• Written by Sonali Subhadarsini, Product Manager

When the category manager and the shift supervisor sit down with the latest sales performance figures, they often find themselves waiting for the finance team to consolidate the numbers, only to discover that the operational KPIs they need are not up to date, and the headcount figures are still being reconciled. This is the point at which many organisations consider introducing AI process automation for executive leadership, in the hope of streamlining the decision-making process. However, as many have found, this is not always a straightforward solution, and the sequence of events that unfolds can be complex and nuanced.

Mapping the current process

Before automating any part of the decision-making process, it is essential to map out the current process in detail. This involves identifying all the stakeholders, including the category manager, the shift supervisor, and the finance team, and understanding their roles and responsibilities. It also requires a thorough analysis of the data flows, including the sources of the data, how it is processed, and how it is used to inform decisions. By doing so, organisations can identify the pain points and bottlenecks that are causing delays and inefficiencies.

A thorough mapping of the current process can also help to identify areas where automation can add the most value. For instance, automating the consolidation of financial data or the updating of operational KPIs can free up staff to focus on higher-value tasks, such as analysis and decision-making. However, if the underlying process is not well understood, automation can sometimes exacerbate existing problems, rather than solving them.

The handoffs that create most of the delay

One of the main causes of delay in the decision-making process is the handoff of information between different teams and stakeholders. When the finance team finally consolidates the numbers, they may need to pass them on to the category manager, who then needs to review and analyse them before making a decision. Each of these handoffs can create a delay, as the information is passed from one person to another, and each person reviews and checks the data before passing it on.

These handoffs can be particularly problematic when different teams have different versions of the truth, or when the data is not up to date. For instance, the sales team may have one set of figures, while the finance team has another, and the operational team has yet another. Reconciling these different versions of the truth can be a time-consuming and laborious process, and can often hold up the decision-making process.

The executive leadership data this depends on

AI process automation for executive leadership depends on a range of data sources, including consolidated financials, operational KPIs, sales performance, and headcount. This data needs to be accurate, up to date, and consistent, in order for the automation to be effective. However, in many organisations, this data is scattered across different systems and teams, and may not be easily accessible or consistent.

For instance, the finance team may have a consolidated set of financials, but the operational team may have a different set of KPIs that are not aligned with the financials. Similarly, the sales team may have their own set of performance figures that are not consistent with the financials or the operational KPIs. Reconciling these different data sources can be a major challenge, and requires a thorough understanding of the underlying data flows and systems.

What it changes about cross-functional questions

One of the main benefits of AI process automation for executive leadership is that it can help to answer cross-functional questions that nobody can answer in the room. For instance, the category manager may need to know the impact of a decision on both sales performance and operational KPIs, but may not have access to the relevant data. By automating the process, organisations can provide a single, consistent view of the data, that can be used to inform decisions.

This can also help to reduce the problem of each function reporting its own version of a figure. For instance, the sales team may report one set of figures, while the finance team reports another, and the operational team reports yet another. By providing a single, consistent view of the data, organisations can reduce the confusion and miscommunication that can arise from these different versions of the truth.

For more information on how AI process automation can support executive and strategic leadership, please see our related article.

Measured with decision latency, actions, and definition conflicts

The effectiveness of AI process automation for executive leadership can be measured in a range of ways, including 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, and can be reduced by automating the process and providing a single, consistent view of the data.

Actions of the form come back with numbers refer to the ability of the automation to provide a clear and consistent set of numbers to support decision-making. This can help to reduce the confusion and miscommunication that can arise from different versions of the truth, and can provide a clearer understanding of the impact of different decisions.

What this does not do

While AI process automation for executive leadership can be a powerful tool for streamlining the decision-making process, it is not a panacea for all organisational ills. It does not, for instance, replace the need for human judgment and decision-making, and it does not provide a substitute for strategic thinking and planning. Rather, it is a tool that can be used to support and inform decision-making, by providing a single, consistent view of the data and automating routine tasks.

Organisations that are considering introducing AI process automation for executive leadership should carefully evaluate their needs and requirements, and should consider seeking advice from experts in the field. For more information on how to get started with AI process automation, please contact us.