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Agentic AI - Finance & the ‘Do It For Me’ Economy

An artificial intelligence that can make autonomous decisions without human intervention. Meet agentic AI.&nbsp;AI, and agentic AI could have a bigger impact on the economy and finance than the internet era.This paradigm shift in AI is powered by a combination of technological breakthroughs in contextual understanding, memory, and multi-tasking abilities.Agentic AI effectively turbocharges the Do It For Me (DIFM) economy. In financial services, users will have their own bots or AI agents helping them choose products and execute transactions. Competition will tick-up as starts-ups grow.The nature of work could change. Those tasks that are outsourced today to contractors or third parties will be increasingly done by agentic AI.The financial services sector is the second largest consumer of GenAI after the telecom and media sector. Historically, banks are the biggest spender on technology (outside of the tech industry). This trend will likely continue with GenAI and agentic AI too.<h5>Agentic AI Use Cases Across Financial Services</h5><img style="border-radius:10px;" src="/rcs/v1/media/citigpa/akpublic/storage/retrieveMediaFile/678a4e5e4a24ce43ec43ec8d.png" alt="Chart showing different use cases in the financial industry." width="900" height="693"><div class="lmn-container infographics-template" style="color:#000000;font-family:Citi-Sans-Text-Regular;margin:auto;"><div style="background-color:#F0F5F7;border-radius:20px;margin:auto;padding:48px;"><h3 style="font-family:Citi-Sans-Text-Regular;font-size:16px;line-height:24px;margin-bottom:24px;">Detailed infographic description</h3>This infographic, titled "Agentic AI Use Cases," is a table outlining the applications of Agentic AI across four key sectors of the financial industry: Wealth Management/Retail Banking, Corporate Banking, Institutional Investor and Insurance. The use cases are organized into seven distinct categories, each marked by an icon. The seven categories are: Personalized Offers, Customer Engagement, Operational Efficiency, Risk and Underwriting, Financial Forecasting, KYC / Onboarding and Fraud Prevention.</div></div>&nbsp;Agentic AI is largely in an experimental phase – and this report offers up insights gleaned as we looked under the hood on what is being built now for future production.We spoke to 30+ AI startup founders, BigTech executives and other external experts in preparing this GPS report.It highlights some of the key use cases of agentic AI in financial services, ranging from compliance, deepfake and fraud prevention, onboarding and KYC, wealth, credit, treasury workflows, and much more.

Finance & the ‘Do It For Me’ Economy

An artificial intelligence that can make autonomous decisions without human intervention. Meet agentic AI.&nbsp;&nbsp;

Agentic AI

<ul><li>AI chatbot adoption in China is broad but remains shallow, with heavy users signaling a potential tipping point toward faster and deeper usage</li><li>Even at this early stage, AI is delivering measurable efficiency gains, especially for heavy users who trust it for higher order tasks needing reasoning and creativity</li><li>Labor market disruptions have sparked anxiety but remain largely theoretical, with most users expressing a mix of job anxiety and optimism about AI driven income opportunities as policy governance takes shape</li></ul>

