Trusting machine-powered financial forecasting has been saved
Trusting machine-powered financial forecasting
Four success factors for algorithmic forecasting solutions
Investments in machine learning-powered financial forecasting technologies are only as strong as the trust users put into them. How can CFOs and their teams best address these sorts of people-centric challenges? Explore some of the key success factors for deploying algorithmic forecasting solutions.
- Would you trust machine learning-powered financial forecasting?
- Levers for algorithmic and machine-enabled forecasting success
- Looking ahead
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Do you trust machine learning-powered financial forecasting?
In recent years, algorithmic forecasting has elevated from a “nice to have” productivity advantage to a foundational finance capability. Companies are increasingly investing in sophisticated machine learning-powered forecasting tools to keep pace with strategic objectives, overcome Finance bandwidth constraints, and navigate changing market conditions quickly and effectively.
Despite organizational investment in these capabilities, users struggle to trust machine-powered financial forecasting and fail to adopt these technologies or incorporate them into their ways of working. As a result, many companies have yet to unlock the true potential of algorithmic forecasting solutions.
Levers for algorithmic and machine-enabled forecasting success
In our experience, addressing the below six themes will enable the successful rollout of algorithmic forecasting in financial planning and analysis (FP&A) so companies can accomplish their strategic objectives and drive long-term adoption.
As companies continue to invest in the latest, top-of-the-line algorithmic forecasting solutions, they will also need to consider how their users will receive them. Read on to explore the most salient, people-centric challenges that are highlighted in the top four themes.
Across the marketplace, reliance on automation is rapidly increasing as a means to scale. While other functions are adopting these capabilities and realizing the intended value and advantage, Finance is still catching up to its counterparts.
The use cases for machine learning-powered algorithmic forecasting are well established and have been reinforced by the vulnerabilities exposed by COVID-19. The user-focused framework we've laid out can be leveraged to navigate a successful solution rollout, which if done properly, can unlock the power of the significant investments made in data and technology. With the accelerated insights and the power of algorithmic forecasting, organizations can stay competitive and responsive to change in a rapidly evolving marketplace.
1 Richard H. Thaler and Cass R. Sunstein, Nudge (London: Penguin Books, 2009).