Finance Leads AI Adoption with Practical Use Cases

In a recent episode of AI Now with Oracle, Miranda Nash, alongside PwC Global Chief AI Engineer Scott Likens, explored how AI is revolutionizing finance and positioning it as a testing ground for broader enterprise AI adoption. Here are the key insights:

Finance: An Unlikely AI Pioneer

Despite its risk-averse reputation, finance has become a front-runner in AI adoption. Likens highlighted that finance's data-rich environment and compliance demands make it ideal for AI integration. Regulations, often seen as hurdles, have instead accelerated adoption by driving efficiency needs.

PwC has adopted a "client zero" approach, deploying AI innovations internally before offering them to clients. Their "human-in-the-loop" model ensures human oversight in AI systems, particularly in audit and tax workflows, where AI reduces manual tasks like anomaly detection and compliance checks.

AI Agents: Beyond Chatbots

Likens introduced AI agents as digital coworkers capable of reasoning, planning, and automating workflows. These agents, powered by generative AI, can read regulations, suggest controls, and monitor compliance, significantly enhancing risk management.

Sustainability and Innovation

While AI's energy demands are a concern, Likens remains optimistic, citing advancements in chip efficiency and greener data centers. He emphasized the importance of sustainable AI use and its potential to drive green energy innovation.

The Case for Acting Now

Likens warned that "waiting is not a great strategy" for AI adoption. Early adopters are already seeing tangible benefits, such as faster product development and market entry. He advocated for a portfolio approach, balancing quick wins (efficiency gains) with bold moonshots (process reimagination).

Governance as a Catalyst

Contrary to fears, governance can fuel innovation if designed into systems from the start. Likens praised "trust by design" and highlighted companies with AI innovation councils as examples of .