Step 1: Map Automatable Processes
The first pitfall of automation is diving in without a clear vision: automating a visible but marginal task while the true value opportunities remain overlooked. The initial step, therefore, is to map the processes in order to objectively identify candidates for automation.
Process mining is the key tool at this stage. By analyzing the logs of information systems, it reconstructs the processes as they actually unfold — not as we imagine them — revealing bottlenecks, detours and repetitive tasks. This objectivity guards against bias and directs the effort to where it matters. The task mining, which observes users’ actions at their desks, complements it by detailing the individual gestures involved.
Next, each process is scored against criteria of automatability: volume, frequency, degree of standardization, stability of rules, and the share of structured data. A high-volume, repetitive, well-marked process is an ideal candidate for RPA; a process that relies on judgment or unstructured data will call for AI.
This mapping yields a surprising and valuable insight: it exposes . By examining a flow with the aim of automating it, one often uncovers redundant steps, unnecessary validations, or workarounds inherited from the past. It’s an invitation to rethink the process before automating it — because automating an inefficient process merely speeds up inefficiency. The mapping phase is thus as much about optimization as it is about selection.
Step 2: Prioritize by ROI
Not all automatable processes are equal. Prioritization blends two axes: the expected value (time savings, cost or error reductions, improved experience) and the feasibility (technical complexity, process stability, data availability). The result is a matrix that differentiates quick, demonstrable wins from longer-term projects.
The golden rule for a first wave is to target quick, demonstrable gains: high-volume processes with clear rules and measurable ROI. These early successes lend credibility to the initiative, galvanize teams, and fund the next set of efforts. Conversely, starting with a complex and uncertain process risks a discouraging failure.
This prioritization must remain realistic. The fact that 70% of automation projects fail to meet their objectives is often explained by overly ambitious goals or unstable processes that were automated too soon. Automating a failing process merely accelerates disorder: sometimes the first step is to simplify the process before automating it.
Step 3: Combine RPA, BPM and AI
A mature automation strategy does not rely on a single technology, but on a relevant combination of them. The choice of tool depends on the nature of the process, in a logic of assembly typical of hyperautomation.
RPA is suited to repetitive, rule-based tasks that don’t modify underlying systems. The BPM structures and orchestrates end-to-end processes, coordinating humans and machines. AI takes over whenever you must process unstructured data, handle exceptions or make decisions. IDP handles documents, and AI agents extend automation to tasks described in natural language.
The art lies in assembling these building blocks as needed, without over-engineering. A simple process does not require AI; a complex one cannot be solved by fragile RPA robots alone. The right architecture combines the technologies where each is most effective, preferring platforms that integrate them natively to limit complexity.
A pragmatic approach is to reason in terms of a growing complexity ladder. Start by automating the core repetitive and stable tasks with RPA, add IDP for documents, then AI for decisions and exceptions, and finally agents for the most variable cases. This gradual ramp-up delivers value at each step, rather than waiting for a single perfect grand system. It also limits risk: each layer is validated before adding the next.
Step 4: Industrialize and Govern
Moving from a handful of automations to a full program requires industrialization and governance. Without these, you accumulate disparate, fragile and undocumented robots — an automation debt as costly as the technical debt you sought to avoid.
Industrialization rests on standards: development conventions, reuse of components, testing, and monitoring of execution. A robot that fails quietly or breaks at the slightest interface change undermines trust. Robustness and maintainability are as important as the initial gains.
A Dedicated Governance
Governance often takes shape as an Automation Centre of Excellence (CoE): a team that defines standards, prioritizes the project portfolio, pools skills, and disseminates best practices. It also ensures supervision of the robot fleet, access management and compliance — a robot handling sensitive data is subject to the same security requirements as a human user.
The CoE also plays a crucial role in upskilling and change management. It trains business teams to design their own simple automations, assists developers with complex cases, and ensures that automation is seen as an enabler rather than a threat. This spread of an automation culture across the organization is what enables moving from a handful of pilot projects to a scaled program, driven by the business lines themselves.
Measuring and Improving
Finally, the approach is driven by measurement: realized ROI, time saved, reduction in errors, number of automated processes, robot availability. These indicators prove value, guide the next priorities and fuel a cycle of continuous improvement. Building an automation strategy is not a one-off project but the establishment of a lasting capability — technological, organizational and cultural — to automate intelligently. It is this structured approach, more than tool sophistication, that separates programs that scale from those that become stuck in proofs of concept.
This content is published by Mentioned
