Where do decisions live?
The core problem: SKT, Deutsche Telekom, KDDI, and T-Mobile are all building AI-native telcos. But becoming AI-native raises a question most haven’t answered: where do decisions live? Decisions in humans are slow, decisions in code are fragmented, and decisions at inference time hallucinate at scale. An operational ontology—like the Totogi Ontology—makes decisions “deterministic,” i.e. correct by construction. With an ontology handling decisions securely, you can let inference handle everything else.
In the age of AI, where decisions get made is changing. Telcos are moving them out of human hands and code, and into models and agents. But is that the right place?
This is a question for your board, not your IT department. Because where decisions live determines how badly things break, how fast you can move, whether AI acts on your behalf, who controls your business logic, and whether your organization compounds knowledge or loses it. It determines if you are AI-native, or AI-abled.
Decisions inside enterprises can live in four possible places: in humans, in code, in inference, and in an ontology. You’ve been in the first two for decades. You’re considering the third. But I’m going to make the case for the fourth: an ontology. Here’s why.
1. Minimize the impact of wrong decisions.
When a human customer service agent makes a wrong decision, it affects one customer. When code is wrong, it’s worse. A miscoded eligibility rule would apply the wrong logic to every subscriber who hits it, silently, for months, until someone notices. That’s a slow, concealed blast radius. Unfortunate, but manageable.
When an AI model infers a wrong decision at runtime and executes it autonomously, it affects thousands of accounts in seconds. That’s a huge blast radius. The agent queries your BSS APIs, gets data back in three different schemas with three different definitions of “customer,” resolves the contradictions, and picks an action. Every step is probabilistic. Every step can hallucinate. And the impact of a wrong inference on an operational decision—misbilling, misprovisioning, a wrong offer applied across thousands of accounts—is orders of magnitude worse than anything that came before. It’s the automation of mistakes at scale.
With the Totogi Ontology, the decision that provisions a service, applies a credit, or changes a billing record never touches inference. Business rules, eligibility constraints, valid state transitions are deterministic—which means they’re known, correct by construction. Decisions are defined. With an ontology, the blast radius is zero, because it literally cannot execute wrong decisions. They’re architecturally impossible.
2. Increase your agility.
Encoding decisions in software takes six months. Decisions coordinated by humans take two to four weeks to execute. And decisions in inference? When your business rules change, the model doesn’t know. It was trained or prompted on the old rules. There’s no single place to update. You just retrain, reprompt, and hope the model picks up the change. None is compatible with an AI-native telco.
When decisions live in the Totogi Ontology, they’re updated once and reflected everywhere instantly. A new eligibility rule, a changed margin constraint, a modified state transition: update the ontology and every agent, every workflow, every system that touches that decision sees it immediately. One update happens everywhere, immediately.
TM Forum’s survey of 110 operators across 72 companies found that 95% believe intent-based operations is the future but 58% say they lack the stack to get there. Your BSS/OSS stack is the bottleneck—not the network. Decisions are trapped in code and humans that can’t move at the speed AI demands.
3. Act quickly, every time.
The real cost of decisions living in the wrong place is your AI becomes a recommendation engine. It generates an insight. A dashboard displays it. Nobody acts on it—or five teams spend a month acting on it manually and by then the subscriber has churned. You haven’t transformed your telco; you’ve built a(nother) recommendation engine everyone might not follow.
Telcos have scaled only 26% of predefined AI use cases despite having more data than most industries. The other 74% is trapped, because decisions live in places AI can’t reach.
When decisions live in the Totogi Ontology, the insight-to-action gap collapses. The model identifies churn risk, the ontology resolves what actions are valid for that subscriber, and the system executes in seconds, not weeks. That’s the difference between AI that advises and AI that operates.
And it changes what you can build. A retention offer engine that used to take six months of requirements gathering, data mapping, and integration development? That can turn into describing what you want in natural language and the ontology already knows what those concepts mean, how they relate, and which systems to orchestrate. The backlog of “someday” becomes something that can be done “today.”
4. Take ownership of your business logic.
If decisions live in vendor code, the vendor controls your business logic. If decisions live in consultant knowledge, the consultants are irreplaceable. Right now, most Tier-1 telcos are paying both.
And if decisions live in inference, you can’t explain them. If a regulator asks why that offer was applied to that subscriber, all you can say is that the model inferred it. There’s no audit trail, no version history, no logic you can point to. Try explaining a probabilistic decision to a compliance team.
When decisions live in the Totogi Ontology, you control the business logic. Every decision has a clear audit trail: this rule, this constraint, this state transition, applied to this subscriber, at this time. Plus, swapping a vendor becomes a configuration change, not a three-year program.
