How do finance leaders move from AI adoption to orchestration - and create finance functions that are predictive instead of reactive?
KPMG answers the question finance leaders must resolve in May 2026: how do they move from adoption to orchestration and make AI predictive instead of reactive? KPMG explores how the value of orchestration is unlocked when the finance function adopts a human-led, agent-operated model that embeds judgment where it matters most.
How do finance leaders move from AI adoption to orchestration - and create finance functions that are predictive instead of reactive?
Finance leaders are already past the question of whether AI belongs in the function. Two years ago, many were piloting isolated use cases. Today, they are racing to deploy and orchestrate AI across core workflows, with nearly all (93%) US companies planning to scale AI in finance in the near term, according to KPMG’s 2026 AI in Finance study.
Yet scaling alone does not guarantee impact. The opportunity leaders now see is not simply faster processing, but a finance function that anticipates outcomes and supports forward‑looking decisions. Achieving that shift allows technology to provide deep insights, freeing professionals to apply critical judgment and become true strategic partners to the business.
Why It’s More Complex Than It Looks
Scaling AI effectively is not just about the technology, but also about the operating models that drive the change. While many organizations report that AI investments are delivering expected returns, those that fall short cite slow organizational adoption and change management as the primary barrier.
The same pattern appears in workforce readiness. Leaders struggle to embed AI into day‑to‑day work when teams lack clear, role‑specific use cases or environments to practice with real tools and data. Without those foundations, AI remains something that happens to finance, rather than within it.
The Evidence
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KPMG’s Answer
Finance leaders successfully move from adoption to orchestration by redesigning how people and AI work together.
The ultimate goal is not just automation, but orchestration--by using AI to surface insights earlier and more accurately, allowing professionals to focus on interpretation, risk assessment, and strategic decision‑making.
This is the essence of a human‑led, agent‑operated finance model. In this model, sophisticated AI agents handle data‑intensive tasks and scenario generation, while humans remain accountable for judgment, context, and outcomes. When implemented effectively, AI shifts finance from reacting to results toward predicting them.
Organizations that fail to make this shift risk plateauing. Automation alone delivers efficiency gains, but without adoption, training, and redesigned roles, it does not unlock the predictive advantage leaders increasingly expect from AI investments.
Redesign roles before scaling technology. Predictive finance requires clarity on where AI supports decisions and where human judgment must remain central.
Invest in practical adoption, not just platforms. Clear use cases and hands‑on environments are what turn AI from a tool into a capability embedded in everyday finance work.
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