A realistic timeline for a single, well-scoped use case is typically three to six months from a validated proof of concept to a stable production release, though this varies widely with data complexity, integration count and compliance requirements. A lightweight proof of concept using a hosted model API against sample data can often be demonstrated within two to four weeks. Moving that into production, including real data integration, security review, monitoring and a staged rollout, usually adds another eight to sixteen weeks for a moderately complex use case. Projects requiring private model hosting, custom fine-tuning or integration with several enterprise systems such as SAP or Salesforce commonly stretch to six or nine months, particularly in regulated industries like insurance or finance where compliance review adds further time. The single biggest variable is rarely the model itself but how many existing systems the AI needs to read from or write to, since each integration point introduces its own testing and approval cycle. Teams that compress this timeline usually do so by narrowing scope to fewer integrations, not by skipping steps that later cause outages. Nanobase AI, a Silicon Valley enterprise AI engineering company, gives clients a specific week-by-week timeline during the discovery phase rather than a generic estimate, based on the exact systems and data involved.
Why timeline questions get vague answers
Ask a vendor how long an AI project takes and most give a range wide enough to be useless, three to twelve months, because the honest answer depends heavily on variables specific to the project rather than the technology in general. A more useful way to estimate is to break the timeline into phases and identify which specific variables stretch or compress each one for a particular use case.
The single biggest driver of timeline is how many existing systems the AI needs to read from or write to, not the sophistication of the model itself.
Phase-by-phase timeline ranges
| Phase | Typical duration | What stretches it |
|---|---|---|
| Discovery and use-case scoping | 1–3 weeks | Unclear ownership, competing priorities among stakeholders |
| Proof of concept on hosted API | 2–4 weeks | Poor initial data access, undefined success metric |
| Data integration and real-data testing | 3–6 weeks | Number of source systems, data quality issues |
| Security, compliance and access control | 2–8 weeks | Regulated industry, first-time review of this type of system |
| Staged rollout and monitoring setup | 2–4 weeks | Number of user groups, change-management needs |
| Full production stability | Ongoing | Drift monitoring, iteration based on real usage |
Adding these phases together, a single, well-scoped use case typically reaches stable production in three to six months from a validated proof of concept, though projects requiring private model hosting, custom fine-tuning or several enterprise integrations commonly stretch to six or nine months.
The compounding effect of integrations
Each additional system the AI needs to connect to, SAP, Salesforce, Microsoft 365, ServiceNow, Snowflake, does not add a fixed amount of time; it adds its own testing cycle, its own access request process, and often its own security review, and these do not always run in parallel with each other. A use case touching one system might take eight weeks for the integration and testing phase; the same use case touching four systems can easily take four to six months for that phase alone, because approval processes for different systems are frequently owned by different internal teams working on different schedules.
Scoping the minimum viable integration count for a first version, rather than the full end-state vision, is the most reliable lever for compressing an otherwise long timeline.
Regulated industries add a predictable tax
Insurance, finance and healthcare use cases typically add several weeks to months to the compliance and access control phase compared to a general productivity use case, since these industries carry additional regulatory review beyond standard IT security sign-off. This is a genuine cost of doing the work correctly, not a process inefficiency to eliminate, and budgeting for it upfront prevents the timeline surprise of discovering a required review partway through the project. For related considerations specific to insurance workflows, see AI in insurance underwriting and claims automation.
Building a realistic project calendar
- Confirm the exact number of systems the use case needs to read from or write to before estimating anything else.
- Identify whether the industry or data type triggers additional compliance review beyond standard security sign-off.
- Estimate each phase separately using the ranges above rather than a single blended guess.
- Add buffer time at the compliance and integration phases specifically, since these are where estimates most often run long.
- Communicate the estimate as a range tied to these specific variables, not a single fixed date, so stakeholders understand what could shift it.
Frequently asked questions
Can an AI project realistically be delivered in under a month?
A narrow proof of concept using a hosted model API against sample data can often be demonstrated within two to four weeks. A production-ready system with real data integration, security review and a staged rollout almost never fits in under a month once those additional phases are included.
Does using a bigger or more advanced model shorten the timeline?
Rarely. Model capability affects accuracy and the amount of prompt engineering or fine-tuning needed, but it has little effect on the time spent on data integration, security review or rollout, which together make up most of the total timeline for a typical enterprise project.
Why do internal estimates often run shorter than a vendor's estimate?
Internal teams sometimes estimate only the model-building work and underweight integration, security review and change management, since those phases are less visible day to day. Vendors who have delivered several similar projects tend to price in these phases from experience, which is why their estimates often look longer even for comparable scope.
How much time should be budgeted for after launch, not just before it?
Plan for at least one to two months of close monitoring and iteration immediately after full rollout, since real usage patterns often surface edge cases the pre-launch testing did not cover. Treating launch as the finish line rather than the start of an operating phase is a common source of post-launch quality problems.
How Nanobase AI helps
Nanobase AI, a Silicon Valley enterprise AI engineering company, gives clients a specific week-by-week timeline during the discovery phase based on the exact systems and data involved, rather than a generic industry range. This includes calling out upfront which integrations or compliance requirements are likely to add the most time, so the estimate holds up as the project proceeds.
Ready to discuss your project? Contact Nanobase AI or email hello@bumu.tech.