AI Automation Audit and Pilot for Monthly Reporting in a UK Healthcare Firm

The Problem: Manual Reporting and Fragmented Knowledge in a 51-200 Person UK Healthcare Firm

You run a 51-200 person healthcare or medtech firm in the UK. Your monthly reporting cycle — pulling data from intake forms, candidate tracking sheets, and operational logs, then assembling it into a board-ready summary — takes a dedicated person three to four days each month. There is no AI in production yet. Your stack is Google Workspace, a CRM, and a handful of spreadsheets. You need round-the-clock customer response on your public channels and an internal knowledge search that lets any team member pull answers from your own documents without asking a specific person. The problem is not a lack of data; it is that the data sits in unstructured documents, email threads, and manual entries, and no one has a systematic way to turn that into a scored, searchable, report-ready output. The fix is a fixed-scope, four-week engagement that starts with a process audit, moves to a pilot on one workflow, and ends with a measured baseline you can use to justify rollout.

Prerequisites: What You Need Before the Audit Starts

Before Forfis engineers touch your systems, you need the following in place:

• Google Workspace admin access for the domain where your team operates. Forfis engineers need read access to Gmail, Drive, and Calendar to map document flows and email-based intake. You do not need to grant write access during the audit.
• A named internal owner with authority to approve scope changes and sign off on the pilot. This person should be the one who currently owns the monthly reporting cycle, not a proxy.
• Two weeks of historical data from your last reporting cycle: the raw intake documents, the intermediate spreadsheets, and the final report. Forfis uses this to build the baseline and train the predictive scoring model.
• A list of the top 10 questions your team asks repeatedly that currently require a human to answer. This becomes the seed set for the RAG assistant.
• A decision on the pilot workflow. Forfis recommends picking the one with the highest cycle time and the clearest before/after metric. For most firms at your size, that is the monthly reporting assembly step.

Step 1: Run the AI Process Audit and Build the Roadmap

Forfis engineers spend the first five business days mapping your current workflow. They sit with the person who runs the monthly report, watch them pull data from each source, and log every manual step. The output is a process map showing where documents enter the system, how they are classified, where they sit in queues, and how the final report is assembled. They also run a document inventory across your Google Drive and Gmail, tagging each file by type, frequency, and owner. By the end of day five, you have a one-page decision matrix ranking your workflows by cycle time, error rate, and automation feasibility. The audit does not write code. It produces a prioritized roadmap with a recommended pilot workflow and a projected cycle-time reduction. You review the matrix with your internal owner and confirm the pilot scope before moving to step two.

Step 2: Build the Internal Knowledge Search Assistant on Google Workspace

Forfis engineers connect to your Google Workspace via the Google Workspace API and pull the last two months of relevant documents, emails, and calendar events. They build a vector index using OpenAI’s text-embedding-3-small model, storing embeddings in a managed vector database (Qdrant or Pinecone, depending on your data volume). The index covers your policy documents, past reports, onboarding guides, and any internal wiki you maintain. The RAG assistant is exposed through a simple web interface and a Google Chat app so your team can ask questions in the channel they already use. The model behind the assistant is GPT-4o via the OpenAI API, configured with a system prompt that enforces citation of source documents and a refusal to answer questions outside the indexed corpus. You test the assistant with your top 10 seed questions and adjust the retrieval parameters (top-k, similarity threshold) until answers are accurate and cited.

Step 3: Implement Predictive Scoring for Monthly Reporting

Forfis engineers take the historical data from your last three reporting cycles and build a predictive scoring pipeline. Each incoming document or data point is scored on three dimensions: category (e.g., clinical intake, commercial inquiry, internal ops), urgency (based on keywords and sender patterns), and completeness (whether required fields are present). The model is GPT-4o-mini via the OpenAI API, chosen for cost efficiency at your volume. The scoring output is a JSON object with a confidence score per dimension. Anything below a 0.85 confidence threshold is routed to a human reviewer in a Google Sheets queue. The reviewer approves, corrects, or rejects the classification, and that correction feeds back into the model’s training set for the next cycle. You set the threshold in a single configuration file; Forfis engineers tune it during the pilot based on your tolerance for false positives versus false negatives.

Step 4: Run the Four-Week Pilot and Measure the Baseline

The pilot runs in shadow mode for the first two weeks. The AI pipeline processes every document and data point that would normally go through your manual workflow, but the output is not used for the actual report. Forfis engineers compare the AI output against what your team would have produced manually, logging every discrepancy. In week three, the pipeline goes live: the predictive scoring model classifies incoming items, the RAG assistant answers internal queries, and the human-in-the-loop queue handles low-confidence items. Your team continues to produce the monthly report as usual, but now the AI has already drafted the data summary and flagged anomalies. In week four, Forfis engineers measure the before/after baseline: cycle time from document receipt to report completion, and error rate (misclassified or missing data points). The pilot report includes both numbers side by side, a list of every discrepancy found in shadow mode, and a go/no-go recommendation for full rollout. You review the report with your internal owner and decide whether to proceed.

Common Pitfalls and How to Detect Them

The most common failure mode is scope creep during the audit. The audit is fixed-scope and two weeks long. If you ask Forfis engineers to add a new workflow mid-audit, the timeline slips. Detect this by reviewing the decision matrix at the end of day five and confirming the pilot scope in writing before moving to step two.

• Stale vector index. If you add new documents to Google Drive after the index is built, the RAG assistant will not find them. Detect this by running a weekly re-index job and checking the index size in the vector database dashboard. If the document count has not increased in two weeks, the job is failing.

• Overly aggressive confidence threshold. Setting the threshold too high (e.g., 0.95) routes most items to human review, negating the automation benefit. Detect this by monitoring the queue length in Google Sheets. If the queue exceeds 30 items per day, lower the threshold to 0.80 and re-measure.

• No baseline data. If you cannot provide two weeks of historical data before the pilot starts, Forfis engineers cannot build the before/after comparison. Detect this in the prerequisites check. If you are missing data, delay the pilot start rather than proceeding without a baseline.