Putting the future promise of self-driving cars and precision medicine aside for a moment, let’s look at how AI can already support a handful of crucial business needs.
These smaller-scale implementations are definitely not what you would call splashy, but they have the power to impact your bottom line in meaningful ways. Best of all, each of the ideas covered here is based on existing technology that can be implemented in a reasonable timeframe and at a reasonable cost.
1. Automation of business processes
In nearly every area of business, a huge amount of employee time is dedicated to executing rote and repetitive tasks. Tasks like data input and transfer are necessary to running a business, but they’re also time-consuming and a major drain on resources.
This is where robotic process automation (RPA) comes in. RPA is a form of business process automation technology in which software (the “robot” in robotic process automation) is deployed to perform logic-based tasks. If there’s a rule for how a task is done, it can likely be accomplished with RPA. Back-office administration, financial services and even human resources are all areas where RPA can help reduce the burden of monotonous tasks on employees.
RPA can be deployed across the organization at large, bringing greater efficiencies to virtually every department. Best of all, it’s easy and inexpensive to implement and does not require an onerous 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 actionable insights. We live in a world that is replete with available data about consumers and their behaviors. The sheer quantity of data presents its own problem — how to make sense of it all.
Here again, AI is perfectly positioned to offer a solution. Algorithms are faster and far better than humans at detecting patterns in huge troves of data. Rather than wasting valuable employee time sifting through data hoping to find the proverbial needle in the haystack, machine learning applications can do much of the legwork to extract the most meaningful insights.
Machine learning algorithms can also analyze past data to predict future outcomes and behaviors, which makes this form of AI indispensable to marketers. The “learn” in machine learning means algorithms get smarter over time. The more they’re 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 like chatbots, recommendation engines, and intelligent agents can help fill the customer service gap. By managing a range of lower-level customer requests and issues, these technologies reduce the load for customer service employees, freeing up their time to handle more complicated tasks.
Building more personalized, custom experiences for users is a core objective for marketers. Recommendation engines powered by machine learning and natural language processing help expand opportunities for the personalization initiatives that are drivers of consumer engagement and sales.
By now it should be clear that it isn’t necessary to undergo a top-to-bottom overhaul 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 meaningful organizational improvements with AI immediately.