Author name: Chahak Thakur

Meet the Expert: Gerald JacksonRole: Vice President, Strategy, and SCM Solutions Evangelist at Oracle With decades of experience at the intersection of supply chain strategy, enterprise technology, and digital transformation, Gerald Jackson, Vice President of Supply Chain Solutions Strategy at Oracle, brings a wealth of industry knowledge to the conversation on supply chain resilience. His career spans influential leadership roles at some of the world’s most recognized technology and consulting organizations, including Oracle, Microsoft, Google, GE Digital, and Accenture. From leading global supply chain technology initiatives to advancing AI, industrial IoT, automation, and digital transformation, Jackson has worked across the systems and strategies that shape modern supply chain operations. His experience combines strategic leadership with practical execution, including product innovation, supply chain engineering, operational efficiency, and technology-driven business transformation. This breadth of experience provides a valuable perspective on how organizations can move beyond traditional visibility and build supply chains that are more intelligent, adaptive, and resilient. At TechSommet’s Digital Supply Chain & Resilience Virtual Summit – 2nd Edition 2026, Jackson shared his insights on a critical question facing today’s supply chain leaders: How can AI help organizations anticipate disruption, make better decisions, and respond before risks escalate? Supply chain disruption is no longer an occasional challenge. It has become part of the operating environment. For businesses, the real competitive advantage lies not just in identifying risks, but in responding to them faster and more intelligently. Disruption Is the New Operating Environment Jackson highlighted the growing impact of disruption on businesses. The figures he shared show a clear upward trend in revenue-impacting disruptions: These figures underline a critical shift: organizations can no longer treat disruption as an exception. They need systems that continuously identify risks, coordinate responses and adapt to changing conditions. Closing the Resilience Gap Supply chain resilience depends on reducing the time between detecting a potential disruption and completing a coordinated response. Jackson explained that the faster an organization can sense a risk, respond, adapt and learn, the better positioned it is to protect operations and maintain a competitive advantage. AI can support this process by helping teams interpret signals, evaluate potential risks and make decisions more quickly. Moving from Visibility to Intelligent Action Visibility helps organizations understand what is happening across their supply chains. However, visibility alone does not resolve disruptions. Jackson emphasized the importance of embedding AI into the foundational transactional layer of supply chain operations rather than isolating it in disconnected systems. With AI integrated into core workflows, organizations can connect operational information with external signals, identify anomalies and support more coordinated decision-making. AI agents can also use system memory and policy understanding to help teams recognize situations that traditional forecasts may not capture. The objective is to move from simply seeing problems to taking informed action. Predicting Disruptions Through AI Jackson discussed how AI can use system memory and policy understanding to identify anomalies and recognize signals that may indicate an emerging disruption. By combining operational context with external signals, organizations can improve their ability to anticipate potential risks before they fully materialize. This creates an opportunity to act earlier rather than waiting for a disruption to affect operations. Building Trust in AI-Driven Decisions AI-generated risk signals must be validated before organizations rely on them for critical decisions. Jackson emphasized the importance of checking information against multiple trusted sources to reduce the risk of inaccurate or unreliable outputs. He also discussed the potential for industry-wide trusted-data clearing houses that could help verify information using reliable sources, such as weather services and official international organizations. For supply chain leaders, the lesson is clear: AI-driven decisions require both intelligence and trustworthy evidence. The Way Forward Building a resilient supply chain requires more than better dashboards or greater visibility. It requires the ability to connect signals, interpret risks, coordinate decisions and learn continuously. Jackson’s insights highlight how AI, when embedded into core supply chain operations and supported by trusted data, can help organizations move toward faster, more informed and more adaptive decision-making. For procurement and supply chain leaders, the next step is to move beyond detecting disruption and build the capability to respond before it escalates. Watch the Full Session and Explore More Continue exploring the discussion and learn more about the summit: Explore more insights, upcoming events and industry conversations at techsommet.com.

