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Most businesses have AI on their radar as something they know can increase operational efficiency and improve customer satisfaction. However, in the vast ecosystem of AI implementations, it’s not always obvious where to begin to generate the best ROI. What’s more, it’s easy to be seduced by the flashiest and boldest AI initiatives which are expensive and difficult to pull off.

Generally speaking, dreaming big is a good thing, but for most businesses getting started with AI it’s far smarter to focus on targeted, manageable initiatives and build from there.

This article focuses on how organizations can begin to build AI into their operations through the use of technology that is already available. No waiting for the robot army to arrive. We’re talking about where AI can have the greatest impact in the immediate future.

Broadly speaking, AI is already capable of supporting three important business needs: automating business processes, gaining insight through data analysis, and engaging with customers and employees. Below we’ll detail what implementations in each of these three categories look like and how they can help your company’s bottom line.

Business process automation

If you close your eyes and imagine the kinds of tasks you would outsource to a robot if you had one at your disposal, the first things that come to mind are probably the most rote and monotonous items on your to-do list.

Good news: AI can help with that.

Process automation is one area where AI is already helping companies bring greater efficiency to their operations. Using a form of AI known as Robotic Process Automation (RPA), companies can effectively delegate administrative tasks that are low complexity but still highly time-consuming.

Examples include data input and transfer, query management, forms processing, and various customer account management tasks. Automating these types of processes frees up valuable employee time to spend on projects that require analysis, problem-solving, and decision-making skills.

Data analysis and insights

The digital era has created a deluge of consumer data available for brands to collect and analyze. However, most companies struggle to make effective use of the data they collect because it’s simply too difficult to parse actionable insights from such large swaths of information.

Fortunately for us, AI is very good at finding patterns in large data sets. Machine learning algorithms can sort and interpret data to pinpoint trends and predict likely outcomes.

These algorithms get smarter automatically the longer they are used and the more exposure to data they get. Thus, over time, machine learning predictions become even more accurate and reliable.

Practical examples of this technology at work include real-time fraud detection, predictive analytics, and personalized content curation.

Engagement

As marketers, much of our time is spent devising ways to better connect with our audiences. AI is already well-positioned to help provide solutions. Machine learning can personalize experiences and fill customer service gaps. Chatbots and intelligent agents are currently capable of dealing with low complexity tasks and issues, freeing up the time of employees to tackle more complicated requests.

But AI doesn’t only power customer engagement strategies, it can have an even bigger role to play in employee engagement.

Understanding employee satisfaction is key to reducing turnover and keeping your best people in their roles. AI offers companies the ability to generate real-time feedback. Getting this feedback quickly, as opposed to generating it once or twice a year during review periods, gives better visibility into employee satisfaction and enables companies to be nimble in addressing issues.

Conversational AI can also serve an internal role. For example, it can augment human resources teams by handling simple questions and requests. Elsewhere, intelligent agents can be used to help customer service representatives resolve customer issues and act as IT support.

AI for the real world

In the future we are sure to see plenty of bold AI implementations that will disrupt business as usual, but we don’t have to wait for that future to arrive before putting AI to work in the real world.

Focusing on smaller, more achievable AI implementations allows organizations to harness existing technology to bring greater efficiency to operations, communication, and customer service.

 

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