How long before AI shows results in executive leadership?
Executive leadership can expect to see results from AI within weeks, as a production manager uses AsscherAi to analyse maintenance logs, identifying patterns that inform scheduling and resource allocation, and drive business outcomes by reducing equipment downtime and increasing production throughput.
A dusty dashboard, once intended to track key performance indicators, now gathers dust in a forgotten corner of the executive suite, a relic of a failed attempt to integrate AI into leadership decision-making. This is a common sight, as many organisations struggle to derive meaningful insights from their data, and even more, to see tangible results from their AI investments.
Initial Setup and Connection
The first fortnight of implementing AI in executive leadership is often spent on connecting data sources, configuring systems, and establishing access controls. This initial setup is crucial, as it lays the foundation for the entire AI ecosystem. However, it is surprising how many organisations underestimate the time and effort required for this phase. A shift supervisor, for instance, may need to work closely with the IT department to ensure that the maintenance log is properly integrated with the AI system.
Most people get wrong the assumption that AI can be plugged in and start producing results immediately. This is wrong, as the initial setup requires careful planning, coordination, and testing to ensure that the system is functioning correctly. The category manager, for example, may need to compare two suppliers and negotiate contracts to ensure that the necessary data is available and accessible.
The First Question Worth Answering
The first question worth answering is often one that has been debated by executives for a long time, but has never been fully resolved due to lack of data. For instance, a question like "what is the most common reason for equipment downtime?" can be a good starting point. Answering this question can provide valuable insights and help executives make informed decisions. A worked example would be a production manager using AsscherAi to analyse the maintenance log and identify patterns that can inform scheduling and resource allocation.
By answering this question, executives can begin to see the potential of AI in driving business outcomes. It is essential to start with a question that has a significant impact on the organisation, rather than trying to tackle a complex problem that may not yield immediate results. The goal is to build momentum and demonstrate the value of AI to stakeholders.
When a Leading Measure Should Move
A leading measure should move within a few weeks of implementing AI in executive leadership. This could be a metric such as "reduction in equipment downtime" or "increase in production throughput". The key is to identify a measure that is closely tied to the question being answered and can be influenced by the insights generated by the AI system. For example, if the question is "what is the most common reason for equipment downtime?", a leading measure could be "reduction in downtime due to maintenance scheduling".
It is crucial to establish a clear baseline and track progress over time. This will help executives understand the impact of AI on the organisation and make adjustments as needed. By monitoring leading measures, executives can identify areas where the AI system is having a positive impact and areas where further improvement is needed.
What Makes it Take Twice as Long
One of the most significant factors that can make AI implementation take twice as long is the lack of data quality and standardisation. If the data is incomplete, inaccurate, or inconsistent, it can be challenging to generate reliable insights. Additionally, if the data is not properly formatted or standardised, it can be difficult to integrate with the AI system. This can lead to delays and frustrations, as executives may need to spend more time cleaning and preparing the data before it can be used.
Another factor that can slow down AI implementation is the lack of clear goals and objectives. If executives are not clear about what they want to achieve with AI, it can be challenging to focus efforts and resources. By establishing clear goals and objectives, executives can ensure that everyone is working towards the same outcomes and that the AI system is being used to drive meaningful change. For more information on setting clear goals and objectives, visit our page on executive and strategic leadership.
When AI is the Wrong Choice
AI is not a silver bullet, and there are times when it may not be the best solution. If the problem is relatively simple and can be solved with existing tools and processes, AI may not be necessary. Additionally, if the data is not available or is of poor quality, AI may not be able to generate reliable insights. In such cases, it may be better to focus on improving data quality or exploring alternative solutions. If you are unsure about whether AI is right for your organisation, contact us to discuss your options.
Frequently asked questions
How long does it take to see results from AI in executive leadership?
Results can be seen within weeks, as AI starts to generate insights and inform decision-making.
What is the first step in implementing AI in executive leadership?
The first step is connecting data sources, configuring systems, and establishing access controls.
What makes AI implementation take longer than expected?
Lack of data quality and standardisation, as well as unclear goals and objectives, can slow down AI implementation.
When is AI not the right choice for executive leadership?
AI may not be necessary for simple problems or when data is unavailable or of poor quality, and alternative solutions may be more effective.