A new Citi Research report from a team led by Head of Pan-Asia Internet Research Alicia Yap looks at the possibility that adoption of artificial intelligence (AI) is nearing a tipping point in China, and explores the potential impacts of such a shift.We worked with Citi Research’s Innovation Lab to run a proprietary survey on the adoption of AI consumer products in China, covering 1,800 respondents with broad representation across age groups, genders and city tiers. The survey focuses on how people actually use AI chatbots today — and what that behavior might suggest about productivity, labor market implications and policy direction.&nbsp;The picture that emerges, as explored in our Macro-to-Micro report, is one of impressive breadth but limited depth. AI is already embedded in daily life for many users, yet overall usage intensity and formal workplace adoption remain relatively shallow. At the same time, several indicators suggest China may be approaching an inflection point where adoption and impact could accelerate.Consumer engagement is already high. Nearly 70% of respondents use AI agents or chatbots daily, with average usage of 49 minutes per day. Most users rely on one or two apps, which underscores how competitive the space has become.&nbsp;And AI is becoming an all purpose tool: General search, content generation and work assistance are the most common uses, while travel planning and health related inquiries are also widespread. Patterns vary across demographics: Students gravitate toward homework help, working age adults toward productivity tasks, and older users toward health questions. Content creation is a particularly important use case, especially image generation, with video and other formats trailing behind.Platform backed AI chatbots dominate consumer adoption, but the market isn’t homogenous. Users tend to differentiate providers by strengths such as speed and simplicity, trustworthiness, accuracy and logic, or creative and emotional capabilities. Nearly half of respondents have also experimented with foreign AI tools, particularly in larger cities, often in search of different features or better performance.Despite high engagement, adoption remains shallow. Average monthly usage remains modest relative to overall internet time, and AI related revenues still account for only a small share of gross domestic product (GDP). Our survey data are consistent with this: Only a small minority of respondents qualify as heavy users, spending multiple hours per day with AI. These heavy users, however, behave very differently — and their behavior may matter disproportionately.Heavy users are more engaged, rely more on AI for work and idea generation, and expect to increase usage more rapidly than the average user. They also display higher trust in AI’s outputs and are more willing to use it for higher order tasks involving reasoning or creativity. This pattern points to a J shaped adoption curve, where early adopters drive deeper usage that eventually spreads more broadly. In this sense, current adoption may understate future momentum.Even at this early stage, efficiency gains appear meaningful. Using trust in AI outputs as a proxy for time saved, we estimate that respondents may already be saving about 30 minutes per day on average — equivalent to some 4.3% of waking hours. Adjusted for overall penetration, this suggests an economy wide efficiency gain of around 1.8%. For heavy users, the gains are far larger, reflecting a compounding effect between greater usage, higher trust, and more advanced applications. As adoption deepens, productivity impacts could accelerate.By contrast, the labor market effects of AI remain largely potential rather than realized. Our earlier macro work suggested that AI could affect roughly 31% of jobs in China, with around 9.6% facing displacement risk. Survey evidence indicates little of this has materialized so far, largely because AI use is still more personal than institutionally driven. Employers are increasingly aware of AI’s relevance, but formal workplace deployment remains limited.Sentiment around AI reflects this early stage. Most respondents believe AI will replace many jobs in general, but views are more divided when it comes to personal job security. At the same time, a majority believe AI can improve earnings opportunities. This combination of substitution anxiety and complementarity optimism closely mirrors our macro framework. Heavy users tend to hold both views most strongly, while younger respondents — despite lower adoption — show the highest job insecurity, consistent with greater exposure among junior roles.Finally, policy is beginning to catch up with technology. AI governance in China is gaining momentum, with a clear emphasis on human centric deployment and safeguards against misuse. Beyond governance, policymakers appear focused on ensuring broader distribution of AI’s gains, including measures to improve work life balance and gradual progress on fiscal reforms. Taken together, the survey suggests an economy still early in the AI transition — but one that is steadily moving toward deeper adoption, greater productivity impact and more visible labor market implications.

Our new Macro-to-Micro report, AI Transition: Survey Findings Suggest Tipping Point Is Near, also considers use cases for AI and top picks among stock names. It’s available in full to existing Citi Research clients <a href="https://www.citivelocity.com/t/eppublic/4OwP3" target="_blank">here.</a>

A new Citi Research report suggests that a tipping point is near for AI adoption in China. Our Innovation Lab survey finds AI adoption is widespread but shallow — but once the tipping point is reached, we see potential labor-market impacts and increased momentum for AI governance.

AI Transition: Survey Findings Suggest Tipping Point Is Near

Transcript

<ul><li data-list-item-id="ed12a2a586d80d53c8075751712c3eea6">AI has reached a critical inflection point, moving from a back-office productivity tool to a core catalyst for business model evolution</li><li data-list-item-id="ea74ee4a4c3e9e5c5ecd3e8ae570d5322">Businesses are approaching AI investments strategically, focusing on areas that deliver the greatest value, whether in productivity, new product development, or customer experience enhancements</li><li data-list-item-id="e1bc797e5bd4203648c77d60df06a5d31">The emergence of "Agentic AI" poses new challenges around identity, authorization, and accountability that the industry is still grappling with, but also presents opportunities to simplify and improve payment processes</li></ul>

