AI agents are transforming enterprise operations, and NVIDIA CEO Jensen Huang believes they are the future of work. These agents go beyond simple chatbots - they are goal-driven systems capable of managing complex workflows, making decisions, and learning over time. Financial institutions are leading the way, using AI agents for tasks like due diligence, compliance monitoring, and portfolio management.
Huang’s vision highlights a future where AI agents act as digital coworkers, reshaping enterprise workflows and IT departments. The shift is already underway - businesses that adapt now will lead the next wave of innovation.
AI agents are reshaping how artificial intelligence operates in enterprise settings, marking a significant departure from the traditional AI systems we’ve grown accustomed to. While chatbots and large language models (LLMs) have been the cornerstone of AI applications, these tools are primarily reactive - they respond to inputs but don’t take initiative. AI agents, on the other hand, are proactive systems that can independently execute tasks, fundamentally changing their role in business operations.
Think of traditional chatbots as conversational tools - they take an input, process it, and deliver an output. This reactive model works well for straightforward tasks like answering customer questions or handling basic support issues. But when it comes to automating intricate, multi-step processes, this approach falls short.
AI agents operate on a different level. They’re designed for independent task execution, meaning they can set objectives, make decisions, and complete entire workflows without constant oversight. Instead of waiting for a command, they actively pursue goals.
The architecture behind this difference is crucial. Traditional chatbots rely on LLMs for text-based interactions - essentially a back-and-forth exchange. AI agents, however, combine LLMs with tools like memory systems, planning modules, and execution capabilities. This enables them to handle complex, multi-step tasks across sessions, unlike chatbots that are limited to single, isolated interactions. For example, financial institutions tackling due diligence or compliance monitoring benefit immensely from this proactive and goal-driven approach.
"You can define agentic AI with one word: proactiveness. The agentic AI system understands what the goal or vision of the user is and the context of the problem they are trying to solve", says Enver Cetin, an AI expert at Experience Engineering.
AI agents bring a range of capabilities to the table, making them more like intelligent coworkers than simple tools. These features set them apart from traditional AI models:
The impact of these features is already being felt in real-world applications. For instance:
The scalability of AI agents further highlights their value. While traditional chatbots excel in handling large volumes of simple, repetitive interactions - like basic customer service - AI agents thrive in environments requiring intelligence, flexibility, and adaptability. They’re built to manage complex, variable workflows across different systems and platforms, making them indispensable for industries like finance, where processes often demand a higher level of sophistication.
These examples and capabilities underscore how AI agents are not just an upgrade but a complete reimagining of what artificial intelligence can achieve in enterprise settings.
AI agents are transforming enterprise automation, but for financial institutions, the way these agents are deployed is just as critical as their capabilities. It’s not just about adopting AI - it’s about deciding where and how it will function. For highly regulated industries like finance, on-premises deployment isn’t just a technical choice; it’s a strategic move.
A staggering 83% of business data remains housed in on-premises or co-located IT facilities. For financial firms managing sensitive client information, proprietary trading data, and regulated transactions, moving this data to external cloud environments can introduce risks that outweigh the benefits. This reliance on local data storage makes a strong case for keeping operations close to home.
The concept of data gravity - where applications and services naturally cluster around large datasets - is reshaping AI deployment strategies. Between 2020 and 2024, data gravity surged by 139%, creating immense pressure on IT infrastructure and making large-scale data migrations increasingly impractical.
For financial institutions, the stakes are even higher. Sensitive client data and compliance records are not only difficult to move securely but also subject to strict regulatory oversight. Transferring this data across borders can complicate compliance, with fines under the AI Act reaching as high as €35 million or 7% of global turnover. These risks make cloud migration a less appealing option for many in the financial sector.
On-premises AI offers a practical solution to these challenges, delivering benefits that cloud-based systems often can’t match. Security and control sit at the core of its appeal. By keeping operations local, financial firms can implement tailored security measures that align with their risk profiles. This setup isolates critical data and IT systems from external networks, ensuring greater protection and compliance with stringent data laws.
Cost is another major advantage. On-prem AI can be three to five times cheaper than cloud solutions. Over a five-year period, private AI data centers can reduce total cost of ownership (TCO) by 35% and operational expenses (OpEx) by 70% compared to public cloud alternatives. Additionally, processing data closer to its source minimizes latency, enabling faster and more accurate decision-making - an essential feature in the fast-paced world of finance.
