In my work helping people think through retirement planning decisions, I often see people focus heavily on preparing their tax return but spend very little time reviewing it afterward.

By the time tax season ends, most people treat the document like a receipt: They file it, save a copy somewhere and move on.

The push mirrors Adobe’s broader strategy around agentic AI. For customers, the company lets them choose models, combine them with their own data and Adobe’s, and point agents at specific business outcomes.

Internally, Durn, who is also in charge of technology, security and operations, has taken a similar approach to finance: pairing a rules-based, data-heavy function with AI, within a structure where finance, IT, and security report to one leader so pilots can move to production quickly. “Accuracy is non-negotiable,” he adds; that’s why Adobe is investing in structured data and governance so it can move fast without sacrificing precision, he says. 

The rise of AI is rapidly reshaping corporate leadership, accelerating turnover and elevating executives who can deliver fast, tangible results. Even long-tenured leaders face increasing pressure from investors to move aggressively on AI. Recent leadership changes, including the announced retirement of Adobe CEO Shantanu Narayen, highlight how little patience markets now have for perceived hesitation. At the same time, Adobe reported that annualized revenue from its AI-first products more than tripled year over year in its first quarter of fiscal 2026, which ended Feb. 27. Across Fortune 500 companies, this dynamic is creating a new internal proving ground where executives are judged by how effectively, and how quickly, they deploy AI to drive growth, efficiency, and innovation.

Using AI in finance

Inside finance, Durn groups AI use into three buckets: forecasting, anomaly detection, and general productivity.

For forecasting, AI uncovers patterns and signals in data that would be difficult for humans to detect quickly, he explains. Anomaly-detection agents flag performance that’s unexpectedly strong or weak—“things that can get lost in the sea of data”—so finance can intervene faster, he says.

However, Durn says the best examples now sit in productivity, citing three use cases:

1. Extracting information from PDFs

One of the most developed use cases involves “containers” of information—collections of PDFs such as investor transcripts, quarterly reports, and analyst research. Finance teams use Adobe’s PDF Spaces to load documents into a shared digital workspace and use an agentic AI assistant to surface themes, insights, and messaging cues in minutes rather than hours.

A recent Forrester TEI study found Acrobat’s agentic AI Assistant increases efficiencies in document summarization and analysis by 45%. Durn says that matters because “the world’s information lives in PDF,” and AI that turns static content into insights that can be used.

2. Cutting contract review time in half

Adobe is also using agentic AI to overhaul contract reviews across finance and procurement functions including revenue assurance, contract operations, product fulfillment, and vendor management. Instead of finance professionals combing through every clause, an AI assistant scans thousands of contracts, highlights provisions relevant to each function, and flags non-standard terms.

The system has cut review time roughly in half, speeding individual reviews and allowing teams to query the entire contract repository—for example, identifying which contracts include auto-cancellation features or foreign-exchange adjustment windows, Durn says. Adobe built its first prototype by April 2024 and began onboarding teams in January 2025.

3. Automating “common” inboxes

A third area is the “common inboxes” that handle high-volume internal and external email—shared addresses for sales, treasury, finance, and supplier questions. Adobe deployed an agentic AI assistant that auto-tags, prioritizes, routes, and, when criteria are met, auto-responds to emails. Typical queries include supplier billing issues or standard credit-quality questions coming into the treasury from Salesforce.

“In 2025 alone, the system auto-responded to about 300,000 emails across 19 inboxes, saving more than 5,000 hours of manual work and freeing teams to focus on more complex issues,” he says. The tool took about six months to build; beta teams began using it around August 2024, with full rollout in January 2025.

The payoff, he stresses, isn’t headcount cuts but the ability to scale more efficiently as Adobe grows.

Grassroots ideas, decade-long build

Durn traces these finance use cases to Adobe’s long AI journey and a bottom-up idea pipeline. The company has invested in machine learning and AI for more than a decade, initially to understand customer usage patterns and embed intelligence into products—work that laid the groundwork for generative and agentic AI.

Many of the best applications come from “reaching down into the organization” and asking employees where AI could remove friction or make their jobs easier, he says. There are more ideas than capacity, so the team prioritizes those with the greatest impact.

