By ALICE Technologies, in collaboration with Global Edge Advisory Group
Schedules can look robust on paper and still unravel in the field. As engineering evolves, access constraints shift, craft labor ramps unevenly, equipment availability changes, and critical packages slip—delays compound, cost exposure grows, and teams spend more time reacting than steering.
That’s when planning teams get pulled into familiar questions:
● If resources or equipment shift, what happens to the milestones?
● If a critical package slips, what’s the fastest buildable recovery path?
● If we pull a lever to improve a date, where does risk move downstream?
Most teams can get to answers—but often too late. Under deadline pressure, planners rebuild logic, chase versions, and test only a narrow set of scenarios because time is short. When scenario analysis lags, decisions are made with less clarity and greater risk.
Leading project organizations are now pushing toward a different operating model: not fewer SMEs, but better leverage of them. The goal is to move planning and risk experts out of “report production mode” and into the rooms that matter—working with delivery teams to evaluate options early, stress-test trade-offs, and choose executable paths before issues escalate. In other words: shorten the loop from signal → options → decision → action, with assumptions that are explicit and traceable. In practice, this moves teams from one baseline defended to multiple executable options reviewed—often within the same governance window.
ALICE was built to make that speed practical. It enables teams to generate and compare scheduling options rapidly—shifting planning from defending a single “best guess” schedule to managing a living set of feasible alternatives that can be evaluated, refined, and used to steer execution governance.
That’s where ALICE and Global Edge Advisory Group (GEAG) come together—combining computational scheduling with real-world execution strategy and governance.
ALICE rapidly generates and compares options. GEAG pressure-tests scenarios against constructability, site constraints, logistics, workforce reality, interfaces, and execution strategy— turning computational outputs into recommendations teams can act on. Just as importantly,
GEAG helps establish scenario governance, so speed translates into decisions, not noise: a clear scenario cadence, explicit assumptions, traceable changes, and decision-ready outputs.
What this looks like in practice:
● A repeatable scenario cadence aligned to execution governance
● A traceable assumption set that makes trade-offs explicit
● A decision-ready options set built for delivery and leadership forums
That governance layer shows up through questions like:
● Are the inputs credible for this site, workforce, and governing rules?
● Does the sequence hold once access constraints and interfaces are factored in?
● Does an “improvement” milestone simply shift risk downstream?
● Can crews actually execute it in the field?
● What assumptions changed—and who approved them?
Across today’s AEC landscape—industrial, infrastructure, energy, buildings, and mission-critical —teams are using generative scheduling to make faster, higher-confidence calls in three recurring areas:
AEC teams are being asked to move faster under greater scrutiny. That requires scenario analysis that matches the real rhythm of execution—enabling earlier, clearer trade-off decisions, with transparent assumptions and outputs that hold up in governance forums.
That’s the intent behind the ALICE–GEAG collaboration: computational speed paired with execution governance, so teams can explore options quickly, choose confidently, and shift from reactive schedule defense to proactive project steering.