Even a program such as Excel can become overwhelming — just ask a finance staff racing to meet a hard deadline. Amid the seemingly endless data, one incorrect input is all it takes to trigger an avalanche of regulatory fines.
For automation platform DataSnipper, Excel need not be rocket science. Vidya Peters, the company’s Chief Executive Officer, spoke with Frontier Enterprise on why automation is no longer optional, but essential in finance.
Perfect storm
Put simply, finance teams are just drowning in data — a situation exacerbated by complex regulatory requirements that make traditional manual processes unsustainable, Peters noted.
“Over 70% of finance leaders cite inefficiency as a key barrier to productivity, and human error accounts for up to 40% of financial restatements. This creates strong demand for DataSnipper, which automates data extraction, matching, and analysis. Enterprise demand is driven by the need to improve accuracy, streamline workflows, and shift teams away from administrative tasks toward higher-value insights,” she said.
Additionally, skilled audit professionals are increasingly hard to find, and businesses are competing for a limited talent pool.
“Automation is the only way teams can keep pace, ensure consistency and quality, and meaningfully reduce risk at scale,” Peters added.
A 2023 study found that a large majority of both large enterprises and SMEs always or often use Excel to conduct substantive or detailed testing. According to Peters, embedding DataSnipper and AI in Excel meets auditors where they already work, ensuring minimal disruption and maximum adoption.
AI integration
While DataSnipper can be leveraged within Excel, the platform also offers AI capabilities in the cloud, helping automation integrate with enterprises’ existing tech stacks.
“The future of enterprise automation is hybrid,” Peters remarked. “It involves a mix of integrated AI in existing tools like Excel, ERP, audit platforms, as well as cloud-based solutions that offer scalability.”
She added that enterprise customers are especially interested in how AI can enhance tools they already know and trust, like Excel, and that there is growing demand for solutions that balance local control with cloud scalability. In her view, DataSnipper’s ability to automate directly within Excel allows teams to see immediate efficiency gains without disrupting their existing workflows.
Ultimately, the decision on where AI processing takes place depends on a mix of security requirements, data sensitivity, IT architecture, and regulatory considerations, hence the appeal of hybrid models.
“Hybrid lets enterprises move fast with AI while staying in control of their data and infrastructure,” Peters said.
Deployment hurdles
One of the core challenges in applying AI in regulated industries like finance is maintaining accuracy and transparency. Since AI can produce hallucinations, even minor misinterpretations can lead to significant consequences.
DataSnipper addresses this by emphasising traceability. According to Peters, every conclusion drawn within DataSnipper is backed by verifiable source data, enabling auditors to trust the insights.
“Unlike black-box models, DataSnipper’s AI provides assistive automation while keeping auditors in control, aligning with strict regulatory standards in audit and finance,” she said.
Another challenge is the varying AI regulatory landscape across regions, requiring DataSnipper to stay agile in its data strategy.
“Europe’s evolving AI Act and strict data privacy rules require very deliberate implementation, while in the United States, firms are more focused on audit defensibility and SEC expectations. Our approach is to build adaptable AI that aligns with regulatory frameworks, while helping them stay ahead of what’s coming next,” she noted.
In Asia-Pacific, the company is likewise taking note of cultural and regulatory nuances: “This allows us to adapt fully to local requirements, different decision-making structures, and business cultures, while also ensuring that AI and automation align with regional expectations in a sector where trust is a cornerstone principle. It’s why we are making such a large investment in the region with local teams that can serve our customers in their language and time zones,” Peters said.
Future projections
In the coming years, Peters expects AI will continue to automate the most tedious parts of finance and audit workflows, though it will not replace the auditor.
“Audit and risk assessment require the interpretation of changing rules and regulations, and the balancing of risk across various areas, which calls for expertise and judgement. Ultimately, clients pay for that expertise, not just a stamp of approval,” she said.
At their core, AI tools like DataSnipper are designed to automate high-risk, manual tasks and help prevent compliance failures stemming from human error. While it also enables faster, more efficient work, Peters said the goal is to empower users with more than just productivity.
“We’re moving quickly toward a future where auditors spend more time applying higher-level professional judgement, supported by verified insights and analysis of their organisation’s data. AI-powered automation isn’t just a productivity booster; it’s a fundamental shift toward smarter, more compliant, and more strategic audit and finance functions,” she concluded.