In this episode of The Citi Institute Podcast, host Alex Miller is joined by Ranjeet Kapur, Global Head of Fintech at Citi Commercial Bank and James Simcox, Group Chief Product Officer at Equals.They explore how Artificial intelligence has moved from experimental back-office tool to a core driver of business model change in Fintech and corporate banking, focusing on payments, fraud prevention, customer experience, and emerging Agentic AI.Ranjeet Kapur comments that AI and machine learning have existed for decades, but recent advances in hardware and processing power have unlocked their ability to deliver value at scale.James Simcox explains how AI has rapidly shifted from small-scale experiments to pervasive deployment across Equals, with AI now writing around 86% of the firm’s code, up from 5–10% a year earlier, and transforming functions such as reconciliation, finance, fraud detection, and customer service.Together, they examine the rise of Agentic AI and discuss why a strategic approach to AI investment is becoming increasingly important.Citigroup, Citi, and Equals are not affiliated and are independent of each other. Equals maintains a client relationship with Citi Commercial Bank. The speakers' views are their own and may not necessarily reflect the views of Citi or any of its affiliates. All opinions are subject to change without notice. Neither the information provided nor any opinion expressed constitutes a solicitation for the purchase or sale of any security. The expressions of opinion are not intended to be a forecast of future events or a guarantee of future results.

Ranjeet Kapur leads the FinTech segment for Citi Commercial Bank (CCB) within the Digital, Technology &amp; Communications (DT&amp;C) group. In this role Ranjeet supports his team’s efforts in better understanding and growing Commercial Bank’s engagement with this segment. Clients include payment intermediaries, alternative finance platforms, WealthTech and other models in the Fintech ecosystem.Prior to that, and as part of the of DT&amp;C segment, he led the process for launching the Western European digital business. He has also held roles in the Commercial Subsidiaries Group and Business &amp; Planning team at CCB. Before Citi Ranjeet worked in the technology field, first for a Canadian telecom and after that for a publishing house in Guatemala.Ranjeet holds an MBA from IESE Business School in Barcelona and B.Sc. in Computer Engineering from Rochester Institute of Technology in New York.

Ranjeet Kapur, Global Head of Fintech at Citi Commercial Bank

James Simcox is Chief Product Officer at Equals, where he leads product, delivery and strategy across the Group's embedded finance and payments platform. James has held progressively more senior roles throughout the growth of Equals, an embedded money movement platform powering exceptional businesses globally. Before Equals, James held a role as Business Analyst at Procter &amp; Gamble and led digital innovation and central projects at Quintessential Brands Group, reporting to the board. He is a recognised voice on the intersection of AI, security and fintech.

James Simcox, Group Chief Product Officer, at Equals

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From Productivity Tool to Business Transformation

In this episode of The Citi Institute Podcast, host Alex Miller is joined by Ranjeet Kapur, Global Head of Fintech at Citi Commercial Bank and James Simcox, Group Chief Product Officer at Equals. They explore how Artificial intelligence has moved from experimental back-office tool to a core driver of business model change in Fintech and corporate banking, focusing on payments, fraud prevention, customer experience, and emerging Agentic AI.

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AI at the Inflection Point

<ul><li data-list-item-id="e10801c14488c85f5ceec0dba986ef83e">AI innovation is fundamentally reshaping global economics driving accelerated adoption and significant market performance shifts across diverse industries</li><li data-list-item-id="e9b83375de46c0c05046868bb75e2f7e3">Businesses are strategically leveraging AI for revenue growth and market expansion moving beyond mere cost savings to maximize creative application</li><li data-list-item-id="e7a7f87239c9172eaf22493172daa7d73">Critical AI deployment factors include rising output token costs persistent infrastructure constraints and evolving cybersecurity and regulatory challenges</li></ul>

Rob Rowe and Heath Terry offer a mid-year review of AI, ranging from the accelerating pace of adoption and how companies are leveraging AI to regulatory issues and infrastructure challenges such as the much-discussed token bottleneck.&nbsp;&nbsp;This podcast contains thematic content and is not intended to be investment research. Nor does it constitute financial, economic, legal, tax or accounting advice. This podcast is provided for information purposes only and does not constitute an offer or solicitation to purchase or sell any financial instruments. The contents of this podcast are not based on your individual circumstances and should not be relied upon as an assessment of suitability for you of a particular product, security, or transaction. The information in this podcast is based on generally available information and, although obtained from sources believed by Citi to be reliable, its accuracy and completeness are not guaranteed. Past performance is not a guarantee or indication of future results. This podcast may not be copied or distributed, in whole or in part, without the express written consent of Citi. © 2026 Citigroup Global Markets Inc. Member SIPC. All rights reserved. Citi and Citi and Arc Design are trademarks and service marks of Citigroup Inc. or its affiliates and are used and registered throughout the world.

