A controller menu is tempting: select the method that sounds most advanced. A better start is to write the requirement: recover a load, reduce settling time, keep voltage below a limit, or tolerate model error. Lead, lag and PI buy different things.
Three mechanisms, not three labels
A lead network places its zero below its pole and can add positive phase over a chosen band, often to improve a speed–margin tradeoff. A lag network places its pole below its zero and can increase relative low-frequency loop gain while costing phase and speed. A PI controller has an integrator: in an ideal stable unsaturated loop it can eliminate constant-error components, but it can wind up against an input limit.
A finite-gain lag is not a disguised integrator. It may reduce a constant-load error without driving it exactly to zero. Nor is lead automatically faster for every motor and gain; the chosen zero, pole, crossover and voltage limit decide the observed result.
Run a fair three-way comparison
Open the Lead, lag or PI lesson. Pin the PI baseline. Keep target, steady load, motor parameters, sample period and seed fixed while switching controller type to Lead and Lag. Use final error, rise or settling time and integrated absolute control effort as separate criteria. The default presets are examples, not optimized contenders; tune each within the same declared constraints before claiming a winner.
Then inspect the Bode plot for the small-signal mechanism and the requested/applied voltage plot for any nonlinearity. If one design saturates and another does not, a simple margin comparison alone is not fair evidence for the limited task.
Write the reason for the choice
A meaningful answer says which requirement was primary and which performance was sacrificed. For example: 'I chose PI because the persistent load error mattered more than settling time, and anti-windup kept the voltage-limited recovery acceptable in the tested range.' Change the load or target once more before trusting that decision.
Choose by a stated target, disturbance and input constraint. A preset's attractive curve is a hypothesis, not an optimized design comparison.