Categories: AI
Once a project begins, keeping it on budget and on schedule is the biggest challenge for project managers. Facing a variety of colorful status dashboards and a myriad of project metrics, what we really need is an indicator of potential future issues. Machine learning algorithms provide early detection of deteriorating budget and schedule performance, and those metrics must be included or prioritized. However, another metric may be of even greater importance and it is based on genetic algorithms, another form of machine learning. The new factor is called stickiness. Although this is a new concept for managing projects, entrepreneurs and new start-up companies often use it to retain customers. In marketing, stickiness refers to the likelihood that your customer will stay with your brand, make repeat purchases, and upgrade to a newer version of the same product.
From a project perspective, a stickiness factor adds resiliency to maintaining project performance. There are two steps in this process. First, the machine learning algorithm identifies the most critical metrics to prevent the project from deteriorating. Once the stickiness metrics are identified, the project manager determines what actions can be taken to increase the probability that those metrics will remain positive. Sticky factors consistently provide benefits to project progress. This might include a project complexity metric or a stakeholder volatility metric. The value is that the algorithm can identify the right metrics for each project rather than relying on a standard set of common metrics.
This might sound like key performance indicators (KPIs), but KPIs tend to be quantifiable metrics for each critical project area, such as budget, schedule, quality, or risk. KPIs are still important, but stickiness is what AI uses to keep the KPIs on track. Project management needs to be open to a new way of thinking. A machine learning algorithm finds the factors that result in a high level of stickiness, and a genetic algorithm determines the best actions to maintain the metrics.
