Intelligent Automation Services

The first step in any automation effort is understanding the business processes—including the individual work activities and the overall workflow—that exist today. Methodologies such as Business Process Model and Notation (BPMN) and software like Nintex Promapp® provide a consistent way to document and discuss those processes, along with opportunities for optimization and automation.

Once well-documented, highly scriptable processes are in place, the automation journey can be defined. An ideal business process automation journey identifies quick wins and includes robust change management. With the plan in place, organizations can implement intelligent automation solutions using tools such as a software robot (e.g., UiPath), workflow automation software that can streamline processes (such as Nintex and Microsoft Power Platform, including Power Automate, Power Apps, etc.), Electronic Data Interchange (EDI) integration(s), or AI assistance (e.g., Microsoft Azure Cognitive Services).

RPA optimizes business operations by having a software robot perform repetitive, rule-based business activities. For example, imagine a sales person must enter a pursuit both in a CRM system and in a revenue forecasting system. Instead of typing the details in both, the sales person could fill out a master deal sheet and then press a button to populate both systems. RPA works well with structured data and repeatable, well-defined tasks. RPA can benefit an enterprise through:

RPA implementation offers a way for organizations to start or expand automation initiatives at the individual employee level. Those who are closest to business processes and know them best can leverage tools like UiPath’s robots to create individual automations. Then, these automations can be assessed to determine if they can provide benefits to additional teams and be expanded to realize the greatest value across the organization. This method empowers employees to lift the buden of manual tasks, increasing their productivity and allowing them to focus on more satisfying tasks, and ultimately driving business impact.

Whereas RPA focuses on the individual tasks, workflow automation uses technology to connect these tasks to accelerate and streamline an entire business processes. For example, it could be used to automate the pricing approval process for a new opportunity, from sending the reqest, approving or rejecting the request, notifying the requester, and updating the CRM entry appropriately. This automation improves employee collaboration and productivity by moving work seamlessly across systems and people so it is done by the right person, at the right time, and decreasing bottlenecks. Working together with automation technologies like RPA, it simplifies and increases the efficiency of work processes while eliminating human error. By reducing manual and paper-based processes, organizations can save time and money, and redirect resources to new and more strategic priorities. Workflow automation software like Nintex is making it easier than ever for businesses to streamline processes. No longer is an in-depth knowledge of code required; users can visualize their processes and automate workflows with a few clicks. These low-code and no-code solutions are helping businesses digitally transform faster and be more agile when adapting to changing needs, while also reducing the need to hire a full-time developer.

Processes that are more repeatable and structured are optimal candidates for RPA and workflow automation; AI can help with unstructured data and unscripted queries. For example, a sales person could ask how changing a contract’s structure would impact commission. Previously, operations might have answered the sales person’s question. With AI, a chatbot could provide the same answers.

AI is not just one technology, but rather multiple technologies working together to enable machines to take in inputs (to “see” or “hear”), to then understand probable outputs (“learning”), and then act based on the “experience” gained. AI uses learning, reasoning, and self-correction to determine how to answer questions, predict the future, and make decisions.

This Machine Learning (ML) takes place through tools such as:

•  Optical character recognition (OCR)
• Natural language processing (NLP), to “understand” speech and writing (such as voice recognition)
• Image recognition, to analyze images to perform a task Sentiment analysis, to understand how the speaker is feeling
  

That learning then determines output, which could come in many forms, such as:

• An answer from a chatbot
• A decision that determines how a variable process moves forward
• A data analysis
• A prediction of customer behavior
  

AI lets organizations:

• Automate decision making
• Create actionable insights and forecasts
• Provide digital assistance
• Work 24x7
• Determine and take calculated risks
• Further optimize processes