How can you ensure that your RPA project meets business objectives?

How to Ensure RPA Projects Meet Business Needs: Ask yourself a few questions: 1. Define Your Goal: Identify the business objectives and the type of process to automate. 2. Determine the Approach: Note that the automation solution doesn't necessarily have to be an RPA solution. It could be a hybrid solution involving API integration, RPA tools, and human involvement. 3. Monitor and Measure: Assess how you're measuring metrics and the performance of the automation solution. This allows you to track ROI and other key performance indicators (KPIs). 4. Continuous Improvement: Establish a feedback loop with business stakeholders and utilize metric input to optimize processes continuously, achieving ongoing improvement.

Automate without any disruption in the current process, just it should be more accurate and efficient. I have emphasized that the scope is strictly limited to the accurate process output from input. By implementing automation, existing processes can be streamlined and optimized for greater accuracy and efficiency. This will allow for a more seamless and efficient workflow, reducing the likelihood of errors and freeing up resources for more productive tasks.

?? How can you ensure that your Automation projects exceed expectations? 1?? ROI - Return on Investment Matters Don’t start any initiative where you don’t know the value it brings & ensure that the value exceeds the costs to create it. 2?? Under Promise & Over Deliver Expectations are important. It’s better to complete your project earlier than late. Too often are people pressured & agree to timelines that are not realistic. 3?? Communication Make it clear, consistent & tailored to your stakeholder groups. People should know what’s coming before it arrives, not after. Best of luck with your automation / digitization journeys. Do you have a question? Don’t hesitate to reach out :) ?? Like to support this #ai #automation #rpa #ia

To ensure your RPA project meets business objectives, start by clearly defining your goals, involving relevant stakeholders, and conducting a thorough process analysis. Develop a well-defined implementation plan, monitor progress, and continually assess and adapt the project to align with evolving business needs.

Keep in mind that RPA is essentially a glorified Excel macro, except it can work across more platforms. It will require governance, an add'l skillset to develop and maintain, and more thought leadership on how it'll be triggered. While RPA has major benefits, these are some of the new characteristics that should be considered when deciding if RPA is the right solution.

RPA is best suited for tasks and processes that are highly structured and rules-based. If a process involves a high degree of complexity, variability, or requires human judgment and decision-making in a dynamic environment, it may not be a good candidate for RPA. Often this is the case with (virtual) desktop automation, from my personal experience it was fun doing that, but it wont get you much result in the long run. Therefore, please take it from me, avoid using virtual desktop automation. Try to enforce robotisation through server-sided integrations such as API's. Robot flows that hold API services are from a best-practice perspective, the easiest to maintain in an enterprise business environment, no matter what industry you are in.

Selecting the appropriate processes and tools is crucial for a successful RPA project. Identify processes that are rule-based, repetitive, and have a significant impact on business objectives. Choose RPA tools that match your organization's needs and provide scalability, security, and robust automation capabilities.

Selecting the most suitable processes and tools is a pivotal step in ensuring your RPA project aligns with business objectives. 1. Process Selection - Prioritize processes based on frequency, volume, complexity, and stability. - Assess data availability and quality as crucial factors. - Choose processes with high automation potential for quick, tangible results. 2. Tool Evaluation - Evaluate compatibility with existing systems and applications. - Align tool selection with your budget and capabilities. - Compare features, functionalities, and vendor support. - Test RPA tools before purchasing to ensure they meet your requirements.

To contradict Martin below me, RPA is best suited to situations where there is no alternative and you need to directly mimic actions to interact with, often a legacy, systems UI. One of the most overlooked aspects of automation strategy, its defining tools and their use cases. This is critical as: it sets the correct expectations with the organisation; it allows you to clearly identify demand for emerging technologies e.g., process mining lets say; it means you can start quantifying your service. In short, define the opportunities right for automation. If those opportunities leverage more modern integration techniques such as APIs, consider something like Azure Logic Apps which for one use case I worked was at minimum 400 times cheaper.

What is missing here is the communication with the business. I am assuming you are robotising a process for the internal customer often referred to as business end-users. Their involvement is critical for a succesfull implementation. Work with them to get the requirements, specifications, the visuals, anything. Make a brief documentation before you begin, in a collaboration tool. Try the approach of pair-programming, from my experience, it is a very powerful way of working and it empowers the bonding between the developers in a scrum team. Once your process is robotised, you need a pair of external eyes to review the robotised process. Check on a regular base with stakeholders to see if enhancements are required when looking at robot output

Creating an effective automation workflow is crucial for a successful RPA project. Here's a concise expert-level perspective on this section: 1. Workflow Design - Map out process steps, inputs, outputs, and exceptions. - Define human and bot roles and responsibilities. - Document business rules, logic, and standards. - Ensure alignment with business objectives. 2. Thorough Testing - Rigorous testing in both development and production environments. - Identify and rectify errors, bugs, or gaps. - Ensure the automation meets quality and performance requirements. - Test scenarios that simulate real-world use.

After selecting the right processes and tools, design the automation workflow by creating detailed process maps and defining clear automation logic. Test the automation thoroughly in a controlled environment to identify and resolve any issues or bottlenecks. Ensure it aligns with business objectives and makes necessary adjustments.

