Data & stats to lean on
What Autonomous Finance Means for the IT Leader
Enhancing IT Leadership with Autonomous Finance
Systems, data flows, security – those are all your domain. The limitations today often lie in inconsistent data, custom integrations, legacy infrastructure, and a lack of standardization. You’re often firefighting issues, rather than enabling forward motion.
Autonomous finance demands that the tech backbone be clean, scalable, secure, and ready for AI-scale workloads. But it also gives you a chance to modernize in ways that reduce total burden over time.
What this enables
- Secure, centralized data models usable by finance, operations, and projects.
- Consolidation of platforms, fewer bespoke integrations to maintain.
- Cloud, AI agents, analytics that scale.
- Systems that improve through use, feedback, and learning.
$3.70
For every $1 invested in generative AI, which autonomous finance relies on, the average ROI is $3.70, with leaders reaching $10x. (IDC’s AI opportunity study)
75%
Generative AI adoption jumped from 55% in 2023 to 75% in 2024. (IDC’s AI opportunity study)
64%
64% of CFOs believe autonomous finance will be a reality at their company within the next six years. (SSO Network)
Considerations for successful integration
- Cloud still a priority: Nearly 20% of orgs list cloud platforms as a top 2025 investment—foundation for scalable finance/AI workloads. (SSO Network)
- Data is the choke point: 59% cite data management as the top GenAI hurdle—make “single source of truth” and adaptive data governance front-and-center. (SSO Network)
- Finance 2030 preview: Plan for AI agents, a finance-talent crunch, and discontinuous regulatory change—architect for agility and compliance-by-design. (Gartner CFO Report)
How your role evolves
You’ll become a strategist and enabler: deciding architecture, integrations, security and governance. You’ll need to ensure reliable data inputs and design systems that support automation, AI, and real-time insights.
First steps
- Audit the data architecture: find silos, inconsistencies, outdated systems.
- Define governance and security around AI tools and financial data.
- Run pilot integrations with finance/business systems.
- Set evaluation metrics: time to value (e.g. speed of deployment), system uptime, integration cost, data quality.

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