Artificial intelligence has become the corporate obsession of 2025. From banks to builders, few boardrooms escape the refrain: “Where is our AI project?” The urgency is palpable, with senior executives increasingly equating AI adoption with competitiveness, innovation and investor confidence.
Yet, for the IT managers tasked with delivering these initiatives, the reality is less straightforward. They are caught between the board’s enthusiasm and the sober practicalities of governance, compliance and long-term integration.
The temptation is to rush into a high-profile pilot, perhaps a chatbot or generative AI assistant, simply to satisfy executive pressure. The risk is that such projects often lack clear return on investment, and may expose organisations to reputational or regulatory risk.
However, a more measured route is beginning to emerge. Companies are increasingly turning to accounts payable (AP) automation, powered by intelligent document processing (IDP), as a controlled but credible way of introducing AI into daily operations.
The Economics of Invoice Automation
The business case for automating AP processes is well established. Analysts estimate that ROI frequently exceeds 200 per cent, driven by reductions in processing costs, cycle times and error rates. Industry surveys suggest:
• The cost per invoice can fall from €10 to €13 when manually entered to as little as €3 to €4 when automated.
• Processing times shrink by as much as 80%, with approval cycles reduced from weeks to days.
• Error rates decline sharply, from nearly 40% in manual entry systems to under 1% with automation.
• Labour savings are significant, with AP teams freeing up as much as 40% of their time for higher-value work.
Intangible benefits add further weight to the benefits of accounts payable automation. Automated systems create reliable audit trails, reduce the risk of fraud, and improve supplier relations by accelerating approvals and enabling early-payment discounts. In an era where liquidity and working capital are under renewed scrutiny, such gains carry strategic as well as operational value.
A Case in Point: Graham, Sysco and Montgomery
Case studies from Inpute underline the practical impact.
• Graham, the construction and facilities management group, processes more than 13,000 invoices each month. By implementing an intelligent capture system, the company was able to eliminate manual handling, an efficiency gain that would be difficult to replicate by any other means.
• Montgomery Transport Group, a major logistics operator, cut invoice processing time by 50%. More tellingly, it freed up resources equivalent to 2.5 full-time staff, who could then be redeployed to value-added activities.
• Sysco, the food distribution giant, reported that automation “made life a lot easier” across its AP function, reducing the time and cost of managing a high volume of supplier invoices.
Such examples demonstrate that AI in the AP context is not speculative but applied: measurable savings, reduced bottlenecks, and improved control.
Why Accounts Payable Automation Appeals to IT Leaders
For IT departments, AP automation offers a series of advantages compared with more experimental AI projects.
First, it addresses a clear business problem. Invoices arrive in multiple formats, from paper to PDF to electronic data. Traditional optical character recognition (OCR) solutions struggle with this variability, requiring templates and frequent manual intervention. IDP systems, by contrast, employ machine learning and natural language processing to classify documents, extract fields, and validate data against finance systems, all while learning and improving over time.
Second, it is governable. Unlike generative AI deployments, which raise thorny questions about data leakage or hallucinated outputs, AP automation operates within well-defined boundaries. It is easier to enforce compliance policies, ensure GDPR alignment, and provide audit-ready trails.
Third, the ROI is transparent. Whereas pilots in areas such as customer service often struggle to quantify value beyond soft measures, AP automation translates directly into cost savings, headcount efficiencies and improved supplier relationships.
Finally, it lays the groundwork for broader adoption. By introducing AI incrementally in a controlled process, IT leaders can develop governance frameworks, integration patterns and user familiarity before deploying AI more widely across the enterprise.
How the Technology Works
The AI embedded in IDP should not be confused with science fiction. It is targeted, task-specific and pragmatic. In practice, it manifests in four areas:
1. Classification
Recognising whether a document is an invoice, purchase order, or credit note, without templates.
2. Extraction
Capturing key fields such as supplier name, invoice number and VAT amount across varied formats.
3. Validation
Cross-checking extracted values against ERP or master data, and flagging anomalies.
4. Continuous learning
Improving accuracy each time a correction is made by a user.
In other words, it is AI in service of a business outcome. Not AI for its own sake.
Balancing Pressure and Prudence
The pressure on IT leaders to deliver AI projects will not ease. Boards and chief executives want demonstrable progress, investors want evidence of modernisation, and staff are eager to see routine tasks reduced. But the risks of ill-considered projects are equally real: from data breaches to regulatory penalties to reputational damage when pilots fail to deliver.
AP automation, underpinned by IDP, provides a pragmatic compromise. It demonstrates that AI can be introduced responsibly, with quantifiable benefit, while laying the groundwork for more ambitious deployments.
The Inpute Approach
Inpute has positioned itself at the intersection of these pressures. With more than 25 years’ experience in intelligent document processing, the firm focuses on delivering AP automation solutions that are:
• Strategically aligned, identifying processes where automation delivers the greatest impact.
• Secure and compliant, built around ISO standards and GDPR requirements.
• System-agnostic, integrating with SAP, Oracle, Microsoft Dynamics, IFS and Exchequer.
• Continuously optimised, with AI models that learn from human corrections to improve accuracy over time.
For clients such as Graham, Sysco and Montgomery, the results speak for themselves. For IT leaders under pressure to “do AI,” these implementations offer proof that AI can be harnessed safely, responsibly and profitably.
Artificial intelligence may be the corporate imperative of the moment, but pragmatism remains a virtue. By focusing on AP automation, companies can satisfy executive demand for innovation while delivering tangible ROI. More importantly, they can establish the governance, integration and cultural foundations necessary for broader AI adoption.
In the rush to “do AI,” those who start with intelligent document processing are likely to be among the few who can point to results rather than experiments when the board asks for evidence.
