Process Mining to Roadmap: Pick the First Three Automations

Most teams end process mining with a dashboard and a backlog, not shipped automations. This article makes the jump from process mining to an actionable automation roadmap, focused on picking and delivering the first three high-value automations with enterprise-grade rigor.

The gap between insights and shipped automations

Process mining surfaces friction, rework, and variance. The failure mode is analysis paralysis: too many candidates, unclear ownership, and no delivery muscle. Your automation roadmap should be a thin, controlled pipeline that moves a few bets to production quickly while protecting compliance and service levels.

• Too broad: Backlogs mix strategic transformations with small wins; nothing ships.
• Too narrow: Teams pick trivial automations that don’t move KPIs.
• Unscored: Decisions become subjective without a scorecard for automation candidates.
• Ungoverned: No controls for changes, data, or risk acceptance.

Close the gap by defining a scoring model, delivery path, and guardrails before selecting use cases.

Why now: compress discovery-to-value

Markets expect operational leverage without adding headcount. Using process mining and task mining together lets you quantify impact at the activity level, then validate with desktop telemetry and SMEs. Modern stacks make it feasible to ship a small wave—two RPA flows and one AI assistant, for example—without a year-long program.

• Cost pressure: Prioritize automations that return hours to the line.
• Risk posture: Bake compliance by design into workflows, not after the fact.
• Tech maturity: IDP engines, agent frameworks, and orchestration layers are stable enough for targeted pilots.

From process mining to delivery: architecture and roles

Keep the architecture simple and auditable. Your delivery path should be explicit about components, environments, and controls.

Core components

• Discovery: Process mining for system logs; task mining for desktop actions; SME validation.
• Backlog & scoring: A lightweight pipeline with a standard scorecard (value, feasibility, risk, time-to-impact).
• Execution: RPA platform for deterministic workflows; IDP engine for unstructured documents; agent framework for AI assistants with human-in-the-loop.
• Integration: API-first where possible; UI automation only for non-API legacy; event bus for orchestration.
• Controls: Secrets management, audit logging, RBAC, change control, and model governance for LLMs.

Environments and pathways

• Dev → Test → Prod: Promote artifacts with versioned releases; automate approvals for low-risk changes.
• Data protection: Pseudonymize in discovery tools; GDPR-safe process analytics; keep PII out of dev.

Roles

• Product owner: Owns value hypothesis and KPIs.
• Process engineer: Links mining insights to candidate definitions.
• Automation engineer: Builds RPA/IDP/agent components.
• Risk & compliance partner: Applies policy, reviews controls.
• Operations lead: Owns runbook, SLOs, and incident response.

Pick the first three automations: a pragmatic, 4-week plan

Week 1: Frame and shortlist

• Define scope boundaries by process (e.g., exceptions handling, KYC refresh, invoice intake).
• Apply a standard scorecard for automation candidates: impact ($/hours/errors), feasibility (APIs, rules), risk (data, auth), and time-to-impact.
• Shortlist 6–8 candidates; capture as one-page briefs with current-state maps and expected outcomes.

Week 2: Validate and size

• Task mining or shadowing to confirm real keystrokes and variance.
• Technical spikes: API ping, sample document sets, auth flows, and error paths.
• Rough order of effort: hours for bot logic, estimating effort for IDP extraction and validation, and agent prompts and guardrails.

Week 3: Design for controls and change

• Define human-in-the-loop checkpoints, four-eyes rules, and production readiness criteria for automations.
• Draft SLOs for throughput, latency, and success rate; capture service level objectives for bots and assistants.
• Author a process mining to delivery handoff checklist for ops and support.

Week 4: Commit and schedule

• Pick the top three based on value per week to first value (not just total value).
• Sequence: 1 deterministic RPA flow, 1 IDP-backed intake, 1 AI assistant for triage or knowledge.
• Lock delivery windows, owners, and acceptance criteria; start backlog execution with risk-based backlog grooming.

Q: How to prioritize automations after process mining?

A: Favor “thin slice” use cases with clear APIs, measurable volume, and controllable risk; avoid cross-team dependencies in wave 1.

KPIs and ROI you can trust

Measure outcomes, not just deployments. Tie KPIs to clear baselines and set targets that reflect system constraints.

• Time-to-first-value: Days from selection to first production transaction (varies by context).
• Cycle time reduction: Avg. minutes saved per case × volume (varies by context).
• Human hours returned: (Manual steps removed × frequency) ÷ 60.
• Quality: Reduction in defects/rework rate; first-pass yield uplift.
• Risk: Control coverage: % of required approvals and logs captured.
• Reliability: SLOs and error budgets for bots/assistants; on-call runbooks and paging.

Package each candidate with an automation business case template that includes: baseline, expected impact, dependencies, data classification, and rollback plan.

Risks and guardrails: controls, compliance, and operational resilience

• Data protection: Minimize PII in training data; GDPR-safe process analytics; DPIAs for new data flows.
• Operational resilience: DORA-aligned testing for critical services; chaos drills and failure modes; controls for automated decisioning under DORA.
• Access and segregation: Service accounts, least privilege, and break-glass procedures; independent approval for prod changes.
• Model risk management: For LLM-backed steps, enforce LLM verification and human-in-the-loop, input/output logging, and prompt versioning.
• Change management: Communicate impacts and a change management plan for frontline staff; update SOPs and training.
• Integration risk: Prefer APIs; document integration patterns with core banking systems and fallbacks when screens change.

Mini-case: payments exceptions in a mid-market bank

Context: A bank’s payments operations team handles daily ACH/SEPA exceptions—name mismatches, sanctions hits, limits, and formatting errors. Process mining shows rework loops and long dwell times between queue hops.

Chosen three

• Deterministic RPA: Auto-close formatting errors with clear rules, log outcomes, and notify originators.
• IDP intake: Extract fields from inbound PDFs/attachments, validate against schemas, and route exceptions.
• AI assistant: Triage notes and suggest next best action to analysts with explainable rationale and approval step.

Why these worked

• High volume, low ambiguity for the RPA flow.
• Contained document set for IDP; measurable “touch reduction.”
• Assistant improves analyst throughput without removing oversight.

Governance included four-eyes approvals, immutable logs, and SLOs for turnaround. Quantified benefits used baselines from mining data; exact gains varied by context.

From insight to impact: commit to your first three

Your goal is not a perfect map; it’s a credible path to value. Use mining to focus, a scorecard to decide, and a thin delivery pipeline to ship. Lock controls early so risk teams say “yes” faster.

If you want a push, run a fixed-fee “Process Mining to Roadmap” sprint: four weeks to a ranked backlog, scorecard, three one-page designs, SLOs, and a deployment plan. You’ll leave with the first three automations scheduled and a repeatable playbook.

Next step: Book a 90-minute roadmap workshop to validate candidates and commit to a delivery sequence within seven days.