Modern Expert Systems, Not AI, Are What Most Orgs Actually Need

Though my IT career I have worked everything from SS7 and SIP to DSL, GPON, CMTS, Linux, databases, and large scale distributed systems. I am currently building an internal platform we have named Helix. It is a system designed to correlate operational data across an entire telecom network, from signaling edges to physical access layers.

I am saying that up front for one reason. This perspective did not come from reading marketing decks or experimenting with chatbots. It came from years of being the person who can dig into the internals of any system and extract truth from the data, no matter how fractured it is.

Right now, everyone thinks they need AI.

They do not. At least not in the generalized, large language model sense that dominates headlines.

What most organizations actually need is a modern expert system.

And those are very different things.

A short history of expert systems and why they failed

Expert systems are not new. They peaked in popularity in the 1980s and early 1990s. The idea was simple and ambitious. Capture the knowledge of human experts as rules and let computers reason over them.

If condition A and condition B are true, then conclusion C follows.

In theory, this would preserve expertise, reduce dependency on a few senior people, and allow faster decisions. In practice, most expert systems failed.

Not because the idea was wrong, but because the assumptions were.

They assumed experts could fully explain how they think. They cannot. Most real expertise is situational and contextual. It emerges while solving the problem, not beforehand.

They assumed rules would remain stable. They do not. Networks change, software changes, vendors change, humans make mistakes, and edge cases become the norm.

They assumed reasoning could happen without deep, rich context. It cannot. The systems had thin inputs and brittle logic, so when reality deviated even slightly, the system collapsed.

As a result, expert systems became rigid, expensive, and fragile. They froze knowledge in time and failed to keep up with the environments they were meant to support.

If this all sounds familiar, it should. Expert systems were taken seriously enough in the 1980s to warrant mainstream coverage. There is a Computer Chronicles episode that walks through the promise and limitations of early expert systems, and it is worth watching with modern eyes. The ideas were not naive. The tooling and data simply were.

So the industry moved on.

What changed, and why the idea deserves resurrection

What we have now would have been unimaginable when those systems were built.

We have cheap storage, fast search, and massive compute. We can retain raw operational exhaust instead of summaries. Logs, events, metrics, state changes, and historical traces can all live side by side.

We also understand systems better now. We know that truth in complex environments is rarely found in a single signal. It emerges from correlation across layers.

Most importantly, we no longer need to pretend the computer is the expert.

That is the key shift.

A modern expert system does not try to replace human judgment. It preserves context, memory, and causality so that human experts can do what they do best.

Think less rule engine, more institutional memory.

What a modern expert system actually is

A modern expert system is not a chatbot. It does not guess. It does not hallucinate. It does not generate answers because answers are rarely the problem.

The problem is that the evidence is scattered.

A modern expert system collects, retains, and correlates evidence across domains. It understands time. It understands sequence. It understands that SS7 signaling, SIP dialogs, application server logic, access network behavior, and physical infrastructure failures are all part of the same story.

In the system I am building, Redis functions like short term memory. Relational databases store structured facts. Search engines preserve long term narrative history. That architecture is not accidental. It mirrors how humans actually think under pressure.

The system does not say, here is the answer.

It says, here is everything that happened, in order, across layers, with enough fidelity that the answer becomes obvious to someone who understands the domain.

That is an expert system.

Why most organizations think they want AI

AI sounds powerful. It sounds modern. It sounds like progress.

But when you look closely at most enterprise use cases, what people are really asking for is not intelligence. They are asking for visibility, correlation, and memory.

They want to know why something broke.
They want to know what changed.
They want to know whether this has happened before.
They want to know the blast radius.
They want to know what matters and what does not.

Those are not language problems. They are context problems.

Dropping a generalized LLM on top of fragmented, low quality, uncorrelated data does not solve that. It just adds another opaque layer between the operator and reality.

In many cases, AI is being used because it sounds cooler than expert system.

The uncomfortable truth

If you tune an expert system deeply to your environment, your network, your workflows, and your failure modes, it will outperform a generalized AI every time for operational decision making.

Not because it is smarter.

Because it is grounded.

It understands your topology, your vendors, your history, your weird edge cases, and your human processes. It does not need to reason abstractly about the world. It needs to remember what actually happened last Tuesday at 3:17 AM.

That is where expertise lives.

So what is Helix, really

Helix is a modern expert system for telecom operations.

It does not automate decisions. It removes fog.

It does not predict. It reconstructs.

It does not replace humans. It keeps them sane.

And this is the part that matters most to me personally. It reduces the need for heroics. It turns hard won experience into durable infrastructure instead of tribal knowledge locked inside a few burned out people.

Final thought

AI is not evil. Large language models are impressive tools when used for the right problems.

But most organizations chasing AI are skipping a step.

Before you ask a machine to think for you, you should make sure it can remember for you.

For many real world systems, especially infrastructure, networks, and operations, the future is not artificial intelligence.

It is modern expert systems, rebuilt with humility, context, and the power we finally have to make them work.

--Sometimes Thinking Ahead of the Curve Means Looking Behind the Curve
-Bryan