This past week, AI has continued to make significant strides in reshaping the landscape of finance and operations. Key developments include the integration of agentic AI in enterprise resource planning, strategic partnerships aimed at digital transformation, and the growing emphasis on AI proficiency in the workplace.

Agentic AI is revolutionizing enterprise resource planning by enabling autonomous financial workflows.

A newly released research paper introduced Generative Business Process AI Agents (GBPAs), which integrate large language models with structured business logic to drive autonomous decision-making within finance processes. These agents reduced processing time by 40% and virtually eliminated manual errors in tasks such as budget planning and payments. The implications for finance teams are significant, suggesting a future where routine tasks are managed by AI while teams focus on strategic insights.

Building on this momentum, major enterprises are forming long-term AI partnerships to embed intelligence across operations.
Virgin Atlantic has extended its partnership with Tata Consultancy Services (TCS) to roll out AI-driven platforms for process efficiency, customer engagement, and sustainability goals. TCS will use tools like Cognix and AI WisdomNext to centralise operational insights through a real-time command centre. This collaboration highlights how AI is moving beyond pilot programs into core operational infrastructure.

It’s Time To Get Concerned As More Companies Replace Workers With AI?

As organisations operationalise AI, they are now demanding AI fluency across their workforce. Companies such as Duolingo, Shopify, and Meta are mandating employees to adopt AI tools as part of their daily responsibilities. Performance reviews increasingly hinge on how effectively staff can integrate AI into their workflows. This cultural shift reflects a new era of performance expectations, but also raises concerns over job security, automation anxiety, and skills gaps.

Klarna, a leading buy-now-pay-later fintech company, made headlines in 2022 when it announced the elimination of over 1,000 jobs, or about 10% of its global workforce, as part of a strategic shift toward AI, the company’s CEO announced.

The company heavily invested in AI to handle customer service inquiries, process transactions, and optimize its operations. Klarna implemented an AI assistant that manages the workload equivalent to 700 full-time staffers.

Anthropic CEO Dario Amodei cautioned that half of all junior roles in finance, law, and consulting could disappear within five years. He urged policymakers and AI firms to acknowledge the disruption AI may bring and to prepare strategies for re-skilling and economic transition. His remarks lend urgency to the growing discussion on balancing innovation with social responsibility.

In response, regulators are cracking down on companies exaggerating their AI capabilities.
The U.S. Securities and Exchange Commission has intensified investigations into “AI washing”—where companies misrepresent AI usage to attract investment. Firms may now face legal consequences for making vague or misleading claims. This development underscores the need for transparency as AI becomes a central driver of enterprise value.

At the same time, major service providers are launching new AI platforms to support trustworthy and scalable deployments.

Infosys has introduced its Agentic AI Foundry—a comprehensive environment to build, test, and scale enterprise-grade AI agents. By focusing on governance and reliability, Infosys aims to help companies automate decision-making across finance and operations without compromising on trust or accountability.

Global race to dominate AI

China’s next-generation AI models are rapidly closing the performance gap—and in some cases, surpassing their Western counterparts—reshaping the global landscape for enterprise AI in finance and operations.

Chinese models like Tencent’s Hunyuan-Large, boasting 389 billion parameters, recently outperformed Western models on the MMLU benchmark—an AI “IQ test” across 57 disciplines including law, economics, and business strategy—with an accuracy of 90.8%, ahead of Meta’s Llama 3 (88.5%).

Meanwhile, Alibaba’s Qwen 2.5 is rivalling GPT-4 in coding and automation tasks, critical for financial modelling and operations workflow design. DeepSeek’s R1 also competes with top-tier U.S. models while reportedly requiring just 1–3% of the training cost—an efficiency breakthrough that could drastically lower AI deployment costs in finance departments.

These advancements suggest that CFOs and operations leaders will soon have a broader, more competitive set of AI tools to choose from, increasing accessibility, accelerating adoption, and driving down total cost of ownership for intelligent planning, forecasting, and automation platforms.

Thats a wrap

AI continues to challenge, disrupt, and inspire every corner of finance and operations—from back-office processes to boardroom decisions. As automation deepens and expectations evolve, leaders must remain agile, informed, and transparent in their adoption of AI technologies.

Next week, we’ll explore how generative AI is influencing budgeting, compliance, and strategic planning, and what trends to watch as fiscal year-end planning begins across many industries. Stay tuned for the next edition of your AI edge.