Use and deployment
Discovery AI and deployment
A structured diagnosis process that starts from people's everyday work — not from choosing a model or a tool.
Discovery AI is a structured process for diagnosing an organization. Its goal is to find where AI, automation or better organized knowledge can improve efficiency, reduce errors, shorten work time or increase scalability. The process starts from people's everyday work, not from choosing a model or a tool.
What we analyze
- repeatable tasks and their exceptions;
- the flow of information, documents and decisions;
- knowledge sources and their owners;
- systems and integration possibilities;
- current AI use and the risk of Shadow AI;
- volume, time, cost, errors and wait times;
- organizational readiness, data, security and potential ROI.
Discovery outputs
| Output | Answers the question |
|---|---|
| Problem and process map | Where are we really losing time or quality? |
| Knowledge map | What sources exist, who owns them, and what's missing? |
| Use-case list | Where can AI support a person or a process? |
| Scoring | What has the highest impact, feasibility and measurability? |
| ROI scenarios | What value could be created under conservative assumptions? |
| Roadmap | Where to start and what dependencies to prepare? |
| PoC or MVP recommendation | What first scope will let us validate value? |
A typical rollout rhythm
| Stage | Scope | Outcome |
|---|---|---|
| Week 1–2 | Mapping the problem, sources, roles and metrics | An agreed scope and a first knowledge corpus |
| Week 3–4 | Launching the first system or PoC | A working scenario and a Golden Set |
| Month 2 | Assistants, agents, adoption and knowledge improvement | Regular use by a selected group |
| Month 3 onward | Integrations, skills, workflow, ROI measurement | Scaling to further processes |
Client responsibilities
- Naming a sponsor and a business owner.
- Providing representative documents, processes and test data.
- Naming knowledge owners and people authorized to approve content.
- Involving experts in building the Golden Set and evaluating answers.
- Agreeing on access, retention, data classification and model-use rules.
- Measuring the baseline before rollout and the results afterward.
Measuring impact and ROI
We present ROI as a methodology and an example, not a guarantee — the actual value depends on data quality, adoption, work volume, the cost of errors and process maturity.
| Impact category | Example KPIs |
|---|---|
| Access to knowledge | Time to find information, share of answers with sources, number of unanswered questions |
| Reduced load on experts | Number of questions to senior staff, hours of interruption, share of cases resolved without escalation |
| Onboarding | Time to independence, number of questions in the first weeks, knowledge-test score |
| Drafts and communication | Preparation time, share of accepted drafts, number of revisions, response SLA |
| Documents | Analysis time, number of gaps detected, share of expert corrections |
| Processes | Cycle time, number of manual steps, exceptions, errors and rollbacks |
| Adoption | Active users, return rate, number of assistants and skills used, satisfaction |
| Governance | Share of sources with an owner, review time, number of revoked consents, incidents |
Basic formulas
- Hours saved per month = number of people × number of repetitions × task time × realistic time reduction.
- Value of time = hours saved × fully loaded hourly cost.
- Net monthly result = value of benefits − cost of maintaining the solution.
- Payback period = implementation cost / monthly value of benefits.
- For risk and quality we additionally report errors avoided, the cost of an error, and the share of cases requiring review.
Last updated: 2026-09-23 · Content owner: Slimaco AI team