Let Us Prepare for AI by Improving Data Quality and Infrastructure

Mohamed Al-Shater Al-Amin Al-Hassan
We often talk about the power of Generative AI (GenAI) to cut costs and speed up decision-making. Yet we tend to overlook the more important question: are we actually ready for it?
In cost accounting systems, the challenge is no longer about having the latest AI model. The real questions are: Is our data standardised? Can our infrastructure cope? Can we trust the outputs? Those who can answer these questions today will be the ones who reap the benefits of AI tomorrow.
In cost accounting, GenAI is not magic. It is a mirror reflecting the quality of the information with which it is fed. When the necessary data and infrastructure are in place, its impact becomes clearly visible across four areas:
1. Faster, less costly decisions
It can analyse input prices, energy costs and exchange rates in real time, and generate future cost scenarios. Analysis that once required a week can now be completed within hours. This alone reduces the cost of time lost to meetings and delays.
2. Deeper understanding of variances
Instead of simply reporting a figure such as a “15% variance”, the system can explain the reason: “The increase was caused by supplier X during period Y.” This interpretation makes decision-making easier for management and can reduce errors in judgement by more than 25%.
3. Direct operational savings
Automation has reduced the time required to prepare periodic reports from several days to fewer than two days. Estimates indicate potential savings of 15% to 30% in overall costs, including a 40% reduction in report preparation costs.
4. Oversight without gaps
The system can immediately detect anomalies in invoices and cost items. Early detection means preventing waste and losses before they accumulate.
In short, AI does not reduce costs simply because it is intelligent; it reduces them because it enables decisions based on accurate, timely information.
The Greatest Obstacle: Data Quality Before Model Quality
This is where successful implementations distinguish themselves from those that rush ahead and stumble. Field studies have consistently identified inadequate data readiness and infrastructure as the number-one obstacle.
Generative AI requires:
Clean, standardised data: A single repository for cost data, free from duplication and inconsistencies.
Stable technological infrastructure: Servers, networks and cybersecurity capable of protecting the confidentiality of cost data.
Models that understand the local context: Models that support Arabic and understand the characteristics of the local market and industry.
Governance and accountability: An audit trail for every recommendation, while keeping a “human in the loop” for final approval.
Without these four layers, you may get answers quickly—but they may be wrong. And the reality is that the resulting cost could be far greater than the cost of waiting.
Conclusion: Readiness Begins with the Foundations, Not the Interface
Generative AI will reshape cost accounting. It will transform the accountant from a data-entry professional into a strategic analyst, and turn the report from a historical document into a compass for the future.
But the journey does not begin with purchasing software. It begins with improving data quality and building the necessary infrastructure.
The companies and institutions investing today in cleaning their data and securing their infrastructure will be the ones able to use AI tomorrow as a lever for competitive advantage.
Those who rush to buy the tool before preparing the workshop, however, will discover too late that the most expensive cost of all is the cost of a decision made by artificial intelligence using poor-quality data.

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