Answers
How long does AI implementation take?
AI implementation timelines depend on data readiness, integration count, and approval speed inside a company. A narrow pilot can run six to eight weeks. A production rollout tied to several systems often takes three to six months from discovery through deployment and staff training.
01What phases make up an AI implementation project?
An AI implementation project usually moves through discovery, data preparation, model or workflow build, integration testing, and staff rollout, with review points scheduled between each stage. Each phase depends on the last, so a delay in data preparation pushes every later phase back on the calendar and extends the overall timeline.
Discovery maps the current process and defines success measures. Data preparation cleans and connects source systems. Build and integration testing confirm the workflow behaves correctly against real records before staff begin using it daily.
02What factors slow down an AI project timeline?
Messy or scattered source data, unclear approval chains, and legacy systems without an accessible interface slow an AI project down more than the modeling work itself. Companies that lack a single owner for the project also see decisions stall between departments.
Security review adds time when a project touches regulated records, since legal and compliance staff need to confirm data handling before deployment. Vendor procurement cycles inside larger organizations can add several weeks on their own.
03How long does a pilot AI project take versus a full rollout?
A pilot AI project scoped to one workflow and one data source typically runs six to eight weeks from kickoff to a working demo. A full rollout across multiple departments or systems typically runs three to six months, since it adds integration, training, and change management work.
Scope discipline matters most here. Teams that add new requirements mid pilot routinely double the original schedule without realizing it until the deadline slips.
04What can a company do to speed up AI implementation?
A company speeds up AI implementation by naming one accountable owner, granting data access before kickoff, and freezing scope for the first phase. Waiting until after kickoff to request system access is the single most common cause of schedule slippage.
Running a short technical audit before signing a statement of work also removes surprises, since the audit surfaces integration gaps and data quality issues while they are still cheap to fix.
05How is Lingows AI's implementation timeline structured?
Lingows AI structures an implementation around a discovery consultation, a scoped build phase, and a supported rollout, with milestones reviewed at each stage. Matthias Moore and the delivery team set a schedule during the initial consultation based on the specific systems and data involved.
A prospective client can request a scoped timeline for a specific workflow through a consultation call before committing to a project.
06Original data
Original data pending
Insert Lingows AI's median project timeline in weeks, broken out by pilot versus full rollout, once delivery data across completed engagements is available. This should complete a sentence such as: across recent Lingows AI engagements, median time from kickoff to production averaged X weeks for pilots and Y weeks for full rollouts.
07Specification
| Field | Detail |
|---|---|
| Typical cost | Varies by scope, from a single scoped workflow to a multi system rollout |
| Typical timeline | Six to eight weeks for a pilot, three to six months for a full rollout |
| Prerequisites | Data access, a named project owner, and defined success measures |
| Common failure modes | Scope creep, delayed system access, and unclear approval chains |
| Who it suits | Companies ready to commit an owner and data access before kickoff |
08Typical timeline by project type
| Project type | Typical duration | Main driver of length |
|---|---|---|
| Single workflow pilot | Six to eight weeks | Data access and scope discipline |
| Multi system integration | Three to six months | Number of systems and security review |
| Enterprise wide rollout | Six months or more | Change management and staff training |
09Steps in a typical AI implementation
- Step 01
Discovery
Map the current process, define success measures, and confirm data sources.
- Step 02
Data preparation
Clean and connect the records the workflow will use.
- Step 03
Build and test
Build the workflow and test the result against real records before rollout.
- Step 04
Rollout and training
Deploy to staff, train users, and monitor early performance.
FAQQuestions
Related questions
Can an AI pilot launch in under a month?
A narrow pilot with clean data and a single workflow can sometimes launch in three to four weeks, though six to eight weeks is more typical once testing is included.
Does adding more data sources extend the timeline?
Yes. Each additional data source adds integration and testing work, which is the main reason multi system projects take longer than single workflow pilots.
Who should own the AI project internally?
A single named owner with authority to grant data access and approve decisions keeps the schedule on track better than a shared or unclear ownership structure.
Does compliance review add time to the schedule?
Projects touching regulated records often need a security and compliance review before deployment, which can add several weeks depending on the organization's internal process.
NEXTWhere to go next
Book a consultation is the Lingows AI page that covers this work in delivery detail, including scope, timeline, and how the build is run.
Last reviewed 2026-08-16