• Written by Azhar Aliyar, Director

In practice, executive leadership deployments of AsscherAi often struggle to provide complete answers because they lack access to specific systems and data fields, despite being designed to provide real-time insights from an organisation's own data. The consolidated financials system, for example, is a critical component that is sometimes overlooked, leading to incomplete or inaccurate answers. A shift supervisor checking the maintenance log may not see the full picture if the financial data is not integrated.

The systems

The systems that executive leadership deployments depend on include consolidated financials, operational KPIs, sales performance, and headcount. These systems provide the necessary data for AsscherAi to generate accurate and comprehensive answers. The consolidated financials system, in particular, is essential for providing a complete view of the organisation's financial performance. Operational KPIs, such as production volumes and quality metrics, are also critical for understanding the organisation's operational performance.

A category manager comparing two suppliers, for instance, needs access to the sales performance system to see the sales data for each supplier. Similarly, the headcount system is necessary for understanding the organisation's staffing levels and structure. Without access to these systems, AsscherAi may not be able to provide complete or accurate answers, which can lead to poor decision-making.

What a missing system does to an answer

A missing system can significantly impact the accuracy and completeness of an answer. If the consolidated financials system is not integrated, for example, AsscherAi may not be able to provide a complete view of the organisation's financial performance. This can lead to inaccurate or incomplete answers, which can have serious consequences for decision-making. A missing operational KPIs system, on the other hand, may mean that AsscherAi is unable to provide insights into the organisation's operational performance, making it difficult for executives to make informed decisions.

Most people assume that a missing system will simply result in a missing piece of data, but this is not the case. A missing system can have a ripple effect, impacting the accuracy and completeness of multiple answers. This is because the systems are often interconnected, and data from one system is used to inform answers in another. For example, the sales performance system may rely on data from the consolidated financials system to provide accurate sales data.

The definitions that must be agreed

For AsscherAi to provide accurate and comprehensive answers, there must be agreement on the definitions of key terms and concepts. This includes definitions of financial metrics, such as revenue and profitability, as well as operational metrics, such as production volumes and quality metrics. Without a common understanding of these terms, AsscherAi may provide answers that are inconsistent or inaccurate.

Agreeing on definitions can be a challenging task, especially in large and complex organisations. Different departments and teams may have different understandings of key terms, and reconciling these differences can be time-consuming and difficult. However, it is essential for ensuring that AsscherAi provides accurate and comprehensive answers. For more information on how AsscherAi can support executive and strategic leadership, see our page on executive and strategic leadership.

Quality problems that only surface later

Quality problems with the data can often only be detected after AsscherAi has been deployed and is providing answers. For example, if the data in the consolidated financials system is inaccurate or incomplete, AsscherAi may provide answers that are also inaccurate or incomplete. Similarly, if the operational KPIs system is not providing accurate data, AsscherAi may provide insights that are misleading or incorrect.

These quality problems can be difficult to detect, especially if they are subtle or intermittent. However, they can have serious consequences for decision-making, and it is essential to identify and address them as quickly as possible. This may involve reviewing the data and systems, identifying the source of the problem, and taking corrective action. For support with identifying and addressing quality problems, contact us.

When AsscherAi is the wrong choice

AsscherAi is not always the right choice for every organisation or every question. If the question requires external data or expertise, for example, AsscherAi may not be able to provide a complete or accurate answer. Similarly, if the organisation's systems and data are not well-integrated or are of poor quality, AsscherAi may not be able to provide reliable answers.

In these cases, other approaches or tools may be more suitable. For example, if the question requires external data or expertise, a human analyst or researcher may be able to provide a more complete and accurate answer. Alternatively, if the organisation's systems and data are not well-integrated or are of poor quality, it may be necessary to invest in data integration or quality improvement initiatives before deploying AsscherAi.