The finance world is speeding toward AI adoption.
From anomaly detection to automated reconciliations, the technology is advancing faster than anyone could’ve expected. The pressure to keep up with everything can be stressful.
But the reality is technology alone won’t create change.
Many organizations invest in AI tools but often neglect their most crucial asset: people. Without the necessary skills and processes, AI initiatives can fail, leading to wasted budgets and increased compliance risks.
In fact, Gartner has noted that only 48% of AI projects progress from prototype to production, meaning more than half never scale.
The potential of AI will be unlocked by finance leaders who set their teams up for success.
Those who don't?
They will observe investments accumulating dust as your competitors gain ground.
Mistakes can become very costly very quickly, especially in finance. A single misclassified transaction can trigger compliance issues, erode stakeholder trust, or ripple into flawed decision-making.
That’s why this function faces more scrutiny than almost any other when it comes to adopting new technology.
AI isn’t something you simply “switch on.” Without the proper preparation, it can backfire badly by amplifying bad data, creating black-box outputs that regulators won’t accept, or draining budgets on tools that no one uses.
A recent survey found that 60% of business leaders lack confidence in their organization’s AI readiness, showing the gap is as much about people and processes as it is about technology.
That’s where readiness comes in. It equips teams to use AI with confidence, but also with healthy skepticism. They know when to trust outputs, when to dig deeper, and how to fold AI into existing controls.
They become the ones who drive change rather than oppose it because they are self-assured, competent, and prepared to provide value.
So what does “readiness” look like? It's not about overhauling your entire tech stack or recruiting a horde of data scientists. For AI to contribute value without posing new risks, readiness entails laying the proper foundation, including guardrails, culture, and fluency.
Here are the dimensions that matter most:
Finance employees do not need to become programmers to be AI-ready. Literacy, not coding, is the primary focus of training.
Teams only need to have the confidence to comprehend what AI is doing, challenge its results, and use it responsibly. They don't need to know how to create models from scratch.
Here are some practical ways to build that confidence:
The goal is not to trust AI fully, but rather to adopt informed skepticism. When finance professionals are fluent enough to challenge and apply AI outputs, that’s when the technology becomes an asset.
Being prepared for AI doesn’t happen overnight. Finance executives can guide their teams by taking deliberate, phased steps:
No. Teams need AI literacy, not PhDs. Partner with IT or data teams for the technical heavy lifting.
It varies, but many finance teams can establish a pilot within 3–6 months if they prioritize training and data preparation.
Begin by assessing current capabilities, then identify one or two low-risk use cases to pilot.
Treat compliance as a design requirement, not an afterthought. Document processes, validate outputs, and maintain human oversight.
When it comes to AI readiness in finance, people come first, not just the newest software or gaudy tools. Strong governance prepares teams to use AI responsibly and confidently, transforming it from a risky experiment into a competitive advantage.
With professionals who view themselves as active collaborators rather than passive observers, finance departments that make readiness investments now will be well-positioned to fully realize AI's potential.
Take the next step by joining our AI Readiness Workshop to start building your roadmap, and watch the on-demand Beyond Buzzwords Webinar for real-world examples of finance leaders already putting AI readiness into practice.