For all the grand pronouncements made in boardrooms about artificial intelligence, the reality of AI finance implementation inside many financial institutions is a bit of a mess. It’s a classic case of strategy-reality disconnect. Everyone wants to talk about a revolution, but very few are willing to do the hard, unglamorous work of actually making it happen. The pilot projects are plentiful, but scaling them? That’s where the story often ends.
The truth is, many organisations are tripping over the same AI adoption barriers. They’re mesmerised by the technology itself, chasing a grand, all-encompassing solution instead of focusing on what actually matters: solving tangible, costly business problems. This obsession with the “big bang” approach is why so many promising AI initiatives fizzle out, leaving behind a trail of frustration and wasted budgets.
What Does “Good” AI in Finance Actually Look Like?
Before we get to the solutions, let’s reset our definitions. We’re not talking about some sentient AI running the entire bank. We are talking about operational AI – a targeted, practical application of AI designed for process optimization. Think of it as inserting intelligent assistants into the complex, often fragmented, workflows that define modern finance.
The goal isn’t to build a mysterious black box. It’s to use financial technology to make existing processes faster, more accurate, and less of a headache for the people involved. The current landscape is littered with organisations struggling to connect their shiny new AI tools with the legacy systems and data silos that make up their operational backbone. The challenge isn’t a lack of ambition; it’s a lack of a coherent and grounded strategy.
Start with the Boring Problems
If you want to succeed, you need to stop chasing AI unicorns and start fixing the plumbing. That’s the first, most crucial step. Instead of asking, “What can we do with AI?”, leaders should be asking, “What is our most painful, repetitive, and measurable problem, and can AI help us solve it?”
According to an excellent analysis from IBM Consulting, this is the secret sauce. They highlight a global building materials manufacturer that wasn’t trying to reinvent finance from the ground up. Instead, they targeted one specific, irritating problem: resolving customer queries. By deploying AI agents to handle the initial triage and resolution, they achieved a 60% improvement in efficiency. That’s not a hypothetical; it’s a real, measurable gain.
This small-win approach does two things beautifully:
– It delivers immediate, quantifiable value that builds trust and a business case for further investment.
– It provides a safe, contained environment to learn about data requirements, system integration, and change management before you bet the farm.
Orchestrating Intelligence, Not Just Automating Tasks
Here’s where the strategy gets more sophisticated. Successful AI finance implementation isn’t about replacing individual tasks. It’s about orchestrating intelligence across entire workflows that are currently broken and disjointed.
Think of your current financial processes – like billing or reporting – as a chaotic city centre with lots of individual cars, each trying to get somewhere on its own. It’s inefficient and prone to gridlock. Now, imagine an air traffic controller overseeing the entire system. The controller doesn’t fly each plane, but coordinates their movements, ensuring they all get to their destinations safely and on time. This is what operational AI can do. It acts as the intelligent orchestration layer.
The IBM article mentions how AI agents can be integrated into the order-to-cash cycle, handling everything from billing validation to dispute resolution. In one case, this level of orchestration helped a company slash the time needed for monthly reporting from over 11 hours down to just two or three. The key is that a human is still in the loop, acting as the senior controller, managing exceptions and overseeing the entire system. It’s about augmentation, not abdication.
Making Your Humans Even Smarter
This brings us to one of the biggest misconceptions about AI: that it’s here to replace people. In the best financial technology rollouts, AI is a partner, not a replacement. It excels at the repetitive, data-heavy tasks that humans find tedious and are prone to making errors on. This frees up your finance experts to do what they do best: apply critical thinking, manage complex relationships, and make strategic judgements.
Imagine a credit analyst who no longer has to spend 80% of their day manually pulling data from ten different systems. Instead, an AI assistant presents them with a synthesised report, flagging anomalies and highlighting key risk factors. The analyst can now spend their time actually analysing the situation and making a more informed decision. This is how you elevate expertise, not eliminate it.
This collaborative model is the most powerful form of process optimization. It combines the raw computational power of machines with the nuanced wisdom and contextual understanding of experienced professionals. Who wouldn’t want that?
Are You Actually Ready to Scale?
So, you’ve started small, got a win under your belt, and you’re orchestrating workflows. Now what? Now comes the hard part: getting the entire organisation ready to scale. This is where most initiatives hit a brick wall.
Organisational readiness for AI isn’t a single switch you can flip. It’s a combination of several critical factors:
– Data Quality: Is your data clean, accessible, and well-governed? AI is powered by data, and if you feed it rubbish, you’ll get rubbish results.
– System Integration: Can your new AI tools actually talk to your old systems? Getting your modern tech to play nicely with legacy infrastructure is a huge, often underestimated, hurdle.
– Change Management: Have you prepared your people for this new way of working? You need to communicate the “why,” provide training, and create a culture that embraces AI as a helpful colleague, not a threat.
Without a serious commitment to these three pillars, your AI strategy will remain stuck in the pilot phase forever. As HFS Research often points out, technology is only one part of the equation; process and people are the other, arguably more important, parts.
The Agentic Future of Finance
Looking ahead, the evolution of AI finance implementation is trending towards more autonomous, or “agentic,” AI. These aren’t just tools that follow a script; they are intelligent agents that can be given a goal – say, “reduce days sales outstanding by 10%” – and then proactively orchestrate the necessary tasks across different systems and teams to achieve it.
This future isn’t as far off as it might sound. The groundwork is being laid now in the systems that effectively orchestrate intelligence. As these platforms become more sophisticated, the level of human intervention required for routine operations will decrease, freeing up the finance function to become a truly strategic partner to the business. The focus will shift entirely from transaction processing to value creation.
So, the real question for your organisation is not if you will adopt AI, but how. Will you continue to chase the hype, or will you get serious about solving real problems? What’s the one boring, painful, and measurable process in your finance department that you could fix first?
