Latest news with #Generative
Finextra
21-05-2025
- Business
- Finextra
Operational Alpha: How Agentic AI Will Rewrite Securities Operations: By Vijay Mayadas
Since ChatGPT's 2022 debut, the financial services industry has keenly anticipated AI's transformative impact. That era of speculation is over; its practical application is here. Advanced Generative AI (GenAI) models, particularly when deployed through agentic workflows, are not just reshaping but fundamentally rewriting how broker-dealers, investment banks, asset managers, and custodians process and settle trades. By orchestrating complex decision-making and automating intricate tasks like dynamic inventory management through collaborative AI agents, these GenAI-powered operations solutions will actively reduce risk, slash costs, and propel markets towards a real-time future. The impact will be so profound and arrive so swiftly that early adopters of these next-gen platforms can forge a significant competitive advantage - "operational alpha." This GenAI-driven evolution couldn't be timelier. Soaring trading volumes strain legacy infrastructure, escalating operational risks. The shift to accelerated settlement and 24x7 trading compels market participants to accelerate post-trade processes, pushing towards an eventual necessity for real-time capabilities across all firms. Simultaneously, widespread capital market electronification squeezes margins. The pressure is undeniable: institutions need a next-generation operations model capable of this speed and scale, while minimizing costs and risks. Agentic AI offers a compelling answer. From Language Models to Autonomous Agent Orchestration The evolution from powerful language models to sophisticated reasoning engines (like OpenAI's o1, for instance) has paved the way for AI agents – specialized, autonomous entities designed for specific operational tasks. These agents can interpret vast, often unstructured datasets, and make context-aware decisions, often without human intervention. This leap enables AI not merely to support human operators, but to autonomously orchestrate entire operational sequences through coordinated agent activity. Think of it as a digital "special ops team'. These capabilities are fueled by rapid AI advancements and the critical creation of large, standardized datasets. Even the most sophisticated AI agents are only as effective as the data they access. Over the past decade, financial services firms and technology providers have strived to dismantle data silos, aggregating information into unified governance frameworks. At Broadridge, for instance, we've invested significantly in BRx, a global, multi-asset harmonized data ontology. This structured data becomes the bedrock upon which our OpsGPT™ platform deploys AI agents to execute complex tasks with precision. Agentic Workflows in Action: The Digital Workforce Today's GenAI platforms, architected around agentic principles, are a leap beyond previous AI tools. Instead of merely flagging issues for human intervention, the platforms will deploy teams of AI agents that act autonomously and collaboratively. Imagine a digital workforce: An Intake Agent reads and interprets an inbound email query regarding a settlement discrepancy. reads and interprets an inbound email query regarding a settlement discrepancy. A Data Retrieval Agent is dispatched to query multiple internal (and potentially external) operational systems for all relevant trade details, positions, and counterparty information. is dispatched to query multiple internal (and potentially external) operational systems for all relevant trade details, positions, and counterparty information. An Analytical Agent processes this data, identifies the root cause of the discrepancy, and determines the optimal resolution path. processes this data, identifies the root cause of the discrepancy, and determines the optimal resolution path. A Communication Agent drafts an explanatory email or SWIFT message, or even initiates a corrective transaction, based on pre-defined rules and confidence scores. drafts an explanatory email or SWIFT message, or even initiates a corrective transaction, based on pre-defined rules and confidence scores. An Orchestrator Agent oversees this entire process, ensuring tasks are routed correctly and completed efficiently, escalating to human experts only for true exceptions. This is agentic AI. In the critical area of trade fails, for example, specialized AI agents can now autonomously analyze root causes, classify fail types with high precision, and even initiate resolution protocols—often involving direct, automated communication with other internal systems or even counterparty agents. This cuts resolution cycles from days