From RPA to AI: Mastering Intelligent Automation

Today’s business-driven software stacks contain intelligent automation to help streamline processes. These intelligent automation platforms leverage multiple technologies, including process mining, Robotic Process Automation (RPA), Intelligent Document Processing (IDP), Artificial Intelligence (AI), Enterprise Content Management (ECM), and Business Process Management (BPM). Low-code/no-code tools are fueling new ways to rapidly automate back-office processes and customer-facing experiences, but there are considerations to contend with.

The success of digital transformation projects is primarily focused on the core technology and its ability to learn and adapt to the complexities of a business process and the unstructured nature of the data. For example, RPA has led these transformation initiatives, providing connectivity to modern cloud-based and legacy systems where content is stored. Where manual work may have been performed in the past, a human swiveling between applications, RPA bots filled a need to connect systems where integration either did not exist or lacked the functionality required.

Automation Nation

Enterprises are betting big on intelligent automation. However, they must realize that, unlike a patchwork of monitoring tools that can fill observation gaps, intelligent automation requires comprehensive monitoring and management capabilities aligned with business needs. Therefore, to ensure successful, intelligent automation implementations, there are five essential requirements to consider:

1. Fortified Integration of AI and Automation – AI-driven automation is moving so fast that IT security can’t keep pace. Now is the time for enterprises to fortify the oversight and monitoring of AI automation tools such as RPA, IDP, BPM, ECM, and chatbots. These intelligent automation tools commonly connect and share sensitive data as a solution. If not adequately managed with the proper application monitoring and observability tools, outages within critical processes will happen, company data will be put at risk, and the goals of scaling intelligent automation will be lost.
2. Secured Bots – The “bot” software that automates repetitive tasks is called Robotic Process Automation (RPA). RPAs can interface with virtually any system – modern web-based, legacy, and desktop – and automate simple repetitive tasks that a human performs. However, there must be a complete audit trail of all actions beyond simply storing and reviewing the raw RPA log data. The potential risk of bots making mistakes, being granted new access rights, or even employees failing to secure the bot properly is a real problem. 
3. Bolstered Intelligent Automation Oversight – Bolstering operational oversight of intelligent automation requires providing monitoring and security views into operations. With this visibility, IT operations, security, and Center of Excellence automation teams will all have valuable input. However, monitoring requires having the correct data views into the use of RPA, IDP, and the systems of record (ERP, CRM, ECM) that are core to the processes, and the needs of each team will be different. For example, for the Center of Excellence automation owners, the insight they require is directly connected to processes running smoothly and taking corrective action quickly when failures occur. By contrast, IT security operations want to know if bots have been compromised and if a complete audit trail of these bots’ work is available.
4. Data-Driven Security Decision-Making Adoption – Data from logs, systems, and process data provides information to be correlated and surfaced within a monitoring and observability tool. In addition, incident intelligence views are formed based on raw audit log data that is further analyzed and triggers alerts to the appropriate people. It’s important to note that a complete audit trail of AI bot activity, human interactions, and other AI data-driven services must be observed to take proactive security measures before a threat or unauthorized bot or human activity becomes a significant problem.
5. IT Security As A Perpetual Process – Enterprises continue to leverage AI automation across all facets of the business, and with the expanded use comes more complexity in providing insight and observability of the applications and processes involved. IT operations and security teams need to adapt and enhance the security around bots, people, and the use of AI technology that powers applications and processes. With the increasing use of Generative AI, application monitoring tools will become even more essential.

Conclusion

Intelligent automation boosts efficiency and enhances business operations. Maintaining extensive monitoring and oversight of the internal operational experience post-implementation is crucial to ensure the success of automation initiatives. IT security is paramount for a secure digital environment with new software access to business processes. Applying the IT security layer supports risk management and prevents automation projects from being a silo environment that could introduce another attack vector to bad actors. The quality of the internal operational experience (behind the firewall) should match or exceed the customer experience (outside the firewall) to achieve a significant impact and meet business expectations. By following these procedures, organizations can safeguard and insulate their automation processes from internal and external threats.

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