Designing an automation should not involve the mapping of the process, that it for process analysis. Designing the automation should achieve two objectives: can the business understand and communicate about the solution operationally; can the automation developers understand and communicate the solution technically. This is best achieved by thoroughly mapping the process and then converting the process into requirements (akin to software development). Those requirements can then be numbered, bucketed, and demonstrable tested from identification to deployment. Critical here is to bucket requirements against functionality, as the client does not need to sit through configuration or solution testing, but should be confident about its outcome.

Assurance... An essential contributor to success and acceptance is to make sure your automation is delivering what it is supposed to deliver. I would think about the assurance processes in parallel to designing the automation. For a deeper discussion on the subject, please feel free to reach out!

I have worked with quite a few BOTs in Production environment. One of the very important things (after the BOT goes live, passes the hyper care stage and goes into BAU) is to have robust process for error handling of the BOT. You should have defined business/tech owners of the BOT who should be reached out to by the BOT BAU Support team in case the BOT stops functioning or errors out. It is already mentioned here that dashboard should be available to track the Production run status of the BOT - all relevant stakeholders should be given access to the dashboard. Once the business owner of the BOT notices a BOT failure through the dashboard (or otherwise), he should know the process of how to raise the ticket/issue and the SLAs.

The live environment mentioned in the AI generated text is often referred to as Production environment when looking at software environments. This is no different for robot processes. If your enterprise organisation is working via scrum framework, make good use of the Sprint Review to see how end-users reflect on the robotised processes. Avoid going waterfall on robotised processes are changing very frequently in a short time frame, be prepared for this. Besides that, it is a good and best practise to have a Dashboard set-up ideally one that is able to fetch data from your database software. Make sure that both are compatible with eachother. Train the business/end-users to use the dashboard, this will eventually reduce the amount of contact

Deploy the tested automation into the production environment and continuously monitor its performance. Establish KPIs and performance metrics to track its impact on business objectives. Regularly update and optimize the automation to adapt to changing business needs and ensure it continues to meet objectives.

If someone mentions hyper-care, do not trust them. Hyper-care is an ill-defined and poorly executed concept. Implementation testing demonstrates a robust understanding of the target process. Implementation testing is the agreement that the production automation will handle a discrete and increasing number of cases successfully until an acceptance criteria has been met. Monitoring is critical for the service management of the automation team, as they must meet specific SLAs etc. Monitoring the benefits is the businesses responsibility. As an automation leader, I should know the average handling time per case. The business should be able to articulate why this average handling time is beneficial for their operations.

Understand that RPA was becoming mainstream for the first time around 2012. It is not a new or innovative platform for technology. There are currently big changes happening to topics like Generative AI and all things connected to that. RPA may not be around forever, we are slowly shifting into a directory where AI becomes more and more mainstream to replace process automation application platforms such as RPA. Consider yourself if you really want to start on a fresh and new RPA journey in 2023. Besides that, scaling and fixing technical debt is the way for massive success. Without scaling, your RPA environment is doomed to fail. Very hard and fast.

Continuously optimize the automation by identifying opportunities for efficiency improvements and cost reduction. As the project matures, scale automation by expanding it to cover additional processes or departments. This helps in ensuring that the RPA project aligns with evolving business objectives and keeps delivering value.

Optimizing and scaling automation should be pinned to a clear roadmap for a diverse product portfolio. The reviewing and refining of RPA based automations if they are underpinning examples of operational debt is not an effective use of resource. RPA when fixing operational debt needs to make a big impact in capacity and insight to allow the debt to be addressed. Adding in intelligent technologies to RPA can increase this breadth, as long as you are clearly focused on taking high volume high frequency processes, that is what RPA is for. The real goal of RPA is to unlock as many employees from operational debt as possible. However, more agile solutions now exist that can actually add value. So scaling automation is diversifying technologies.

To ensure your RPA project meets business objectives: 1. Align RPA goals with overall business objectives. 2. Define clear, SMART objectives for the project. 3. Involve stakeholders in planning and execution. 4. Maintain regular communication. 5. Evaluate progress against KPIs. 6. Be flexible and adapt as needed. 7. Focus on delivering tangible business value.

In addition to the steps mentioned, consider the following to ensure your RPA project aligns with business objectives: 1. Security and compliance: Implement robust security measures and ensure compliance with relevant regulations to protect sensitive data and maintain business integrity. 2. Change management: Address employee concerns and provide adequate training to ensure a smooth transition to automation, minimizing resistance. 3. Return on investment (ROI): Continually assess the project's ROI and make necessary adjustments to maximize the value generated. 4. Business continuity: Develop contingency plans to handle potential failures or disruptions in the automation process.

All of the topics lack a little bit of in-depth information but all comes down to all of the phases mentioned in the generic version of RPA Lifecycle & Portfolio management. It forms a good fundament for any RPA initiative, it contains all key components that need to be addressed for a minimum version of a succesfull RPA implementation approach.

People are at the heart of this, not technology. It all begins and ends with humans embracing or rejecting it. The importance of effective change management cannot be overstated. On our team, we have a rule: asking humans to change is always the last resort. Automation Teams must prioritize ease of use and user experience in all things.