The Future of Agentic Finance
Learn about trends affecting the global supply chain, the challenges facing organizations, and strategies you can adopt for resilience.
The shift to agentic AI is inevitable, with those who don't adapt likely to be left behind. This transition requires reimagining processes, breaking down functional silos, and connecting end-to-end value chains. Legacy infrastructure needs to be rewired, and the workforce must be reshaped through reorganization, talent strategies, and learning and development (L&D) curriculums. Strong governance is critical to ensure trusted and reliable AI systems. Performance measurement needs to be redefined to include digital labor and enterprise value creation.
Panelists discussed the following topics:
The Race for AI Value is On
AI is one of the most transformative technologies in our lifetime and its effect on enterprise value creation and productivity cannot be overstated. As a result, the workforce is undergoing a massive transformation as organizations are figuring out ways to redefine existing roles and syncing them with the deployment of new roles that together will represent a new hybrid digital-human workforce.
Insights from the KPMG Q3 AI Pulse Survey show that strategic investments are on the rise but so are demands for results that are visible and tangible. Nearly 80% of leaders are facing significant pressure from investors and boards to demonstrate value from AI while, at the same time, the same leaders are struggling with how to measure value as traditional metrics fail to capture true business impact. According to respondents, the barriers to AI success remain data quality (82%), data privacy and cyber security (78%), & AI misuse (45%).
What AI deployment looks like in the future can be seen in the results: Resistance from workers has plummeted from 47% to 21% in a single quarter. Nearly three-quarters of leaders also expect employees to manage agents within the next two to three years. Leaders are under increased pressure from investors, boards, and shareholders to prove that the value is there, which means organizations need to increase training, create governance, and leverage tools throughout the organization as swiftly as possible to overcome, not just the barriers, but anxieties raised by stakeholders.
Where AI is Headed
Public awareness of AI began in 2022 when platforms like ChatGPT became generally available to individuals and organizations alike. The impact was immediately obvious and by the next year, AI platforms were enterprise ready. Today we are seeing a significant amount of advancement that is allowing rapid scalability that will ultimately break down traditional silos, creating new capabilities.
We are now entering the Agentic Enterprise Era marketed by significant productivity gains and faster value creation driven by fleets of interconnected agents throughout the organization. By 2030:
- Enterprises will operate as self-optimizing networks of autonomous agents.
- Agents will collaborate, transact, and continuously learn under governed guardrails.
- There will be continuous agent refinement and scale across all value chains.
Agentic AI in Finance by 2030
By 2030, Finance will serve as the strategic growth engine and value orchestrator for the enterprise. In doing so, Finance will run autonomous, multi-cloud ecosystems in which agent coverage exceeds 80%. The cloud will be the dominant platform. AI will serve as the operating system, which means the organization will not just build with AI, it will instead build atop AI. An agentic command center will provide actionable intelligence delivered through a suite of ambient agents, which will orchestrate performance.
On the human side, the workforce will shift from a pyramid structure to that of a diamond with lower-level employees augmented by a suite of digital employees that works build, manage, and supervise.
AI will no longer serve as a tool to support workers, but instead AI will represent the majority of the workforce. Leaders will evolve from operators within the business to becoming value orchestrators while employees will begin curating intelligence and building, managing, and refining their digital counterparts.
Performance metrics will be redefined as a result. With 80% of today’s tasks now automated, there will be between a 40-60% reduction in finance costs, a 30-50% reduction in the cash conversion cycle, a 70-90% reduction in decision latency, and a 60-80% increase in forecast accuracy.
Increasing Productivity
With humans performing manual and repetitive tasks, growth is slow, siloed, and linear. Scaling up means hiring more people. Today, AI programs like ChatGPT augment those tasks by providing analysis, content creation, summarizing meeting minutes, etc., which produces 20-30% increase in productivity.
Productivity increases when AI agents begin to autonomously handle specific workflows and humans focus on supervision, insight validation, and exception handling. Finally, organizations will see 20 times the productivity gains when Finance functions operate as digital command centers overseeing networks of autonomous agents. This is what happens when human workers are shifted from doing the work to designing the systems that think, act, and deliver specific outcomes.
Companies can focus on measuring the return on investment (ROI) of agentic AI solutions by tracking metrics such as reduced full-time equivalent employees (FTEs), decreased hours, and enhanced productivity.
Implementation Strategies
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There is ultimately a strong need for a thoughtful and multi-faceted approach to implementing agentic AI in finance workflows that focuses on both technological and organizational transformations.
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