What Is Being Compared
The two options are not alternatives in a vacuum; they are different scopes of the same engagement. Option A is a full AI process audit and roadmap: Forfis maps every back-office and customer-facing workflow, measures baseline cycle time and error rate on each, and produces a prioritised automation roadmap across the company. Option B is a single-process pilot: one workflow — here, an internal knowledge search assistant built on retrieval-augmented generation over the company’s Google Workspace documents — is scoped, built, and measured in a fixed three-month window. Both use the OpenAI API as the model layer, both integrate through existing APIs rather than replacing tools, and both ship with a human-in-the-loop approval gate. The difference is breadth: Option A covers the whole operation; Option B covers one process and proves the pattern before scaling.
Criteria for the Comparison
The judgment rests on seven criteria that matter to a 201-500 person healthcare company in Austria with no specific compliance mandate and a three-month timeline:
- Time to first measurable value — how many weeks until a workflow runs with a before/after baseline.
- Upfront cost — the fixed-scope fee for the audit or the pilot, before managed operation.
- Breadth of coverage — how many workflows are mapped or automated by the end of the engagement.
- Integration surface — which existing systems (Google Workspace, CRM, helpdesk) the AI layer touches.
- Model-agnostic flexibility — whether the architecture can swap OpenAI for an open-weight model on client hardware if data-residency needs emerge.
- Human-in-the-loop overhead — how many approval steps a support agent must complete per query.
- Scalability path — how the engagement extends from one process to the next without re-scoping.
Side-by-Side Comparison
| Criterion | Option A: Full Audit + Roadmap | Option B: Single-Process RAG Pilot |
|---|---|---|
| Time to first measurable value | 8-10 weeks (audit) + 4-6 weeks (first pilot) | 3 weeks (audit slice) + 4-6 weeks (pilot) |
| Upfront cost | Higher: covers all workflows, multiple integrations | Lower: one workflow, one integration (Google Workspace) |
| Breadth of coverage | All back-office and customer-facing workflows mapped | One workflow: internal knowledge search |
| Integration surface | CRM, ERP, helpdesk, Google Workspace, messaging | Google Workspace (Gmail, Drive, Calendar) |
| Model-agnostic flexibility | Full: per-workflow model selection | Full: OpenAI API default, swappable |
| Human-in-the-loop overhead | Varies by workflow; set during audit | Light: internal search, no money/health/contract decisions |
| Scalability path | Roadmap already built; next process is a scheduling decision | Must re-scope for the second process |
When Each Option Wins
Option B wins when the company’s immediate pain is concentrated in one workflow and the three-month timeline is a hard constraint. A 201-500 person healthcare company whose support team spends 25-40 minutes per ticket searching through Drive documents and Gmail threads will see a measurable cycle-time reduction within six weeks of the pilot starting. The RAG assistant indexes the existing Google Workspace content, retrieves the relevant SOP or device manual passage, and returns a grounded answer with a citation. The support agent approves the answer before sending it to the requester. No new hires are needed; the senior staff who previously handled routine knowledge lookups are freed to work on complex cases. The before/after baseline on time-to-answer and accuracy is captured in the first two weeks and compared at the end of the pilot.
Option A wins when the company has multiple workflows with similar automation potential — invoice processing, document extraction, ticket triage, data entry — and the leadership team wants a single prioritised roadmap rather than a sequence of ad-hoc pilots. The audit maps all of them, measures baselines on each, and ranks them by expected cycle-time reduction and error-rate improvement. The cost is higher, but the company avoids the re-scoping overhead of going back to Forfis for every second process. For a company that has already automated one process and is now asking “what next?”, the audit is the natural next step.
Recommendation for This Scenario
For the scenario as specified — a 201-500 person healthcare and medtech company in Austria, no compliance mandate, three-month timeline, one process already automated, need to free senior staff from routine work, and a Google Workspace integration — Option B is the correct starting point. The company has already proven the pattern with one automated process; the next step is to apply the same pattern to internal knowledge search, not to commission a full audit that would extend the timeline beyond three months. The RAG pilot on Google Workspace is the highest-leverage single workflow for a support-heavy operation: it directly reduces the time senior staff spend on routine lookups, it integrates with the tools the team already uses, and it ships with a measured baseline that justifies the next investment. Once the pilot is live and the before/after numbers are in hand, the company can decide whether to commission the full audit (Option A) to map the remaining workflows, or to run a second pilot on a different process. The model-agnostic architecture means that if data-residency requirements emerge later, the OpenAI API layer can be swapped for an open-weight model on the company’s own hardware without re-architecting the integration.
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