What are the steps to evaluate process automation in the cloud?

In the RPA space, success isn't just about immediate savings. While clients often focus on financial and time benefits, they might overlook RPA's intrinsic costs like development and upkeep. Beyond tangible savings, setting grounded, measurable expectations is key. While time savings are common goals, the value of new opportunities created by automation cannot be ignored. Plus, RPA's consistency and reduced errors, though harder to quantify, offer long-term benefits. Beyond mere cost metrics, RPA's real strength is in unlocking efficiency and innovation. Recognizing these broader benefits is essential for true RPA success.

Key Insights: - Define Objectives - Process Assessment - Cloud Suitability - Cost-Benefit Analysis - Technology Selection - Data Integration - Security and Compliance - Testing and Validation - Monitoring and Reporting - Scalability and Flexibility

If all the information and processes you have to put on the cloud is already optimized and you have a certain degree of automation, you have to follow these steps: 1 - Start a well known base of knowledge of the process you want to automate; 2 - Have a Clear Objective of How do you want to have the processes on the cloud; 3 - Verify if the infrastructure you have is capable of handling all the information you want to place in the cloud.

1. Identify objectives: Define clear goals for process automation in the cloud. 2. Assess current processes: Analyze existing workflows and identify automation opportunities. 3. Choose a cloud platform: Select a cloud service provider that aligns with your needs. 4. Select automation tools: Pick suitable tools for your specific processes. 5. Design automation workflows: Create detailed automation workflows. 6. Test and iterate: Ensure automation processes work effectively and refine as needed. 7. Monitor and optimize: Continuously track and improve automation for efficiency and cost savings. 8. Ensure compliance: Comply with data security and regulatory requirements. 9. Scale as needed: Expand automation as business needs grow.

For effective automation, a thorough process analysis is vital. Engaging with both leaders and on-the-ground workers reveals practical process nuances often overlooked. Workers' real-world adaptions can offer valuable insights that deviate from standard documentation. When mapping, challenge data origins and step justifications to pinpoint inefficiencies. For prioritization, I use a matrix: the x-axis for payback period, y-axis for benefits, helping discern immediate gains from long-term strategies. In essence, successful automation stems from deep process understanding and strategic prioritization, ensuring the best outcomes.

1-Understand the business processes relevant for the industries. 2-Any processes which are repeatable in nature or conditional (e.g. if this happens , Do this)?.. 3-Try to find known scenarios like use of chatbot in customer service..another scenario is like finding inappropriate content in social media .. e.g. YouTube uses DocAI to find objectionable content in videos uploaded 4- start small but effective process, get the confidence of leadership and scale

When walking clients through the initial workshop, we lay out all enhancements that are requested and then analyze them on a cost/benefit analysis to set priorities. For each enhancement, we identify the benefit to the organization on a scale of 1-5 where 1 is the best. "Benefit" is define by the client. It can be time saved, but it can also be better accuracy, reduced compliance risk, etc. Then, we identify the cost (which includes risks). Again, "cost" has multiple definitions. There is automation setup, complexity, risk of automation issues, risk of maintenance issues (such as does it involve relying on a 3rd party to not change their UI). Rank 1-5 with 1 being the least complex (or least cost). Cost * Benefit = Priority Done.

The discovery phase of any project is like unwrapping a box. Some hurry to get to the problem statement and aaddress it. Most often than not, the solution will not be scalable. The surrounding processes, the upstream and downstream systems involved. The humans involved (in various roles) have to well analysed. Als most always, when we propose an automation solution, there is process optimization. There is an opening to automate surrounding processes to achieve maximum efficiency and many times, some intermediary steps can be removed.

In addition to using flowcharts, diagrams, or process maps there is now an opportunity to use process mining solutions to identify problem processes and in some cases these solutions can also suggest process improvements to align with best practices.

Selecting the right cloud platform for process automation is like choosing the perfect recipe for a dish. Consider the unique ingredients each platform offers - SaaS, PaaS, or IaaS. Find the one that aligns with your budget, technical requirements, and business goals, just like selecting the right ingredients for a delicious meal.

One often forgotten about testing element is testing for scale. Often times we develop and test our code and platforms for various use and edge cases but we neglect to test for larger throughput, higher data consumption and performance. Running automation at scale, performance considerations more than use case can often be the deciding factor on what tech stack to use.

While designing your automation it is important to take advantage of this opportunity to change the end to end process. It is easy to automate your existing process flow, but if you take that approach you waste this valuable opportunity to transform the business process and see additional benefit beyond automating the manual steps. If you automate a bad process then you just get a bad automated process.

In its core essence, the steps are essentially the same as any other automation initiative - not necessarily that of cloud implementations. The only differences are the tools used and the processes that can in fact be automated via the cloud. The reason why we have technologies like Robotic Process Automation is because we do not live in a perfect world where every organization can drop their legacy systems and migrate to the cloud easily. As such, every automation initiative should be evaluated and checked whether it covers the entirety of the needs of the user/client. It might be worth looking into technologies/platforms that can cover both the cloud and the on-premise needs. The Microsoft Power Platform is a good example of that.