Wie managen und optimieren Sie den Automatisierungslebenszyklus und die Governance in der digitalen Transformation?

When a company is introducing automation, it's crucial to make sure everyone in the organization understands what automation is, what tasks are suitable for it, and how it can improve efficiency. Instead of setting goals that are too hard to reach, the focus should be on finding low-hanging fruits and sharing the success with the whole organization. In the automation lifecycle, it's important to identify a suitable process to automate and involve experts to avoid surprises once the project starts. Deciding whether to automate tasks as they are or to change them first and then use automation requires careful thought. It's about making smart choices to make the work easier and better for everyone.

🤔 How do you manage the Automation lifecycle? Defining your problems to solve, desired outcomes and success metrics are important. Assessing opportunities based on benefits, feasibility, complexity, risks & resource availability to automate it are also important. Arguably, the most important part of managing the lifecycle for your automations is not allowing SCOPE CREEP. It continues to be one of the top reasons why projects fail worldwide, including automation projects. Scope creep (adding more to the scope of your project after your scope should be finalized) is what slows & or triggers failure for many teams. Not sure how to scope a project so it’s just the right size? Contact an automation expert! 👍 Like to support this

This is a very important exercise and needs the blessings from the higher management for the due diligence to be adhered in finding the automation scope and objectives. This is both a top down and bottom up approach. It is top down as it is driven by the higher management and bottom up as the people involved in the processes are the best candidates to find the automation scope for the processes. The best way to start is to find the processes that have the highest benefit and are the most easy to implement. The pilot processes should be the one's wherein user's can see the benefits within few days rather than few months. The success of the pilot processes is very important for setting the future automation trends.

By combining both top-down strategic direction and bottom-up insights, organizations can effectively identify, prioritize, and implement automation initiatives that deliver tangible benefits and drive long-term success

Effective automation in digital transformation starts with clear objectives - use SMART criteria to align with business goals. Establish governance through a Center of Excellence and standardize practices for consistency. Many companies have cut costs and improved efficiency by automating customer service with AI chatbots. Continuous optimization is key—track metrics like process speed and employee engagement. Empower teams to suggest improvements and align automation with your future vision. Automation isn’t just about efficiency; it’s about driving strategic value.

Automation solutions and architecture should be aligned and augment the Enterprise Architecture to have complete journey automation be it customers, employees and partners. It’s should combine RPA, BPM, Analytics and AI to make sure it’s continuously improve or change the process to provide faster processing, reduce cost, accelerate the growth and continuous monitoring the performance. In fact I see most of Low Code No Code Platforms, RPA OEMs and Enterprise Products are extending their products to cover the complete automation leveraging same ecosystem. Microsoft Platform, Appian, Pega etc are good examples of such platforms.

Managing and optimizing the automation lifecycle in digital transformation requires a structured approach. Start with Planning and Design to align automation with strategic goals and choose the right technologies. Move to Development and Testing to build and rigorously test solutions, ensuring they function effectively. Deployment and Monitoring require careful rollout and continuous performance tracking. Maintenance and Improvement are crucial for long-term success. Establish a clear framework, manage risks like data security, and promote continuous learning. Focus on designing scalable, interoperable automation solutions to support growth and innovation. This approach drives efficiency and ensures successful digital transformation.

This is a very important step as the architecture defines the entire gamut of your future automation processes. The security and scalability of the solution is most important. The use of the runtime VM for the automation (whether they need to be the client VM or the cloud VM's provided by the automation vendor?), the number of bot's to be deployed (you need to utilize the bot's 24 hours for maximum returns), the modularity of the processes (design the processes to be serving single function) the data persistence and processing, the archival policy, the robust error handling and system downtime scenarios are very important aspects to be considers. The due diligence on this factor's go a long way to creating a scalable and robust architecture

The process of architecting automation solutions is pivotal for optimizing efficiency, scalability, and effectiveness. To guide this endeavour, consider the following essential questions: Workflow Development: How easy is it to create and modify workflows? Integration: Can it seamlessly connect with other systems? Scalability: Can it adapt to growing workloads? Maintenance: How much effort is needed for ongoing upkeep? While there are numerous other considerations, addressing these questions can significantly assist in narrowing down the best-suited automation solutions.

I ensure that the architecture is scalable and adaptable. I’ve learned that flexibility is key, especially in fast-changing digital environments. In one case, we built an automation framework that could easily integrate new tools and processes as the project evolved, allowing for seamless updates without disrupting the workflow.

