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VP of Finance

Manage cash flow forecasting and working capital

Enhances◐ 1–3 years

What You Do Today

Forecast cash positions, manage banking relationships, and ensure the company has adequate liquidity. Optimize working capital across receivables, payables, and inventory.

AI That Applies

AI cash flow forecasting that predicts daily cash positions based on historical payment patterns, seasonal trends, and upcoming commitments.

Technologies

How It Works

The system ingests historical payment patterns as its primary data source. The processing layer applies the appropriate analytical models to the structured data, generating scored outputs that surface the most actionable insights. The output is a forecast with confidence intervals, showing both the central estimate and the range of likely outcomes.

What Changes

Cash forecasting accuracy improves significantly. AI predicts which customers will pay late and which invoices will be disputed before they're due.

What Stays

Managing banking relationships, negotiating credit facilities, and making strategic working capital decisions — those require financial expertise and business relationships.

What To Do Next

This section won't tell you what your numbers should be. It will show you how to find them yourself. Every instruction below produces a real, verifiable result in your organization. No benchmarks, no projections — just the steps to build your own evidence.

1

Establish Your Baseline

Know where you are before you move

Before adopting AI tools for manage cash flow forecasting and working capital, understand your current state.

Map your current process: Document how manage cash flow forecasting and working capital works today — who does what, how long it takes, where the bottlenecks are. You need this baseline to measure improvement.
Identify the judgment points: Managing banking relationships, negotiating credit facilities, and making strategic working capital decisions — those require financial expertise and business relationships. These are the boundaries AI won't cross.
Assess your data readiness: AI tools for this area need data to work. Check whether your organization has the historical data, integrations, and data quality to support Kyriba tools.

Without a baseline, you can't measure whether AI actually improved anything. You'll adopt tools without knowing if they're working.

2

Define Your Measures

What to track and how to calculate it

Time per cycle

How to calculate

Measure how long manage cash flow forecasting and working capital takes end-to-end today, then after AI adoption.

Why it matters

The most visible improvement is speed. If AI doesn't save time, question whether it's adding value.

Quality of output

How to calculate

Track error rates, rework frequency, or stakeholder satisfaction scores before and after.

Why it matters

Speed without quality is just faster mistakes. Measure both.

When to check: Check after 30 days of consistent use, then quarterly.
The commitment: Give new tools at least 30 days before judging. The first week is always awkward.
What NOT to measure: Don't measure AI adoption rate as a KPI. Adoption follows value — if the tool helps, people use it.
3

Start These Conversations

Who to talk to and what to ask

your board chair or lead independent director

What's our current capability gap in manage cash flow forecasting and working capital — and is it a people problem, a tools problem, or a process problem?

They shape expectations for how AI appears in governance

your CTO or CIO

What's the biggest bottleneck in manage cash flow forecasting and working capital today — and would AI address the bottleneck or just speed up something that's already fast enough?

They own the technology infrastructure that enables AI adoption

4

Check Your Prerequisites

Confirm readiness before you invest

Check items as you confirm them.