AI Adoption Is Rising. AI Value Is Not.
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Finance leaders are investing in AI—but struggling
to turn that investment into measurable value.
AI is being talked about in just about every corner of business, including finance, but now, the conversation is changing from adoption to value.
From “Should we use it?” to “How can we prove the value of our AI investments?”
Dive in to understand what’s getting in the way for finance teams and how to move from AI adoption to transformation.
![It’s no secret that AI adoption in finance is accelerating…
63%
of finance leaders say AI is fully deployed and actively used in at least one finance function
14%
have gone further, integrating AI agents into parts of finance
Across finance, adoption is uneven — some functions are experimenting; others are already running AI day-to-day.
[Source: Deloitte, Finance Trends 2026: Navigating the Expanded Scope of Finance]
Here’s the catch…adoption isn’t translating to value
21%
of finance leaders say AI investments have delivered clear, measurable value
[Source: Deloitte, Finance Trends 2026: Navigating the Expanded Scope of Finance]
5%
of task-specific enterprise GenAI pilots reached successful implementation
[Source: MIT, The GenAI Divide: State of AI in Business 2025]
Two different lenses, one consistent signal: deployment is outpacing results.
What’s getting in the way?
Both studies point to the same root causes: poor workflow fit and lack of integration — AI applied to isolated tasks rather than built into how work actually flows.
From AI Adoption to Transformation
AI delivers greater value when it operates within a strong finance operating model:
Connect the process
Link data, tasks, approvals, exceptions and evidence across the end-to-end workflow.
Standardize the routine
Create consistent policies, definitions and processes so AI isn't being asked to navigate unnecessary variation.
Build trusted data into the workflow
Preserve lineage, context, timing and ownership as financial information moves through the process.
Embed governance and controls
Build approvals, evidence, thresholds, escalation and human review into execution—not around it.
Keep people accountable
Define who owns decisions, where human judgment belongs, where AI can act and when intervention is required.
The research backs it up.
Workflow redesign
has the closest relationship to reported impact.
[Source: McKinsey & Company, The State of AI: How Organizations Are Rewiring to Capture Value]
3–6X performance improvement
for finance organizations that can produce AI-related audit evidence.
[Source: KPMG International, Global AI in Finance 2026: The Decision Advantage]
End-to-end execution > Layering AI onto isolated tasks
[Source: IBM Institute for Business Value, Finance Execution Unlocks AI Value at Scale]
It’s no secret that AI adoption in finance is accelerating…
63%
of finance leaders say AI is fully deployed and actively used in at least one finance function
14%
have gone further, integrating AI agents into parts of finance
Across finance, adoption is uneven — some functions are experimenting; others are already running AI day-to-day.
Here’s the catch…adoption isn’t translating to value
21%
of finance leaders say AI investments have delivered clear, measurable value
5%
of task-specific enterprise GenAI pilots reached successful implementation
Two different lenses, one consistent signal: deployment is outpacing results.
What’s getting in the way?
Both studies point to the same root causes: poor workflow fit and lack of integration — AI applied to isolated tasks rather than built into how work actually flows.
From AI Adoption to Transformation
AI delivers greater value when it operates within a strong finance operating model:
Connect the process
Link data, tasks, approvals, exceptions and evidence across the end-to-end workflow.
Standardize the routine
Create consistent policies, definitions and processes so AI isn't being asked to navigate unnecessary variation.
Build trusted data into the workflow
Preserve lineage, context, timing and ownership as financial information moves through the process.
Embed governance and controls
Build approvals, evidence, thresholds, escalation and human review into execution—not around it.
Keep people accountable
Define who owns decisions, where human judgment belongs, where AI can act and when intervention is required.
The research backs it up.
Workflow redesign
has the closest relationship to reported impact.
3–6X performance improvement
for finance organizations that can produce AI-related audit evidence.
What that means for ROI
Up to 18% reduction in total finance cost for organizations using AI within mature, end-to-end operating models.](https://www.trintech.com/wp-content/uploads/2026/09/IG-GL-Q3-AI-Adoption-Success-Stats_short-480x1024.png)
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