Account for technical, compliance, and human challenges — particularly complexities in AI deployment related to data readiness and integration, legacy systems, security, privacy, technical issues, culture and organisational change
Engage experienced professionals — Involve experts who have previously managed AI or technical implementations to provide inputs on timeframes
Define the project activities and scope in detail — Break down the project into clear, manageable tasks with well-defined deliverables and milestones
Develop a phased roadmap with clear milestones and deliverables — Start with pilot projects and iterate, then scale up gradually while tracking progress
Benchmark similar projects — Review past AI projects or industry case studies
Build in contingency time — Include buffer periods in the project schedule to account for unforeseen challenges and necessary adjustments
