The Rise of Internal AI Systems
2 Min Read
Let’s be honest. If you go a week without hearing the word “AI,” you’ve probably misplaced your phone or finally took that off-grid silent retreat.
The AI landscape, just keeps seeming to gain momentum, and while the general use case at the moment is LLMs (Large Language Models) due to their accessibility by the mass market and the fact that I can brainlessly type “do my homework assignment for me” - these only begin to scratch the surface of the AI systems out their in business and other use cases.
So, what happens when AI becomes a standard component of every business, as much as you see Property, Plant, and Equipment (PPE) on every balance sheet?
That’s what we’re looking at today.
AI Systems Taking The Forefront
While there’s basically an ocean of AI systems in play, some of the most viable business use cases at the moment are systems such as:
AI Trading & Algorithmic Investment Systems: At an enterprise level, these are among the most sophisticated and high-stakes AI deployments in existence. They encompass high-frequency trading (HFT) algorithms that execute in microseconds, sentiment analysis engines that parse news and social signals, quantitative portfolio optimisation models, and risk management systems that monitor exposure in real time. Major banks, hedge funds, and asset managers rely on these systems for market-making, arbitrage, execution optimisation, and alpha generation. Firms like Renaissance Technologies, Two Sigma, and Citadel have built entire business models around this category, and traditional institutions like Goldman Sachs and JPMorgan have invested billions in similar capabilities.
AI-Driven Analytics & Decision Support These systems ingest structured business data and surface insights, forecasts, and recommendations covering demand forecasting, financial modeling, risk scoring, churn prediction, and operational optimisation. Embedded in CRMs, ERPs, and BI layers, they shift focus from reporting what happened to predicting what will happen and recommending action.
AI-Powered Search & Knowledge Management These systems help employees find information across vast organizational data sources (documents, emails, wikis, databases) using semantic search and retrieval-augmented generation (RAG). They replace traditional keyword search with context-aware answers drawn from internal knowledge bases.
How Do We Recognise These From The Accounting Lens
In essence, an AI system is like any other software we’d develop or purchase for business and more specifically accounting purposes.
What’s more important is looking at how the business intends to use that AI system.
SaaS (Software as a Service): The AI system would likely be recognised as an internally generated intangible asset under IAS 38, and then any subsequent revenue would be recognised as a service contract and be bound by the guidance under IFRS 15 with regards to service contracts and the way revenue is recognised as performance obligations are .
Sold As Inventory: If the company were to develop an AI system to be sold as a whole, i.e. sell the fundamental code and foundational design behind it, it would most likely be recorded as Inventory under IAS 2 and then revenue recognised at a point in time under IFRS 15.
Solely Used Internally: This is the easy one, if you’ve gone about developing an AI system to be used internally in your company, and don’t intend in selling it, it’ll sit in intangible assets under IAS 38.
The Strategic Lens
What I think is more important as aspiring chartered accountants is knowing how to refine business processes by implementing AI where it actually makes sense.
There is a fine balance here. You do not want to implement AI just for the sake of it. The goal is to inform decision-making rather than removing thinking from processes that fundamentally require a human brain. Think of AI as your high-speed calculator. It is brilliant at the math, but you still need to decide if the answer actually makes sense.
Because at the end of the day, AI is trained on some input somehow, somewhere, someway, and if you blindly accept its output you overlook any inherent flaws in the AI’s training.
The Bottom Line
AI is moving from a "tech trend" to a line item on the financial statements. Whether it sits in Intangibles or Inventory, our job is to ensure the reporting reflects the economic reality. We should use the technology to handle the data so we can focus on the strategy and underlying thinking.
Until next week,
The Journal Entry Team
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