In this day and age, everyone has a project. Whether it’s for passion, work, or a practical DIY home upgrade, basic project management skills come to play in getting something done within a time frame and with the most efficient use of available resources. That is, however, easier said than done.
Time and time again, natural human conflicts get in the way of the process. As a result, the project may encounter delays, go over budget, or not go as planned. Team dynamics and human relations can help and, with the onset of technology, so can artificial intelligence (AI).
However, before you go all, Isaac Asimov gives some credit to how Siri and Alexa have made things easier at home or while you’re on the move. Just think of the times these AI assistants have helped set a timer for even the most talented individuals while they were cooking pasta and chasing a toddler around the living room or found the title of a song you couldn’t figure out through the Internet! Predictive analytics and machine-learning methods can track human errors and blind spots in numerous project planning and execution processes.
Here are 3 common problems in project management that AI can help solve:
- Data Collection
Simple input can be organized and interpreted by AI in complex ways. From storing names, dates, preferences, or outcomes and more to finding recurring patterns or measuring large samples, data collection can be elevated to computer-level analysis with AI. Certain apps and programs can be set with time-tracking or binary codes for the intended purpose of data collection while also minimizing the time (and effort) spent sorting through or identifying specific details needed in this process. This later benefits accuracy in predictions, including pinpointing weaknesses from past events that indicate errors when repeated without proper corrective measures.
Of course, there are pros and cons to the objectivity of AI in viewing information, so even in the beginning — as in the extraction process of the figures and specifics — various factors may be considered essential to data collection. It goes without saying that while this aspect seems like simple note-taking, it’s a step that could determine the success of future executions with the project or similar. In which case, the added aspect of data security in accumulating information becomes paramount in applying AI.
Long to-do lists are one thing; organizing that to-do list and then delegating the tasks is a whole different task altogether. Add to that, weighing the probability of success for one choice over another and trying to decide to move forward from there. If not analysis-paralysis, a complete lack of accountability could sabotage this stage of project management.
With separate data collection conducted, AI can execute an algorithm to help project managers achieve decision-making. By eliminating needless steps through the objective analysis and data extraction, the process is simplified for the human proponent to focus on essential movement forward with the project.
Besides simplification, providing shortcuts and easy access, and completing low-value tasks so project managers can focus on high-value items on the list, AI can also identify distractions. You can see this in how AI can filter emails from “Very Important” to “Trash” based on the number of previous messages you’ve deleted. Or, you will find it in the environments of various group platforms or office chat rooms that encourage clear communication and productivity tracking among its features. In such cases, mobile and browser UX/UI integration also become integral to the work culture that drives the project towards a direction free of misunderstandings, bruised egos, and performance delays.
Ultimately, while human interaction and ingenuity make up the spirit of a project, it takes a well-oiled machine to execute it. Sometimes that’s a great team alone, but most times, it’s a great team that’s got a little help from AI.