to mere minutes, paving the way to eventually both predict and prevent settlement failures proactively. Beyond Fails: Systemic Operational Alpha The applications of agentic workflows extend further, driving systemic improvements: Capital Efficiency: AI agents can proactively manage global securities inventory, almost like a digital treasurer, by identifying mismatches, recommending optimal asset transfers, and executing rebalancing actions to enhance capital utilization and reduce funding costs. AI agents can proactively manage global securities inventory, almost like a digital treasurer, by identifying mismatches, recommending optimal asset transfers, and executing rebalancing actions to enhance capital utilization and reduce funding costs. Holistic Risk & Transparency: Agentic systems can integrate and mine data from siloed platforms, presenting a unified, real-time view of operational risk and performance across the entire firm. This firm-wide transparency enhances decision-making speed and strategic execution. Agentic systems can integrate and mine data from siloed platforms, presenting a unified, real-time view of operational risk and performance across the entire firm. This firm-wide transparency enhances decision-making speed and strategic execution. Elevated Client Experience: Client interactions are upgraded through AI agents powering intuitive chat interfaces or managing automated, yet contextually relevant and personalized, email communications for inquiries and updates. Crucially, these agentic systems incorporate self-learning feedback loops. Each successfully (or unsuccessfully) executed workflow refines the agents' adaptive logic and improves the underlying models, making them progressively smarter. This means firms achieve operational alpha not by scaling headcount, but by cultivating increasingly intelligent digital workers. Firms can scale operational intelligence, not just operational capacity. The First-Mover Imperative While GenAI operations platforms with sophisticated agentic capabilities are relatively new, their power to transform securities operations is undeniable and imminent. According to Broadridge's 2025 Digital Transformation & Next-Gen Technology Study, 72% of firms are making moderate to large GenAI investments this year, a significant jump from 40% in 2024. The urgency is clear: over a third expect ROI within six months of deployment. What does this ROI look like? By embedding real-time intelligence via agentic AI directly into post-trade processes and integrating these digital workers into daily operations, firms gain a measurable edge. They'll see rapid reductions in operational complexity, manual workloads, settlement penalties, and capital costs, alongside a corresponding surge in risk management prowess and efficiency. This isn't just theoretical; this is operational alpha in action, offering a critical, tangible advantage to early adopters ready to embrace the agentic AI revolution in securities operations.
Associated Press
08-05-2025
- Business
- Associated Press
$47.3 Bn Generative AI in DevOps Market Analysis and Strategies to 2034: Asia Pacific and Middle East Drive Expansion with Remarkable Growth, Opportunities Abound in Cloud-Based Deployments
DUBLIN--(BUSINESS WIRE)--May 8, 2025-- The 'Generative AI in DevOps Market Opportunities and Strategies to 2034" has been added to offering. This report describes and explains the Generative AI in DevOps market and covers 2019-2024, termed the historic period, and 2024-2029, 2034F termed the forecast period. The global Generative AI in DevOps market reached a value of nearly $1.87 billion in 2024, having grown at a compound annual growth rate (CAGR) of 38.80% since 2019. The market is expected to grow from $1.87 billion in 2024 to $9.58 billion in 2029 at a rate of 38.53%. The market is then expected to grow at a CAGR of 37.63% from 2029 and reach $47.3 billion in 2034. Growth in the historic period resulted from the increasing internet penetration, rapid growth of the Large Language Model (LLM), increasing demand for personalized AI solutions, increasing digital transformation across industries, and rising adoption of cloud computing. Factors that negatively affected growth in the historic period were limited availability of skilled personnel and technical expertise. Going forward, the growing adoption of automation, rising investment in AI startups, increasing usage of artificial intelligence, favorable government initiatives, and expansion of the Information Communication Technology industry will drive growth. A factor that could hinder the growth of the Generative Artificial Intelligence in Development and Operations (DevOps) market in the future includes concerns regarding