Forrester says only isolated wins are likely, while 'good enough' open-weight models soak up the tokens
More than half of CIOs are likely to miss their AI-related ROI targets next year, according to Forrester.
Some tech leaders will get better at tying AI spend to planned returns. However, Forrester expects success to be confined to isolated use cases rather than whole programs, which won't be enough.
“As with prior emerging technologies, organizational structures, processes, and ways of working need to change to drive measurable, firmwide results with AI,” the report says.
Only 22 percent of AI decision-makers report a positive AI impact on EBITDA (earnings before income tax, depreciation and amortization) over the past 12 months.
“This mismatch between the investment into technology adoption and organizational change will delay the realization of heightened ROI targets,” the report adds.
The money will keep flowing through IT budgets regardless. In its Technology & Security Predictions, Forrester claimed AI will account for 20 percent of all business technology spend next year.
Sharyn Leaver, chief research officer at Forrester, said that will increase the pressure on leaders to prove AI's value while keeping costs, infrastructure, and risk under control. “At the same time, emerging technologies, geopolitical pressures, and the growing use of AI across technology operations will introduce new complexities and potential points of failure,” she said.
While tech teams struggle to get returns from growing AI spending, they will try to get more bang for their buck by investing in open-weight GenAI models, which can approach similar performance to the leading proprietary systems, at a fraction of the cost. Open-weight models will represent half of AI tokens but only five percent of enterprise spend, Forrester predicts.
“Open-weight models such as Gemma, Llama, or Qwen are good enough for the enterprise workloads that generate the most token volume, such as document processing, customer support conversations, internal knowledge search, and the routine substeps inside agentic workflows,” Forrester said.
Frontier models, including Anthropic's Claude Opus 5.5 and Claude Sonnet 5.5, OpenAI's GPT-6.1 Sol and GPT-6 Astra, and Google's Gemini 4 Argon, will be reserved for “the few deep knowledge and critical task automations where errors are expensive, but the token count will tip toward open weight”, Forrester said.