AI Adoption in Back Office Commodity Workflows

By Jonathan Alger, Head of Customer Success and Co-Founder, ClearDox

I recently had the opportunity to join Michelle Bruce, Partner at Optimus, in a discussion hosted by Irina Reitgriber, Affiliate Analyst at Commodity Technology Advisory, on CTRM Radio. Our topics were the role of artificial intelligence in back office operations and the growing significance of what we at ClearDox call “commodity intelligence” – data insights that identify errors, reduce risk, and reveal opportunities.

The conversation reinforced what many of us already sense: artificial intelligence is no longer a distant vision or executive buzzword. It is entering the mainstream of how commodity businesses manage complexity, protect revenue, and drive growth. Given this evolution, it’s inevitable that speculation about the potential of the technology gives way to more prosaic questions about implementation, compliance, team adaptation, and business objectives.

In this post, I’ll share some of the key points from our discussion and give you my takeaways on what they mean for commodity business that are thinking about AI.

The AI Reality Check

Across the commodities industry, AI adoption is uneven. Some companies are deep into implementation, using machine learning and agentic automation to streamline workflows and gain data insights that would previously have been impractical or unaffordable. Others are just starting to explore what AI could mean for operational processes and the potential business impact.

Michelle described how Optimus is helping its clients gather, normalize and analyze large amounts of data and turn it into actionable intelligence. For an industry that has long struggled with consistency in how it defines, stores and shares information (and where physical, even handwritten documents remain common), overcoming the data challenge is foundational. There needs to be certainty of clean and complete data for AI to deliver tangible value, not just theoretical promise.

We also discussed a cultural dimension that is often overlooked. Many prospective employees expect their future employers to use AI in meaningful ways. As Michelle put it, companies that aren’t exploring AI risk being perceived as less innovative, which can make attracting top talent more difficult.

The Compliance Question

Clients not only ask me about what AI can do, but also how to use it safely: “Can I put this document into this platform? Does that violate our AI compliance policy?”

It’s a fair concern. Today, more firms understand that they need to establish internal governance for AI, but it’s important that this isn’t limited to compliance officials. The teams that use AI need to know that the technology aligns with their organizational and career goals, as well as corporate and regulatory standards.

At ClearDox, we address this by embedding AI directly into familiar workflows that clients already trust. Instead of asking users to paste sensitive data into a generic model, we integrate AI into specific trade-lifecycle applications where data is controlled and auditable. This approach allows companies to benefit from AI while maintaining transparency, compliance and data security.

Protecting Revenue

The front office has always been seen as the engine of profit, casting the back office and operations into the contrasting position as cost centers. That perception is changing.

Dare I say that more enlightened firms now recognize that while the front office generates revenue, it’s the back and middle offices that collect and protect it. Missed payments, duplicate invoices and slow reconciliations quietly chip away at margins. Misaligned contract terms, discrepancies in bills of lading and missing records can be amplified into downstream risks and financial losses.

By applying AI to core back office tasks, such as trade confirmations and payment processing, companies are uncovering significant opportunities to reduce risk and preserve value. Data and AI make it easier to see the interconnected reality of how revenue flows.

Empowering People

Another theme that emerged from our discussion was how AI affects employee experiences and leadership expectations of work. Michelle put it well when she said that AI “allows employees to be more analytical instead of just button-pushers.”

We’re seeing this transformation firsthand. In one project, we helped a client integrate AI with their electronic data interchange process (EDI) for gas nominations and actualizations. The system automatically reconciled data and provided near real-time visibility into their positions. As a result, they could close their books faster and spend more time analyzing trends instead of fixing mismatches. It’s a prime example of how AI can make work more rewarding and profitable.

Build or Buy: Getting to Value Faster

Any organization ready to start with AI in earnest faces the question: should we build our own software or buy something already proven?

My advice was the same as Michelle’s: if the use case is the same as your competitive advantage, it might make sense to build. But if it involves a standard process – like invoice reconciliation, document validation, or position tracking – you’ll likely reach value faster by partnering with a vendor that has solved the problem many times before.

For example, we’ve implemented AI-powered invoice reconciliation for several clients trading natural gas. Because we’ve already worked with the same vendors and counterparties across the industry, our solution can go live in two to three weeks instead of the two to three months an in-house project might require.

There’s also the ongoing maintenance factor. AI models evolve every few months. Keeping up requires data science expertise that many firm simply don’t have.

The Key to AI Adoption: Be Intentional

AI is here to stay, it’s changing the way commodity operations work, and expanding the potential for our industry in the process.

The most successful AI adopters typically approach in the same way as they would with any business software decision:

• Clearly define the problem you want to solve.
• Identify what success looks like.
• Determine whether a proven solution already exists that can meet your goals faster and with less risk.

As I said during the discussion, “Think first about the business use case, then decide if there’s a solution already available that can shorten your time to market.”

AI is only now becoming mainstream. But if you’ve not yet started experimenting in at least one area, you are at risk of becoming less competitive than more innovative firms.

Whatever your level of maturity, the technology will continue to evolve. What matters is how we align that evolution with real business outcomes: faster decisions, reduced risk, and better use of human expertise.