When deciding whether to green-light AI investments, Durn focuses on organizational velocity—the ability of back-office functions to keep pace with faster product innovation. If finance doesn’t adopt AI, he argues, it risks becoming a “rate limiter of growth.”

The actual spend is modest, he adds; much of the work involves change management and process redesign layered onto Adobe’s technology.

Durn’s perspective on change management coincides with new research from McKinsey. To capture the full value of AI, organizations need to go beyond “a piecemeal approach and push for a double transformation—both technical and organizational—that includes reimagining how work gets done across functions and workflows,” according to the report. While 88% of organizations surveyed are now experimenting with AI, fewer than 20% report tangible bottom-line results,, the research finds.

How AI is changing his own job

For his own workflow, Durn relies on AI primarily for insight generation. Ahead of earnings, his team loads pre-earnings research reports, Adobe filings, and peer transcripts into an AI-powered workspace to surface themes and likely investor questions.

Scripts and Q&A preparation are then run through models with guardrails to test whether messaging addresses those themes and to ask, “If I were an investor, what are my key takeaways?”

He sees it as a useful check on clarity and consistency—using AI to validate instincts and sharpen how Adobe communicates with the market.

A select team of students from the University of St. Thomas’ Cameron School of Business has officially launched its bid for the FY 2025–2026 Texas Region CFA (Certified Financial Analyst) Institute Research Challenge, a prestigious competition often referred to as the “Investment Olympics” for university students. 

The CFA Institute Research Challenge is an annual competition that provides university students with hands-on mentoring and intensive training in financial analysis. The competition tests students’ analytical, valuation, report writing and presentation skills, challenging them to take on the role of real-world research analysts. The 2025–2026 cycle involves more than 6,000 students from more than1,000 universities worldwide. 

Representing UST, the team is comprised of Team Captain Chih Jung Ting, MSF; Vice-Captain Daria Kostyukova, BBA/MSF; Reginald Paolo Laudato, BBA/MSF; Simon Wong, BBA in Finance; and Anjali Sebastian, BBA in Finance. 

The team of five students has been selected to conduct an exhaustive equity analysis of a target company, competing against top-tier universities from around the Texas area. 

“Taking part in the CFA Research Challenge has been the most intense and rewarding experience of my academic career,” said Chih Jung Ting, team captain. “We aren’t just reading case studies anymore—we are digging into real balance sheets, forecasting real economic shifts, and learning how to defend our ideas under pressure. It’s given us a true taste of what it means to be an analyst.” 

The team is supported by Department Chair of Economics and Finance Dr. Joe Ueng, CFA, and faculty advisor Dr. Dan Hu. Assisting the team was industry mentor Matt Caire, CFA, CFP®, CMT from Vaughan Nelson, a seasoned professional who provides guidance on the intricacies of equity research. 

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“Our participation in the CFA Research Challenge is a testament to the caliber of our students and the strength of our curriculum,” said Dr. Ueng. “By applying advanced financial theory to a live market scenario, our students demonstrate that they are not just learners, but emerging professionals ready to contribute to the global financial community. We are incredibly proud of their dedication to academic excellence.” 

Dr. Sidika Gülfem Bayram, the Cullen Foundation Endowed Chair of Finance and UST associate professor of Finance said participating in the CFA Research Challenge this year creates a pivotal moment for UST students.  

“I’m impressed to see our students apply their curriculum knowledge to meet the depth and vast nature of the analysis required in such a fierce competition,” Dr. Bayram said. “I’m so proud of the effort the students put into the challenge.” 

This year, the team has been tasked with analyzing Green Brick Partners, a publicly traded company in the consumer cyclical sector. During the past several months, the students have dedicated more than 150 hours to conducting a deep-dive analysis of the company’s business model and industry position, interviewing company management and financial experts, building complex financial models to determine the stock’s intrinsic value, and compiling an “Initiation of Coverage” report with a buy, sell or hold recommendation. 

“Participating in the CFA Research Challenge allows our students to bridge the gap between classroom theory and the fast-paced world of investment management,” said Dr. Hu. “It demands a level of rigor and professional ethics that prepares them for the highest levels of the finance industry.” 

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The team will presented its findings and defended its recommendation before a panel of judges from leading investment firms at the CFA Society local final in late February. Winners of the local competition will advance to the subregional and regional rounds, with the goal of reaching the global finals in May 2026.