Global and regional challenges
The growth in generative AI spending is not limited to one region, but organisations across Asia Pacific face particular challenges. The report points out that issues such as data sovereignty, diverse regulatory regimes, and persistent skills gaps add distinct layers of complexity for enterprises in Asia Pacific implementing AI at scale.
By adopting a structured financial framework that aligns AI investments with business priorities, Forrester suggests organisations can drive both cost-effectiveness and ongoing innovation. The aim is to create a sustainable foundation for AI that delivers measurable returns and integrates smoothly into wider business operations.
"As generative AI embeds itself into every layer of enterprise operations, organizations must grapple with escalating costs amidst increasing demand. Forrester's framework equips leaders with the tools to optimize spend, manage risks, and strategically align their AI cost models with their business objectives," said Michele Goetz, Forrester VP, Principal Analyst.
Asia Pacific context
For Asia Pacific, the report recommends that leaders factor local considerations into their AI strategies. This includes addressing market specifics such as local regulations, data localisation requirements, and regional talent development for AI and data analytics roles.
"In Asia Pacific, organizations 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 prioritize 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."
Forrester's report emphasises the importance for enterprises to approach AI investment with a holistic view that incorporates both the visible and hidden costs. This approach aims to reduce financial unpredictability while building the capacity for sustainable, value-driven AI adoption.
