Redesign the work.
Prepare the people.

The technologies can work together. RPA follows defined steps; agents can interpret goals and choose actions. The appropriate combination depends on the process and the oversight it needs.

Use the nature of the work to decide. Stable, structured activities may suit established automation, while tasks involving less predictable information may benefit from an agent’s flexibility. Kashyap’s comparison examines where the approaches overlap and why a combination can be more appropriate than wholesale replacement. Read more about AI agents and RPA.

New tools change responsibilities, skills and working practices. RPA2AI’s change management offering addresses the people and organizational work involved in adopting AI and automation.

People need to understand how their roles are changing and have a credible route to learning the required skills. Managers also need to address uncertainty and create room for feedback. Kashyap’s reskilling article treats communication and behavioral change as part of implementation, alongside technical training. Read more about reskilling for AI.

Map tasks, handoffs and exceptions. Assign people responsibility for intent and judgment, and define what agents may execute and when they must escalate.

Consider which parts of a workflow are predictable and which require interpretation. This makes it easier to decide where rules remain sufficient and where more adaptive software may help. Kashyap’s comparison of agents and RPA provides examples of dividing work across the technologies rather than assuming one approach fits every task. Read more about choosing an automation approach.

Employee experience also matters. Kashyap’s published commentary argues that improving employees’ working experience contributes to better customer experience.

A wider view of success also asks how the work changes after deployment. In HR, for example, administrative tasks and activities involving relationships or sensitive judgment have different requirements. Kashyap’s analysis of AI in HR illustrates why automating part of a function does not settle the purpose of the whole role. Read more about AI and the changing HR function.

Both are possible, but task exposure does not automatically mean job loss. Kashyap’s analysis emphasizes job transformation and the uneven timing of change across roles and industries.

Planning should consider the work that remains and the capabilities needed to perform it. Organizations may need more evaluation, integration and oversight even as some execution becomes automated. Kashyap’s employment analysis examines how AI changes demand across roles, with different effects on hiring and career progression. Read more about AI and job creation.