Andrew Barto and Richard Sutton. Image credit: Association for Computing Machinery.
The Association for Computing Machinery, has named Andrew Barto and Richard Sutton as the recipients of the 2024 ACM A.M. Turing Award. The pair have received the honour for “developing the conceptual and algorithmic foundations of reinforcement learning”. In a series of papers beginning in the 1980s, Barto and Sutton introduced the main ideas, constructed the mathematical foundations, and developed important algorithms for reinforcement learning.
The Turing Award comes with a $1 million prize, to be split between the recipients. Since its inception in 1966, the award has honoured computer scientists and engineers on a yearly basis. The prize was last given for AI research in 2018, when Yoshua Bengio, Yann LeCun and Geoffrey Hinton were recognised for their contribution to the field of deep neural networks.
Andrew Barto is Professor Emeritus, Department of Information and Computer Sciences, University of Massachusetts, Amherst. He began his career at UMass Amherst as a postdoctoral Research Associate in 1977, and has subsequently held various positions including Associate Professor, Professor, and Department Chair. Barto received a BS degree in Mathematics (with distinction) from the University of Michigan, where he also earned his MS and PhD degrees in Computer and Communication Sciences.
Richard Sutton is a Professor in Computing Science at the University of Alberta, a Research Scientist at Keen Technologies (an artificial general intelligence company based in Dallas, Texas) and Chief Scientific Advisor of the Alberta Machine Intelligence Institute (Amii). Sutton was a Distinguished Research Scientist at Deep Mind from 2017 to 2023. Prior to joining the University of Alberta, he served as a Principal Technical Staff Member in the Artificial Intelligence Department at the AT&T Shannon Laboratory in Florham Park, New Jersey, from 1998 to 2002. Sutton received his BA in Psychology from Stanford University and earned his MS and PhD degrees in Computer and Information Science from the University of Massachusetts at Amherst.
The two researchers began collaborating in 1978, at the University of Massachusetts at Amherst, where Barto was Sutton’s PhD and postdoctoral advisor.
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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.
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.
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:
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.
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:
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.
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:
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.
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.
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