Here's how you can harness artificial intelligence for process automation.

It is important to have an understand of what this new class of AI models can and cannot do. And not only that, you need to understand how well they can do it. This only comes with actually trying out and playing with the models. For example, one can't just assume that because an AI model can generate images from a text prompt that it can create the exact images you're looking for. You need to test these models out and see the quality of what they produce. You also need to be aware of what steps in automation can have value in automating. Generative AI can introduce amazing new opportunities, but it can also create new challenges as well.

La integración de la IA en la automatización de procesos no solo amplía el alcance de lo que podemos automatizar, sino que también mejora la calidad y eficiencia de nuestras soluciones. Como RPA Engineer, es esencial mantenerse al día con los avances en IA y ML para aplicar estas tecnologías de manera efectiva en nuestros proyectos de automatización.

? Have an in-depth understanding of key Artificial Intelligence (AI) technology areas such as Machine Learning (ML), Computer Vision (CV), Natural Language Processing (NLP), Conversational AI, and even Generative AI (GenAI), among others ? More importantly, it is imperative to understand the applicability of AI technology within process automation in order to amplify the outcomes from process automation alone. For instance, RPA is often augmented with Intelligent Document Processing (IDP) for document heavy processes to ensure greater degree of automation and better outcomes

AI can handle tasks that usually need human intelligence, like understanding complex data or recognizing speech and images. By using AI, we can automate these tasks, making processes faster and more efficient.

Identify the tasks or use cases which are ideal candidates for AI powered process automation. For instance, ? Invoice Processing: Leveraging RPA alone in invoice processing would help automate not more than 50-60% of the process, while augmenting RPA with AI-powered Intelligent Document Processing (IDP) would help automate 90%+ of the process, while ensuring a superior Customer Experience and Employee Experience ? Contact Centre Automation: leveraging AI powered chatbots/IVA can help automatically cater to customer queries at scale, alow self-service, and automatically log tickets and take the next best action. GenAI is now further enhancing this by understanding ‘intent’ behind the customer call/query and responding in real time

One way to identify tasks is to look for a task that you can have an intern perform. Typically something that can be completed with a little bit of instruction, but something that may be particularly tedious and not very desirable for a seasoned employee to tackle.

En un proyecto reciente en el departamento de operaciones, analizamos el flujo de trabajo para detectar tareas automatizables. Identificamos procesos repetitivos como la descarga de archivos y el cruce de información. Implementamos IA para gestionar estas tareas, lo que incrementó la eficiencia y redujo errores significativamente. Evaluar y seleccionar estas tareas iniciales fue clave para una integración exitosa de la IA en nuestros procesos de automatización.

Once you've identified these tasks, you can assess how AI can take over and do them faster and more accurately. For instance, AI can enter data without making mistakes and process information at high speed, which saves time and reduces errors.

Como Senior RPA Engineer, he aprendido que elegir las herramientas y plataformas adecuadas es crucial para la automatización de procesos. En un proyecto reciente, seleccionamos soluciones que se alineaban con nuestras necesidades específicas, incluyendo capacidades avanzadas para la extracción y análisis de datos. Al considerar las tareas a automatizar y asegurar una integración fluida con nuestros sistemas existentes, logramos mejorar significativamente la eficiencia y precisión. Este enfoque refuerza la importancia de seleccionar tecnologías que no solo cumplan con los objetivos del negocio, sino que también permitan una escalabilidad y adaptabilidad continuas.

Explore AI Option ,NLP,Predictive Analytics and make sure the tools you select align with your business goals and the specific tasks you plan to automate. This ensures you get the most value from your AI investment.

Yes, you need to know the tools available and what their capabilities and limitations are. You also need to be well versed with the business problem you are trying to solve. For example, if you are automatically generating/gen-editing images at scale and you need a quicker way to have these go through an initial QA process, you can use a multimodal LLM as part of your workflow to ask the LLM questions about the image. For example, if the image has a dark background, it might not work well in certain cultures or with certain audience segments. This is the kind of task that can be automated to a fairly sufficient degree.

Gradual implementation is a safe way to go. We've recently seen large public companies roll out AI features to much chagrin, such as woke founding fathers or recipes for glue pizza. In my previous example of multimodal LLM filtering for culturally sensitive imagery, this should be piloted with full human oversight to double check the analysis of the LLM. Then gradually accept the judgement of the LLM as you feel more confident with it.

Pilot project we can start and . it can be a single, manageable task to automate. This allows you to test the AI tool in a controlled environment and understand its impact.

Training the AI model is an important step to ensure accuracy and relevance to your business / processes. Interestingly, GenAI is further helping short-circuit this training by providing 'synthetic data' that mimics real world data, enabling large scale training of AI models!

If your AI needs to recognize patterns or make decisions, the data must be labeled correctly. For example, if you’re training an AI to recognize images, each image should be labeled with what it represents.

Monitoring the results is key to making sure the AI-driven process step is performing at the same level or better as a human. With my previous example, one needs to make sure the LLM is catching the images that may trigger cultural sensitivity. You need to watch for false positives and try to understand what the cause of these may be. It could be possible that the question you're asking the LLM to use might not generate the right kind of inference response you're looking for.

To harness AI for process automation, begin by identifying processes that can benefit from AI’s ability to learn and adapt. Integrate AI with existing systems to analyze data and optimize workflows. Utilize AI’s pattern recognition to streamline decision-making and predict outcomes. Implement cognitive automation to handle complex tasks that require understanding and reasoning. Enhance customer experiences by employing AI for personalized and efficient service. Continuously monitor and refine AI-driven processes to ensure they align with evolving business objectives.