\n Today, the question for banking leaders is not whether AI can help solve longstanding challenges and open new avenues for growth, but how to deploy it in both the near and long term.\n<\/p>\n\n\n\n
With the global banking system intermediating hundreds of trillions of dollars in funds, corporate and commercial banking sits at the center of the global economy.1<\/sup> It spans geographies, regulatory regimes, client types, and balance sheet structures. And critically, it relies on decision\u2011making that unfolds across extended, non\u2011linear processes.<\/p>\n\n\n\n \n Where past transformation efforts often fell short in accelerating decision-making and streamlining cumbersome processes, the emergence of agentic AI is changing what is possible. Combined with enterprise\u2011grade platforms, new AI capabilities are being designed to help coordinate work securely, governably, and at scale.\n<\/p>\n\n\n\n \n This shift is best understood through four ways that AI is starting to reshape how corporate and commercial banking can operate.\n<\/p>\n\n\n\n \n Relationship managers have long been the lifeblood of positive client experiences, often serving as the trusted human face of the business. In this role, the ability to synthesize insights from complexity is a critical skill. Yet even the best managers often struggle to manage the growing sprawl of data and requirements.\n<\/p>\n\n\n\n \n In many cases, relationship managers function as human middleware\u2014responsible for manually aligning information that the organization itself cannot easily connect as environments become more fragmented across treasury and credit\/lending systems, client data, risk platforms, documents, and collaboration across channels.\n<\/p>\n\n\n\n \n Agentic AI addresses this by shifting from passive or responsive assistance to active work coordination. AI-powered agents can assist in monitoring where a client, deal, or request sits across multiple systems, identify what is missing, and surface the right context at the moment decisions are made.\n<\/p>\n\n\n\n Standard Chartered<\/a> equipped more than 6,000 bankers with a unified platform spanning 53 markets, giving relationship managers real-time insights and more time to spend on client-centered engagement. Likewise, UBS<\/a> deployed Microsoft Copilot<\/a> across its employee base (including relationship managers), transforming legal research with an AI assistant that surfaces precise clauses across 26 million documents using natural language queries. The system eliminates manual search and accelerates information retrieval, freeing experts to focus on judgment-intensive work and giving relationship managers more time for client interactions.<\/p>\n\n\n\n \n The result is not \u201cAI advice,\u201d but improved decision readiness for teams. Relationship managers spend less time assembling inputs and more time applying expertise, consistently, audibly, and earlier in the process.\n<\/p>\n\n\n\n \n Clients do not experience the bank as a set of systems. They experience it as a series of moments across onboarding, credit, treasury, and servicing. Too often, these moments are disconnected, which creates friction and can erode confidence. \n<\/p>\n\n\n\n \n Agentic AI helps close these gaps not just by connecting interactions, but by making them more meaningful. AI agents enable financial professionals to understand the client in context by maintaining continuity across all channels, while also helping anticipate needs, surface relevant insights, and making sure requests move forward without repeated inputs or dropped handoffs.\n<\/p>\n\n\n\n Commerzbank<\/a>, for example, built an AI agent that now handles more than 30,000 customer conversations per month, resolving approximately 75% of requests autonomously. By maintaining shared context across interactions and orchestrating workflows in real time, the bank reduces the need for customers to repeat information while ensuring requests move forward without disruption. The result is a more consistent, responsive experience at scale.\n<\/p>\n\n\n\n \n AI also helps bankers maintain continuity across interactions and generate consistent, empathetic responses by unifying previously siloed workflows in sales, service, and communications. First National Bank<\/a> is using Copilot for Sales to reduce fragmented touchpoints and strengthen ongoing, relationship-driven engagement with commercial clients across channels and teams. \n<\/p>\n\n\n\n \n This extends across other complex and high-stakes moments in banking. In investment banking, for example, AI-assisted meeting preparation pulls together internal context and external market data, so bankers walk into conversations already aligned to the client’s situation. In corporate and commercial banking, onboarding and KYC agents can intelligently scan documents, cross-reference sanctions lists and adverse media, and surface key findings for relationship managers. \n<\/p>\n\n\n\n \n This can turn what has traditionally been a weeks-long, manually intensive process into a more efficient and consistent experience for both the bank and the client. \n<\/p>\n\n\n\n \n The result is a shift from fragmented, episodic service to coordinated, proactive engagement, where interactions are more timely, more relevant, and more aligned to the client\u2019s evolving objectives.\n<\/p>\n\n\n\n \n Banking operations have rarely struggled because of a lack of automation alone. They often struggle because complexity compounds.