Als DevOps-Spezialist sehe ich die strukturierte Bereitstellung und das systematische Testen als Kernelemente erfolgreicher Automatisierungsprojekte. Die Integration agiler Methoden wie DevOps und Scrum ermöglicht eine iterative Entwicklung mit kontinuierlicher Qualitätssicherung. Durch Unit-, Integrations- und Benutzerakzeptanztests validieren wir Funktionalität, Zuverlässigkeit und Compliance. Diese systematische Herangehensweise minimiert Risiken und beschleunigt die Implementierung. Der Erfolg basiert auf der Balance zwischen agiler Entwicklung und gründlicher Qualitätssicherung. Regelmäßiges Feedback und schnelle Anpassungen sichern die nachhaltige Implementierung automatisierter Lösungen.

I always make sure to thoroughly test automation solutions in a controlled environment before rolling them out. This has helped avoid issues in live environments, ensuring that the automation delivers as expected. In one project, testing in a staging environment revealed bottlenecks that we resolved before deploying to production, saving us from costly downtime.

Deployment Planning, Environment Configuration, Data Migration, System Integration, Test Development, Test Execution, Monitoring during Deployment, Performance Evaluation, Issue Resolution, Post-Deployment Documentation, User Training.

Deploying and testing automation solutions is a critical step in managing the automation lifecycle during digital transformation. Ensuring seamless deployment involves using continuous integration/continuous deployment (CI/CD) practices. These help automate the code delivery pipeline and minimise errors, leading to faster, more reliable deployments. Testing automation tools like Selenium can validate the functionality and performance throughout different stages. I recall working on a project where introducing robotic process automation (RPA) streamlined our testing phase, cutting lead times significantly and boosting team morale.

Achieving 100% efficiency from a bot on the first attempt is quite rare unless the automated process is very simple. At @RPATech, we place great importance on optimization after going live. For several weeks after implementation, we closely monitor the transactions that are processed by the bot and, more importantly, the transactions that the bot is unable to process and thus require intervention from the "human in the loop". We use the 80:20 rule to ensure that these unprocessed transactions are added back to the robot queue in order to improve efficiency.

Unless the automated process is very simple, there will always be exceptions not accounted for during the documentation and development phases. Therefore, at Connections Consult, after deploying the newly developed process, we collaborate with the process owner in what we call a "hyper-care period" for one month. During this time, we closely monitor how the new process operates in a real-world environment. Additionally, after the hyper-care period ends, we regularly review the process parameters and propose improvements to enhance its performance.

Establishing proper automation ROI/value monitoring & optimization tools and programs is essential since the lack of it is often one of the biggest impediments to scaling automation at firms of all sizes. A few important callouts: 1) Choose the right set of metrics based on whether you are automating a full-fledged process such as Invoice Reconciliation, or just a task like Data Entry 2) Look at business metrics that go beyond just the hours saved and money saved 3) Look at the specifics of the workload being automated, e.g. automating a data source of 10,000 records will deliver a much greater impact than automating a source of 10 records. Make sure you have the tools in place to measure the impact relative to the size of the workload

Once deployed, monitoring automation performance is crucial for ongoing success. I use real-time monitoring tools to track key metrics like efficiency gains, error rates, and resource usage. Regular performance reviews allow me to make adjustments and optimize as needed. In one case, continuous monitoring helped us spot inefficiencies early, enabling us to tweak the automation process and boost overall system performance.

Sobald die Lösungen bereitgestellt und getestet sind, solltest du ein System einrichten, das Leistung, Effizienz und Zuverlässigkeit kontinuierlich überwacht. Dies ermöglicht es dir, proaktiv auf Probleme zu reagieren, bevor sie größere Auswirkungen haben. Ebenso wichtig ist es, einen Mechanismus für die ständige Verbesserung der Automatisierungslösungen zu etablieren. Nutze die gesammelten Daten und Feedbacks, um die Systeme zu optimieren und anzupassen. Dies kann alles von kleinen Anpassungen in der Software bis hin zu umfangreicheren Änderungen in den Prozessabläufen umfassen.

Businesses maintain one consistent data governance policy when automation (and AI/ML) solutions originate natively in their data warehouse. But when data teams start to outsource with 3rd party automation platforms, they relinquish those governance policies. My clients automatically inherit Snowflake’s best in class data governance by automating business processes natively within their Snowflake instance, using Elementum. Work where the data is!

