Can AI reduce cross-functional questions nobody can answer in the room?
AI reduces cross-functional questions by providing rapid access to consolidated financials and operational KPIs, enabling teams to investigate issues like a shift supervisor checking maintenance schedules, and identify areas for improvement, then act while the deviation is still small, driving business outcomes that are more effective.
The objection that AI cannot reduce cross-functional questions nobody can answer in the room because it is too complicated is misguided, as the real challenge lies in having the right data in place, not in the complexity of the questions themselves. In reality, the ability of AI to answer such questions depends on the availability of consolidated financials and operational KPIs, which provide a comprehensive view of the organisation's performance.
What has to be in the data for this to be possible
To enable AI to answer cross-functional questions, the data must include consolidated financials, such as income statements and balance sheets, as well as operational KPIs, like production volumes and customer satisfaction metrics. This data should be up-to-date, accurate, and easily accessible, allowing the AI system to draw connections between different aspects of the organisation. A shift supervisor, for instance, can use this data to investigate the impact of maintenance schedules on production volumes, and identify areas for improvement.
The inclusion of both financial and operational data is crucial, as it allows the AI system to provide a holistic view of the organisation's performance, and answer questions that span multiple functions. Without this comprehensive data, the AI system would be limited in its ability to provide meaningful answers, and would likely struggle to identify the root causes of problems.
The mechanism, stated plainly
The mechanism by which AI answers cross-functional questions is straightforward: it uses natural language processing to understand the question, and then searches the available data to find the relevant information. This process is facilitated by the use of platforms like AsscherAi, which enable organisations to query their own data in real-time, using natural language. The category manager, for example, can use AsscherAi to compare the performance of different suppliers, and identify areas where costs can be optimised.
By providing a simple and intuitive interface, AsscherAi makes it easy for users to ask questions, and receive answers that are relevant and actionable. This, in turn, enables organisations to make better decisions, and drive business outcomes that are more effective.
What it will not fix
Most people are wrong when they assume that AI can fix underlying issues with data quality, or compensate for a lack of standardisation in data collection. In reality, AI is only as good as the data it is given, and if the data is incomplete, inaccurate, or inconsistent, the answers provided by the AI system will be similarly flawed. A common mistake is to assume that AI can somehow "magically" fix these problems, when in fact, it is simply a tool that relies on the quality of the data it is given.
Furthermore, AI will not fix issues with organisational culture, or overcome barriers to communication between different departments. If an organisation is struggling with siloed thinking, or a lack of collaboration between teams, AI will not be able to overcome these challenges on its own. Instead, it is up to the organisation to address these underlying issues, and create an environment in which AI can be used effectively.
How you would know it worked: decision latency
One way to measure the effectiveness of AI in answering cross-functional questions is to look at decision latency, which refers to the time it takes for organisations to make decisions. By providing rapid access to relevant information, AI can help reduce decision latency, and enable organisations to respond more quickly to changing circumstances. This, in turn, can drive business outcomes that are more effective, and give organisations a competitive edge.
For executive leadership, the ability to make informed decisions quickly is critical, and AI can play a key role in supporting this process. By using AI to answer cross-functional questions, organisations can tap into the expertise of their leaders, and make decisions that are more informed, and more effective. To learn more about how AI can support executive leadership, visit our page on executive and strategic leadership.
What this does not do, or when it is the wrong choice
AI is not a replacement for human judgement, or a substitute for critical thinking. While it can provide valuable insights, and answer complex questions, it is not a panacea for all organisational challenges. In some cases, the use of AI may even be the wrong choice, particularly when the questions being asked are highly subjective, or require a deep understanding of organisational context. In these situations, it is better to rely on human expertise, and the judgement of experienced leaders.
For organisations that are considering the use of AI to answer cross-functional questions, it is essential to carefully evaluate their needs, and determine whether AI is the right tool for the job. To discuss your specific requirements, and learn more about how AsscherAi can support your organisation, visit our page on contact us.
Frequently asked questions
How can AI help answer cross-functional questions in our organisation?
AI can help by providing rapid access to consolidated financials and operational KPIs, enabling teams to investigate issues and identify areas for improvement.
What data is required for AI to answer cross-functional questions effectively?
Consolidated financials, such as income statements and balance sheets, as well as operational KPIs, like production volumes and customer satisfaction metrics, are required for AI to answer cross-functional questions effectively.
Can AI fix underlying issues with data quality or organisational culture?
No, AI is only as good as the data it is given, and it cannot fix underlying issues with data quality or organisational culture, such as siloed thinking or lack of collaboration between teams.
How can we measure the effectiveness of AI in answering cross-functional questions?
Decision latency, which refers to the time it takes for organisations to make decisions, can be used to measure the effectiveness of AI in answering cross-functional questions, with AI helping to reduce decision latency and enable organisations to respond more quickly to changing circumstances.