Increased competition and changing customer expectations have organizations rethinking how they operate. Speed and efficiency are more important than ever, and enterprises across industries are exploring ways to use and apply Intelligent Automation solutions to reduce manual work, unlock cost savings, and free up employee resources to focus on activities that can drive the business forward.
Intelligent Automation merges Artificial Intelligence [AI], Machine Learning [ML], and automation technologies to streamline increasingly complex processes, achieve greater efficiency and data accuracy, and enable the operational agility and flexibility organizations need.
When implemented effectively, Intelligent Automation enables smarter, more dynamic business processes that can learn and become more efficient and effective over time.
But with the wrong tech stack, businesses can find themselves drifting further away from operational efficiency- bound tighter to existing processes and systems with high maintenance and overall total cost of ownership.
Why Legacy Systems Are Holding You Back
Unlike outdated, legacy tech with more rules-based approaches, Intelligent Automation can handle more complex processes, deliver greater speed and accuracy, and is capable of extracting information that can be fed into downstream systems.
Often, our customers who kick-started data automation projects with legacy tech discovered a large portion of their workflows involve document processing and data extraction. As a result, they needed to find a more intelligent, flexible solution capable of unlocking and lifting the data, so it can be used downstream, like IDP.
While RPA can automate simple, existing, well-defined tasks, such as dragging files into various folders or manipulating data, it lacks the flexibility and intelligence needed to handle increasingly complex data inputs and business processes.
Today, the vast majority of information that enterprises need to run their operations lives in unstructured document formats that are inaccessible to RPA.
This is a major roadblock since reliable, accurate data is the critical first step for decision-making and taking action as a part of a larger business process; whether it’s approving someone for a mortgage, processing a claim, or onboarding a new customer.
RPA also keeps companies bound to their existing way of doing things – even if the underlying process is flawed. Without realizing it, companies that invest in RPA are unable to make changes or upgrade their systems without people attending bots and tracking the systems to ensure the process doesn’t break.
Legacy tech tools aren’t living up to their expectations, and companies are looking for a better alternative.
Why Accuracy is Critical to Intelligent Automation
In your world, accuracy is fundamental. One incorrect digit can be the difference between a claim, mortgage or other application being approved or rejected. The final response can have a big impact on the lives of your customers, and potentially derail their overall satisfaction with your company.
What’s more, once in your system, inaccurate data has the potential to multiply. An account number or invoice tally that is off by one keystroke can cause confusion and errors that impact more than your day-to-day operations – throwing your decision-making a curveball, too.
But what happens to accuracy when you automate?
Most tech vendors today compromise on accuracy. Legacy extraction software like OCR needs pristine conditions to achieve accurate results, requiring high-quality documents that conform to standard templates with fields for extraction in the same location across pages. In addition, since OCR delivers low accuracy rates with extraction, this incorrect data can cause errors downstream.
Organizations can’t afford to be wrong, so even with legacy data automation software in place, employees are still required to validate transcription outputs or manually identify which fields are incorrect and must be fixed.
If people have to redo the work done by software, it isn’t automated.
Intelligent Automation solutions like IDP are already helping companies surpass conventional performance tradeoffs to achieve new levels of accuracy and automation.
At Hyperscience, accuracy is paramount. Clients set the software to their desired target accuracy based on internal SLAs or other compliance requirements, and the machine automates against it, generally achieving over 80% automation out-of-the-box.
The more Hyperscience is used, the smarter and more confident it gets. Our built-in quality assurance mechanism drives the Machine Learning improvements that make us best-in-class.
Choosing the Right Solution for Your Business
Implementing Intelligent Automation is a practical way to use AI to transform business operations and drive value. With Intelligent Automation, you can eliminate paper backlogs, reduce errors, increase employee productivity and retention, and improve overall customer experience.
But that doesn’t mean all solutions are created equal. In the case of Intelligent Document Processing, there are varying standards of what an ideal solution is, what capabilities it should have, and the value it can bring.
So, how do you select the right solution for your business needs?
It can be difficult to navigate the evolving Intelligent Automation landscape and select the solution that will work for your business now and in the future. A successful framework for Intelligent Automation requires:
Intelligent Automation technology continues to evolve and advance every day, so it’s important to prioritize a robust, full-suite solution that will effectively, and with high degrees of reliability, achieve what you’re looking to achieve and also grow and adapt with your business as your needs change.
What Intelligent Automation Success Looks Like
Every organization can – and should – become good at Intelligent Automation, but it’s important to start out the right way and focus on a first project that will be successful.
When it comes to introducing Intelligent Automation into your operations, Intelligent Document Processing is a good first step because it’s mission-critical, can be deployed quickly, and has a high chance of successfully automating a time-consuming and resource-intensive business process in multiple industries like finance, insurance, healthcare, transportation & logistics, and the public sector.
Ready to learn more about Hyperscience?