• Written by Azhar Aliyar, Director

A sales manager checks the forecast for the upcoming quarter, but the numbers do not match the order book. This discrepancy is a common problem when integrating AI systems with sales data, and it stems from the way these systems interact with the underlying data. To avoid becoming a second source of truth, it is crucial to understand the read and write paths in the integration process.

Read paths, write paths and why the second needs more care

The read path is the process by which the AI system retrieves data from the systems of record, such as the enterprise resource planning system or the customer relationship management system. This path is relatively straightforward, as the AI system is simply reading data that already exists. However, the write path is more complex, as it involves writing data back to the systems of record. This is where the AI system can potentially become a second source of truth, if the data it writes back is not properly validated and reconciled with the existing data.

A good example of this is when a sales team uses AsscherAi to analyze their sales data and identify trends. If the AI system writes back its findings to the sales database without proper validation, it can create inconsistencies and inaccuracies in the data. To avoid this, it is essential to carefully design the write path and ensure that the AI system is writing data back to the systems of record in a way that is consistent and accurate.

Reconciling definitions that differ between systems

One of the biggest challenges in integrating AI systems with sales data is reconciling definitions that differ between systems. For instance, the definition of a sales region may vary between the sales database and the enterprise resource planning system. To overcome this challenge, it is necessary to create a common data model that defines the key concepts and terms used in the sales process. This data model should be used consistently across all systems, to ensure that the data is accurate and consistent.

Reconciling definitions is a time-consuming and labor-intensive process, but it is essential for ensuring the accuracy and consistency of the sales data. Most people get wrong the idea that this process can be automated, but it is a task that requires careful human judgment and oversight. By taking the time to reconcile definitions, organizations can ensure that their sales data is accurate and reliable, and that their AI systems are providing valuable insights.

The sales data this depends on

The integration of AI systems with sales data depends on a range of sales data, including historical sales at planning grain, order book and pipeline, inventory positions, and promotional calendar. Historical sales data provides a baseline for analyzing sales trends and forecasting future sales. The order book and pipeline data provide insights into current and future sales, while inventory positions data helps to identify potential stockouts or overstocking. The promotional calendar data provides context for sales trends and helps to identify the impact of promotions on sales.

A sales team can use AsscherAi to analyze this data and identify trends and insights that can inform their sales strategy. For example, a sales team can use the AI system to analyze historical sales data and identify seasonal trends, and then use this information to adjust their sales forecast and inventory management. By linking to sales teams and providing them with accurate and timely data, organizations can improve their sales performance and competitiveness.

What it changes about

The integration of AI systems with sales data can have a significant impact on the sales process, particularly in terms of forecasting and inventory management. A forecast that is based on inaccurate or inconsistent data can lead to stockouts or overstocking, which can have serious consequences for the business. By providing a single, accurate view of the sales data, AI systems can help to improve forecast accuracy and reduce the risk of stockouts or overstocking.

For instance, a forecast that nobody can interrogate can lead to a lack of trust in the sales data, and can make it difficult to identify the root causes of errors or inconsistencies. By providing a transparent and explainable forecast, AI systems can help to build trust in the sales data and improve the overall sales process. This is particularly important in industries where inventory management is critical, such as retail or manufacturing.

Measured with

The success of AI systems integration for sales can be measured using a range of metrics, including forecast accuracy, win rate, and stock cover. Forecast accuracy is a key metric, as it provides insights into the accuracy of the sales forecast and the effectiveness of the AI system. Win rate is also an important metric, as it provides insights into the effectiveness of the sales team and the sales process. Stock cover is a critical metric, as it provides insights into the inventory management process and the risk of stockouts or overstocking.

By tracking these metrics and using them to evaluate the effectiveness of the AI system, organizations can identify areas for improvement and optimize their sales process. For example, if the forecast accuracy is low, the organization may need to review its sales data and adjust its forecasting model. If the win rate is low, the organization may need to review its sales strategy and adjust its approach to sales. By using these metrics to drive continuous improvement, organizations can achieve better sales outcomes and improve their competitiveness.

What this does not do

The integration of AI systems with sales data is not a panacea for all sales problems, and it is not a replacement for human judgment and oversight. While AI systems can provide valuable insights and improve the sales process, they are not a substitute for experienced sales professionals who can provide context and nuance to the sales data. Additionally, AI systems integration for sales is not suitable for all organizations, particularly those with limited sales data or inadequate IT infrastructure.

For organizations that are considering AI systems integration for sales, it is essential to carefully evaluate their needs and capabilities before proceeding. This may involve consulting with experts and evaluating the potential benefits and risks of the integration. By doing so, organizations can ensure that they are making an informed decision and achieving the best possible outcomes for their sales process. For more information, please contact us.