Guide · IT
AI Adoption Guide for Mid-Sized Businesses
Where AI genuinely pays back today, how to run a pilot with a measurable baseline, and the governance to put around it.
Start where the hours are
AI pays back fastest on high-volume, rules-heavy, low-judgement work: document extraction, first-line support, lead qualification, reconciliation. Strategic use cases sound better in a board meeting and deliver later.
Measure the baseline first
Record how long the task takes today, its error rate and its cost. Without that number, no honest ROI claim is possible afterwards — and the pilot becomes a matter of opinion.
Keep a human in the loop
Set a confidence threshold. Above it the system acts, below it a person reviews. Track the review queue: if it shrinks over time the system is learning your edge cases correctly.
Data governance
Use enterprise endpoints with training disabled. Know which data leaves your network. For sensitive workloads, deploy inside your own cloud tenancy. Write the policy before the pilot, not after an incident.
- Training disabled on all vendor endpoints
- PII redaction before any external call
- Audit log of every automated decision
- Documented escalation path for failures
FAQs
Questions this guide gets asked.
Is our data used to train models?
Not on enterprise endpoints with training disabled, which is what we deploy by default.
What does a pilot cost?
Typically ₹1.5–4 lakh for a single workflow including the audit, build and measurement.
How do we know it worked?
Compare against the pre-pilot baseline on hours, error rate and cost per transaction. Nothing else counts.
More resources
Related guides and checklists.
Next step
Tell us the problem. We will tell you what it takes.
A 30-minute consultation with the practice lead who would actually run your mandate — no sales layer in between.