In this guest piece, Brandon Till, Head of Business Solutions at Soldo, explores how rapid AI adoption is outpacing traditional enterprise governance frameworks. Till argues that Shadow AI is a symptom of operational friction, demonstrating why establishing proactive guardrails allows finance leaders to secure enterprise data while unlocking true ROI.
Old habits die hard. But even finance teams, often known for their more traditional ways of working, are moving quickly to capitalize on AI. In fact, nearly all (93%) of CFOs expect AI and digital investment to increase over the next year. That comes after AI adoption across the finance function has already more than doubled since 2024.
But while businesses race to keep pace with the technology’s evolution, governance is falling behind. Our research found that almost half (49%) of UK finance leaders admit their organization has gaps in its AI governance strategy.
And that’s a concern because governance isn’t just about ticking compliance boxes. Done well, it gives employees the freedom to embrace and experiment with new AI tools safely, creating the right conditions for innovation and greater ROI.
Without those guardrails, however, experimentation doesn’t stop. It simply happens outside of the organization’s view. Employees may adopt AI tools independently, without malicious intent, but also without realizing the risks involved.
That creates fertile ground for shadow AI, leaving businesses without the visibility and control they need to understand how AI is being used and effectively manage the risks that come with it.
For an industry that often gravitates towards tried and tested ways of working, it’s genuinely encouraging to see AI embraced so openly by finance teams. In fact, 83% of finance leaders believe AI will play an important role in helping them achieve their business goals.
What’s less encouraging is that almost a quarter (23%) admit they have little to no AI governance measures in place. That represents a disconnect finance leaders can’t afford to ignore.
Too often, governance is treated as something to think about and address only once the adoption of new technology is in motion. In reality, the two have to be developed alongside one another. There’s often a concern that governance can put the brakes on innovation. But that’s missing the point. Good governance doesn’t stop organizations from innovating, it exists to make sure that innovation happens safely and in a way that business can measure and trust.
Without that foundation, AI adoption can quickly become fragmented, increasing a business’ exposure to compliance and security risks that will only intensify as AI becomes more deeply embedded.
There’s no question that governance gaps are linked to organizational risk, but they also shape employee decisions in a way many businesses may not understand.
When approved tools are difficult to access, limited, or policies aren’t clear, people won’t just stop using AI until they have those guardrails in place, they’ll look elsewhere to get the job done.
And that’s exactly what our research demonstrates. More than a quarter (27%) of UK employees admit they have purchased AI tools for work without approval in the past year. Beyond AI usage, 67% say they regularly bend rules or find loopholes to access company money, while 27% report missing business opportunities because of delays accessing spending.
This isn’t a sign that employees are deliberately trying to undermine company policy. More often than not, it indicates that existing processes aren’t keeping pace with the way people now expect and want to work.
Shadow AI is often a ripple effect when an approved route is harder or less clear than the unofficial one.
Businesses may see it as easy to think of Shadow AI as the problem itself. But it’s usually a symptom of something bigger.
When employees feel they need to work around approved processes to be productive, businesses quickly lose visibility over which AI tools are being used and how company data is being handled and shared. For finance teams, this often means losing track of where money is being spent.
That unmonitored use of AI can lead to data leakage, compliance failures, poor record-keeping and inconsistent decision-making. So, it’s critical to tackle the issue before it spirals out of control.
Addressing these issues early on is far easier than trying to untangle them once they have become embedded.
AI governance isn’t about restricting innovation or putting a dampener on experimentation.
It’s about creating a safe environment and freedom for employees to explore new tools and ways of working, with clear guardrails.
When it’s done right, governance gives businesses the visibility they need to prevent shadow AI without stifling experimentation. In turn, this helps lay the foundations for AI to improve processes and deliver tangible value, ultimately paving the way for businesses to generate greater returns on investment.