Integration for executive leadership
Executive leadership relies on integrated AI systems to provide a unified view of organisational data, enabling informed decisions by reconciling disparate definitions and reducing data inconsistencies, thus facilitating cross-functional queries and improving decision-making processes.
A finance director reviewing the quarterly budget often needs to check the latest sales forecasts, which are stored in the sales force automation system, and the current headcount, which is stored in the human resources system. This requires connecting to the systems of record without becoming a second source of truth, a challenge that is particularly relevant to executive leadership.
Read paths, write paths and why the second needs more care
When integrating AI systems for executive leadership, it is crucial to distinguish between read paths and write paths. Read paths refer to the process of retrieving data from various systems, whereas write paths refer to the process of updating data in these systems. While read paths are relatively straightforward, write paths require more care to avoid creating inconsistencies and discrepancies between systems.
A common failure in AI systems integration is the lack of careful consideration of write paths, which can lead to data inconsistencies and errors. This failure is often caused by inadequate testing and validation of the integration process, resulting in incorrect or incomplete data being written to the systems of record.
Reconciling definitions that differ between systems
Different systems often have different definitions for the same data elements, which can lead to inconsistencies and errors when integrating AI systems for executive leadership. For example, the definition of a sales region may differ between the sales force automation system and the customer relationship management system.
Most people get wrong the idea that reconciling these definitions is a simple task that can be done quickly. However, it is a complex and time-consuming process that requires careful analysis and validation to ensure that the definitions are consistent and accurate.
The executive leadership data this depends on
The integration of AI systems for executive leadership depends on a range of data elements, including consolidated financials, operational KPIs, sales performance, and headcount. This data is often stored in different systems, such as the enterprise resource planning system, the sales force automation system, and the human resources system.
A shift supervisor checking the maintenance log, for example, may need to access data from the enterprise resource planning system, the computerized maintenance management system, and the supply chain management system. The category manager comparing two suppliers may need to access data from the procurement system, the contract management system, and the supplier relationship management system.
What it changes about
The integration of AI systems for executive leadership changes the way cross-functional questions are answered and the way each function reports its own version of a figure. With integrated AI systems, executive leaders can get answers to cross-functional questions, such as "What is the impact of sales performance on headcount?" or "How does operational performance affect financial results?"
Without integrated AI systems, each function may report its own version of a figure, leading to inconsistencies and errors. For example, the sales team may report a different sales figure than the finance team, leading to confusion and misunderstandings.
Measured with
The success of AI systems integration for executive leadership can be measured with decision latency, actions of the form "come back with numbers", and definition conflicts resolved. Decision latency refers to the time it takes to make a decision, which can be reduced with integrated AI systems. Actions of the form "come back with numbers" refer to the need for additional data or analysis, which can be minimized with integrated AI systems.
Definition conflicts resolved refer to the number of conflicts between different definitions of data elements that are resolved through the integration process. By measuring these metrics, executive leaders can evaluate the effectiveness of their AI systems integration and identify areas for improvement. For more information on executive leadership, visit our page on executive and strategic leadership.
What this does not do
The integration of AI systems for executive leadership does not replace the need for human judgment and decision-making. While AI systems can provide insights and recommendations, executive leaders must still use their judgment and expertise to make decisions. Additionally, AI systems integration is not a one-time task, but rather an ongoing process that requires continuous monitoring and maintenance.
If you are considering integrating AI systems for executive leadership, but are unsure about how to proceed, contact us for more information and guidance. We can help you evaluate your options and develop a plan that meets your needs and goals.
Frequently asked questions
How does AI systems integration support executive leadership?
AI systems integration provides a unified view of organisational data, enabling executive leaders to make informed decisions.
What are the challenges of integrating AI systems for executive leadership?
Integrating AI systems for executive leadership requires careful consideration of read and write paths to avoid data inconsistencies and errors.
How does AI systems integration improve decision-making for executive leaders?
AI systems integration enables executive leaders to answer cross-functional questions and reduces decision latency by providing a unified view of organisational data.
What metrics can be used to measure the success of AI systems integration for executive leadership?
Decision latency, actions of the form "come back with numbers", and definition conflicts resolved can be used to measure the success of AI systems integration for executive leadership.