Companies across all sectors have learned that they can transform their businesses by embracing Intelligent Process Automation, or IPA, which combines Robotic Process Automation (RPA) with AI to automate a myriad of back-office processes. By automating repetitive, mundane work, they free up their employees to spend more time on higher value work, such as providing a better customer experience and finding more efficient operations.
Yet, while RPA works well to automate an employee’s rote responsibilities, many organizations struggle to scale to hundreds of production bots when automation is not coupled with AI and machine learning tools. With the pairing of AI and RPA, IPA adds a new layer of intelligent decision-making processes to automated tasks that standard RPA tools lack. In addition, IPA allows organizations to see ROI that is often in the triple-digit percentages, according to a McKinsey study. But in order to reap these kinds of rewards, organizations must first educate themselves and prepare for the adoption of IPA.
Steps to Realize the Value of IPA
With the promise of double or triple-digit percentage returns from IPA, it is understandable that many executives would wonder if these returns are too good to be true. But what is clear is that the companies that don’t implement Intelligent Process Automation will be left behind. Many organizations underestimate the complexities of these initiatives and what it takes to scale up. Even though the technology is relatively straightforward, making sure that IPA works for your business and fits seamlessly into your operations is essential to ensuring success. Towards this end, there are certain steps that you should take in order to scale appropriately:
1. Invest in centers of excellence.
With governance structures in place to support the deployment of IPA solutions, a center of
excellence can help you sustain the value that your IPA solution creates. By creating a centralized location with blueprints for future success, you can build on your team’s capabilities and offer them training to understand why and how IPA will make both their jobs and the company more efficient.
2. Plan for change management.
As you implement IPA and scale up your initiatives, some employees may worry that this technology may replace their jobs. Stress that the change actually opens up great opportunities for your employees to learn new skills and build on existing knowledge. Institute an employee education program that can both propel your business forward and enhance employees’ skills.
3. Assess risks proactively.
Any new solution will introduce risk into your organization. Work with your IT team to be ready for security audits that will catch problems ahead of time
4. Build a roadmap.
In order to reach your destination of realizing significant ROI, you need to have a roadmap that shows where to start and how to get to your destination. By identifying which inefficient processes you want to replace, you will set yourself up for long-term success.
5. Understand AI basics.
There are many different categories for AI with each of the functionalities being useful for specific problems, such as OCR, process mining, chatbots, and computer vision, along with predictive algorithms, natural language processing, and deep learning. Companies now have so many different tools and solutions to use when solving their biggest problems, thus defining the problem is critical.
6. Collaborate on IPA adoption.
Oftentimes, the biggest hurdle to bringing IPA capabilities into organizations is adoption by the business users. If business users do not trust the automation or the predictions, they are less likely to act upon the results, meaning the IPA solution creates little to no value. To overcome this hurdle, automation CoEs and data and analytics teams should collaborate to solve the business problem together. This holistic approach will drive value-based KPIs and show higher ROI than simply building one-off solutions.
7. Understand IPA lifespan.
Over time, RPA automations and ML models can lose their value due to process engineering, system updates, or drift in accuracy and data. IPA solutions need to be monitored to make sure the performance is meeting business end users’ expectations. It’s good practice to periodically retrain machine learning models with new historical data to continue to improve upon predictions, helping the business continue to retain confidence in the solutions’ outcomes.
Download Intelligent Automation: Boosting Bots with AI and Machine Learning, and learn how the coupling of AI and RPA can remove common bottlenecks and yield more ROI from your process automation bots.
