A Process Audit is a structured “current-state diagnostic” that turns a workflow you feel is messy into something you can see, measure, and improve—with a clear plan for what to eliminate or optimize first, and what to automate (and how).

This service is designed to mirror the best-practice shape you’ll see in large firms’ transformation work: diagnostic → analysis → future-state options → roadmap, grounded in evidence (workshops + data) rather than opinions.

What you get (deliverables)

1) An industry-leading process map (visually clean, boardroom-ready)

You receive a high-quality "current state" process map that makes the flow of work understandable to both business and technical stakeholders—using a recognised modelling approach (commonly BPMN-style) so it’s precise, shareable, and easy to maintain.

2) Throughput analysis (where time really goes)

We quantify performance so you’re not guessing. This typically includes:

• Volume (cases per day/week/month) and seasonality

• Lead time (start → finish), touch time (time spent working), and wait time (time spent stuck)

• Work-in-progress (WIP) and queue build-up

• Bottleneck identification (true constraint step(s))

• Service-level performance (SLA hit rate, aging, abandonment)

• Scenario thinking (what changes if we remove approvals, standardise inputs, or automate a step)

Where helpful, we use a simple flow relationship (often described as WIP = Throughput × Lead Time) to explain why queues grow and how to shrink them without heroics.

If your systems have usable event logs, we can also incorporate process mining to validate the “as-is” flow with objective data and reveal variants you won’t hear about in workshops.

3) An efficiency scorecard (with an automation readiness view)

You get a scorecard that makes the audit actionable: what’s healthy, what’s risky, and what’s a good automation candidate.

Below is a template you can publish (and we’ll deliver a completed version for your process):

Efficiency Scorecard Template (1 = poor, 5 = excellent)

Outputs we provide with the scorecard:

• Process Health Score (operational stability)

• Automation Readiness Score (feasibility)

• Impact Potential Score (value if improved/automated)

• A short list of top constraints and top levers (the “why this is slow/expensive” summary)

4) Optimise before you automate (recommended improvements first)

Automation amplifies whatever process it touches—good or bad—so we identify the improvements that will make automation cheaper, faster, and safer.

Typical optimisation recommendations include:

• Delete steps that exist “because we always have”

• Reduce approvals (or change approval thresholds)

• Standardise inputs (forms, required fields, naming conventions)

• Fix upstream data to stop downstream reconciliation

• Move decisions closer to the information

• Clarify ownership and eliminate “someone should probably…”

• Batching → flow where it reduces queues and aging

• Controls-by-design (logging, exception handling, evidence capture)

Where appropriate, we’ll use value-stream thinking to distinguish value-added vs. waiting/waste so optimisation is targeted, not cosmetic.

5) An implementation roadmap (AI + automation, sequenced)

You receive a pragmatic roadmap that answers:
What should we do first? What can we automate now? What needs process or data work first?

The roadmap typically includes:

• Opportunity backlog (ranked by Impact × Effort × Risk)

• Recommended solution types, for example:

  • Workflow automation (intake, routing, approvals, SLAs)

  • Integrations (API-first, eliminate swivel-chair work)

  • RPA where UIs are stable but APIs aren’t

  • Document automation / OCR where inputs are messy

  • AI enablement where work is language-heavy (triage, summarisation, classification) with clear guardrails

• Dependencies (data, access, security, SMEs, change management)

• Testing and rollout approach (pilot → iterate → scale)

• Measurement plan (KPIs, monitoring, exception reporting)

This aligns with the diagnostic-and-roadmapping approach you see in large transformation programmes.

Book a process audit or discovery call

If you’re exploring AI in business process automation and want to avoid expensive experiments, start with a structured discovery.

A process audit will identify where workflow automation, RPA, and AI actually fit, quantify value (time, cycle time, errors, cost-to-serve), and define the controls needed for a safe rollout.

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