10 Ways You Can Use AI to Turbocharge Project Productivity

There is an old joke in the artificial intelligence industry that a technology is only AI until it is useful. Today, the application space for intelligent applications is growing so fast that you may not even realize that you are using an AI-based application.

Knowing where AI is embedded in your project systems will allow you to gain maximum value from the available options.

Here are 10 ways that you can improve your project delivery right now:

1. Improve Your Writing

Spell-checking has been a part of word processors for decades. Advances in the AI field of natural language processing have expanded the humble spell-checker to review your grammar as you type, but this is only the beginning. Several companies offer writing plugins for most mainstream word processors. These supercharge your writing by considering your style, suggesting improvements to clarity and conciseness.

The more advanced of these plugins offer access to an AI that learns your company style and can offer suggestions that match previous documents.

2. Make Eye-catching Presentations

A second form of AI that often goes unnoticed is the concept of embedded expertise. Most mainstream presentation packages offer a tool that will help you design a good-looking presentation. You may have already clicked on a button that will rearrange the elements on a slide to make them stand out. What you may not realize is that these tools often incorporate sound graphic design principles such as the rule of thirds, or complementary colors.

3. Analyze your Cost and Effort Trends

Data analysis is another form of embedded expertise that is being used in spreadsheet applications. These allow you to ask natural language questions about your data and also provide suggestions on the best ways to analyze your information.

For example, an extract from a finance system will provide prompts showing graphical trends in spending, or tables showing average spend by category or month, or perhaps some other characteristic of the data.

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