Speaker: S. RameshRole: Global Head of Payments, Risk, and Data Solutions; formerly TD Securities From AI Adoption to Business Transformation During his presentation, S. Ramesh explored how businesses can move beyond experimenting with AI and focus on achieving measurable business value. He began with an analogy from the retail industry, comparing Sears, Kmart, and Walmart during the rise of the internet. While Sears and Kmart moved their marketing and catalogues online, Walmart rewired its operating model around its supply chain. As Ramesh explained: “Sometimes the very first thing that you see is not the thing that’s going to give you the best benefit.” The lesson was to look beyond adding technology to existing processes and consider how it can improve the way a business creates value. Three Stages of AI Adoption Ramesh described three phases of AI adoption: He emphasized that the most significant opportunity lies in reimagining how businesses operate, rather than simply introducing new tools. Managing Risk and Measuring Results Ramesh highlighted that AI can produce inaccurate information because it is probabilistic by design. Businesses therefore need appropriate controls and risk management rather than relying entirely on technological solutions. When selecting AI use cases, he stressed the importance of defining and measuring business benefits while ensuring compliance requirements are addressed. He also discussed how faster software development alone does not guarantee faster delivery. Operational bottlenecks, meetings, and approval processes can still slow down implementation. To measure progress, Ramesh recommended using actionable metrics, regular checkpoints, and a willingness to pivot when results fall short. As he concluded: “You measure at regular checkpoints, you pivot, and you prune relentlessly.” The central message was clear: successful AI adoption requires measurable outcomes, continuous learning, and a focus on solving real business problems. Q&A — unfiltered expert insights Question 1: Where does AI have the strongest near-term business case in payments and cross-border? Moderator — Dr. Angela Murphy: “We do have one question for the Q&A, and it says, in payments and cross-border, where do you think AI has the strongest near-term business case? Is it fraud and risk? compliance, operations, customer experience, pricing, or something else?” S. Ramesh: “Ah, yes. So I think the short answer is that there’s a lot of use cases around it. There’s no shortage of use cases around it. Certainly in risk and compliance, there are use cases. It’s a little touchier here because you do have to have explainability, but there’s also a lot of advantages, things like day zero attacks.” He also highlighted operational issues, production problems, and customer support as areas with potential benefits. He emphasised that businesses should define and measure the business benefit and ensure compliance approval before proceeding. Question 2: How do you prevent AI initiatives from becoming innovation theatre? Moderator — Dr. Angela Murphy: “Okay. And then the final question in Q&A for today before we move on to our next speaker is, how do you prevent AI initiatives from becoming innovation theater where there’s lots of pilots and impressive demos, but very few things that materially change the P&L?” S. Ramesh: “Exactly. And I think that is key. I think the way that I do it is I have a scorecard, a business scorecard, which we use to measure the value of the use cases. We measure them across multiple parameters such as risk, revenue growth. cost reduction and so on, alignment with strategy, and then have actionable metrics that are defined before you start the use case.” He further explained that teams should define what they will measure, track progress continuously, and pivot or stop initiatives that fail to deliver value. Source: Next-Gen Payments & Cross-Border Innovation — AI, Stablecoins & The Future of Payments. The Conversation Doesn’t End Here. Want to explore the insights in more detail? Discover the key takeaways in our post-event report on LinkedIn. Continue exploring how AI, stablecoins, and emerging payment innovations are shaping the future of global payments.

