AI and Research Scientist Publication Plan: Two-Year Trajectory

Publication plans drift without structure; AI proposes a 24-month plan with venue and milestone choices.

What AI does well here

• Draft a 24-month publication plan with venues
• Suggest collaborations that strengthen the trajectory
• Format a milestone-based progress tracker

What AI cannot do

• Predict acceptance at top venues
• Replace genuine novel research ideas

Two-year publication plans: how to build trajectory that reads as coherent in retrospect

Research scientists in industry and academia are evaluated not just on individual papers but on research trajectory — is there a coherent line of inquiry that builds, or a collection of disconnected projects? A strong two-year publication plan serves two functions: it forces the scientist to articulate the through-line of their research agenda, and it creates a milestone structure that helps prevent the drift toward whatever is easiest to publish next rather than what advances the scientist's actual thesis. AI can draft a 24-month plan structure from the scientist's research description and ideas: suggesting venue sequences (workshops before conferences, conferences before journals for an emerging area; journals before conferences in more mature fields), proposing collaboration partners whose work complements the trajectory, and formatting a milestone tracker tied to key submission deadlines. The plan is a scaffolding for thinking, not a commitment that can be used against the scientist at review time. Research surprises — a method that doesn't work, a dataset that reveals something more interesting than the original question — are features of good research, not failures to follow the plan. The scientist's annual review conversation goes better when there is a plan to point to: here is where I am relative to where I planned to be, here is what I learned that changed the plan, here is where I am going next and why.

• Publication plans force articulation of research trajectory — the coherent through-line committees evaluate
• AI can draft venue sequences, collaboration suggestions, and milestone timelines from a research description
• The plan is a thinking scaffold — research surprises should update the plan, not be hidden from it
• Venue prestige matters less than citation impact; prioritize venues where the community reads closely