Rob Rowe is the Head of Citi Research’s Global Strategy &amp; Macro Group (GSMG) as well as the Global Head of Citi Research. GSMG unites all of Citi’s Economists with Fixed Income, FX, Equity and Commodities strategists to create a common team providing research on the world’s major economies and asset classes. Rob has been with Citi for 35 years in various capacities. He originally joined Smith Barney, Harris Upham &amp; Company in 1989 working in investment banking, fixed income sales, and research. In addition to his overall responsibilities, he continues to provide portfolio strategy for select clients as part of his previous role overseeing the Global Bond Portfolio Analysis team. Rob is a graduate of Hartwick College with a BA in European History and the Theatre. He is also a graduate of the American Academy of Dramatic Arts.

Robert Rowe, Global Head of Research, Citi

Heath Terry is the Global Sector Head for Technology &amp; Communications Research at Citi Research. Prior to joining Citi in May 2025, Heath led the centralized research effort in TMT for Balyasny Asset Management and was a Managing Director at Third Point Ventures, leading its growth stage venture capital practice. Prior to that Heath worked for more than 20 years in equity research, most recently as the head of Goldman Sachs’ Global Investment Research’s Technology, Media and Telecom Group. While at Goldman Sachs Heath led research's working group on Artificial Intelligence and created the firm’s venture capital research practice. He is a Trustee for the Southern Environmental Law Center and Vice Chairman of The University of Alabama President’s Cabinet.

Heath Terry, Head of Technology & Communications Research

Rob Rowe and Heath Terry offer a mid-year review of AI, ranging from the accelerating pace of adoption and how companies are leveraging AI to regulatory issues and infrastructure challenges such as the much-discussed token bottleneck. &nbsp;

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E83: AI's Mid-Year Review: The Token Bottleneck 

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As agentic commerce emerges, AI-powered agents making purchases on behalf of consumers will require payment rails built for machine-native transactions. Instant payments are uniquely equipped to meet this demand, providing the speed, finality, and data-rich settlement needed to power this new era of e-commerce.

<ul><li data-list-item-id="e229e267c2a9eb4cdb1f2933b89027a21">Agentic commerce is the new frontier where AI agents execute e-commerce transactions on behalf of consumers demanding payment rails optimized for speed security and cost</li><li data-list-item-id="e9903579ec5c85c572bff6bbc60fa30eb">Instant payments are uniquely positioned to power agentic commerce by providing the speed, finality, and rich data reconciliation that machine-native API-driven transactions require</li><li data-list-item-id="e6e16b6a0f1a4479eb4ed9926ecab6601">Markets like India, Brazil, and Thailand have shown the scale instant payments can achieve with the right regulatory clarity and incentive design, and agentic commerce gives other markets a fresh reason to close the gap</li></ul>

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Agentic Commerce: A New Chapter for Instant Payments

Agentic commerce is more than a shift in how the world buys, sells and pays. Agentic commerce is a future in which artificial intelligence (AI) agents can discover, decide and transact on behalf of people and businesses across retail, travel, financial services and beyond. It has the power to redefine industries and transform how businesses and individuals interact with and experience their entire value chain.Citi Services is helping shape what comes next. For more than 200 years, Citi has been at the forefront of innovation across the global financial ecosystem, from enabling the movement of money around the world to helping advance instant payments, digital assets and other emerging capabilities. Today, we are bringing that experience, global network and continued focus on innovation to the development of agentic commerce.For our clients, that's a rare first-mover advantage: the chance to build for tomorrow's customer today. By bringing together Citi’s global network, capabilities and expertise, we help clients navigate this next chapter with greater ease and connectivity. By connecting clients to a single global network and the breadth of Citi’s insights and solutions, we can help them explore the opportunities presented by agentic commerce while addressing the evolving demands of an increasingly connected digital economy.Explore Citi’s latest thinking, research and perspectives on agentic commerce, AI and the technologies shaping the future of commerce.

Agentic commerce is more than a shift in how the world buys, sells and pays.