Governance is another important aspect. I implement strong governance frameworks to manage automation risks like data security and compliance. By involving key stakeholders and creating clear policies, we ensure that automation operates within the organization’s risk tolerance. For example, in one financial project, we integrated strict access controls and auditing processes to meet regulatory requirements while still reaping the benefits of automation.

Es ist wichtig spezialisierte Frameworks für das Risikomanagement in deinen Automatisierungsprojekten zu implementieren. Diese Frameworks sollten spezifische Richtlinien und Verfahren enthalten, die auf die Identifizierung, Bewertung und Minderung von Risiken ausgerichtet sind. Sie können beispielsweise Frameworks für IT-Sicherheit, Datenschutz oder Compliance umfassen. Diese Frameworks sollten auch klare Richtlinien für das Rollen- und Verantwortlichkeitsmanagement beinhalten, um sicherzustellen, dass jeder im Team weiß, wie er bei der Identifizierung und Bewältigung von Risiken vorgehen soll. Zusätzlich sollten regelmäßige Schulungen und Sensibilisierungsmaßnahmen für das Personal durchgeführt werden.

Managing and optimising the automation lifecycle in digital transformation requires a proactive approach to govern and manage automation risks. This involves establishing a robust framework that includes risk assessment, compliance checks, and continuous monitoring. Implementing standardised protocols ensures that automation processes align with organisational goals and regulatory requirements. Regular audits and updates are critical for identifying potential risks early. From my experience, clear communication between IT and business units helps preemptively address issues, preventing costly disruptions and ensuring smooth integration.

Besonders wertvoll ist die Förderung eines offenen Austauschs innerhalb deines Teams und unter den Stakeholdern. Ermutige zu regelmäßigen Brainstorming-Sitzungen, bei denen Teammitglieder und Stakeholder zusammenkommen, um neue Ideen zu entwickeln und Herausforderungen zu diskutieren. 3 Beispiele: Interdisziplinäre Projekte: Starte Projekte, bei denen Teammitglieder aus verschiedenen Abteilungen zusammenarbeiten. Peer-Review-Sessions: Etabliere regelmäßige Peer-Review-Meetings, bei denen Teammitglieder ihre Arbeit präsentieren und konstruktives Feedback von Kollegen erhalten. Mentoring-Programme: Setze Mentoring-Programme auf, in denen erfahrenere Mitarbeiter ihr Wissen und ihre Erfahrungen mit jüngeren Teammitgliedern teilen.

In managing and optimising the automation lifecycle during digital transformation, learning and innovation are crucial. Companies must foster a culture of continuous improvement, where teams are encouraged to explore new automation tools and strategies. Regular training and workshops can boost skills, allowing employees to experiment and generate innovative solutions. I recall a project where we adopted this approach, significantly enhancing our process efficiency—a powerful reminder of how embracing learning and innovation can drive successful digital transformation.

Automation is inevitable in this age of digital transformation. Be it Agile delivery, DevOps or Cloud computing, automation helps realize: - optimizing value delivery to customer - time to market - feedback based solutions The delivery team, management & customer: - collaborate to identify various processes in Business, IT & Operations that can benefit from automation - through continuous reviews & sampling, iteratively & incrementally identify candidates & implement mutually agreeable automation solutions - need to consider data integrity, automation costs & expected benefits while rolling them out This inclusivity in the lifecycle is essential to make it work. Periodic governance will help review progress and take it to completion.

I did had a opportunity to work with one clients which wanted to start automation into governance. 1. Usually developers prefer to have a great useful output file but most of clients in governance prefer reconciliation. Just for a example That means your output of today should be input for tomorrow and bot should keep of tracking. Every minute detail client perspective is essential should he added. 2. It's a continuous process. More optimize. 3. Divided into phases to make sure you deliver what's expected I hope this was helpful 💡

Most companies, after automating their initial set of processes, do not continue to assess how these automations have transformed their organizations. They fail to take the next step, which should be to reevaluate and identify new processes that can now be optimized and further automated. This lack of ongoing evaluation is one reason why automation penetration remains low in many organizations.

When managing and optimising the automation lifecycle and governance during digital transformation, continuity and adaptability must be considered. Automation isn't a one-time effort but an ongoing process requiring constant monitoring and refinement. Best practices involve setting clear KPIs to measure success and ensure alignment with business goals. It reminds me of a project I worked on where the success hinged on periodic assessments and allowing the flexibility to adjust our tools and processes, much like fine-tuning a musical instrument to achieve the right harmony in an ever-changing environment.