As businesses navigate their way around various technological advancements, finance teams are faced with the task to integrate analytics and automation into their existing processes, determining at the same time which specific system to transform first for maximum operational impact.
New research from global data and cloud solutions company Hitachi Vantara found that while Asia is outpacing the world in AI adoption, poor data quality and security risks threaten to stall progress.
According to the Hitachi Vantara State of Data Infrastructure Survey, there are critical gaps that could undermine the region’s AI momentum, despite ambitious investments.
This includes how data security is viewed as a top concern for 44% of Asian enterprises, exceeding the global average and how AI model accuracy is just 32% on average, with only 30% of data is structured, revealing messy data foundations.
Addressing such gaps is crucial for the organisation, including the Finance function, as it can impact cost planning and decision-making processes.
Matthew Hardman, chief technology officer, APAC, Hitachi Vantara, believes finance leaders must take a strategic approach to AI adoption, ensuring that technological investments align with broader business objectives.
He concedes that AI has significantly transformed finance teams by automating processes, improving forecasting, and enhancing risk management, but he notes that its effectiveness depends on access to up-to-date data.
"Finance teams rely on a mix of historical and real-time data, but if an AI model is only trained periodically, it risks generating outdated insights that could lead to poor decisions," Hardman explains.
AI in Finance today
Hardman observes that finance professionals have made notable progress in digital transformation, particularly in automating routine tasks, streamlining workflows, and enhancing financial forecasting.
"The adoption of AI-driven analytics allows teams to move from traditional reporting to real-time insights, enabling more proactive decision-making," he says. "Cloud-based financial systems have improved collaboration, making data more accessible while ensuring compliance with evolving regulations."
Additionally, Hardman notes that AI-powered fraud detection has significantly strengthened risk management by identifying anomalies faster than traditional methods.
"The ability to integrate AI into financial planning also means we are seeing improved efficiency, allowing Finance teams to focus on strategic initiatives rather than administrative tasks." Matthew Hardman
In his view, there is still room for growth for Finance teams, particularly in fully leveraging AI-driven automation. However, he says they have noticed that organisations' Finance departments are becoming increasingly comfortable to use advanced technologies to improve accuracy, efficiency, and overall business performance.
"The ability to process large datasets in real time has helped finance leaders identify trends and risks more efficiently," says Hardman. "Platforms like Hitachi iQ enable advanced financial modelling and anomaly detection, while automation has reduced the burden of reconciliation and regulatory reporting, allowing teams to focus on strategic decision-making."
He believes that addressing the probable issue on up-to-date data addressed by Retrieval-Augmented Generation (RAG) by combining AI-driven insights with live data, ensuring more accurate, real-time responses.
"Unlike static AI models, RAG combines large language model (LLM) responses with live data sources to ensure the most current insights," he explains. "For example, if you ask a free AI model like ChatGPT about yesterday’s market trends, it may only provide information based on its last training update."
Hardman says AI platforms like CoPilot and Gemini enhance their responses by integrating real-time data from external sources. "This highlights the ongoing need for high-performance, reliable infrastructure to support ERP, CRM, and financial systems."
Key skills
In terms of the key skills concerning AI and other technological advancements that finance teams need to develop to better understand and drive business value, Hardman believes finance teams must develop a combination of analytical, technical, and strategic skills to fully leverage its potential.
As AI becomes increasingly embedded in financial operations, data literacy becomes crucial, as professionals need to interpret AI-driven insights and validate the accuracy of financial models.
"Understanding automation tools is also essential, as AI is reshaping processes such as financial planning, risk management, and audit compliance."
Further, Hardman says that one emerging skill finance teams must develop is prompt engineering, which is the ability to structure precise and effective inputs for AI models.
He posits that as AI adoption grows, finance teams will need to ask increasingly complex questions to extract meaningful insights. "The way queries are phrased will directly impact the quality and relevance of AI-generated responses, making prompt engineering a valuable capability," he continues.
Hardman also highlights how important it is that Finance teams do not shy away from cybersecurity awareness, which is critical to ensuring the integrity of the data and regulatory compliance. "We should encourage collaboration with IT and data teams and embrace continuous upskilling to drive innovation and longer-term success," he says.
Moreover, to achieve meaningful business outcomes, Finance teams must focus on data attributes such as revenue growth, expense trends, cash flow projections, and risk exposure.
"Accurate, real-time data is essential for effective financial planning and strategic decision-making," Hardman points out. "Ensuring data integrity requires robust governance frameworks, automated validation processes, and continuous monitoring for inconsistencies."
He says AI-driven anomaly detection can help identify discrepancies in financial data, while real-time reconciliation tools can ensure accuracy in reporting. According to him, strict access controls and encryption methods also play a crucial role in protecting sensitive financial information.
"Collaboration between finance and IT teams is vital in maintaining compliance with regulatory standards and data security protocols," he adds. "As businesses increasingly rely on AI-driven insights, maintaining the reliability and accuracy of financial data should be a top priority to prevent errors, misinterpretations, or security risks that could compromise business performance."
Leveraging AI
As AI has admittedly made its way to the Finance department, imposing its relevance and importance, it is necessary that finance leaders know how to leverage the technological advancement to enhance analysis and decision-making.
Hardman says AI is transforming financial analysis by providing real-time insights, improving accuracy, and enhancing predictive capabilities. Meanwhile, machine learning allows finance teams to process large datasets quickly, identifying patterns and anomalies that traditional methods may overlook.
He explains, "predictive analytics improves cash flow forecasting, helping businesses anticipate financial risks and opportunities, while natural language processing (NLP) makes financial reporting more accessible by generating clear, concise summaries."
Beyond this automation, Hardman sees that the next evolution of AI in finance likely lies in Agentic AI, where AI agents are able to act autonomously and make decisions without constant human intervention.
"Unlike traditional automation, which follows predefined workflows, Agentic AI can analyse financial results, flag performance trends, and even trigger follow-up actions such as notifying stakeholders about financial variances. Combining AI automation with Agentic AI will allow organisations to unlock new levels of financial intelligence and agility." Matthew Hardman
Addressing skill gaps
To ensure that necessary capabilities and skillsets are intact among staff, Hardman suggests a proactive approach that combines assessment, training, and strategic hiring.
"Regular skills audits can help finance leaders understand where gaps exist," he says. "Training programmes can then be tailored to equip teams with the necessary digital competencies."
He adds that encouraging cross-functional collaboration with IT and data science teams can also help finance professionals gain hands-on experience with emerging technologies.
Organisations can also consider hiring specialists or third-party consultants with expertise in AI-driven financial analysis to complement existing teams.
"Ultimately, upskilling must be an ongoing process, with finance leaders fostering a culture of continuous learning and adaptability."
Advice
Hardman says the foundation of any AI-driven finance strategy is a secure, scalable data infrastructure, which enables seamless data access and ensures compliance.
He believes that leaders should also focus on leveraging AI-powered analytics solutions such as Hitachi iQ, which can enhance financial forecasting, risk assessment, and operational efficiency. "The key is ensuring that the AI tools and solutions selected are the right fit," he notes. "However, maximising AI’s benefits requires more than just the right technology, it also demands investment in talent."
Hardman says upskilling finance teams in AI literacy and data analytics will be essential for extracting maximum value from these innovations. Collaboration between finance, IT, and data science teams can also accelerate AI adoption and improve decision-making.
"By fostering a culture of continuous learning and AI-driven financial strategy, finance leaders can ensure their teams are well-equipped to leverage AI for long-term business growth." Matthew Hardman
