Most energy and commodity trading firms are testing artificial intelligence (AI) right now. Many are running trials with large language models (LLMs). Others are building small automation projects. Almost none of them are getting real results.
The problem is not the AI. The problem is the data feeding it. LLMs need clean, structured, consistent data. Without this they become unusable. They break or give incorrect results when you feed them incomplete records, missing tags, or timestamps that do not match across systems. Feed an LLM incomplete data and it will fill in gaps with plausible-sounding garbage. Feed it conflicting timestamps and it cannot build an accurate trade history. Give it broken tags and it cannot categorize trades correctly. The model will still give you answers, but those answers will be wrong. You will not notice immediately because the errors are subtle i.e. a missing counterparty here, a rounded volume there, wrong risk calculations, incomplete surveillance alerts etc. By the time you notice, you have made decisions based on bad outputs.
Why AI struggles with E/CTRMs
Your E/CTRM was designed for trade capture and settlement. It was not designed to feed data to AI models in real time. Core systems still handle trade capture, settlements, invoicing, and daily operations. The issue is that they were never designed to feed AI models. They were built for operational workflows, not for real-time streaming data or LLM consumption.
The data is siloed across modules. Position data lives in one place, market data lives in another, counterparty information lives somewhere else. Each system uses different field names, different formats, different timestamps. Access is slow because teams have to export CSVs manually or wait for overnight batch processes. They query interfaces that were designed for humans, not machines. The data itself is inconsistent. One system rounds to two decimals while another uses four. Timestamps are in different time zones. Reference data does not sync across modules. If you try to let your AI model query the E/CTRM directly, you will watch system performance degrade.
Add enough AI queries and you will slow down trading operations. This is not theoretical. This is what firms are dealing with right now.
Moving towards a curated data layer
Every AI project starts the same way. Your team needs data from the E/CTRM, IT builds a custom integration, data engineers write transformation scripts then someone gets stuck maintaining the pipeline when it breaks. Then you start another AI project and need different data fields, so you build another integration. More transformation scripts, logic code buried in code, growing developer time, and more maintenance. Each project needs custom work because the E/CTRM was not designed to expose data this way. You end up building the same data layer over and over, project by project.
Meanwhile, your AI models are underperforming because the data quality is inconsistent. You are paying an integration tax on every single AI initiative.
This is why firms are now focusing on curated data layers. Without one, every AI project becomes a custom integration. Every model needs its own data pipeline. Every team solves the same problems repeatedly. This slows progress and increases cost. If the E/CTRM is not built for AI, the environment around it has to be.
The missing layer that makes AI work
What firms need is a data layer that sits between the E/CTRM and AI systems. Something that pulls data from across the trading stack, normalizes formats, resolves inconsistencies, and structures everything into a single coherent layer. Format mismatches get resolved. Different decimal precision, date formats, and field names get standardized. Missing fields get filled in from other sources or flagged for review, timing gaps get aligned, timestamps get converted to a single time zone with precision, and source inconsistencies get tracked so every field traces back to its source system. You know where data came from and can audit it. AI models skip the cleaning phase entirely and go straight to analysis, forecasting, and generating insights.
This is what BroadPeak’s Data Integration solution does. We extract data from your E/CTRM, execution platforms, and risk systems. We normalize everything into a single, structured format that AI systems can consume directly. Your data stays private because everything runs in your environment. Nothing gets sent to external AI platforms. Every field is traceable, which matters for compliance and model governance. It works with your existing systems because you do not rip out your E/CTRM or rewrite integrations. BroadPeak sits on top of what you already have and supports multiple AI workflows, including anomaly detection, forecasting, surveillance models, risk analysis, and workload automation. All built on the same clean data foundation.
Analysts adjust curves and settle prices without waiting on IT. Developers avoid constant maintenance when, for example, CME updates a contract specification and can focus on higher-value projects
Compliance, risk, trading and IT benefits
The impact varies by role. Compliance gets stronger auditability and faster investigations. When a regulator asks about a trade, you have clean data with full lineage back to the source. Risk gets clearer exposure views, cleaner position histories, and better scenario inputs that do not require manual cleanup before analysis. Trading gets less friction with the E/CTRM and faster access to trade context without waiting for slow exports or fighting with clunky interfaces. IT gets fewer downstream workarounds, less ETL maintenance, and fewer late-night calls about broken data pipelines.
Custom or pre-built data layer?
If your E/CTRM is not built for AI consumption, something has to bridge that gap. You can build it yourself by writing custom integrations for every AI project, maintaining transformation scripts, handling data quality issues as they come up, and building the same infrastructure over and over. Or you can use a purpose-built solution that handles normalization, enrichment, reconciliation, and real-time access out of the box
The question is not whether you need this layer. You need it if you want AI projects to work. The question is whether you build it or buy it.
Clean, structured, real-time data is the foundation for AI. Without it, your models will underperform, your teams will waste time cleaning data, and your AI projects will stall. BroadPeak gives you that foundation without rewriting your E/CTRM. Once data is normalized, timestamped, enriched, and reconciled, AI systems can actually answer questions, spot issues, and reduce manual work. Your competitors are solving this problem right now. The ones who figure out the data layer first will have AI that actually works.
Getting AI to work
The industry is not limited by AI models. It is limited by the data that feeds them. Energy and commodity trading firms that solve the data layer first will move faster with AI. They will run cleaner models, reduce manual work, and support decisions with reliable information. More importantly, they will stop wasting engineering resources on repetitive integration work and start focusing on the AI applications that actually matter for their business.
This is not about replacing existing systems or launching a multi-year modernization program. It is about adding a layer that makes your current infrastructure work with AI. The firms making progress right now are the ones that recognized this early. They stopped trying to make their E/CTRM do something it was never designed to do.
They built or bought a data layer that sits on top of their existing systems and feeds their AI initiatives with data that actually works.
BroadPeak makes that data layer work. It gives energy and commodity trading firms a foundation that supports AI across compliance, risk, trading, and IT without restructuring the entire stack. No rip-and-replace. No system downtime. No disruption to daily operations. Just a data layer that finally lets your AI projects deliver results.
