Most companies are in the early stages of determining how best to deploy AI to work for them.
Keeping in mind the future promise of self-driving cars and precision medicine for a moment, let’s take a look at how AI can already support a handful of critical needs.
These small-scale implementations are certainly not what you would call a sham, but they do have the power to affect your bottom line in meaningful ways. The best thing is that each of the ideas covered here are based on existing technology that can be applied at reasonable timeframes and reasonable costs.
1. Automation of Business Processes
In almost every area of the business, a large amount of employee time is devoted to performing repetitive and repetitive tasks. Functions such as data input and transfer are essential to running a business, but they are also time-consuming and a major drain on res.
This is where robot process automation (RPA) comes in. RPA is a form of business process automation technology in which software (“robots” in a robot process process) is deployed to perform logic-based tasks. If there is a rule for how a task is performed, it can be accomplished with an RPA. Back-office administration, financial services and even human res are all areas where RPA can help reduce the burden of monotonous tasks on employees.
RPAs can be deployed in a large-scale organization, allowing more capabilities in almost every department. Best of all, it is easy and inexpensive to implement and does not require a great onboarding process to get it up and running.
2. Mining actionable insights
One of the most promising AI implementations for brands and marketers is the ability to mine data for flexible insights. We live in a world full of available data about consumers and their behavior. The sheer volume of data presents its problem – how it all makes sense.
Here again, AI is fully positioned to offer solutions. The algorithm is faster and better than humans at detecting patterns in huge pieces of data. Machine learning applications can do much to extract the most meaningful insights, rather than wasting precious employee time, hoping to find cosmic needles in the high-stake through data.
Machine learning algorithms can also analyze past data to predict future outcomes and behaviors, making this form of AI unavoidable for marketers. “Learn” in machine learning means that algorithms become smarter over time. The more they are trained, the more accurate they become.
3. Engagement with customers and employees
Engaging customers and internal employees is another way businesses can put AI to work in the immediate future.
Cognitive engagement technologies such as chatbots, recommendation engines, and intelligent agents can help fill customer service gaps. By managing a range of low-level customer requests and issues, these technologies reduce the load for customer service employees, freeing up their time to handle more complex tasks.
More personalized creation, custom experience for users is a core objective for marketers. Recommendation engines powered by machine learning and natural language processing help expand opportunities for personalization initiatives that are the drivers of consumer engagement and sales.
It should be clear by now that it is not necessary to go from top to bottom to benefit from artificial intelligence. Starting with business process automation, mining data to generate rich insights and predictions, and focusing on cognitive engagement, brands can begin making immediate organizational improvements with AI.