In this era of convergence, thought leaders are developing new use cases, where the efficacy of one technology is improved exponentially through different permutations and combinations with different technologies. One such example is that of Robotic Process Automation (RPA) solutions, which can be cross leveraged with latest cutting edge technologies such as Artificial Intelligence (AI)/Machine Learning (ML) to unleash the proverbial Genie.s
What is RPA – Robotic Process Automation?
RPA is the next level automation achieved by the erstwhile screen scraping, which was followed by Robotic Desktop Automation. You can use it for automating simple to complex use cases through two-way communication between digital systems. The purview of RPA solutions is quite wide and can automate business use cases not only in the Banking and Financial Services domain but also Insurance, Manufacturing, Healthcare, Retail, Public Services, etc.
What is AI?
AI facilitates the tasks that normally require human intelligence. It has a wide footprint and can enable functions, such as speech recognition, visual perception, decision making, natural language processing, etc.
What is ML?
ML is a sub-branch of AI. It allows a computing machine to learn, interact, and perform tasks with high volume quality data sets with precision and accuracy.
What is the potential and business impact of RPA solutions using AI/ML?
AI/ML imparts cognitive capabilities to the technology it is mashed-up with. It extends RPA solutions and allows it to infer contextual information from high volume data sets and act. It offers decision making capability in workflows that are not discrete process steps. With this convergence, the combined technology is able to process diverse data sets, such as text, email, documents, SMS, video, etc.
The technology combination allows you to scrutinize high data volumes and records as well as classify them into categories. It allows you to identify unusual trends and detect anomalies. It also allows you to improve business forecasting. The technology also allows you to scan images and identify instances with pre-defined criteria. It also allows you to perform sentiment analysis with audio files. It also enables you to run business applications with voice commands as well as create business summaries from long running documents. RPA along with AI/ML offers personalized recommendations to visitors on eCommerce sites.
How do you start implementing RPA solutions with AI/ML in your organization?
RPA and AI/ML works well in data intensive and process intensive domains. It can be extended to paper environments by involving Intelligent Data Capture in the mash-up. This converged technology is useful in most business domains such as BFSI, Healthcare, Government enterprises, Communications and Retail, Manufacturing, and Logistics.
In order to embark on this digital transformation journey, the enterprise needs to ensure certain per-requisites:
- Key result areas & expectations: Review the critical business areas and ascertain the key result areas that you intend to drive. Normally, businesses pin their transformation agenda around productivity, risk reduction, and growth. Set realistic expectations and evaluate the outcomes at the solution design stage.
- Data and data-readiness: As the transformational exercise revolves around transactional as well as historical data, determine the data sets and evaluate their quality. Prepare the data sets using contemporary data enrichment techniques. Validate the data sets and their readiness before moving from development to production.
- Internal and external resources: Engage resources who understand the program as well as the business objective. Alternatively, engage solution providers and get them to train your internal resources, who can quickly identify automation opportunities.
- Governance team: Set up a cognitive center of excellence (CoE) to oversee the program and identify business opportunities to leverage the RPA and AI/ML automation. Shortlist processes and conduct pilots with the CoE at the helm to evaluate and assess outcomes. Create roadmaps towards improving top and bottom lines. Start simple with O2C, P2P, and R2R processes to reduce latency, where it is easy to visualize growth and impact of AI/ML on automation.
Conclusion
Convergence extends the capabilities of a particular technology as well as helps to overcome its shortfalls. RPA along with AI/ML is capable of significantly improving business outcomes and efficiencies. Enterprises can start small and then explore its potential in various business scenarios.