List the business results you seek—faster fulfillment, fewer errors, higher lead conversion—then map which workflows must change to realize them. Only afterwards attach potential tools. This sequencing prevents shiny-object shopping and turns evaluation into an evidence hunt anchored to measurable improvements stakeholders actually feel.
Write a one-paragraph problem statement including who is affected, how often the pain occurs, what it costs in money and morale, and which constraints cannot move. Circulate it for edits. Agreement here eliminates endless detours and future arguments disguised as feature debates.
List every assumption driving your estimates—adoption rates, training time, integration ease—and assign ranges, not absolutes. Capture best case, likely case, and worst plausible case with clear sources. This makes later surprises explainable, comparable, and correctable, rather than emotional shocks that distort executive judgment.

Choose metrics someone on the frontline can verify without dashboards—tickets closed, orders shipped, hours to onboard. Establish starting baselines, expected lift, and acceptable variance. Avoid vanity indicators. Evidence should travel from operators upward effortlessly, shrinking the gap between slideware optimism and operations that actually improved.

Design experiments with hypotheses, checkpoints, and stop-loss rules. In thirty days validate installation and initial fit; in sixty, verify productivity signals; in ninety, test scale, security, and support. If gates are missed, pause or cancel confidently, knowing your process protected time, morale, and budget.

Agree upfront on what constitutes success, what justifies a pivot, and what triggers a shutdown. Publish these gates before funding. When decisions follow precommitted rules, teams feel treated fairly, vendors respect outcomes, and leadership avoids escalation traps that prolong doomed projects out of fear or ego.