As AI quickly catches on among businesses, leaders are increasingly using it to assist with major decisions. While CFO survey data shows only about eight percent of finance teams are currently using AI in their forecasts, new technologies that integrate AI algorithms with a company’s data stack have the potential to be game changers for early adopters.
The past several years of market volatility have shown organizations that traditional approaches to business are no longer enough to stay competitive—especially in planning. As the market continues to shift, companies that integrate AI into their planning efforts will reap the benefits. But to succeed, they’ll need to be ready. In this article, we’ll lay out a roadmap for incorporating AI into your planning infrastructure and recommend important steps.
Historically, companies have expanded their planning capabilities in response to market uncertainties—but planning tools can only be as good as the data that’s fed into them. As organizations rely more and more on connected planning to inform strategic decisions, the underlying data needs to be reliable.
Unfortunately, the past few years have seen a decline in data quality. As many enterprise applications have added data capabilities, companies have seen conflicting sources of truth arise, and less need to collaborate as a result means that those conflicting sources of truth don’t get reconciled. Furthermore, rapid changes in organizational structures and market dynamics have compounded the issue. Anomalies and inconsistencies in data undermine the confidence in its predictive capabilities, making it challenging for companies to leverage historical data for future planning.
To navigate these challenges, organizations have chosen to prioritize either their planning systems or data availability and quality. But companies that prioritize data often wind up with subpar planning tools, feeding excellent data into half-baked systems, and organizations that allocate insufficient resources to data will find inaccurate data affects the results of even the best planning tools.
To avoid these outcomes, companies should see data management and planning tools not as competing priorities, but as two parts of a single system that both must work for the system to function. They should establish a “golden ratio” between investment in planning systems and data infrastructure, and find a holistic approach to data and planning, recognizing that each one reinforces the usefulness of the other. This will be especially important as AI emerges, and that data’s quality is more and more determinative of the quality of the resulting plan.
An organization that wants to achieve more from AI-powered planning will need good data. Start with the establishment of key masters, such as customer master and employee master data sets. These main data sets provide a comprehensive and reliable source of information for each area within an organization. By centralizing and maintaining these key masters, organizations can ensure that all stakeholders have access to accurate and consistent data—data that will be essential for effective planning and decision-making.
Having key masters isn’t just a data quality issue though—it’s also important to ensure that stakeholders are using the correct data. When stakeholders are far removed from the actual data source, they may inadvertently use incorrect or outdated information. This can lead to a lack of trust in the data and a reluctance to rely on it for planning purposes. A true data master is an investment of time and energy, but it ensures stakeholders have easy access to the right data and are educated on how to use it effectively, leading to more reliable planning outcomes.
With data managed well, companies can make better use of AI’s powerful predictive capabilities, allowing you to do more complex analysis without the need for a dedicated data science team. With tools like Cortex, which can be used in conjunction with basic SQL, AI technology empowers more individuals within an organization to leverage advanced analytics and make data-driven decisions.
One of the key advantages of using AI tools in conjunction with a data warehouse is the flexibility and control it offers. Pre-built AI tools often function as black boxes, running periodically and providing limited customization options. However, by integrating AI capabilities directly into the data warehouse, organizations can tailor their predictive models to their specific needs and run them on-demand. This hybrid approach combines the power of AI with the flexibility of a customizable data infrastructure, enabling organizations to adapt quickly to changing business requirements.
While many organizations may not initially plan to work with transactional-level data, having access to granular insights can be invaluable for effective planning. AI tools can provide this level of detail, allowing organizations to make informed decisions based on fine-grained data points. For example, retailers who need to allocate resources down to the SKU level can benefit greatly from AI-driven insights, allowing them to perform complex calculations and optimize their inventory management, ultimately improving efficiency and profitability.
The convergence of data and AI presents exciting opportunities for companies to optimize their capital planning processes and drive business growth. By prioritizing data quality and investing in AI-driven insights, organizations can maximize the ROI on planning tools and gain a competitive advantage in the marketplace. At Spaulding Ridge, we can help you put industry-leading technology like this to work for your organization. Contact us today to learn more!
