Finance ditches excel: AI agents double productivity in 2026

By 2026, finance leaders at companies like Gusto and Tide, including notable figures such as Jeff Cobourn and Rohit Divate, are decisively abandoning traditional tools like Excel and legacy FP&A software in favor of AI-powered platforms such as Claude. This transition to agentic AI workflows has been transformative, doubling productivity and igniting what industry insiders call a 'productivity revolution' that reshapes financial operations and accelerates strategic growth. The shift is not merely about automation but about pioneering next-generation 'company Jarvis' systems that seamlessly integrate human-AI collaboration to enhance decision intelligence and operational excellence.

Integral to this transformation is the evolution of human roles within finance departments, where human-in-the-loop oversight and collaborative workflows are critical to harnessing the full potential of AI agents like Claude. While agentic workflows double productivity, they simultaneously underscore the necessity for evolved human oversight to sharpen decision-making and maintain operational excellence amid a trillion-dollar market shakeup. This nuanced human-AI teamwork is redefining financial management, ensuring that foundational modeling skills remain essential even as AI drives unprecedented efficiency.

Underlying the successful deployment of agentic AI workflows is robust data architecture, which finance leaders recognize as the linchpin for unleashing Claude’s full capabilities. This foundational infrastructure supports the complex integration of AI-driven processes, enabling sharper human-AI collaboration and ensuring that the doubling of productivity is sustainable and scalable across rapidly evolving product roadmaps. As finance departments pivot away from Excel, the emphasis on data integrity and architecture becomes paramount to sustaining this new era of financial automation.

Beyond productivity gains, the adoption of AI-powered Claude platforms is reshaping team efficiency ratios and embedding a culture of continuous innovation within finance functions. This paradigm shift is not just technological but strategic, as agentic workflows empower finance leaders to expand business impact and redefine operational excellence through seamless human-AI collaboration. The move away from Excel symbolizes a broader transformation toward intelligent, agile financial management that aligns with the accelerating pace of modern business demands.

In 2026, finance leaders like Jeff Cobourn of Gusto and Rohit Divate of Tide are spearheading a transformative shift by abandoning Excel in favor of agentic AI workflows powered by platforms such as Claude. This transition has effectively doubled productivity in FP&A and broader financial operations, not merely by automating tasks but by embedding human-in-the-loop oversight that sharpens decision-making and ensures operational excellence amid a trillion-dollar market upheaval. The emphasis has moved from individual AI fluency to fostering company-wide AI impact through human-centric leadership and workflow-native decision intelligence, underscoring that strategic growth now hinges on seamless human-AI collaboration rather than isolated automation efforts.

Agentic AI workflows are not only supercharging productivity but also fundamentally redefining the nature of human-AI teamwork within finance departments, merging traditional finance and business intelligence roles into a unified function. This evolution empowers a new breed of '10x talent' who leverage AI agents to rapidly process complex data and contextualize insights, turning what was once a specialized skill into a universal capability. However, this productivity surge is balanced by a growing recognition of AI’s limitations—93% of finance professionals express concerns about AI accuracy, hallucinations, and bias—making critical human oversight and continuous upskilling in AI literacy indispensable for sustaining operational excellence.

Human oversight in AI-augmented financial workflows has evolved from manual intervention to a sophisticated role of strategic collaboration and quality assurance, where experienced finance professionals apply high judgment to validate AI outputs and safeguard against costly errors. Systems are designed with built-in redundancies and approval rules that flag uncertainties for human review, ensuring that AI-driven automations enhance accuracy and clarity rather than merely increasing output volume. This coordinated approach extends beyond isolated finance functions, as leaders emphasize the importance of aligning improvements across quoting, billing, and revenue processes to avoid bottlenecks and foster organization-wide operational excellence.

By 2026, finance leaders recognize that robust, cloud-native data architectures are the cornerstone for unleashing AI-driven, agentic workflows that double productivity and enhance human-AI collaboration. Platforms like Snowflake, AWS, and tools such as Fivetran or Airbyte enable seamless ELT pipelines feeding into centralized data warehouses, preserving raw source data for traceability and error correction. Adinath Kadam emphasizes that traceability is not an afterthought but an architectural constraint designed from the outset, ensuring every financial insight has an auditable lineage, which is critical for operational determinism and regulatory compliance under frameworks like the EU AI Act and NIST AI RMF.

