Improving cash flow: The AI advantage in financial forecasting

Every CFO knows the pressure of making high-stakes financial decisions with limited visibility. When cash flow forecasts are off, businesses scramble, relying on costly short-term loans, missing financial targets, and struggling to optimize working capital.

Yet, most forecasting tools rely on static assumptions, forcing finance teams to react rather than plan strategically.

This outdated approach leaves businesses vulnerable to financial instability. In fact, 82% of business failures are due to poor cash flow management. 

AI-powered forecasting changes that dynamic, enabling CFOs to anticipate cash flow gaps before they become financial setbacks.

The cash flow blind spot: Where forecasting falls short


Cash flow forecasting challenges cost businesses billions. Nearly 50% of invoices are paid late,  leading to cash flow gaps that force CFOs into reactive borrowing.

Without real-time visibility, finance teams struggle to anticipate cash availability, respond to fluctuations, and prevent shortfalls before they become a crisis.

Yet, many organizations still rely on manual reconciliation processes that can take weeks, pulling data from disparate sources and leaving little time for strategic decision-making. By the time reports are finalized, the information is already outdated, making it impossible to plan with confidence.

The consequence is inaccurate forecasts that lead to last-minute borrowing, unplanned interest expenses, and heightened financial risk.

Instead of proactively managing cash flow, CFOs are left scrambling to plug financial gaps.

To break this cycle, finance leaders need a smarter, more dynamic approach that moves at the speed of their business instead of relying on static reports.

How AI transforms cash flow forecasting


AI has the power to give CFOs the clarity and control they need to manage cash flow with confidence.

That’s why DataRobot developed the Cash Flow Forecasting App.

It enables finance teams to move beyond static reports to adaptive, high-precision forecasting, helping them anticipate risks and opportunities with greater confidence.

By analyzing payer behaviors and cash flow patterns in real time, the app improves forecast accuracy, allowing finance leaders to:

  • Anticipate cash availability
  • Optimize working capital
  • Reduce reliance on short-term borrowing. 


With better visibility into future cash positions, CFOs can make informed decisions that minimize financial risk and improve overall stability.

Let’s look at how a leading company leveraged AI-driven forecasting to improve financial performance.

Cash Flow Forecasting App dashboard
Powered by DataRobot and ERP systems like SAP and Oracle NetSuite, this app provides real-time visibility into cash flow forecasts, payment timing, and credit extension needs.

How DataRobot is improving cash flow at King’s Hawaiian 


For Consumer Packaged Goods companies like King’s Hawaiian, cash flow forecasting plays a critical role in managing production, supplier payments, and overall financial stability. 

With sales spanning grocery chains, online platforms, and retail channels, fluctuations in cash flow can lead to significant disruptions, from production delays to strained supplier relationships, and even increased borrowing costs.

To improve forecasting accuracy and better manage working capital, King’s Hawaiian implemented DataRobot’s Cash Flow Forecasting App.

Using AI-driven insights, the company refined its forecasting process and saw measurable improvements, including:

  • 20%+ reduction in interest expenses. More accurate forecasting reduced reliance on last-minute borrowing, lowering overall financing costs.
  • Improved cash flow visibility. Finance teams had a clearer view of cash reserves, allowing for better short-term planning and decision-making.
  • Operational stability. With better forecasting, the company was able to prevent funding gaps that could disrupt production and distribution.


More precise cash flow predictions helped King’s Hawaiian reduce financial uncertainty and improve short-term planning, enabling the finance team to make more informed decisions without relying on reactive borrowing.

Getting an edge with adaptive, AI-driven forecasting


Traditional forecasting tools rely on rigid assumptions. AI-driven forecasting learns from actual payer behavior, continuously refining predictions to reflect real financial conditions.

This approach improves forecasting precision down to the invoice level, helping CFOs anticipate cash flow trends with greater accuracy.

AI-driven forecasting helps your team:

  • Reduce payment risks. Identify potential late or early payments before they impact cash flow.

  • Eliminate billing blind spots. Compare forecasts to actuals to spot discrepancies early.

  • Optimize inflows. Gain real-time visibility into expected cash movement.

  • Lower short-term borrowing. Reduce reliance on last-minute loans by improving forecast accuracy.

  • Control free cash flow. Adjust spending dynamically based on predicted cash availability.


By seamlessly integrating with systems like SAP and NetSuite, AI eliminates the need for manual data pulls and reconciliation, letting finance teams focus on strategic, proactive decision-making.

Good CFOs plan. Great CFOs use AI.


To transition from reactive to proactive financial operations, businesses must embrace AI-driven forecasting.

With AI, CFOs gain the ability to predict cash flow gaps, optimize working capital, and make faster, more precise financial decisions, all of which drive greater financial stability, security, and efficiency.

Take control of your cash flow management and improve forecasting—book a personalized demo with our experts today.

The post Improving cash flow: The AI advantage in financial forecasting appeared first on DataRobot.

