Analyzing data with the help of business automation.
The future of business productivity is immersed in computer automation.
The first wave of automation has obvious benefits. When computers do busy work, solve business problems, and turn compiled data into analytics, productivity goes through the roof.
The second wave of automation takes the information, loops it back through the automated systems, and heightens the value of the data beyond mere cost-saving measures into an evidence-based plan for company-wide progress.
What does it look like when a company hits this stride of data-driven automation? One company seems to have it figured out.
Employing Automation, AI, and Smart Data Analytics
According to HfS Research, The Services Research CompanyTM, jumped into the deep end of Robotic Process Automation or RPA and found the sweet spot. US-based Corning is on the leading edge of discovering how to implement this combo with success. As their company becomes the subject of best practice articles, let’s take a look at how they do it.
The first step towards the future of automation is getting on board with the basics.
Robotic Process Automation (RPA) is the mechanism by which a company lets computers handle primarily structured data. It allows for human intervention on judgment-intensive tasks and the end result is efficiency.
Artificial Intelligence (AI) is an umbrella term for a host of processes that solve business problems. AI is human-trained machines carrying out certain objectives. The machine then uses unstructured and structured data to mimic human reason, planning, and natural language processing (NLP) to achieve the set outcomes. With AI, computer systems are involved in machine learning, a science whereby computers use statistical techniques to improve or “learn” without being programmed for it.
Smart data analytics are programs that comprehend structured and unstructured data gathered by the system and recommend ways to improve macro processes along the way. This type of smart analytics leaves final decisions up to humans but improves decision-making in the company by sensing all relevant data points.
Business operations experts at HfS Research, in their profile of Corning’s success, are calling the combination of Automation, AI, and Analytics the “Triple-A Trifecta”.
How They Did It
Director of Shared Services for Corning Chad Keenan pulled in tech partners Ataway, a global enterprise software consultant with expertise in RPA and BPM solutions, to advise.
Ataway recommends a step-by-step approach to automation; choosing processes, specific tools, and defining goals are considered best practice for a slow roll-out. With this type of piecemeal introduction of automation being standard, a few obstacles still stand in the way of reaching that second tier of data intelligence.
With Keenan’s vision and input from Ataway, Corning’s data-driven approach to RPA was formed with the big picture in mind.
Ataway gives enterprises the right tools to employ overall automation, not just time-saving tricks.
It’s understandably hard for companies dipping their toes in the waters of automation to clearly recognize return on investment (ROI). With small implementation, businesses fail to see beyond initial cost-reduction. Targets are met and investment ends before large-scale change happens.
By comparison, Corning’s business transformation plan was designed by Keenan with an overall improvement in mind. Automation at every level was purposeful.
The HfS Robotic Process Automation Customer Experience Big Picture View is a report describing broad-view benchmarking in the field. It looks at 75+ RPA players and investigates how large business process change is critical to reaping of the benefits presented by overall automation.
Another obstacle to implementing the Triple-A Trifecta for most companies is visibility of automation programs.
As automation works its way into companies through business process outsourcing (BPO), third-party providers of back and front office outsourcing are cut off from company insight. BPO providers are also black-boxed from each other.
In Corning’s case, the company pioneered a unique precedent for transparent and automated data sharing across departments and between providers. By increasing the visibility of the analytics systems, Corning created a large, closed-loop system with rich data insight feedback.
Following Corning’s Example
Why does Corning’s experience with the deep end of automation matter?
Simply put, it’s repeatable.
Companies with vision, transparency, and the right partnerships can realize similar success.
Instead of tackling automation from the old mindset of computers doing busy work, the data can be opened up to new levels of actionable intelligence.
This “trifecta” of automation, AI, and analytics is the business sweet spot; a new wave of data processing to benefit businesses like never before.