AI Project Timeline: Stages, Milestones & Roadmap 2026 | Meritorious CodeCrafters
                     Roadmap to AI Success
Understanding Project Timelines and Stages
Why AI Projects Need a 
Structured Roadmap
Most AI projects don't run long 
12–24 Weeks because the technology is slow — 
they run long because the invisible 
Typical time from problem work of data preparation and 
definition to production — MLOps infrastructure is consistently 
driven by data prep and underestimated at the outset.
MLOps, not AI complexity
A structured roadmap forces early 
alignment on what success looks 
like, who owns each phase, and 
40–60% Faster
what "done" actually means before 
Teams that define success a single model is trained.
criteria upfront consistently 
outpace those who define it 
retrospectively
The Hidden Cost of Skipping Discovery
Discovery is the highest-leverage 
investment in any AI project
At just 2–4 weeks, it is the cheapest phase by far — 
and the one most often cut to "save time."
Skipping it leads to three compounding problems:
• Wrong problems solved — the model answers a 
question nobody needed answered
• Scope creep — requirements shift once the data 
reality becomes clear
• Wasted effort — six months of development 
pointed at the wrong target
You cannot recover discovery time by moving 
faster later.
Phase 1: Discovery & Problem Definition
WEEKS 1–4
Problem Scoping Data Audit Feasibility Assessment
Define the specific prediction task Survey available data sources and Validate that historical data contains 
with measurable success criteria — assess quality, volume, and sufficient signal to support the 
before anyone touches data or code accessibility before development begins intended prediction task
Phase 2: Data Preparation
WEEKS 3–11
40–60% 2×
Of Total Project Time Your First Estimate
Routinely consumed by data preparation alone — the Rule of thumb: assume data prep takes twice as long as 
single most time-consuming phase in any AI project initially planned, then budget accordingly
Why real data always surprises you
Business data in production is far messier than stakeholders expect or documentation suggests:
• Missing fields in critical columns
• Inconsistent labelling across systems or time periods
• Insufficient volume for robust model training
• Siloed data that is harder to join than anticipated
Phase 3: Model Development
WEEKS 7–19
Baseline Modelling
Simple models establish a performance floor — the 
minimum bar every complex model must beat
Experimentation
Systematic evaluation of complex architectures with 
rigorous experiment tracking and version control
Evaluation
Assessment against pre-defined success criteria on 
held-out test data — not training data
Phase 4: MLOps & Deployment
WEEKS 15–25
Where AI projects most commonly stall
A notebook model is a research artifact — not a 
production system. The gap between the two is wider 
than most timelines acknowledge.
• Containerisation, APIs, and serving infrastructure
• CI/CD pipelines for model updates
• Security, compliance, and access controls
Budget MLOps at 30–40% of model 
development cost — not as a minor final step.
Phases 5 & 6: Monitor, Iterate & Scale
ONGOING
Monitoring & Iteration Scale & Optimisation
Models degrade in production as the real world changes. Reduce inference cost, improve latency, and add 
Define your monitoring plan before deployment — not new model types. Once a model is stable and 
after. Retraining triggers should be time-based, delivering value, expand to adjacent product areas 
performance-threshold-based, or event-driven.
with confidence built from production experience.
Timeline by Project Type
Plan for the range, not the minimum
Every project type has a best-case and a realistic-case 
timeline. Teams that plan for the minimum consistently 
miss it.
• Generative AI / RAG: 8–16 weeks — fastest to 
prototype, longest to harden for production
• NLP / Document Processing: 10–18 weeks — data 
labelling is the key variable
• Prediction / Classification: 12–20 weeks — data 
quality determines the range
• Full AI SaaS Product: 5–9 months — treat as a 
product build, not a model build
The Four Causes of Timeline Overruns
1 Data problems discovered mid-project 2 Undefined success criteria
The most common cause of overruns. Run a Fix criteria in Phase 1 and obtain explicit stakeholder 
formal data readiness assessment in Phase 1 — sign-off. Moving goalposts are a project killer.
before any model work begins.
3 Underestimated MLOps scope 4 Insufficient team specialisation
Plan deployment infrastructure with the same rigour Each phase requires distinct skill profiles. A generalist 
as model development — not as an afterthought. team will slow at every transition between phases.
The Roadmap Is the 
Advantage
AI project success is not determined by model sophistication — it is 
determined by process discipline. Teams that invest in discovery, respect 
data preparation timelines, treat MLOps as first-class work, and define 
monitoring before deployment consistently deliver on time and on brief.
Define success early Double your data 
estimate
Phase 1 alignment saves 
months downstream It will consume 40–60% of 
total project time
Budget MLOps properly
30–40% of model cost — not a minor final step
Explore the Full Guide
This presentation covers the key highlights. For the complete 
breakdown — including detailed timelines, phase-by-phase 
planning, and real-world benchmarks — read the full article on 
our blog.
Read the Full Article  → AI Insights & Engineering Blog