Tag: automation-ready

Automation projects often lose months not because the technology is hard, but because the work is unclear. Teams start building, discover exceptions, rework configurations, and end up with a system that doesn’t match operations.

A requirements pack is a practical way to prevent this. It is a set of documents that makes the workflow explicit and implementable—before any tool is configured.

The hidden cost of unclear requirements

Unclear requirements lead to:

• Endless alignment meetings
• Rework due to late exception discovery
• Conflicting KPI definitions
• Workarounds and shadow spreadsheets
• Low adoption because the system feels “not for us”

The cost shows up as time, frustration, and lost trust.

What to include in a practical requirements pack

A strong requirements pack includes six parts:

1) Workflow definition

• Start/end triggers
• Stages and status definitions
• SLAs and time windows

2) Roles and governance

• Who submits, reviews, approves, escalates
• Decision rights by risk level
• Delegation rules (who can act when someone is absent)

3) Data requirements

• Mandatory fields and definitions
• Valid values (drop-down lists)
• Source of truth (master data references)

4) Business rules and thresholds

• Approval thresholds
• Priority logic
• Trigger logic for notifications/escalation

5) Exception handling

• Top exceptions and their handling paths
• When to allow override, and who approves overrides
• Audit trail requirements

6) Reporting outputs

• KPI definitions and formulas
• Dashboard views by routine (daily/weekly)
• Triggers and action rules

Add acceptance criteria and test scenarios

Implementation becomes smoother when you define:

• Acceptance criteria (“the workflow is correct when…”)
• Test scenarios that reflect reality (including exceptions)

Example scenarios:

• “Urgent request with incomplete data”
• “Approval delayed beyond SLA”
• “Asset ID missing from master list”
• “Override requested with justification”

Test scenarios force clarity—and reduce late surprises.

Make the handover usable

A requirements pack should be written so IT or a partner can implement without constant interpretation. Keep it:

• Structured and consistent
• Clear definitions (no ambiguous language)
• Supported with examples and edge cases
• Tied to operational routines and decisions

Where INJARO helps

This is a core INJARO contribution: we produce automation-ready requirements packs—workflow, governance, data logic, reporting logic, and exception handling—so implementation can be done efficiently by internal IT or an implementation partner. INJARO can support as a functional advisor, but we do not build the systems.

The fastest automation project is the one that starts with clarity.

Many organizations equate automation with software. They start with tool selection, then try to force the operation into the tool. This often produces expensive systems that don’t match reality, leading to shadow spreadsheets, manual workarounds, and frustrated teams.

Automation-ready is different. It is the discipline of defining the work clearly enough that automation becomes straightforward—whether implemented by internal IT or an implementation partner.

Why software-first fails

Software implementations fail when:

• Workflow steps are unclear or inconsistent
• Roles and approvals are debated during configuration
• Exceptions are not defined (so everything becomes “special”)
• Data definitions vary across teams
• Reporting logic isn’t agreed (so dashboards become political)

The software becomes a mirror of organizational ambiguity.

What “automation-ready” actually means

Automation-ready does not mean “we will build a system.” It means the operation has clarity on five elements:

1) Workflow steps and boundaries
What triggers the workflow? Where does it end? What are the stages?

2) Roles and decision rights
Who submits, reviews, approves, escalates? Under what conditions?

3) Data definitions and required fields
What fields are mandatory? What format? What source of truth?

4) Business rules and thresholds
What qualifies as pass/fail? What triggers escalation? What changes priority?

5) Exceptions and handling paths
What are the top exceptions? What happens when they occur?

If these are defined, automation becomes configuration—not discovery.

Requirements that matter in the real world

Operational workflows typically require:

• Audit trail (who changed what, when)
• Visibility (status tracking)
• Approvals aligned to risk levels
• Notifications that reduce chasing
• SLA logic (time windows and escalation)
• Reporting tied to decisions, not just metrics

Most systems can support these—if you define them first.

Common pitfalls that derail automation

Undefined exceptions
Teams define the “happy path” and ignore exceptions. In reality, exceptions dominate. Start by listing top 10 exceptions and designing handling rules.

Unclear ownership
If approval ownership is political or ambiguous, automation exposes conflict. Define decision rights explicitly.

Messy master data
If asset lists, location codes, or product definitions are inconsistent, workflows will break. Align data definitions early.

Reporting without logic
Dashboards fail when KPI definitions are not standardized. Define KPI formulas, thresholds, and triggers before building dashboards.

How to start in 2–3 weeks (without building anything)

A practical automation-ready sprint:

1. Select one workflow with high pain (e.g., work order approvals, shipment release, incident reporting)
2. Map current state with friction markers
3. Define future state workflow + roles
4. Define required data fields + definitions
5. Define business rules and top exceptions
6. Define reporting outputs and triggers
7. Produce a clear requirements pack

This pack becomes the foundation for implementation later—without locking you into a tool prematurely.

Where INJARO helps

INJARO specializes in Automation-Ready Workflow & Reporting Design: we define workflows, governance, requirements, and reporting logic so your internal IT or implementation partner can implement efficiently. We do not build automation systems—we make them easier to build correctly.

Automation starts with clarity. Tools come after.