Adobe’s finance chief, Dan Durn, is transforming the company’s financial operations into a testing ground for agentic AI, employing autonomous software agents to enhance forecasting, review contracts, and manage a large volume of emails. This initiative aligns with Adobe’s overarching strategy to leverage AI, allowing clients to customize models alongside their data and Adobe’s, all directed at achieving specific business objectives.
In his dual role overseeing technology, security, and operations, Durn has integrated AI into finance by merging a data-centric approach with advanced AI capabilities. This structure facilitates swift transitions from pilot projects to full-scale implementation, as all relevant departments report to a single leader. “Accuracy is non-negotiable,” Durn asserts, emphasizing Adobe’s commitment to structured data and governance that enables rapid yet precise advancements.
The ascent of AI is reshaping corporate leadership dynamics, intensifying turnover rates and promoting executives who can deliver immediate, measurable results. This shift is evident in recent leadership transitions at Adobe, including the anticipated retirement of CEO Shantanu Narayen, reflecting growing investor impatience for decisive action in AI deployment. In its first quarter of fiscal 2026 ending February 27, Adobe reported that annualized revenue from its AI-first offerings had more than tripled year over year, underscoring a broader trend among Fortune 500 companies where leaders are increasingly evaluated on their effectiveness and speed in harnessing AI for growth and innovation.
Using AI in Finance
Durn has categorized the application of AI within finance into three key areas: forecasting, anomaly detection, and productivity enhancement. AI’s capabilities in forecasting allow for the detection of patterns and signals in data that might otherwise remain hidden from human analysts. Meanwhile, anomaly detection agents identify performance irregularities, enabling quicker interventions by finance teams.
However, Durn highlights that the most impactful applications currently reside in productivity. Among these is the extraction of information from PDFs, a sophisticated use case where finance teams utilize Adobe’s PDF Spaces to load and analyze documents such as investor transcripts and quarterly reports. An agentic AI assistant streamlines this process, rapidly surfacing relevant themes and insights. A recent Forrester TEI study reported that Acrobat’s AI assistant boosts efficiency in document analysis and summarization by 45%, affirming Durn’s statement that “the world’s information lives in PDF.”
Moreover, Adobe is utilizing agentic AI to significantly reduce contract review times across various financial and procurement functions. Rather than having finance professionals manually sift through each clause, an AI assistant scans thousands of contracts, highlighting pertinent provisions and flagging non-standard terms. This innovation has halved the time required for contract reviews, enabling teams to query the entire repository for specific features like auto-cancellation clauses or foreign-exchange adjustments. The first prototype was developed by April 2024, with team onboarding commencing in January 2025.
A third application involves automating the management of “common inboxes” that handle a high volume of internal and external emails. Adobe has introduced an agentic AI assistant capable of auto-tagging, prioritizing, routing, and even auto-responding to emails when certain criteria are met. In 2025 alone, this system processed approximately 300,000 emails across 19 inboxes, saving over 5,000 hours of manual labor and allowing teams to concentrate on more complex issues. Durn notes that the objective is not to reduce headcount but to facilitate more efficient scaling as Adobe expands.
Durn attributes these finance use cases to Adobe’s extensive AI development history and a grassroots approach to idea generation. The company has invested in machine learning and AI for over a decade to better understand customer behavior and embed intelligence in its products, laying the foundation for both generative and agentic AI. Many innovative applications spring from soliciting input from employees about how AI can alleviate challenges in their work. Given the abundance of ideas, the team prioritizes those that promise the greatest impact.
When evaluating potential AI investments, Durn emphasizes the importance of organizational velocity—the capacity of back-office functions to align with rapid product innovation. He warns that without adopting AI, finance could become a “rate limiter of growth.” He also indicates that the financial commitment for these initiatives remains modest, largely involving change management and process redesign integrated with Adobe’s existing technologies.
Durn’s perspective aligns with recent findings from McKinsey, which suggests that to maximize AI’s potential, organizations must advance beyond a piecemeal approach and pursue a comprehensive transformation, both technical and organizational, to reimagine workflows. Although around 88% of organizations are experimenting with AI, fewer than 20% report significant improvements to their bottom lines.
In his own workflow, Durn utilizes AI mainly for generating insights. Ahead of earnings announcements, his team employs an AI-driven workspace to analyze pre-earnings research, Adobe filings, and peer transcripts to identify themes and anticipate investor inquiries. Scripts and Q&A preparations are evaluated through AI models to ensure clarity and alignment with key messaging points. He views this as a valuable tool for validating instincts and refining Adobe’s communication strategy with stakeholders, illustrating the transformative role AI plays not just in operations but also in executive decision-making.
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