AI apps and agents that scale impact across your business

Despite significant investments in AI, many organizations struggle to convert that potential into compelling business outcomes. 

Only a third of AI practitioners feel equipped with the right tools, and deploying predictive AI apps takes an average of seven months—eight for generative AI. Even then, confidence in these solutions is often low, leaving organizations unable to fully capitalize on their AI investments.

By streamlining deployment and empowering teams, the right AI apps and agents can help businesses deliver predictive and generative AI use cases faster and with greater results.

What’s slowing your success with AI applications? 

Data science and AI teams often face lengthy cycles, integration hurdles, and inefficient tools, making it difficult to deliver advanced use cases or integrate them into business systems.

Custom fixes may offer a fast workaround, but they often lack scalability, leaving businesses unable to fully unlock AI’s potential. The result? Missed opportunities, fragmented systems, and growing frustration.

To address these challenges, DataRobot’s AI apps and agents help streamline deployment, accelerate timelines, and simplify the delivery of advanced use cases, without the complexity of building from scratch.

AI apps and agents  

Delivering impactful AI use cases can be faster and more efficient with custom AI solutions. Specifically, DataRobot’s new features provide:

  • Streamlined deployment by reducing the need for extensive code rewrites.
  • Pre-built templates for business logic, governance, and user experience to accelerate timelines.
  • The ability to tailor approaches to meet your unique organizational needs, ensuring meaningful results.
AI apps and agents

Collaborative AI application library

Disconnected workflows and scattered resources can bring AI deployment to a crawl, stalling progress. DataRobot’s customizable frameworks, hosted on GitHub, help teams establish a shared library of AI applications to:

  • Start with a foundational framework.
  • Adapt it to organizational requirements.
  • Share it across data science, app development, and business teams.

These organization-specific customizations empower teams to deploy faster, enhance security, and foster seamless collaboration across the organization.

Collaborative AI application library

How to streamline fragmented workflows for scalable AI 

Creating user-friendly AI interfaces that integrate seamlessly into business workflows is often a slow, complex process. Custom development and integration challenges force teams to start from a blank slate, leading to inefficiencies and delays. Simplifying app development, hosting, and prototyping can accelerate delivery and enable faster integration into business workflows.

AI App Workshop

Setting up local environments and generating Docker images often creates bottlenecks. Managing dependencies, configuring settings, and ensuring compatibility across systems are time-consuming, manual tasks prone to errors and delays.

DataRobot Codespaces now allow you to build code-first AI applications for your models using frameworks like Streamlit and Flask, simplifying development and enabling quick creation and deployment of custom generative AI app interfaces. 

The new embedded Codespace support enhances this process by allowing you to easily develop, upload, test, and organize interfaces within a streamlined file system, eliminating common setup challenges.

AI App Workshop

Q&A App

Another new DataRobot feature enables you to quickly create chat applications to prototype, test, and red-team generative AI models. With a simple, pre-built GUI, you can evaluate model performance, gather feedback efficiently, and collaborate with business stakeholders to refine your approach.

This streamlined approach accelerates early development and validation, while its flexibility allows you to customize or replace components as priorities evolve.

Adding custom metrics and conducting stress-testing ensures the application meets organizational needs, builds trust in its responses, and is ready for seamless production deployment.

QA App

What’s holding back scalable AI applications?

Delivering scalable, trustworthy AI applications requires cohesion across workflows, tools, and teams.  Without streamlined provisioning, standardization, and integration, delays and inefficiencies stall progress and stifle innovation.

The right tools, however, unify processes, reduce errors, and align outcomes with business needs.

Declarative API framework

DataRobot’s Declarative API Framework simplifies the development of scalable, repeatable AI applications for generative and predictive use cases, enabling teams to replicate work, save pipelines, and deliver solutions faster.

Declarative API

One-click SAP ecosystem embedding

Integrating AI models into existing ecosystems presents multiple challenges, including compatibility issues, siloed data, and complex configurations. DataRobot’s one-click integration with SAP Datasphere and AI Core simplifies this process by enabling you to:

  • Seamlessly connect with minimal effort.
  • Specify SAP credentials and compute resources.
  • Bring models closer to your data for faster, more efficient scoring.
  • Monitor deployments directly within DataRobot.

This integration minimizes latency, streamlines workflows, and enhances scalability, allowing your AI solutions to operate seamlessly at an enterprise scale.

One click SAP ecosystem embedding

Transform your workflows with adaptable AI

Integrating AI shouldn’t disrupt your workflows—it should enhance them.

Imagine AI that adapts to your business: flexible, customizable, and seamlessly deployable. With the right tools, you can overcome challenges, deliver value faster, and ensure AI becomes an enabler, not an obstacle.

As you evaluate AI for your organization, the right AI apps and agents can help you focus on what truly matters. Explore what’s possible with AI apps that help you achieve enterprise AI at scale.

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