"We believe the long-term AI action is on-prem, where customers can keep their data and intellectual property safe and secure", says Clarke, COO of Dell.
Another key benefit is customization. On-prem AI allows financial institutions to design systems that meet their specific needs, from internal policies to regulatory standards and ethical guidelines. Unlike cloud solutions, which often lock users into predefined ecosystems, on-premises setups provide full control over hardware, software, and AI models.
The market is reflecting these priorities. By 2025, nearly 50% of IT decision-makers are expected to consider both on-premises and public cloud solutions for new applications. This highlights a growing recognition of the need to balance cutting-edge AI capabilities with the demands of security, compliance, and performance.
"On-premises infrastructure comes with flexible financial models. As organizations better understand the economics of public clouds - and on-premises solutions evolve to be more flexible - they are revisiting their workload placement strategy to be more purposeful. Those that reside in the public cloud or were initially earmarked to migrate there are being revisited", explains Mindy Cancila, VP of corporate strategy at Dell Technologies.
This shift toward on-prem AI isn’t just about avoiding the limitations of cloud systems. It’s about creating a secure, high-performance environment where AI agents can thrive while meeting the rigorous demands of financial operations.
AI agents are no longer just a futuristic concept - they’re actively reshaping financial workflows in ways that go far beyond basic task automation. Unlike traditional systems that handle routine jobs, these intelligent tools can tackle complex, multi-step processes that used to require significant human oversight. Let’s look at three critical areas where these advancements are making a big difference.
Due diligence has always been a major bottleneck, often taking weeks to complete. Now, AI agents are slashing review times by up to 70%, turning what used to take weeks into tasks that can be done in just hours. These systems automate document analysis and risk assessments, making the process faster and more thorough.
The benefits go beyond just saving time. A study by Accenture revealed that 70% of professionals expect generative AI to improve M&A outcomes, with 84% believing it will make planning and execution more reliable and efficient. By processing massive amounts of unstructured data - like contracts, financial statements, and market reports - AI agents can uncover risks and opportunities that might escape even the sharpest human eyes.
A real-world example comes from Diligent, which introduced AI-powered benchmarking to its Board Reporting for ESG in 2024. Their AI model analyzes 26 categories, including greenhouse gas emissions and supply chain management, saving ESG professionals countless hours of manual work.
"We're thrilled to add the power of AI to our market-leading solutions. We have seen significant demand from our customers looking to leverage AI to drive efficiencies and mitigate risk. This is especially true of ESG, where analyzing a wealth of public information is an essential, but time-consuming task. AI-powered benchmarking in Board Reporting for ESG will provide intelligent insights, while saving time and resources."
– Adam Bailey, SVP and Global Head of Product at Diligent
AI agents also bring consistency to due diligence by applying the same rigorous analysis to every document. Over time, they adapt to industry-specific nuances, allowing human experts to focus on strategic decisions and relationship-building instead of repetitive tasks.
AI agents are also transforming reconciliation processes, particularly for NAV (Net Asset Value) calculations, which are both critical and time-intensive. These systems automate tasks like cross-checks, variance analysis, and reconciliation, all while maintaining the high accuracy needed for regulatory compliance.
For example, some institutions spend up to 21 hours per suspicious activity report (SAR), with 83% of analysts bogged down by ineffective alerts. AI agents simplify this by automatically gathering relevant data and presenting clear, actionable insights. They also enforce anti-money laundering (AML) rules specific to different jurisdictions, log every action, and produce standardized, audit-ready reports.
One notable case involves a large exchange that used AI to investigate trading irregularities like spoofing and layering. By automating complex queries - such as detecting patterns like "five or more rapid cancellations by Trader X followed by favorable price moves" - the system reduced false positives and sped up evidence collection. The financial impact is hard to ignore, with AI agents cutting operational costs by as much as 70% through automation.
AI agents are also redefining how financial institutions handle client onboarding and portfolio monitoring. Traditionally, onboarding involves tedious manual data entry and oversight. AI agents streamline this by automating data extraction and setting up continuous monitoring from the start.
The demand for this technology is clear: 82% of large companies plan to adopt AI agents by 2027 to handle the complexities of onboarding more efficiently. These systems manage documentation, audit trails, and filings within strong governance frameworks, making regulatory compliance less of a headache.
Wealthfront provides a great example of this in action. During the market turbulence of 2020, the platform saw a 68% increase in account sign-ups. Its AI algorithms analyzed users’ saving and spending habits to recommend tailored financial strategies, showcasing the power of automation in portfolio management.
