Automation is Coming to a Workplace Near You- How will Your Business Maximize Its Potential?
Five years ago, my wife and I ventured to the beautiful city of Paris. Far from having even a novice level of proficiency in the local language, we were beyond relieved to see that Google Translate had a French setting. While incredible in theory, Google Translate’s effectiveness ultimately paled in comparison to the use of hand signals when it came to the simplest of tasks— like asking for the restroom, for example.
Today’s version of Google Translate is a different story. It’s powered by the latest advancements in machine learning, capable of navigating the nuances and ambiguities of language and translating whole sentences in a second’s pace– features my wife and I could have used a number of times five years ago!
In the decade following its launch, Google Translate had limited functionality beyond its ability to translate simple phrases. In 2016, everything changed. Google introduced a new type of artificial intelligence called Neural Machine Translation (NMT) to build on the app’s capabilities. Almost overnight, the update carried the application light years into the future, generating swift translations with 95 percent word accuracy. If you paid any attention to Mary Meeker’s Internet Trends 2018 report, that’s the equivalent of human accuracy.
In itself, this development is a modern miracle. However, it’s a breakthrough that also points to a larger truth about the state of AI and machine learning in business today–it’s worlds beyond what it was even a year ago, with little sign of slowing down.
Better still, the Intelligent Enterprise is one that will tap into the endless amounts of data available to explore new business models and revenue streams
AI and Machine Learning for the Enterprise
With artificial intelligence now in the mix, machine translations seem almost human now. Google Translate is just one example of how machine learning algorithms can power advancements leaps and bounds ahead of previous innovation.
In the enterprise, we’re seeing machine learning permeate across all lines of business, fundamentally changing the way in which we work and service our stakeholders. Take conversational AI– also known as chatbots–as an example.
Since NMT was introduced to market, its application has generated substantial process improvements (not to mention cost savings) in chatbot services for a number of industries including banking and healthcare. How? Chatbots cannot only respond to customers on a 24/7 basis, regardless of region, but they can also make phone calls to set up appointments and check order statuses. The time and resources saved by this level of automation, which accentuates human work, is single-handedly driving productivity by giving employees more time to focus on higher-impact tasks.
The biggest takeaway: we’re only scratching the surface with what chatbots can do. Gartner Predicts that 85 percent of customer interactions will be managed without a human in the next two years.
Granted, call centers are only one application of Machine Learning and AI. In manufacturing, for instance, factory data generation is exploding with projects like Intel’s Smart Factory, which generates 5 billion data points a day that are tied to everything from how the company is meeting manufacturing orders to predictive maintenance. Really any activity that requires repetitive tasks is a good candidate for automation, which will require business leaders to do a bit of creative thinking around how they can take advantage of these capabilities.
Preparing for a Future of Change
We are truly entering an era of the Intelligent Enterprise—this is a business that is super smart, uber efficient, and delivers even greater value to customers through its use of the most intelligent software and technologies on the market. It’s an enterprise of the not-so-distant future that will deliver game-changing outcomes, from the automation of complex processes that empower the workforce to do more, to the creation of personalized and unique customer experiences. Better still, the Intelligent Enterprise is one that will tap into the endless amounts of data available to explore new business models and revenue streams.
Here is what you can do to ready yourself:
1. Prioritize where your work will benefit most from the increased quality and throughput of automation. Have customer call centers or issues with customer maintenance orders that are negatively impacting your bottom line? Research the latest advancements in machine learning as it relates to those troublesome business functions you’re looking to improve. Afterwards, set up time with your executive team to discuss implementation and what it can mean for your business’s long-term growth.
2. Embrace the tremendous opportunity offered by reducing the cost of labor and enabling completely new business practices that could not previously have been cost justified. Automation accentuates your employees, allowing them to focus on high-value objectives–leverage these freed resources to ideate around the next market or product offering you can introduce.
3. Embed intelligence into your customer products and services with the unparalleled access to data and new insights available. With thousands of data touchpoints in play, there’s no excuse for not knowing how you can best serve your customer’s unique business needs before they walk in the door. Identify opportunities to integrate smart technology into your products and services.
Machine learning and AI are tools that empower businesses to take these vital steps to succeed in our rapidly evolving landscape. In this new era where automation is tackling monotony, creativity and customer-driven empathy are the chief currency for today’s enterprise.
When my wife takes our daughter to Paris this summer, they will benefit tremendously from Google’s work to integrate machine learning into an area that impacts every one of us: language. That same technology is coming to our enterprise applications very quickly–it’s on us to be creative around how we take advantage of it.