\n<\/p>\n\n\n\n \n Credit assessment depends on risk inputs, risk depends on documentation, documentation depends on counterparties, onboarding depends on compliance and core systems rely on all of the above. These processes span teams, systems, and timelines that were often not designed to operate in sync, and as a result work can slow down, fragment, and stall.\n<\/p>\n\n\n\n \n Agentic AI helps address these challenges by not only automating tasks but also coordinating workflows across stages. It can track progress across processes, resolve handoffs dynamically, and surface exceptions to the right experts when judgment is required, reducing friction by connecting workflows, aligning dependencies, and helping work move more smoothly across systems and teams.\n<\/p>\n\n\n\n Bank of Queensland<\/a>, for example, used Microsoft Copilot<\/a> to streamline complex, multi-step workflows, reducing risk analysis from weeks to a single day while improving quality by 22%. By automating document-intensive tasks and simplifying cross-functional coordination, teams accelerated decision-making and enabled more adaptive, efficient execution across core banking processes.<\/p>\n\n\n\n \n At an ecosystem level, Swift<\/a> applied federated learning and confidential computing to detect financial crime across institutions without centralizing data, which enabled more coordinated risk modeling while preserving privacy and regulatory boundaries. \n<\/p>\n\n\n\n At Scotiabank, rather than replacing core systems, the bank embedded AI agents into its payment operations to coordinate data transformation, reconciliation, and exception handling across fragmented workflows. As a result, processes that once required weeks can now be executed in seconds, with clear auditability and control.2<\/sup><\/p>\n\n\n\n \n When processes are streamlined and coordination costs fall, measurable outcomes can improve. Faster credit decisions, strengthened compliance, improved reconciliation and materially better banker and borrower experiences are enabled, without requiring wholesale system replacement.\n<\/p>\n\n\n\n \n Innovation in corporate and commercial banking typically falls short because complexity has historically resisted scale.\n<\/p>\n\n\n\n \n In trade finance, structured lending, and cross-border treasury, work spans multiple stakeholders, documents, and exceptions across boundaries. These are not linear processes, but complex, judgment-driven environments where traditional automation can break down.\n<\/p>\n\n\n\n \n The shift enabled by agentic AI is reimagining how these processes operate. Instead of forcing complexity into fixed workflows, banks can execute work adaptively, coordinating across participants in real time and responding to exceptions as they arise.\n<\/p>\n\n\n\n \n Agentic systems open the door to different operating models. They can address deviations, help route actions to the right specialists, and capture how complex work is completed. Over time, these patterns can be encoded into reusable playbooks, so banks can scale expertise, deliver more consistent outcomes, and maintain continuity across complex, multi-party processes.\n<\/p>\n\n\n\n A glimpse of this future can be seen in a Microsoft-led trade finance proof of concept<\/a> with ANZ, HSBC, and Lloyds. By embedding AI agents within ERP systems, the solution parses letters of credit, cross-checks them against invoice and shipping data, flags discrepancies, and securely transmits structured, standards-aligned information to bank platforms. Rather than simply accelerating existing steps, it demonstrates how trade workflows can evolve toward a more consistent, data-driven model, ultimately reducing fragmentation, minimizing manual rekeying, and improving traceability across trade finance processes.<\/p>\n\n\n\n \n What emerges here is not just more efficient execution, but a potentially fundamental new way of operating, one in which complex, exception-heavy processes can be coordinated, governed, and continuously improved at scale.\n<\/p>\n\n\n\n \n Realizing this shift depends on enterprise-grade AI platforms that combine advanced AI models, secure integration across systems and partners, and scalable data and analytics foundations.\n<\/p>\n\n\n\n Leading institutions are using GenAI to streamline trade finance<\/a> and drive the shift from paper-based to digital platforms.<\/p>\n\n\n\n \n Experienced banking leaders are right to approach AI with caution. The industry has seen many moments where enthusiasm outpaced operational reality. What differentiates this phase is not model capability alone, but fit. Agentic AI aligns with how corporate and commercial banking functions. It streamlines key processes, respects variability, reacts to change, and reduces coordination costs rather than oversimplifying work.\n<\/p>\n\n\n\n \n Equally important, platforms matter. Without security, governance, and integration at enterprise scale, AI coordination becomes a risk rather than an advantage.\n<\/p>\n\n\n\n \n This is where disciplined adoption\u2014not experimentation for its own sake\u2014will separate leaders from laggards.\n<\/p>\n\n\n\n <\/p>\n\n\n\n 1 <\/sup>McKinsey & Company, \u201cGlobal Banking Annual Review 2025<\/a>,\u201d October 23, 2025.<\/p>\n\n\n\n1. Empowering relationship managers <\/h2>\n\n\n\n
2. Improving the quality of client interactions<\/h2>\n\n\n\n
3. Modernizing risk and core systems<\/h2>\n\n\n\n
4. Transforming trade finance<\/h2>\n\n\n\n
From experimentation to operating advantage<\/h2>\n\n\n\n
Learn more<\/h3>\n\n\n\n
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