The "tokenmaxxing" trend has drawn scrutiny from auditors and risk officers, who are now conducting deeper reviews of how artificial intelligence tokens are consumed, not just how many are used.
As enterprises grapple with mounting AI bill shocks, they are increasingly tracking metrics such as cost per workflow, cost per transaction, cost per document reviewed, cost per customer query resolved, cost per software change delivered and productivity gains per process, said Ashvin Vellody, chief strategy and innovation officer, Consulting, at Deloitte South Asia.
“The stronger organisations are also putting in place usage dashboards, model-selection guidelines, consumption thresholds, approval mechanisms for high-cost models, and governance forums to assess whether AI usage is delivering measurable value,” he added.
Companies are drowning in token bills due to heavy spending on AI tools without clear returns, success metrics, or guardrails. Goldman Sachs has forecast global token usage could surge 24 times to 120 quadrillion tokens per month between 2026 and 2030. Cheaper tokens may not necessarily translate into lower AI bills, Gartner says. A recent report by brokerage firm Jefferies noted that “AI for now is costing more money than it is saving”.
At the same time, large IT services firms are under growing pressure to demonstrate measurable business outcomes rather than simply reporting productivity improvements.
To save token costs, HCLTech said it is shifting workloads to sovereign models. “We have AI offerings that leverage sovereign models and GPUs, as well as non-GPU inferencing, to reduce dependency on high-cost external compute,” said chief technology officer Vijay Guntur.
Tech Mahindra, meanwhile, is deploying smaller open-source models that can deliver comparable business outcomes for clients at a lower operating cost. Some clients are consolidating tools and platforms to improve governance and cost control, said Nikhil Malhotra, the company’s chief innovation officer. It has also invested in building its own AI models suited to enterprise needs.
“Auditability is emerging as a critical component of enterprise AI adoption,” said Chandrashekar Mantha, partner-Assurance at Deloitte India. “Large AI programmes are typically reviewed weekly or fortnightly, while smaller projects may be tracked daily to prevent budget overruns.”
The process begins with rigorous testing of models for vulnerabilities such as hallucinations, accuracy, bias, etc., and followed by performance and financial audits to assess whether expected outcomes are being achieved against predefined KPIs (key performance indicators) and reported to steering committees and boards, Mantha said.
“Heavy users are now beginning to track the RoI (return on investment) on AI,” said Sunil Bhadu, leader, risk consulting at PwC India. “Some companies are becoming much more rigorous about auditing AI usage, actively tracking where maximum impact is being felt.”
According to experts, not every use case needs the most advanced frontier model. Hence, companies are routing complex tasks to advanced systems while assigning routine steps to smaller, lower-cost models to optimise token spend.
Deloitte’s Vellody said: “AI bill shocks are like an unmanaged electricity bill. If every team uses the most expensive model for every task, it is like running air conditioners everywhere even when a fan would do.”
He explained that a poorly designed AI workflow can become more expensive than human effort, especially if it requires repeated model calls, large context windows or manual rework. “This is where token consumption has to be linked directly to expected cost benefit,” he said.
