Often seen as bastions of tradition, corporate finance, accounting and taxation teams typically lag in technology adoption within organizations.
Their daily routines are mired in navigating vast compliance complexities, adapting to constantly evolving accounting and taxation standards, and wrestling with distributed financial data scattered across disparate systems. For larger enterprises, these challenges amplify the inherent inertia against embracing new solutions. Despite a growing awareness of generative AI's potential to revolutionize complex workflows, accounting teams remain notably hesitant. Driving gen AI adoption in this critical sector demands a fresh approach, one that precisely targets specific business outcomes and keenly understands how these teams evaluate and procure technology.
Framework to strategically dissect business outcomes
Broadly, accounting teams predominantly engage in one or more of three core categories of tasks: managing incoming order-to-cash (O2C) flows, overseeing outgoing cash from procurement initiatives (CFP) and meticulously recording transactions for reporting under U.S. GAAP/IFRS standards. To identify which business processes are most receptive to change, the initial step involves classifying all relevant activities into these three categories.
For instance, gen AI offers significant potential to reduce intensive manual interventions in common O2C activities like customer contract reviews, forecasting accounts receivable, and predicting delinquencies. Similarly, within CFP, activities such as AP forecasting, expense management, invoice generation and vendor contract reviews can be greatly enhanced. Accountants also dedicate substantial time to recording activities, including (though increasingly automated by software) ledger entries, tax liability entries, transfer pricing, intercompany transactions, tax return preparation and preparing for SEC filings in publicly listed organizations. Ultimately, accounting teams are pivotal in providing near-real-time insights into a company's financial standing that CFOs critically require.
Channels for gen AI access to accounting professionals
For accounting professionals, the integration of generative AI in 2025 can occur through several distinct, yet often complementary, channels. Multimodal systems offer comprehensive interfaces to harness gen AI's capabilities. These systems include chat interfaces, enabling users to effortlessly search for relevant information, receive accounting recommendations and verify actions against established standards like U.S. GAAP or IFRS. Additionally, document generation systems drastically reduce the time spent on creating critical documents by automating tasks such as bookkeeping entries, preparing 10-K SEC filings, annual and quarterly reports, analyst and investor presentations, and various tax filings. Furthermore, AI insight dashboards allow professionals to interrogate complex financial findings using natural language, pinpointing root causes and driving deeper analysis.
While large language models from providers like Google, OpenAI and Perplexity are trained on vast, generic public datasets, domain-specific models offer a more tailored and impactful approach for finance and accounting teams. These models are meticulously fine-tuned by "grounding" them with an organization's proprietary financial data, enabling them to navigate intricate accounting standards with precision, suggest specific tax-saving mechanisms within recorded transactions, and provide highly relevant insights that generic LLMs cannot. Developing such models often requires cross-functional collaboration, involving engineering teams, but the long-term return on investment can be substantial due to their specialized accuracy and direct applicability.
Assistive, autonomous and nearly autonomous AI agents are increasingly adept at taking on human tasks that demand reasoning and rule-based decision-making. Given that many accounting activities are governed by strict guidelines, AI agents are perfectly positioned to thrive in this field, working synergistically with their human counterparts. A significant number of SaaS-based accounting software providers are actively integrating AI agents natively into their platforms, a development that suggests these could become one of the easiest generative AI tools for accountants to adopt within the next six months. Furthermore, for tasks still beyond the native capabilities of these platforms, organizations can develop custom AI agents that leverage their purpose-built domain-specific models.
Sticking the gen AI landing
Historically, finance and accounting teams have had minimal involvement in technology evaluation or procurement decisions. They have typically wielded little influence in these organizational choices, with the CTO's office often spearheading foundational technology purchases. Consequently, the "build vs. buy" debate for accounting teams has largely been settled: they have almost invariably opted to buy rather than develop in-house solutions. This preference stems from their highly specific business needs, which have traditionally been directly addressed by specialized SaaS products designed for accountants and CPAs.
However, the rapid ascent of generative AI is compelling these teams to rethink their approach fundamentally this year. AI technology is advancing at an exponential pace, rendering solutions just six months old seemingly outdated. Most traditional SaaS platforms struggle to keep pace with this relentless evolution. Encouragingly, leveraging AI is becoming increasingly democratized and accessible to business users. One no longer needs to be an advanced machine learning engineer to construct powerful AI agents; development time has plummeted from several months just two years ago to merely a couple of hours today. This dramatic shift prompts a critical question: why not consider building more alongside buying?
Here are key dimensions to consider when analyzing the build vs. buy decision in the gen AI era: If your current platform merely automates tasks rather than intuitively reasoning and making decisions on your behalf, it's a strong indicator to build. Similarly, if your platform primarily offers a collection of features rather than consistently delivering guaranteed outcomes for your specific accounting challenges, consider a build strategy. Furthermore, if your current platform isn't fundamentally refreshing its AI capabilities and delivery mechanisms at least every six months, it's time to consider a replacement or build your own solution.
Taking the first step toward AI adoption
Finance professionals don't usually think of themselves as technology experts, but getting started with AI isn't as hard as it seems. The crucial first step involves a systematic approach: pinpoint all the real business outcomes that genuinely matter to your teams, aiming for tangible improvements in efficiency, accuracy, insights or cost savings. Next, categorize and prioritize these identified outcomes based on their significance to the organization's strategic goals and the potential impact of AI. With your prioritized outcomes in mind, choose the most relevant channel(s) to access AI platforms — whether through multimodal systems, fine-tuned Small Language Models or specialized AI agents. Finally, rigorously evaluate the market for AI-native platforms available for purchase; if a suitable "buy" option doesn't fully address your prioritized outcomes, the accelerated development capabilities of generative AI now make building a tailored solution from the ground up a surprisingly viable and often superior alternative.
