Finance leaders in my network have shared that they face immense pressure to not only implement artificial intelligence this year, but also to drive near-immediate efficiency gains from it.
This anecdotal sentiment has been reinforced consistently, including in two recent reports:
- Gartner’s Hype Cycle for AI in Finance placed generative AI in the trough of disillusionment and AI governance at its peak of inflated expectations. Causal AI and decision intelligence are rising in importance and will trigger innovation in the coming years.
- The Hackett Group 2026 Finance Key Issues Study found that finance teams are scaling AI to address a productivity gap caused by rising workloads and declining headcount. Accounts payable and travel and expense management are the most prevalent areas for AI, and treasury, tax, and compliance use cases are emerging.
This anecdotal and research evidence proves that AI is spreading in finance as a competitive necessity to address a growing productivity gap and eliminate manual, error-prone tasks. The bubbling focus on governance and decision intelligence, however, highlights a critical consideration: Legacy tools automate the mechanics of accounting while ignoring the context that makes those mechanics defensible.
If leaders focus narrowly on short-term productivity boosts with AI, they risk building a fintech foundation that cannot expand to complex, analytics-driven processes and will repeatedly fail to keep pace with new revenue models and compliance requirements.
Organizations need AI that can not only automate manual work, but deliver auditable, trusted results that preserve governance. The solution lies in an emerging concept: a context graph.
AI adoption pressures sacrifice your foundation
Finance and accounting workflows are notoriously manual and distributed, so early AI application has focused on scenarios like these:
- Automating expense tracking, receipt scanning, and policy compliance to reduce manual effort and errors.
- Extracting data from invoices, receipts, and checks using computer vision to eliminate manual data entry.
- Generating revenue waterfalls from uploaded schedule to maintain ASC 606 compliance.
Tools automate data collection and regulate workflows, which provides necessary relief. However, scaling AI to finance processes that demand complete accuracy while achieving true efficiency gains requires a more intelligent foundation.
The core challenge is that workflows span every system: CRMs, CLMs, ERPs, and email threads. AI tailored for an individual system provides a focused solution, but scaling that AI across systems or applying AI for complex workflows reveals a critical gap.
Without the context stored across systems—and the unwritten institutional knowledge that each employee possesses—AI delivers flawed outputs that can be hard to trace. This problem expands as new data sources emerge, which McKinsey research found is a leading cause of AI pilot failure.
Teams need a foundation that enables context-driven finance decisions, reflecting insights from every system with an audit trail. Without this foundation, AI invites costly mistakes in time, credibility, and regulatory exposure as teams apply AI to risk-sensitive areas.
Context graphs are essential for defensible AI
Bridging the context gap isn’t about larger AI models or better prompts. It’s about a new class of memory and reasoning infrastructure known as a context graph.
A context graph is an evolving framework that guides AI to understand the rules, relationships, and reasoning behind every decision. AI agents use the graph to deliver fully informed, confident outputs, much as a human would with proper training and background.
At a high level, a context graph maps the data landscape within a system’s defined scope. In finance and accounting applications, this typically includes:
- Entities, such as customer or employee profiles, invoices, or transactions.
- Relationships, which detail how information flows across entities, reflecting key dependencies and guardrails.
Every finance process involves nuanced considerations, but generalized AI tools are not built to extract and reflect the appropriate context for each individual process. Databases store facts, and a search index retrieves text. What’s missing is the relationships and sequences among entities over time, across every system.
The context graph fills the gap and is required to move the conversation from “how do I automate more?” to “how do I make my AI trustworthy enough to sign off on?”
Audit cycles, as one example, consume considerable resources to reconstruct transaction history and address auditor questions. A well-architected system fueled by a context graph can deliver outputs that reflect the entire chain—from commercial intent and legal language to business judgment and regulatory interpretation. It enables a system where, by design, every output is quickly verifiable—establishing necessary trust in AI for high scrutiny workflows.
How to enable true agentic intelligence for finance
We are rapidly approaching an inevitable future where AI augments most finance processes. Leaders cannot let the pressure to adopt AI lead them to make short-sighted decisions focused on perceived immediate efficiency gains.
AI’s lasting value for finance centers on its ability to address edge cases—which are a reality of finance—and establish trust in AI outputs. Without a context-aware foundation, teams will continually grapple with outputs that require extensive human oversight and correction.
Forward-looking organizations will avoid these risks and gain considerable competitive advantage by enabling context to flow across each tool. The transition starts with a technology audit. For each tool, ask:
- Does this tool create a data silo, or does it connect to the upstream and downstream systems?
- How does this tool preserve data privacy and security?
- Can this tool orchestrate activities seamlessly with our other systems, supporting a composable architecture?
- Can this tool’s features or capabilities be addressed through an alternate investment, including existing tools?
A lean, composable stack fueled with contextual insights will provide immediate efficiency gains and confidence as teams address emerging use cases; enabling the optimal mix of human expertise and oversight augmented by the speed and scale of AI.
The future of AI for finance is built on context
A context-aware foundation doesn’t just accelerate work and reduce errors. It enables collaborative confirmation where the system understands the full arc of a transaction.
The CFOs who fuel AI applications with a context foundation will close with a confidence their peers can’t match and continually evolve their processes.
ABOUT THE AUTHOR:
Jagan Reddy is the founder and CEO of RightRev and a pioneer of revenue recognition software, leading the company’s corporate vision and product development. Before founding RightRev, Jagan’s extensive accounting, IT, and ERP background led him to build the first-to-market revenue recognition software, RevPro by Leeyo, in 2009. At Leeyo, he grew the company to more than 200 employees and 100-plus customers, with several Fortune 1000 companies among its clients. Jagan then joined Zuora in June 2017 as part of Zuora’s acquisition of RevPro. At Zuora, he led the revenue recognition franchise and was responsible for sales, engineering, product and customer success. His accomplished background, which combines accounting and software engineering, helped him solve a massive gap in revenue accounting. Jagan holds a Bachelor of Commerce in Accounting and Finance.
Photo credit: tadamichi/iStock
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