"AI agents in financial services can reduce the burden of manual processes for regulatory compliance through automation. Agents can handle documentation, audit trails, and filings - all within your established governance framework that ensures the ethical use of AI."
– Salesforce
One standout feature of AI agents is their ability to monitor portfolios continuously. Unlike traditional onboarding processes that end after setup, these systems provide ongoing oversight of portfolio performance, market trends, and regulatory updates. This proactive approach helps catch issues early, ensuring portfolios stay aligned with client goals and compliance standards.
Adopting AI agents comes with its fair share of hurdles, from technical infrastructure demands to regulatory compliance and organizational alignment. However, these challenges also present opportunities for businesses to gain a competitive edge.
Building the right technical foundation is critical for deploying AI agents successfully. Many financial institutions still rely on outdated systems, which often clash with modern AI technologies. According to a 2023 McKinsey survey, 67% of enterprises that struggled to scale AI cited "inadequate infrastructure" as a major obstacle.
To deploy AI on-premises effectively, organizations need a well-rounded setup. This includes composable infrastructure managed with Kubernetes for container orchestration, unified data pipelines for real-time processing, and secure environments to protect sensitive data. Additionally, teams must have tools to manage the entire lifecycle of AI models and agents through ModelOps and AgentOps capabilities.
Key components for a solid infrastructure include:
Beyond these basics, organizations should integrate advanced tools like data fabrics, smart ETL processes, and observability systems. These help AI agents access relevant data, process it efficiently, and provide clear, actionable insights.
Once the technical groundwork is set, the next step is tackling the complex world of regulations.
For financial institutions, compliance with strict regulations from entities like FINRA, SEC, FDIC, and the Federal Reserve is non-negotiable. Introducing AI into the mix adds new layers of complexity, requiring careful planning to balance regulatory obligations with operational efficiency.
One example of success comes from a large exchange that implemented AI to investigate market manipulation tactics like spoofing and layering. Using bitemporal data, the AI agents identified suspicious patterns - such as repeated rapid order cancellations followed by favorable price shifts - while reducing false positives. This streamlined process provided regulators with clear, audit-ready evidence and significantly accelerated investigations.
The benefits of AI in compliance are clear. Firms using agentic AI with bitemporal data can expect faster regulatory responses (often within 24 hours), a 30% reduction in manual effort, and greater confidence in meeting compliance standards. For example, traditional methods often require over 21 hours to complete a single Suspicious Activity Report (SAR), whereas AI can dramatically cut down this time.
Wells Fargo offers another case study, having adopted AI-driven tools to automate risk and compliance reporting. By pulling data from multiple platforms and formatting it according to regulatory templates, the bank reduced manual intervention and sped up report preparation.
To ensure compliance and governance, organizations should:
These measures not only keep AI operations secure and compliant but also pave the way for significant operational improvements.
The successful adoption of AI agents hinges on more than just technology - it requires the right people and a willingness to adapt. Bridging the gap between financial expertise and AI engineering is essential, creating teams that can design, deploy, and manage AI systems effectively.
Statistics highlight both progress and challenges. 42% of IT professionals in organizations with over 1,000 employees report having deployed AI, while another 40% are experimenting with it. However, 37% of respondents cite resistance to change, and 43% believe their workplace isn’t prepared to manage change effectively.
This is where change agents come into play. These individuals act as champions for AI adoption, addressing skepticism, closing knowledge gaps, and encouraging innovation. Organizations need to identify and empower employees who are curious and eager to explore new technologies.
AI adoption isn’t just about technical skills - it’s also about shifting mindsets. 83% of professionals familiar with AI believe it will enhance human capabilities, boosting productivity and creating new opportunities. This perspective is key to framing AI as a tool that complements rather than replaces human effort.
Klarna’s CEO, Sebastian Siemiatkowski, offers a glimpse into how AI is reshaping organizations:
"We've stopped hiring in the last six months. We're shrinking as a company, not by layoffs, but by natural attrition. Klarna tries to apply AI across all products and services and work we do. It's having implications on how many people we need as a company."
To align talent and organizational goals, companies should focus on:
The potential rewards are immense. By 2030, AI agents are expected to become the primary users of most companies’ internal digital systems, and over 40% of Global 2000 businesses will incorporate AI agents into their workflows by 2027. Organizations that address today’s challenges will be well-positioned to lead in the AI-driven future.
