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Home Technology & Industry AI

AI adoption rises, but finance teams remain trapped in manual work

SVJ Writing Staff by SVJ Writing Staff
August 7, 2026
in AI, Enterprise Tech, Financial Planning, Fintech, Leadership & Perspective, SaaS
0
AI adoption rises, but finance teams remain trapped in manual work

Finance teams are increasingly embracing artificial intelligence, but many are still spending significant amounts of time manually updating spreadsheets and checking for errors, according to new research from Kaleidoscope.com.

The State of Financial Modelling & Planning 2026 report, based on a survey of 170 finance professionals working across finance leadership, FP&A and advisory roles, found that 42% of finance teams still rely exclusively on spreadsheets without a dedicated modelling platform.

Meanwhile, 45% spend significant time manually updating data and 44% spend major time checking for errors, highlighting the operational challenges that continue to consume finance teams despite growing interest in AI.

The findings suggest many organisations are layering AI tools onto existing spreadsheet-based processes rather than addressing the underlying systems and workflows that create inefficiencies in the first place.

Michael Gould, founder of financial modelling software company Kaleidoscope.com and former co-founder of Anaplan, said finance professionals should invest time in understanding AI, but warned that technology alone will not solve deeper structural challenges.

“The number one technical skill Financial, Planning and Analysis (FP&A) professionals should be investing in right now is AI. Something has shifted very rapidly over the last few months in terms of what these tools can do and finance professionals need to understand both the opportunities and the risks.

“What it can do now might look rubbish compared to your own work, but that might not be true in six months’ time. The pace of change is extraordinary.”

The report found growing pressure on finance teams to modernise their modelling infrastructure as businesses become more operationally complex. Among respondents, 72% said they were very or extremely interested in adopting more specialised modelling tools. That figure rose to 91% among finance professionals working with highly advanced models.

According to the report, many organisations are reaching a point where maintaining spreadsheets across multiple stakeholders, systems and business functions is becoming increasingly difficult.

“Change management, not model creation, is the core friction,” the report states.

Finance leaders surveyed cited version control, scenario modelling, data cleaning and tracing downstream impacts as some of the biggest operational pain points inside current modelling environments.

Gould said finance teams are increasingly expected to support strategic decision-making across the organisation but are often constrained by fragmented systems underneath.

“Finance should be enabling the decision-making process across the business. The challenge is that many teams are spending too much time maintaining models and not enough time analysing the future states of the business.”

“Businesses do not operate in rows and columns. They operate through products, customers, people, supply chains and operational decisions. The tools underneath finance need to reflect that complexity.”

The report also raises questions about how organisations are adopting AI within finance functions. While AI tools can help automate tasks and accelerate outputs, Gould believes many finance professionals need to think carefully about governance, transparency and trust.

One concern is the growing tendency for employees to experiment with consumer AI platforms using business information.

“If you stick all your company finances into an AI tool and you haven’t got a subscription with a zero data retention policy, you’ve just provided the AI tool with learning data.”

Gould urges finance teams to scrutinise the data policies of any AI tool they use, not just whether data is retained, but how it is processed and whether it could be used to train future models.

“Finance teams should ensure their organisation has a comprehensive AI data policy covering retention, processing and governance across all platforms in use.”

The report argues that AI may help finance teams work faster, but not necessarily with greater confidence if the underlying modelling environment remains fragmented or difficult to validate.

Gould added: “An AI tool that generates 100,000 cell formulae in a spreadsheet sounds impressive, but how do you know it’s done it right? That question of trust is going to become increasingly important for finance teams.”

The State of Financial Modelling & Planning 2026 report was produced by Kaleidoscope.com and surveyed finance professionals working in organisations employing between 20 and 500 people, alongside multi-business finance advisors and consultants.

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