Getting Started with Robotic Process Automation in 2025

Introduction #

Most RPA articles sound like marketing. This one is written from an engineer’s point of view for business and IT teams in Singapore and Southeast Asia.

At SYNKEE, we build and operate software robots that sit on top of your existing ERP, CRM, finance systems and internal tools. Think of RPA as a repeatable script that can see and click like a human, but logs and behaves like software.

This guide explains how we think about RPA in 2025, how to decide what to automate, and how we structure projects so they survive beyond the first pilot.

A simple mental model of RPA #

At a high level, an RPA platform glues together three things: triggers, bots, and systems.

%%{init: {'theme':'base', 'themeVariables': { 'fontSize':'24px'}}}%% flowchart LR Trigger["Trigger (schedule, email, API, queue)"] Orchestrator["RPA Orchestrator"] Bot["Software Bot"] Systems["Target Systems (ERP, CRM, Excel, Web, Legacy)"] Trigger --> Orchestrator Orchestrator -->|"Assign job"| Bot Bot -->|"Clicks / types / reads / uploads"| Systems Systems -->|"Responses, data"| Bot Bot -->|"Logs, metrics, alerts"| Orchestrator

When we design a bot, we treat it like any other production workload:

Inputs: where does the work come from (files, queues, APIs, inboxes)?
Determinism: can we express the rules clearly enough for a machine?
Observability: can we see success rate, latency, and failure reasons?
Safety: what happens when the UI changes or credentials expire?

What RPA is good (and bad) at #

RPA is strong when processes are:

Digital end-to-end: everything happens on a screen or API.
Rule-based: “if A and B, do C” — not “it depends on the mood”.
High volume: hundreds or thousands of cases per month.
Stable: UI and business rules do not change every week.
Sensitive: mistakes are costly but patterns are predictable.

Typical examples we implement:

Invoice and payment processing across multiple banking portals.
Customer onboarding checks (KYC, screening, data validation).
Reconciliation between core systems, spreadsheets, and reports.
Periodic reporting (regulatory, finance, operations).

Telco NOC case: network alert triage #

As a telecom operator whose NOC team handles tens of thousands of network alarms every day across mobile, fixed-line, and enterprise services. Without automation, engineers spend most of their time opening, updating, and closing tickets rather than solving the actual incidents.

With RPA, the first-level triage can be automated end to end:

The network monitoring system (NMS) raises raw alarms (SNMP traps, syslog, vendor APIs).
Alerts are normalised and pushed into an event queue.
The RPA orchestrator picks up new events and dispatches jobs to bots.
Bots look up topology and customer impact from CMDB and OSS tools.
Bots apply routing and suppression rules (maintenance windows, duplicates, flapping).
Bots create or update incidents in the ITSM platform with full context and suggested priority.
NOC engineers focus on non-standard or high-risk incidents instead of mechanical updates.

%%{init: {'theme':'base', 'themeVariables': { 'fontSize':'32px'}}}%% sequenceDiagram participant NMS as Network Monitoring System participant Queue as Event Queue participant Orchestrator as RPA Orchestrator participant Bot as RPA Bot participant CMDB as CMDB / OSS Tools participant ITSM as ITSM Platform participant NOC as NOC Engineer NMS->>Queue: Raise raw network alarm Queue->>Orchestrator: New alert event Orchestrator->>Bot: Start triage job Bot->>CMDB: Fetch topology & customer impact CMDB-->>Bot: Enriched context Bot->>ITSM: Create/update incident with enrichment ITSM-->>NOC: Notify on-call engineer Bot->>Orchestrator: Status + metrics

RPA is usually not the first choice when:

The process itself is broken or unclear.
The data lives in many paper or offline steps.
There is heavy human judgement, negotiation, or exception handling.

In those cases, we often combine process redesign + light custom software + RPA.

Our RPA implementation playbook #

From our projects, a repeatable pattern works best:

%%{init: {'theme':'base', 'themeVariables': { 'fontSize':'32px'}}}%% flowchart LR Idea["Candidate process"] POC["POC in controlled scope"] Wave1["Wave 1: 1-3 production bots"] Platform["Shared platform & monitoring"] Scale["Scaled RPA program"] Idea --> POC POC -->|"Validated ROI & feasibility"| Wave1 Wave1 -->|"Standardised patterns, runbooks"| Platform Platform -->|"More processes onboarded safely"| Scale

This is how a typical journey looks from the first POC to a scaled, governed RPA program.

1. Discover and score processes #

We map candidate processes and score them with a simple matrix:

Volume and time spent today.
Rule clarity and exception rate.
Compliance / audit requirements.
Impact if things go wrong.

The top-right quadrant (high impact, high clarity) becomes the first wave.

2. Design the system, not just the bot #

We document:

Source systems and screens.
Input formats and expected outputs.
Failure modes (UI change, timeout, malformed data).
Required logs and audit trails.

This is where we align business, compliance, and IT before any development.

3. Build bots as code #

Instead of “record and pray”, we:

Store bot projects in version control (Git).
Separate configuration (URLs, credentials, thresholds) from logic.
Standardise error handling and retry patterns.
Integrate with central identity and secrets management where possible.

This keeps RPA aligned with your existing engineering practices.

4. Orchestrate, monitor, and alert #

We connect bots to an orchestrator (from your chosen platform) to:

Schedule jobs and control concurrency.
Track per-run metrics: success rate, average handling time.
Send alerts when error rates spike or SLAs are at risk.

Automation without monitoring is just hidden manual work.

5. Iterate or retire #

After go-live, we review every few months:

Is the process still worth automating?
Have upstream systems or rules changed?
Can we merge multiple bots into a single workflow?

Well-run RPA programs treat bots as living systems, not one-off projects.

Tooling: platform-agnostic, problem-first #

There are many good vendors in the market (UiPath, Automation Anywhere, Blue Prism, Microsoft Power Automate, and others).

Our approach at SYNKEE:

Start with the process, not the tool.
Use the platform that fits your IT landscape (Windows-heavy, cloud-first, etc.).
Design solutions that can be migrated later if your tool strategy changes.

We also frequently combine RPA with:

Custom web services to expose stable APIs over legacy systems.
Data processing pipelines to clean and enrich the data RPA works with.

Common failure patterns we see #

Across organisations, the same issues show up:

Automating a broken process instead of fixing it.
No owner for bots after the initial vendor leaves.
Credentials hard-coded into scripts.
No audit trail for what the bot did and when.
“Shadow IT” RPA running under personal user accounts.

Good governance is not about heavy paperwork. It is about clear ownership, safe credentials, and simple metrics that everyone can see.

How SYNKEE can help #

SYNKEE is a Singapore-based team focused on:

Software customization to connect bots with your existing systems and data sources.
End-to-end RPA project delivery from discovery and POC to production, monitoring, and handover.
Data processing and reporting so that automated work is clearly reflected in your business KPIs.

We work with SMEs and large institutions across Southeast Asia, with a strong focus on regulatory and operational requirements.

If you are exploring RPA or need to stabilise an existing program, we can help you assess your current state and design a roadmap.