From AI Hype to Operational Credibility - Part 2
By Hariprasad Sudharshan
Welcome to From AI Hype to Operational Credibility, a five-part series that explores 1Finity’s targeted, deterministic approach for using Artificial Intelligence (AI) to manage optical networks. In our first installment, we discussed the rapid adoption of AI among optical networks and how the overuse of AI can create more issues than it solves. Many of the problems stem from the fact that the non-deterministic nature of AI creates a black box, where critical decisions and the logic behind them are obscured from network engineers and managers. This creates a domino effect in which operational credibility erodes, the operational spine is disrupted and those in charge begin to question their AI adoption strategy.
In this blog, we define specific areas where AI is particularly suited to help network operators optimize automation without losing visibility and control of how and why decisions are made.
The Deterministic Workflow Gap
The overwhelming majority of today’s successful networks rely on deterministic workflows where steps, sequences, and outcomes are precisely defined. Examples include alarm verification, context assembly, service impact mapping, protection path evaluation, as well as change and reason for outage logging. These deterministic components are both measurable and visible to allow network operations teams to confirm that the customer’s Service Level Agreements (SLAs) are being met.
While a deterministic workflow executes a predictable path with identical inputs yielding identical outputs, often the next step in an automation workflow cannot be decided by rigid “if-then” code alone. This is known as a judgment point. Judgment points act as airlocks to protect the network from catastrophic, unguided automated changes. They are created where conditions may be ambiguous, novel or require weighing tradeoffs that cannot be reduced to rules.
AI at the Judgment Point
In mission-critical systems, judgment points are where the probabilistic nature of production AI adds tremendous value. AI does not replace the workflow, instead it is deployed at the judgment point where it uses detection, referencing, and inferencing to assemble context based on flag patterns, highlight precedents, and present options with visible tradeoffs. The human operator still decides but AI helps to make decisions much faster and with more information.
The judgment point is a perfect example of how AI’s probabilistic capabilities can be used selectively to improve deterministic decision-making. The two don’t compete, they leverage their unique strengths to complement each other while providing the ultimate decision maker, the engineer or manager, visibility and control.
Deterministic & Probabilistic Working Together
When selective AI automation is used within deterministic workflows, it reframes what smarter AI means for networks. It doesn’t mean AI takes over more of the workflow. Rather it involves AI getting better at the specific judgment points it serves with more relevant context, clearer confidence, better-surfaced precedents, and more transparent reasoning. The deterministic parts remain deterministic.
It is critical to note that transparency is absolutely critical to building trust in how AI is being used. Building confidence in the system has less to do with whether technology is always right, but to the degree those involved know where AI is being invoked and why while everywhere else, the system is doing exactly what it’s designed to do, every time and in the same way. In other words, it is the polar opposite of asking those around you to take a leap of faith into a black box.
Start Small, Grow with Intention
The key to building trust in AI starts small by focusing on carefully select judgment points, then expanding only as trust accrues. This measured and planned approach keeps your AI adoption strategy manageable and reduces risk while delivering incremental improvement in workflows and outcomes.
What exactly might the growth model look like? Look for the next installment in this blog series where we discuss mapping workflows, isolating judgment points, and further automating deterministic steps. Until then, don’t hesitate to contact 1Finity with any questions.
Trust isn’t a feature you turn on. Stay tuned for Part 3, where we’ll explore how operators can systematically build confidence in AI through a deliberate, incremental adoption model.
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