By Lyle Malander, CEO & Founder | Malander Advisory
Published: June 4, 2026

AI in Finance

AI in finance is no longer limited to future-focused discussions. Finance teams are already using AI to automate invoice processing, improve forecasting, speed up reporting, support variance analysis and create more time for strategic decision-making. For larger organisations, the real opportunity lies in applying AI with clear governance, strong data controls and a practical implementation roadmap.

What we’re seeing, and where it’s actually making an impact

Over the past 18–24 months, one topic has come up in almost every client conversation: AI.

Not in a theoretical sense, but in a very practical one.

Where does it fit? What does it replace? And more importantly, what actually works?

At Malander Advisory, we’ve been closely tracking how AI is being adopted across finance teams in South Africa, the UK, and the US. And what we’re seeing is a clear shift: AI is no longer experimental. It’s becoming part of day-to-day financial operations.

Where is AI already being used in finance functions?

The perception is often that AI is complex or futuristic. In reality, most adoption is happening in very practical areas.

Across our client base and global benchmarks, we’re seeing AI used in:

• Invoice and transaction processing 
• Accounts payable and receivable automation 
• Cash flow forecasting 
• Financial reporting and variance analysis. 

They’re targeted improvements that remove friction from existing processes.

What the data is telling us

The global shift toward AI in finance is already well underway:

• In the US, over 65% of finance leaders say they are actively investing in AI-driven tools 
• In the UK, adoption in finance functions has increased significantly, particularly in mid-sized firms 
• And, in South Africa, while adoption is slower, there’s a noticeable uptick in interest, especially in sectors under pressure to improve efficiency

Many finance teams aren’t starting from scratch, they’re already using tools with embedded AI capabilities, often without realising it.

Where is AI delivering real value?

1. Reducing manual work

This is where we see the quickest wins. Processes such as invoice capture, reconciliations, and reporting can now be partially or fully automated. What used to take hours can often be done in minutes, and, with fewer errors.

2. Improving forecasting

Traditional forecasting relies heavily on historical data and manual assumptions.

AI tools can process larger datasets and identify patterns that are difficult to spot manually. The result isn’t a perfect prediction, but it’s often better-informed decision-making.

3. Creating space for strategic thinking

This is the shift that matters most. When finance teams spend less time on manual processing, they can spend more time:

• Interpreting data 
• Advising leadership 
• Planning for growth. 

That’s where real value is unlocked.

Where are businesses getting it wrong with AI?

One of the more interesting trends we’ve seen is not just where AI is working, but where it isn’t. The common challenges seem consistent, such as:

• Implementing tools without a clear objective 
• Expecting immediate transformation 
• Underestimating data quality issues 
• Not involving the finance team in the process. 

AI doesn’t fail because the technology is flawed. It fails because it’s approached incorrectly.

How we’re approaching AI at Malander Advisory

We’ve been spending time understanding how AI can be applied practically within finance environments, not just conceptually.

This includes:

• Evaluating AI tools relevant to finance and accounting 
• Understanding implementation challenges within existing systems 
• Identifying where clients can achieve quick wins vs longer-term transformation. 

We’re positioning AI as an extension of sound financial processes.

Why this matters now

The pace of change is accelerating. Businesses that take a measured, practical approach to AI will improve efficiency and decision-making over time. Those who delay may not feel the impact immediately, but will likely find themselves under increasing pressure to catch up.

FAQ’s

1. Why do large organisations often struggle to fully implement AI in finance?

Large organisations typically operate within complex environments, with multiple systems, entities, geographies, reporting structures and governance requirements. AI implementation is therefore not only a technology decision, but also a data, risk, compliance and change management challenge. Concerns around data security, privacy, GDPR, internal controls, auditability and system integration often need to be addressed before AI can be deployed at scale.

2. Why is AI implementation slower in larger finance functions?

In larger organisations, AI adoption usually requires collaboration between Finance, IT, Risk, Legal, Compliance and senior leadership. Implementation roadmaps need to consider existing ERP systems, data architecture, approval workflows, cybersecurity requirements and user adoption. Because finance teams are already operating in pressurised reporting environments, change management needs to be carefully planned to avoid disrupting business-as-usual activities.

3. How can AI improve timelines for group reporting?

AI can support faster group reporting by automating repetitive processes such as data extraction, reconciliations, variance analysis, commentary preparation and consolidation checks. This can reduce manual intervention, improve consistency and free finance teams to focus on reviewing results, identifying risks and providing meaningful insights to leadership. In complex group environments, the benefit is not only speed, but improved visibility and control across the reporting cycle.

4. What are the main risks corporates need to consider before using AI in finance?

Key risks include data quality, data privacy, cybersecurity, model reliability, regulatory compliance, access controls, and overreliance on outputs that have not been properly reviewed. For finance teams, there is also the question of auditability: organisations need to understand how AI-generated outputs are produced, reviewed and approved. Strong governance frameworks are essential to ensure AI supports decision-making without weakening financial control.

5. Will AI replace large finance teams?

AI is unlikely to replace finance teams, but it will change how they work. In large organisations, AI can reduce the burden of manual, repetitive and time-consuming tasks, allowing finance professionals to focus more on analysis, business partnering, scenario planning, risk management and strategic decision support. The value lies in using AI to strengthen the finance function, not remove the human judgment that is critical in complex corporate environments.

6. Where should a large organisation start with AI in finance?

Large organisations should start by identifying high-volume, high-effort finance processes where AI can deliver measurable value without introducing unnecessary risk. Examples may include management reporting, reconciliations, accounts payable reviews, forecasting support, variance commentary or consolidation processes. The most successful implementations usually begin with a clear business case, strong data governance, IT and Finance alignment, and a phased roadmap that allows the organisation to test, refine and scale responsibly.

From what we’re seeing, the businesses that benefit most are not those chasing trends, but those applying AI with clarity and intent.

At Malander Advisory, this is an area we’re continuing to explore – not because it’s new, but because it’s becoming essential.

About the Author

Lyle Malander is the CEO and Founder of Malander Advisory, a leading professional services and advisory firm headquartered in Johannesburg with an expanding footprint across the UK and Europe. He is passionate about financial strategy, governance, and leadership that drives sustainable business growth in a changing world.