Taming the Enterprise AI token ogre – maxxing outcomes

Token Taming (noun)
Optimizing AI execution to minimize token consumption while maximizing outcome
Antonym: Tokenmaxxing

Boards are no longer asking what AI can do. They are asking what AI has done. The organizations that answer that question with real numbers are the ones that stop treating AI as a productivity hack and start treating context as infrastructure — the layer that makes every model, every agent, and every token count.

AI generalists stall at the enterprise edge

Frontier models are extraordinary generalists trained on the public internet. They know what an invoice is and how a supply chain works — generally. They do not know how a specific enterprise’s invoices relate to its shipping records, which exceptions employees routinely override, or why the Denmark warehouse is the right transfer point when a Texas distribution center spikes. That knowledge lives in ERP tables, emails, message threads, spreadsheets, and your people.

Without that operational context, agents do what generalists do — they guess. And in the enterprise, guessing is costly.

Experiment broadly, optimize purposefully

Deloitte’s 2026 work on AI tokenomics describes one healthcare enterprise where token usage grew 8–10% per month, translating into more than $6 million in unplanned annualized cost—before finance had visibility into what was driving it. News outlets have reported regularly on “tokenmaxxing” inside companies like Amazon and Meta, where engineers gamed internal leaderboards by routing trivial work to agents. Volume is not value.

Context makes a different posture possible: experiment broadly, optimize purposefully. Not every task needs a frontier model. When an agent grounded in the Celonis Context Model (CCM) knows how work flows, it can route the simple steps to less resource-intensive, faster models and reserve the expensive reasoning for the calls that genuinely require it.

And when an agent is used, Celonis exposes functions it can call directly — a manufacturing lead time, a process variant frequency, an exception rate. The agent consumes the answer and moves on. Without that, the agent has to ingest raw data, decide on a calculation path, and compute the result itself. Every one of those steps spends tokens. And every one of them carries a probability (no matter how small) of being wrong. When Enterprise AI is grounded in business context, pre-structured business logic replaces a chain of probabilistic guesses with a single deterministic call — eliminating wasted tokens and silent retries, and shifting AI from uncapped experimentation to a defensible line item the CFO can budget against.

Think about it like a navigation system giving you the optimal route to your destination—avoiding traffic and road construction. You get to your destination more quickly and use less fuel in the process. The total amount of fuel your tank can hold hasn’t changed, but you’ve used it more efficiently.

The Celonis Context Model is an asset that compounds

The CCM unifies process data, business knowledge, process and decision intelligence to ground Enterprise AI in operational reality and power its effective execution.

1. The process data is pulled from systems of record (ERP, CRM, ITSM, …), desktop-level interactions, and AI agents are mined to understand how work gets done as a sequence of events—steps, decisions, exceptions, and interdependencies.
2. Business knowledge (ontology, rules, benchmarks, and KPIs) helps it learn how things work and interact, and what makes something “good” or “bad” for the enterprise.
3. Process and Decision intelligence enable it to understand why things are happening, get to the root causes of breakdowns and bottlenecks, predict what is likely to happen, simulate different scenarios, and make recommendations for what should happen next.

The benefits of the Context Model get better over time: Every agent interaction, every human override, every exception feeds back into the Context Model. The result is an asset that gets more valuable with every deployment — each new agent is cheaper and more effective than the last because it inherits the operational understanding the previous ones helped build. That is the perfect recipe for optimal execution, not just effective, but also efficient.