Drivers for AI Business Development

1. Process flywheel

A process flywheel refers to a concept often used in business, management or operational contexts to describe a system or process that continuously builds momentum over time. Just like a mechanical flywheel stores energy and releases it smoothly, a process flywheel in a business setting gains increasing efficiency, output or results the longer it is in motion.

In a business process, the "flywheel" is typically used to represent a cycle where actions taken early on such as creating value, acquiring customers, or building resources accumulate and reinforce each other, leading to sustainable growth with less effort as time progresses. The more the system is powered, i.e. the more the process is repeated or refined, the more momentum is gained, creating an increasingly efficient process.

For example, in a customer acquisition flywheel:

1. Customer success: Deliver excellent service or products to customers, ensuring satisfaction.
2. Referrals: Happy customers refer others, bringing in more business.
3. Growth: This increased business drives further success and resources to improve service or products even more, reinforcing the cycle.

The flywheel concept emphasizes the importance of consistency, scalability, and momentum, showing how small efforts can compound over time into significant results.

2. Data flywheel

A data flywheel refers to a self-reinforcing cycle where data is collected, processed and analyzed to improve systems, products, or services. The insights gained from this data are then used to drive further data collection, creating a continuous loop that builds momentum and increases value over time.

In simpler terms, a data flywheel works by leveraging data to improve a product or process, which, in turn, generates more data that can be used to make further improvements, creating a cycle of continuous enhancement. As this cycle grows, the system becomes more efficient and the value generated from the data increases exponentially.

Key components of a data flywheel:

1. Data collection: Gathering data from various sources (e.g., customer interactions, sensors, transactions).
2. Data analysis: Using advanced analytics, machine learning or AI to extract insights from the data.
3. Actionable insights: The insights lead to improvements in a product, service or process.
4. Implementation: These improvements are applied, which leads to better user experiences or outcomes.
5. New data: As the system improves, more data is generated, which feeds back into the cycle.

Over time, this flywheel effect means that the system becomes smarter, more efficient and more valuable without requiring proportional increases in effort or cost, as it continuously leverages data to enhance its performance.

SIPOC process modeling & mapping by the ProcessHorizon web app can significantly enhance the process and data flywheels by providing clarity and focus on each part of the process, from inputs to outputs. It helps identify inefficiencies, optimize workflows, and ensure that both the processes and the data generated from those processes are continually improved, creating sustainable momentum and value over time.