By Luka Mijatović, Co-Founder, Chief Technology and Financial Officer, Farseer
For years, finance teams have been asked to do more with less. More analysis, more forecasting, more strategic input – often with the same resources and an ever-growing volume of data. AI promises to help. But as CFOs increasingly look to AI to support planning and decision-making, one question is becoming more important than how quickly AI can produce an answer: can that answer be trusted?
This marks a significant shift in the conversation around AI in finance ops. The first wave of adoption focused on productivity, with organizations exploring how generative AI could automate routine tasks or summarize information more quickly. Those capabilities remain valuable, but finance is unlike many other business functions. A marketing team can tolerate an AI-generated draft that requires editing. A finance team cannot afford an AI-generated forecast or recommendation that cannot be explained.
Financial decisions underpin investment, hiring, expansion and risk management. They are scrutinized by executives, boards, auditors and regulators. Every number must have a clear origin, and every assumption must be defensible. In that environment, trust becomes as important as speed, if not more so.
Why trust is becoming the defining issue for AI in finance
As businesses continue to invest in AI, many are discovering the technology is only as effective as the financial foundations beneath it. While AI can accelerate analysis, surface trends and answer complex questions in seconds, it cannot resolve inconsistent data, conflicting metrics or fragmented reporting processes.
Many organizations still rely on a patchwork of enterprise systems, spreadsheets and manually maintained reports. Different departments often use different definitions for the same metrics, while multiple versions of the truth coexist across disconnected systems. Finance professionals have learned to work around these inconsistencies over time, but AI has a way of exposing them.
If the underlying financial data is fragmented or inconsistent, AI simply produces faster answers based on unreliable foundations. Asking increasingly sophisticated questions of poor-quality information does not improve decision-making. It accelerates confusion.
This is why conversations about AI in finance are increasingly shifting from capability to governance. Rather than asking whether AI can generate insights, finance leaders are beginning to ask where those insights came from. What data was used? Which assumptions were applied? Can the reasoning be explained? Would the same question produce the same answer tomorrow? These are governance questions that go to the heart of financial accountability.
Continuous forecasting requires reliable financial data
Alongside the rise of AI, finance itself is undergoing a broader transformation. The traditional rhythm of monthly reporting and quarterly planning is giving way to continuous forecasting, where organizations are expected to respond to changing market conditions in real time.
Economic uncertainty, supply chain disruption and geopolitical instability have made static annual plans increasingly obsolete. Finance leaders are now expected to update scenarios, assess risks and provide strategic guidance as conditions evolve, not weeks after the fact.
This evolution creates a significant opportunity for AI. By reducing the time spent gathering information, reconciling data and answering routine questions, AI can allow finance professionals to focus on interpreting results, evaluating trade-offs and advising the business. Rather than replacing finance expertise, AI has the potential to amplify it.
But achieving that vision requires confidence in the numbers. Continuous forecasting only works if everyone is working from consistent, governed financial data. Otherwise, organizations risk making faster decisions based on conflicting information – a problem that technology alone cannot solve.
The future of AI in finance depends on explainability
The concept of explainability is becoming increasingly important as AI becomes embedded in financial workflows. Finance teams need confidence that AI-supported analysis can be understood, challenged and validated. Transparency is not a barrier to innovation; it is what enables organizations to adopt new technologies responsibly.
This is especially important because finance operates under a different level of scrutiny than many other business functions. Recommendations often influence investment decisions, capital allocation and long-term strategy. Boards, auditors and regulators increasingly expect organizations to demonstrate not only what conclusions were reached, but how those conclusions were formed.
Importantly, this does not mean AI should replace professional judgement. Finance has always combined quantitative analysis with commercial understanding, and that balance is unlikely to change. AI can identify patterns, surface anomalies and accelerate analysis, but people remain responsible for interpreting results within the wider business context.
In many ways, the role of finance professionals is becoming more strategic, not less. As repetitive administrative work becomes increasingly automated, their value lies in asking better questions, testing assumptions and helping leadership teams navigate uncertainty. AI should strengthen those capabilities rather than diminish them.
Consequently, the organizations that succeed with AI in finance are unlikely to be those that simply adopt the latest tools the fastest. They will be the ones that first establish trusted financial data, consistent governance and clear accountability for how decisions are made. Once those foundations are in place, AI becomes a powerful accelerator rather than a source of additional risk.
The excitement surrounding AI is understandable, and its potential to transform finance is undeniable. But finance has always relied on confidence as much as calculation. Faster analysis is valuable only when decision-makers believe the results.
Ultimately, the next era of AI in finance will not be defined by who generates answers first. It will be defined by who can make better decisions with confidence, backed by information that is transparent, explainable and trusted.
