Common mistakes when applying AI to executive leadership
Applying AI to executive leadership requires careful planning, starting with mapping out processes and identifying areas where AI can add value, to avoid common mistakes such as automating poorly understood processes or relying solely on consolidated financials for strategic decisions.
A dusty dashboard in a corner of the executive suite, rarely opened and never updated, is a common sight in organisations that have attempted to apply AI to their leadership decisions. This is often the result of a series of mistakes that recur in executive leadership deployments, mistakes that can be avoided with careful planning and consideration.
Automating a process nobody mapped
One of the most significant mistakes made when applying AI to executive leadership is attempting to automate a process without first mapping it out. This can lead to a situation where the AI system is trying to optimise a process that is not fully understood, resulting in suboptimal decisions. A shift supervisor, for example, may try to use AI to automate the scheduling of maintenance tasks, but if the process for requesting and approving these tasks is not well defined, the AI system will struggle to make effective decisions.
A better approach would be to start by mapping out the process, identifying the key steps and decision points, and then using AI to optimise specific parts of the process. This requires a deep understanding of the process and its constraints, as well as the ability to identify areas where AI can add value.
Treating consolidated financials as complete when it is not
Another mistake made when applying AI to executive leadership is treating consolidated financials as a complete and accurate picture of the organisation's financial situation. In reality, consolidated financials often only tell part of the story, and may not reflect the nuances of different business units or departments. A category manager, for example, may use consolidated financials to compare the performance of different suppliers, but if these financials do not account for differences in pricing or service levels, the comparison may not be valid.
Most people get wrong the idea that consolidated financials are sufficient for making strategic decisions, and this is wrong. Consolidated financials are just one tool that should be used in conjunction with other data sources and analysis techniques to get a complete picture of the organisation's financial situation.
Giving the capability only to the analyst who already had it
A common mistake made when deploying AI in executive leadership is giving the capability only to the analyst who already had it. This can limit the potential benefits of the AI system, as it may only be used to automate tasks that the analyst is already performing. A more effective approach would be to give the capability to a broader range of users, such as line managers or department heads, who can use the AI system to inform their decisions and drive business outcomes.
This requires a change in mindset, from seeing AI as a tool for automating specific tasks to seeing it as a way to empower decision-makers across the organisation. By giving more users access to the AI system, organisations can unlock new insights and drive business value.
Declaring success against a metric with no baseline
Declaring success against a metric with no baseline is another mistake made when applying AI to executive leadership. This can make it difficult to determine whether the AI system is actually having a positive impact, or whether any improvements are due to other factors. A better approach would be to establish a baseline for the metric before deploying the AI system, and then use this baseline to measure the effectiveness of the system.
For example, a company might use AI to optimise its pricing strategy, but if it does not establish a baseline for revenue or profitability before deploying the system, it will be difficult to determine whether the system is actually driving business value. By establishing a baseline and tracking progress over time, organisations can get a clear picture of the impact of their AI investments.
When AI is the wrong choice
While AI can be a powerful tool for executive leadership, there are times when it is the wrong choice. For example, in situations where the decision-making process is highly nuanced or dependent on human judgement, AI may not be the best solution. In these cases, it may be more effective to rely on human decision-makers, who can bring their expertise and experience to bear on the problem.
Organisations considering AI for executive leadership should carefully evaluate their options and consider whether AI is the right tool for the job. They can learn more about executive and strategic leadership and how to apply AI effectively in this context. For more information on how to get started with AI in executive leadership, or to discuss a specific project, please contact us.
Frequently asked questions
What are the most common mistakes made when applying AI to executive leadership?
Mistakes include automating poorly understood processes and relying on incomplete data.
How can organisations avoid making these mistakes when deploying AI?
Organisations can avoid mistakes by mapping out processes and identifying areas where AI can add value.
What is the impact of giving AI capabilities only to analysts who already have them?
Giving AI capabilities only to analysts who already have them limits the potential benefits of the AI system.
Why is it important to establish a baseline for metrics before deploying AI?
Establishing a baseline allows organisations to measure the effectiveness of the AI system and determine its impact on business outcomes.