AI-ready finance: how strategy sets leaders apart

AI pressure is rising. Is your strategy keeping pace?

Across the finance landscape, CFOs are being asked to adopt AI faster than ever: shorten the close, tighten controls, deliver real-time insights, and forecast with greater accuracy. But organizations that are seeing real impact aren’t the ones deploying AI tools most quickly—they’re the ones taking a more disciplined, more strategic path. 

These businesses begin by clarifying where intelligence will create measurable value. They align teams around a shared vision and strengthen data foundations before adding complexity. Only then do they evaluate platforms. 

This is the essence of Ready Layer One, a playbook for building a foundation-first approach to AI and data. The concept aligns strategy, data, systems, process, and people, enabling organizations to scale AI and transformation with clarity, control, and confidence. This framework originated across enterprise data transformation, but its relevance within accounting and finance has never been stronger. 

The readiness gap beneath the surface 

Across what seems like every source, the pattern is remarkably consistent. There’s a consensus in research and reports from MIT, IMA, CPA.com, major audit, tax, and advisory consultancies, and even in candid feedback from accounting leaders at professional conferences: 

AI fails when organizations treat it as a software problem instead of a readiness problem. 

Ninety-five percent of AI pilots fail to deliver ROI because they chase tools before defining outcomes. Sixty-five percent of leaders say their teams don’t fully understand their own data. And inside most finance functions, friction doesn’t come from the absence of automation. It’s the result of a lack of standardization, governance, and shared context. 

CFOs know this and are experiencing it every quarter-end. As a result, every major research body is reinforcing the same set of four prerequisites for finance leaders to use as readiness benchmarks. 

1. Data quality and architecture matter more than anything else. AI amplifies what already exists. Metrics and sources must align for intelligence to be trustworthy.  
    

2. Process maturity determines whether automation accelerates outcomes. Many challenges flagged in the Cincinnati forum—inconsistent approval paths, manual handoffs, and outdated workflows—must be resolved before AI can optimize efficiency. 
    

3. Governance and controls are now strategic differentiators. Across major consulting benchmarks, a consistent message is emerging: AI brings new dimensions of reporting risk. CFOs need auditability, transparency, and defensible decision trails. 
    

4. Workforce fluency determines adoption. Fewer than one in four finance professionals feel confident using AI tools. Capacity won’t unlock until confidence does. 

Together, these realities form the finance-specific expression of our Ready Layer One framework, highlighting the critical importance of strengthening your data foundation in preparation for AI transformations. 

Why strategy must lead AI adoption 

The market is full of platforms helping to build the future of intelligent finance: NetSuite, OneStream, Salesforce, Everest, Workday, Planful, Pigment, Mosaic, and a growing list of AI-augmented FP&A capabilities. But for CFOs, technology selection will serve as one of the final steps in a transformation rather than a starting point.  

With a strategy-first approach, you answer questions that empower the tools you choose: 

• Where should intelligence accelerate our outcomes? 

• Which workflows are ready for automation, and which require redesign? 

• What decisions demand better predictive insight rather than more dashboards? 

• What data, controls, and governance must be strengthened before AI enters the process? 

When you have clear answers to these kinds of questions, you enable your technology of choice to position you for sustainable growth and success.  

Where AI creates opportunity and optimization in finance 

While AI use cases are often marketed in broad terms, CFOs consistently point to a handful of domains where intelligence can materially improve performance: 

• Closing cycles. AI can spot anomalies early, draft explanations, and guide reconciliation efforts, but only if mappings and models are consistent.  
• Forecasting. Rolling models and scenario analysis improve dramatically when predictive signals are woven into daily finance workflows.
• Real-time performance visibility. When data is unified across ERP, GL, and FP&A systems, AI can elevate dashboards from static reports to dynamic, decision-ready signals. 
• Early risk detection. When controls, workflows, and policies are clearly defined, continuous monitoring and anomaly scoring surface issues before they can develop.
• Stronger audit readiness. AI-enabled transparency reduces audit effort by flagging exceptions, strengthening documentation, and maintaining a continuous evidence trail. 
• Automation in AP/AR, revenue, and journal-heavy operations. The ability of workflow automation to eliminate manual friction in high-volume areas depends on standardized processes and consistent data inputs. 

Across all of these opportunities for process optimization, there is a visible, consistent through-line: Value is created when the underlying data and processes are ready. 

The modernization mandate for the next 18 months 

The finance organizations winning with AI are not the ones building clarity, alignment, and readiness, and then choosing the tools that reinforce the strategy they’ve defined. Finance and accounting leaders in these organizations know the value of: 

• Standardizing before they automate. 
• Governing before they scale. 
• Elevating data quality before pursuing predictive capabilities. 
• Upskilling their teams before changing their workflows. 

Winning organizations know that AI doesn’t transform finance—AI-ready finance teams do. 

For CFOs planning their next wave of transformation, the message is clear: Lead with strategy, then activate the technology, and choose partners who can help you do both. 

Where Highspring + Vaco help CFOs shift from tools to outcomes 

CFOs and other leaders in accounting and finance don’t need more demos. They need a partner who bridges strategy, data, systems, and the human side of transformation. Highspring and Vaco, our talent solutions division, operate at exactly that intersection. 

Step by step, we help CFOs: 

1. Identify where AI meaningfully improves financial outcomes. 
2. Assess the readiness of their data and workflows. 
3. Determine the best path, whether that’s selecting new platforms or optimizing the ones you already own. 

And because our teams deliver across strategy, architecture, implementation, and adoption, we close the gap between intent and execution. We provide an effective, proven path to intelligence operationalization.