When an organization decides to make the leap to hybrid automation that combines RPA and artificial intelligence, it usually focuses on tools, algorithms, or implementation costs. However, the critical factor that determines the success or failure of these initiatives is human: knowing who should be involved and with what responsibilities. Hybrid automation is not a purely technical project, but a transformation that affects processes, people and business strategies. That's why assembling the right team makes the difference between an implementation that generates sustained value and one that remains in pilot tests without scaling.
To understand the ecosystem of actors needed, it is worth remembering that hybrid automation merges the ability of software robots (RPA) to execute repetitive, rule-based tasks, with the power of artificial intelligence to handle unstructured information, make contextual decisions, or learn from patterns. This combination expands the scope of automation, but also calls for broader governance. Thus, the first essential profile is the executive sponsor. Without a person in senior management to support the program, allocate budget, and remove political barriers, the initiative risks stalling. The sponsor does not need to know the technical details of RPA or AI models, but they must understand the strategic impact: reducing operational costs, improving the customer experience or freeing up talent for higher-value tasks. Their active participation, such as reviewing progress quarterly and communicating the importance of the project to the entire organization, is critical to maintaining priority.
On a more tactical level, the product or process owner is the one who has a thorough understanding of the workflow to be automated. This role is responsible for defining scope, establishing key performance indicators (KPIs), and validating that the hybrid solution actually solves day-to-day problems. For example, if an invoicing process is automated that includes extracting data from PDFs (with AI) and then updating accounting systems (with RPA), the process owner knows what the most common exceptions are, the current times, and the points of friction. Their vision prevents the technical team from building a solution that is misaligned with operational reality. In addition, they are usually in charge of measuring the return on investment, comparing the performance before and after automation.
Another essential group is business users in the affected areas. They are the ones who will interact with hybrid bots on a daily basis, either as supervisors, as receivers of results, or as those responsible for handling exceptions that automation cannot solve. Involving them from the design phase – through workshops, interviews or pilot tests – ensures that the solution adapts to their real needs and does not generate resistance to change. In addition, these users can detect opportunities for improvement that the technical team does not perceive. In contexts where automation touches sensitive data or regulated processes, it is essential to incorporate compliance and risk managers. Their early involvement avoids costly subsequent redesigns and ensures that the solution complies with industry regulations, such as data protection or financial standards. Failure to do so can result in a technically flawless project being rejected for audit.
Of course, technical and IT support cannot be missing. The IT team must take care of integration with existing systems (ERP, CRM, databases), connection security and the underlying infrastructure. This opens the door to using AWS and Azure cloud services, which offer elasticity, scalability, and managed environments to run both RPA bots and AI models. A cloud architect can define the best deployment strategy, while a cybersecurity specialist ensures that data flows between bots, systems, and external services are protected from unauthorized access or information leaks. Hybrid automation, when handling critical business data, requires a security-by-design approach. That's why more and more companies are including pentesting audits and security reviews in the project lifecycle.
The artificial intelligence component adds an additional profile: the data scientist or AI expert. This professional is responsible for selecting, training and evaluating the models that will give cognitive capacity to bots. Whether it's AI agents that process natural language or computer vision algorithms to read documents, the enterprise AI expert must ensure that models perform with acceptable accuracy and are kept up to date. In addition, it is the one who designs the data governance strategy: what information is used to train, how bias is avoided, and how the drift of the model is monitored. On many occasions, these specialists collaborate with custom application developers to integrate the models into customized automated flows, since each business has particularities that standard software does not cover.
Experience shows that programme governance is another pillar. A small steering committee consisting of the executive sponsor, the process owner, an IT representative and a leader from the affected business area meets regularly to review progress, resolve conflicts and prioritize resources. This group prevents the project from being diverted by particular interests and maintains the focus on value generation. It also establishes clear success metrics, such as average processing time, automation rate (percentage of steps executed without human intervention), and user satisfaction.
In this context, having a technology partner with experience in this type of initiative accelerates the learning curve and minimizes risks. Q2BSTUDIO, as a software and technology development company, has accompanied multiple organizations in the design and implementation of hybrid automation. His team knows how to combine RPA with artificial intelligence, but he also understands the importance of putting together the right team. For this reason, they offer not only technical solutions, but also support in the definition of roles and governance. From designing bespoke applications that integrate bots with legacy systems, to implementing Power BI-based business intelligence services to visualize the performance of automated processes, Q2BSTUDIO covers all the necessary layers. Even when automation requires cloud deployments, they advise on best practices in AWS and Azure cloud services, ensuring scalability and regulatory compliance.
A practical example: a logistics company wanted to automate its delivery note reception process, where documents arrived in multiple formats (PDF, images, emails). By combining RPA for orchestration and AI agents for intelligent data extraction, they were able to reduce processing time by 70%. The team involved included the operations director as a sponsor, the warehouse manager as process owner, several operators for usability testing, the compliance area to ensure data traceability, and the IT department to integrate with their ERP in the Azure cloud. The company reported that the accompaniment of an external partner was key to not getting stuck in the early stages. In this case, applied artificial intelligence made it possible to handle variations in documents that previously required manual intervention.
Finally, it should be noted that the team is not static. As the automation program matures, some roles may evolve. For example, business users can become citizen developers who create small bots with low-code tools, always supervised by the central team. Or the risk area can request to incorporate cybersecurity as a permanent requirement in the governance committee. The important thing is to have a clear and flexible initial structure, which allows you to scale automation without losing control. Ultimately, the question of who should participate in RPA and AI hybrid automation is answered with a strategic mix of executive leadership, business knowledge, technical support, AI specialists, and a strong governance approach. Only in this way can technology be made not an end in itself, but a means to transform processes in a sustainable way.