job displacement. The global Generative AI in DevOps market is fairly fragmented, with a large number of small players operating in the market. The top ten competitors in the market made up to 15.8% of the total market in 2023. Microsoft Corporation was the largest competitor with a 2.52% share of the market, followed by Alphabet Inc. (Google LLC) with 2.20%, Amazon Web Services Inc. with 1.81%, International Business Machines Corporation with 1.79%, Oracle Corporation with 1.78%, NVIDIA Corporation with 1.58%, Cisco Systems Inc. with 1.43%, Capgemini SE with 0.98%, OpenAI with 0.91%, and NetApp Inc. with 0.81%. The Generative AI in DevOps market is segmented by component into solutions and services. The solutions market was the largest segment of the Generative AI in DevOps market segmented by component, accounting for 61.64% or $1.15 billion of the total in 2024. Going forward, the services segment is expected to be the fastest growing segment in the Generative AI in DevOps market segmented by component, at a CAGR of 40.28% during 2024-2029. The Generative AI in DevOps market is segmented by deployment mode into cloud-based and on-premise. The cloud-based market was the largest segment of the Generative AI in DevOps market segmented by deployment mode, accounting for 62.38% or $1.17 billion of the total in 2024. Going forward, the cloud-based segment is expected to be the fastest growing segment in the Generative AI in DevOps market segmented by deployment mode, at a CAGR of 40.20% during 2024-2029. The Generative Artificial Intelligence in Development and Operations (DevOps) market is segmented by application into testing, monitoring, deployment, maintenance, and other applications. The testing market was the largest segment of the Generative Artificial Intelligence in Development and Operations (DevOps) market segmented by application, accounting for 36.62% or $687.76 million of the total in 2024. Going forward, the deployment segment is expected to be the fastest growing segment in the Generative Artificial Intelligence in Development and Operations (DevOps) market segmented by application, at a CAGR of 42.68% during 2024-2029. North America was the largest region in the Generative Artificial Intelligence in Development and Operations (DevOps) market, accounting for 33.66% or $632.17 million of the total in 2024. It was followed by Asia Pacific, Western Europe, and then the other regions. Going forward, the fastest-growing regions in the Generative AI in DevOps market will be Asia Pacific and the Middle East. The top opportunities in the Generative AI in DevOps markets segmented by component will arise in the solutions segment, which will gain $4.51 billion of global annual sales by 2029. The top opportunities in the Generative AI in DevOps market segmented by deployment mode will arise in the cloud-based segment, which will gain $5.17 billion of global annual sales by 2029. The top opportunities in the Generative AI in DevOps markets segmented by application will arise in the testing segment, which will gain $2.84 billion of global annual sales by 2029. The Generative AI in DevOps market size will gain the most in the USA at $1.8 billion. Market-trend-based strategies for the Generative Artificial Intelligence in Development and Operations (DevOps) market include a focus on developing innovative solutions to enhance AI model training and deployment efficiency, concentrating on creating innovative solutions, like the Generative AI production stack and focusing on developing innovative products like AI-powered DevSecOps (Development, Security, and Operations) platforms. Player-adopted strategies in the Generative AI in DevOps market include focusing on expanding operational capabilities through new product launches and focusing on strengthening business capabilities through strategic partnerships. Key Attributes: Key Topics Covered: Major Market Trends Global Generative AI in DevOps Market Segmentation Company Profiles Other Major and Innovative Companies Key Mergers and Acquisitions Companies Featured For more information about this report visit About is the world's leading source for international market research reports and market data. We provide you with the latest data on international and regional markets, key industries, the top companies, new products and the latest trends. View source version on CONTACT: Laura Wood, Senior Press Manager [email protected] For E.S.T Office Hours Call 1-917-300-0470 For U.S./ CAN Toll Free Call 1-800-526-8630 For GMT Office Hours Call +353-1-416-8900 KEYWORD: INDUSTRY KEYWORD: TECHNOLOGY ARTIFICIAL INTELLIGENCE SOURCE: Research and Markets Copyright Business Wire 2025. PUB: 05/08/2025 10:02 AM/DISC: 05/08/2025 10:01 AM