Strong data governance frameworks tailored to organizational complexity and regulatory exposure are essential to unify fragmented financial data scattered across banking, payments, accounting, and operational systems. Ben Liebert of SyncHub highlights that AI models cannot deliver reliable operational answers when fed disjointed SaaS data; instead, governance models—centralized, federated, or hybrid—must align policies, ownership, and standardized definitions to prevent data silos and inconsistencies. Finance functions, accountable for reported results even when data originates externally, play a structurally central role in these governance models, enabling cross-domain collaboration and maintaining trustworthiness in AI-augmented workflows.

The primary risk in AI-driven finance workflows lies not in the AI agents themselves but in the governance and management of their use cases, underscoring the critical role CFOs and audit teams play as the custodians of AI quality and trustworthy financial operations. Anthony Habayeb, CEO of Monitaur, argues that organizations need not create entirely new AI governance frameworks but must leverage existing controls owned by finance and audit functions. Continuous executive oversight, automated testing protocols, and integration with audit processes ensure data integrity and compliance, preventing AI from producing confident-sounding guesses instead of verified insights.

Avoiding the pitfalls of deploying AI on fragmented, ungoverned data environments requires agile, scalable cloud-native infrastructures that support large enterprise data volumes and strict reporting deadlines. Adinath Kadam warns that many AI projects fail because demos rely on sanitized datasets that mask real-world complexities, resulting in a 95% failure rate to demonstrate P&L impact in production. By moving financial transformation logic upstream into transparent cloud pipelines and eschewing costly proprietary lock-ins, organizations reduce operational risk and enable reliable, scalable AI-driven financial analytics that can adapt rapidly to evolving business needs.

In 2026, finance leadership is undergoing a profound transformation, shifting focus from individual AI fluency to orchestrating company-wide AI impact through human-centric leadership and workflow-native decision intelligence. Leaders like Jeff Cobourn of Gusto and Rohit Divate of Tide emphasize that executive success now hinges on strategic judgment—knowing what to build, framing the right questions, and exercising discerning taste among AI-generated options—because when intelligence is commoditized, human skill and accountability become the ultimate differentiators. This evolution demands leaders who can diagnose the right problems, balance systematic transformation with operational excellence, and maintain a relentless focus on customer outcomes, employing methods like Amazon’s 'Working Backwards' to ensure clarity and strategic alignment.

The finance workforce is bifurcating into two distinct groups: those performing routine, mechanical tasks increasingly automated by AI, and thinkers whose judgment and strategic insight are amplified by AI’s capabilities. As noted in 2026 research by Robert Half, 87% of finance leaders are now paying premium salaries for specialized skills that enable professionals to leverage AI tools effectively, underscoring that proficiency in traditional tools like Excel is no longer a safeguard against obsolescence. Instead, the value lies in the ability to challenge flawed processes and apply judgment, with AI freeing up time for these thinkers to cover work that once required multiple process-followers, thereby exponentially increasing their leverage and organizational impact.

Effective upskilling and organizational adaptation are critical to realizing AI’s promise in finance, with leadership playing a pivotal role in driving immersive, in-person training that transforms finance professionals into AI power users. Companies like Bausch + Lomb exemplify this approach by embedding AI literacy as a core business skill through enterprise-wide programs and initiatives like their VisionAI Challenge, which empowers employees across functions to identify and implement AI-driven improvements. This culture of continuous learning and knowledge sharing, facilitated by platforms such as AI in Action, accelerates adoption and operational excellence, reinforcing the idea that lasting competitive advantage stems from organizational adaptability rather than technology alone.

Strategic leadership in AI-driven finance increasingly involves bridging advanced AI capabilities with real-world workflows to unlock applied intelligence and market opportunities, as highlighted by investor Joe Lonsdale’s vision of a new organizational layer that integrates technology and business processes. Finance leaders like Samantha Greenberg are adopting rigorous frameworks that balance sustaining and disruptive innovations, closely monitoring AI-driven operational metrics such as customer behavior, platform usage, and model optimization to guide capital allocation decisions. This shift toward outcome-based investment models, where spending is tied directly to measurable business results, exemplifies how AI is reshaping finance from a back-office function into a strategic driver of growth and efficiency.