5. Compound organizational knowledge.
When decisions live in humans, you create tribal knowledge that walks out the door. Decisions in code fossilize. Decisions in AI inference reset to zero with every query, because the model doesn’t learn from last Tuesday’s retention offer.
The Totogi Ontology compounds. Every action enriches it. Edge cases reveal gaps in entity definitions. Success and failure data improves decision logic. Invalid attempts expose missing business rules. Every decision makes the next decision better. Every exception becomes a searchable precedent instead of a Slack thread that disappears. The Totogi Ontology is a living system.
Wait, I thought inference was awesome?
Given all this, why are vendors pushing you to put decisions in inference? Because it keeps your business logic trapped. When decisions live in the model, you still need their consultants to retrain, reprompt, and reintegrate every time a business rule changes. Amdocs generated 66% of its revenue from managed services in 2025. Those consultants exist because your systems don’t speak the same language—by design. An operational ontology like the Totogi Ontology makes that translation layer unnecessary. They will never recommend that.
So where should you use inference? Everywhere else. Generating the communication to the subscriber. Identifying churn risk. Recognizing anomalies. Ranking the best option among the ones the ontology already validated. Creating new workflows from natural language requirements.
Inference is powerful. You want it working hard across your business. You just don’t want it making the decisions that provision, bill, or change a subscriber’s account. The Totogi Ontology constrains the decision space. Inference supports it.
When we show operators the Totogi Ontology—the actual model of their entities, constraints, and valid state transitions—they instantly get it. It stops being a concept and becomes the control system they want to run their business.
Come find us at DTW. We’ll show you. When you see it, I know you’ll get it too.
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Frequently Asked Questions
Deterministic decisions are correct by construction. They can’t be wrong because they’re architecturally defined at the outset, not inferred in real-time. When a business rule lives in the Totogi Ontology, the system doesn’t guess whether a subscriber is eligible for a credit or a state transition is valid. It knows. Inference is probabilistic, so it’s brilliant at pattern recognition, but every step can hallucinate. You don’t want probabilistic decisions touching provisioning, billing, or subscriber accounts. That’s how you automate mistakes at scale. Deterministic decisions give you a control system. Inference gives you a co-pilot. You need both, and they need to be in the right seats.
Because there’s no single place to update. When your rules live in an LLM, whether through training or prompting, changing them means re-training, re-prompting, and hoping the model picks it up correctly everywhere it matters. That’s not agility. That’s prayer. When decisions live in the Totogi Ontology, you update once and the change propagates instantly across every agent, every workflow, every system that touches that decision. That’s what it actually means to move at the speed AI demands. The BSS vendors pushing you toward inference-based decisions will never tell you this, because the update cycle is what keeps their consultants racking up those billable hours.
It’s enormous, and that’s the problem nobody talks about. When a human customer service agent makes a wrong decision, it affects one customer. A wrong rule in code silently hits everyone who triggers it. But a wrong inference executed autonomously by an AI agent? We’re talking about thousands of accounts, misbilled or misprovisioned, in seconds. Every step the agent takes—querying APIs, resolving conflicting data definitions, picking an action—is probabilistic. That means every step can go wrong. And the agent doesn’t stop to ask. With the Totogi Ontology constraining decisions, that blast radius collapses to zero, because invalid actions are architecturally impossible. The ontology simply doesn’t allow them.
Everywhere that isn’t provisioning, billing, or changing a subscriber’s account, which still delivers a huge amount of value. Use inference to identify churn risk, generate personalized subscriber communications, detect anomalies, rank the best offers among the ones the ontology already validated, and translate natural language requirements into new workflows. Inference is powerful. You absolutely want it working hard across your business. You just don’t want it holding the keys to your operational decisions. The Totogi Ontology constrains decisions, exactly directing what the decision will be. That’s the architecture of an AI-native telco. Inference isn’t bad. You just want to make sure it’s doing what it’s good at.
Right now, most Tier-1 telcos don’t actually own their business logic. It lives in vendor code, which means the vendor controls it, or in consultant knowledge, which means the consultants are irreplaceable. Amdocs generated 66% of its revenue from managed services in 2025. That’s not a coincidence. When decisions live in the Totogi Ontology, you own them. Every rule, every constraint, every valid state transition is explicit, auditable, and version-controlled. When a regulator asks why that offer was applied to that subscriber, you can point to the exact logic—not shrug and say the model inferred it. Swapping a vendor underneath becomes a configuration change, not a three-year program. That’s what it means to actually control your business.