10 Ways to Improve Enterprise Workflow with AI and Automation

Of course, machine learning can be used for data analysis — but when paired with automation, it can also save time and resources by running more straightforward data searches. When dealing with a volume of data, whether that’s a few dozen sales or press inquiries or thousands of customer records, ML systems can learn via real-life examples to find and isolate important records based on keywords or senders. This saves time both in finding important information more quickly and in reducing the manpower spent eliminating unimportant information.

Maintaining client and customer connections is important — and also more difficult when many people are working remotely. Intelligent automation can make the process more consistent and less time-consuming, for example, by ensuring that a follow-up email is sent within a certain number of days to customers who make a sales inquiry about a specific product or service.

AI-led follow-up can also be built into employee workflows with the right software bots. Think of reminders about documentation or attachments, or the ability upon completion of a task to assign to-do items to the next person in a workflow. Even emails can be prioritized based on keywords or the sender, making it easy to know which messages should be dealt with first.

A variety of repetitive and time-consuming administrative tasks can now be taken care of via AI and automation. This saves time and frees up administrative resources for tasks that need to be done person-to-person, or for strategic job tasks, and also reduces overall decision fatigue.

For example, instead of sending emails back and forth to decide on an ideal meeting time, allow an automated scheduling tool to choose one based on everyone’s existing schedule. If the same order for coffee and snacks is made before every weekly all-hands meeting, the process of making that order and arranging its delivery can be automated.

Artificial intelligence has a role to play in content creation from its inception to its deployment. Keyword and search analysis can inform what content you include, when it should publish and to whom it is targeted. Increasingly, AI can be used to generate text — from predictive text in email replies to entire paragraphs or longer.

Machine learning tools such as Grammarly analyze your text to ensure it is accurate and effective, allowing you to put your best foot forward with every piece of writing. And intelligent analysis of demographic data for clients, customers or social media followers ensures that you target the right content to those most likely to welcome it.

Thanks to natural language processing, chatbot applications are becoming increasingly effective. There are now many third-party options that can be integrated with an enterprise’s operations, whether that’s forward-facing services such as customer support and sales inquiries, or internal operations like HR queries and IT troubleshooting.

When a well-designed chatbot can answer simple queries easily or point people toward the relevant documentation, professional staff are free to give the appropriate attention to more complex queries.

AI and predictive analytics can be used in powerful, even world-changing ways — but they can also be used to improve enterprise workflows for a variety of roles. Automation can be used to monitor for media mentions, research and reports, and when combined with analytics this can point to the potential for future mentions or ideal areas of focus for upcoming reports. Predictive analytics are also important for risk management, which should only become more valuable in a world dealing with the challenges of the ongoing pandemic, climate change and globalization.

Automation is already commonly used for customer marketing via Facebook advertisements and Google AdSense, where powerful targeting capabilities can help you home in on your desired audience. Similarly, there's value in the approach for email sales marketing as well when AI is used to optimize or scale operations via learning on existing or prospective customer data.

Additionally, analytics can be used to find keywords, analyze search data and make use of data from social media followers to ensure that all sales and marketing efforts are as intelligent as possible.

Robotic process automation is a growing area for enterprise automation, thanks to the ability of RPA tools to automate data entry, form completion, bookkeeping and other documentation tasks. With the right RPA software in place, companies can automate time-consuming tasks such as invoice completion and processing or HR onboarding. RPA also has the potential to reduce human error, especially for repetitive tasks like data entry.

By automating various steps in an enterprise's accounting processes, invoicing and billing can be streamlined — and ideally, kept on track, stabilizing cash flow. Intelligent automation can be worked into the handling of billing and expenses at various stages: to fill out invoices, to ensure that account statements move correctly up the workflow chain, to enter data into records and to send payment or order reminders. Also, machine learning-led analysis of the data that results can point to predictable trends in spending, revenue and cash flow.

Finally, AI-powered tools can increasingly find and compile relevant research and analyze results in order to inform — and improve — future business decisions. Think of looking at the last year’s sales or revenue data to predict what might be coming up the pipe, for example. The limitations of such analysis based on past trends became clear in 2020; however, the insights gathered from the disruptions happening now could be the research informing modeling and risk mitigation in the future.

Finally, AI-powered tools can increasingly find and compile relevant research and analyze results in order to inform — and improve — future business decisions. Think of looking at the last year’s sales or revenue data to predict what might be coming up the pipe, for example. The limitations of such analysis based on past trends became clear in 2020; however, the insights gathered from the disruptions happening now could be the research informing modeling and risk mitigation in the future.