Historically, moving money across borders has relied on rigid, rules-based software. Corporate treasury teams spent years hard-coding if/then statements into their Enterprise Resource Planning systems to handle transaction execution. If a transaction went to Western Europe, the system chose a specific correspondent bank. If the payment size crossed a specific limit, it triggered a slow internal approval loop. However, this rigid approach no longer works in volatile global markets. The rapid introduction of agentic AI in finance is completely replacing old rules-based pipelines with flexible, software-driven execution layers. Moving Beyond Static Rules: What is Agentic AI in Finance? Agentic AI does not simply read spreadsheets or flag odd transactions. Unlike basic chat-bots or predictive tools, an AI agent operates with a specific goal. It evaluates live market conditions, assesses counter party risks, and executes multi-step financial workflows on its own. For example, in international B2B payments, you can assign an AI agent a direct corporate goal: Execute this $500,000 supplier invoice with the lowest total fee, ensuring completion within four hours. The software layer then independently manages the best path forward. How Intelligent Payment Routing Eliminates Settlement Drag Every cross-border payment faces three primary hurdles: AI agents address these issues through intelligent payment routing. The agent monitors live transaction data across multiple digital rails. Instead of relying on a per-set partner bank, the agent instantly evaluates alternative networks like tokenized deposits, real-time clearinghouses, and regional commercial networks. It then calculates the optimal path right at the moment of settlement. Collapsing Payment Rails Into Unified API Policies Building out this level of automation does not require enterprise teams to completely scrap their legacy systems. Modern middle-ware tools pull these varying payment methods into a clean, unified API layer. AI agents run smoothly on top of this single configuration layer. The software dynamically selects the best network rail based on your real-time priorities: This flexibility turns payment architecture into a strategic corporate advantage. If a traditional wire channel slows down or spikes its fees, the software automatically shifts the capital volume onto alternative digital rails. Managing Liquidity with Software-Driven Adjustments Managing cash flow across different global currencies creates constant headaches for mid-market corporate treasurers. Leaving excess funds parked in low-yield local operating accounts just to cover sudden partner invoices wastes valuable capital. Autonomous financial software solves this by tracking live corporate ledger balance sheets. When a time-sensitive supplier bill lands, the agent pulls together the necessary liquidity by initiating instant, micro-hedging currency conversions across your connected accounts. This keeps your working capital working efficiently, right up until the exact second it needs to be sent out. The 2026 Roadmap for Smart Treasury Operations Human-AI collaboration in cross-border financial systems has driven a 50% jump in productivity and execution speed along with a 25% improvement in decision accuracy. Transitioning to an agentic infrastructure requires three clear sequential steps: Enterprise payments are moving fast toward software-directed execution. Treasurers who adopt these autonomous tools can permanently eliminate high transaction fees and settlement delays from their global supply chains.

As the global financial ecosystem undergoes a massive digital transformation, the Next-Gen Payments & Cross-Border Innovation Virtual Summit brought together industry leaders to discuss the critical intersection of AI, fintech, and modern payment infrastructure. The Shift to Modern Infrastructure Payment strategy expert AJ Matthews opened the summit by highlighting that trust remains the primary currency in cross-border trade. He emphasized a necessary shift from legacy, siloed banking systems toward instant, decentralized, data-rich payment networks. Matthews noted the industry’s focus on two key metrics: authorization rates and the cost of payment. He argued that while blockchain technology can facilitate instant settlement, the challenge remains scalability and government-backed interoperability . The Execution Layer: Beyond Just Rails Meredith Perry of Paystand shifted the focus from payment rails to the “execution layer.” She argued that a payment is not just a single point event, but an entire business event. To achieve full automation in B2B payments, context must travel with the payment. Perry stated, “This idea of an entire object or a record around that payment is really the product that B2B needs for faster payments for cross-border payments to become completely automated” . Agentic AI in B2B Payments The panel discussion, “From Autopilot to Agentic,” explored how AI is moving beyond simple automation. Safwan Sah explained that the distinction lies in the ability to reason: “Automated payments executes with rules, and AI-led payments can simultaneously infer, reason, and then operate within risk boundaries” . Key Panel Takeaways on Implementation: Conclusion: The Path Forward Dr. Angela Murphy concluded the summit by noting that the opportunity for innovation is real, but it requires the right data, guardrails, and human oversight. The consensus among the experts is clear: the future of cross-border payments is moving away from manual, legacy processes toward systems defined by smarter infrastructure and better interoperability .Watch the Full Conversation Moreover, to learn more about the topics covered in this summit, you can watch the complete conversation on YouTube.For more information or if you would like to know more about this topic, please feel free to contact us through our website. Techsommet