The journey forward requires a balance between technical excellence and human-centered design. AI agents should act as intelligent collaborators, enhancing human expertise rather than replacing it. As Tamarah Verhoog, Senior Consultant at Valcon, aptly puts it:
"The real challenge lies in guiding employees through the transition and ensuring they not only use but also embrace new AI tools."
The transition from experimental AI tools to fully operational AI agents isn’t on the horizon - it’s happening now. Huang’s vision has set the stage for a transformation in enterprise operations.
The data paints a clear picture. Gartner projects that within three years, AI agents will be embedded in a third of enterprise software applications, a leap from just 1% in 2024. Additionally, 15% of routine business decisions will be made autonomously by these intelligent systems. This shift is reshaping the way businesses operate and underscores the need for a reimagined approach to enterprise workflows.
Huang also highlighted the importance of on-premises systems in this transformation. For industries like finance, where sensitive data, regulatory compliance, and real-time decision-making are critical, relying solely on cloud-based solutions often falls short. Keeping data on-premises is a strategic choice to enhance security, maintain control, and improve performance - not just a resistance to change.
The benefits of AI-driven automation are undeniable. It can increase productivity by as much as 40% while reducing operational costs by up to 30%. These advantages are helping industry leaders pull ahead of the competition.
But the impact of AI agents goes beyond cost savings. As Huang put it, "The IT department of every company is going to be the HR department of AI agents in the future." Enterprises need to start treating AI agents as part of their workforce - complete with onboarding, training, and ongoing management. These agents aren’t just tools; they’re digital team members.
The financial services industry, in particular, stands to gain immensely. AI agents are already managing complex workflows, far surpassing the capabilities of basic chatbots. They understand context, make decisions, and execute tasks with minimal human involvement, streamlining operations like never before.
For companies still experimenting with AI, the time for gradual adoption is running out. On average, organizations plan to deploy 30 additional AI agents in the next year, with a projected investment of $2.56 million per company. This aligns with Huang’s call to rethink the concept of the workforce. The focus now is on how quickly these agents can be integrated while maintaining the security and compliance essential for regulated industries.
Huang’s statement that "Everything that moves will be autonomous" extends far beyond self-driving cars. It envisions a future where artificial intelligence enhances or executes nearly every business process. Companies that embrace this shift today will define the competitive landscape of tomorrow.
This isn’t about humans versus machines - it’s about collaboration. Human teams working alongside intelligent agents that operate around the clock, execute tasks with precision, and continuously learn are setting the new standard for efficiency. This future is already unfolding in data centers, trading floors, and investment offices worldwide. AI agents are not just the future - they’re the present, and they’re here to stay.
AI agents represent a leap forward from traditional chatbots, bringing a whole new level of sophistication to the table. While chatbots are often limited to pre-programmed responses or handling straightforward questions, AI agents excel at understanding nuanced, complex instructions. They can make independent decisions, learn and adapt through memory, and carry out multi-step processes - all while working toward specific objectives instead of just producing static replies.
In the business world, AI agents shine by seamlessly integrating with existing systems, tackling intricate tasks, and maintaining compliance by functioning within secure, on-premises environments. This makes them a perfect fit for industries dealing with sensitive information, as they combine the speed and intelligence of automation with robust security and control.
Financial institutions often lean toward on-premises AI deployments because they provide more control, stronger security measures, and better compliance with stringent regulations. By keeping sensitive financial data within their own infrastructure, these organizations can limit exposure to external threats and ensure they meet industry standards.
Another key advantage is the ability to handle real-time data processing without depending on external cloud services. This reduces latency and boosts operational efficiency. For financial firms operating in tightly regulated environments, this setup strikes a balance between innovation and the critical need for security, privacy, and reliable performance.
Integrating AI agents into business systems often comes with its own set of hurdles. For many companies, dealing with legacy systems is a significant challenge. These older systems can have outdated architectures or incompatible data formats, making it tough to achieve smooth integration. On top of that, ensuring data security is a critical concern, especially in industries like finance where regulatory compliance is non-negotiable. Scalability also becomes a pressing issue as businesses grow and their tech needs evolve.
To navigate these challenges, businesses can adopt modular integration strategies. This approach allows AI agents to function alongside existing systems without requiring a costly and disruptive system overhaul. At the same time, focusing on strong data management practices and implementing robust security protocols can help protect sensitive information and ensure compliance with regulations. Together, these strategies can make the adoption of AI agents more efficient and secure.
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