What if we don't have a history of similar projects?

For early projects the system draws from industry benchmarks and the project structure. After 10–15 completed projects in the system the accuracy of estimates is significantly higher.

Will AI account for the availability of specific people on the team?

Yes — the system assigns tasks to specific people taking into account their current workload from other projects, holidays, and historical work speed.

Does it work for agile projects with sprints?

Yes — the system supports sprint planning. AI estimates story points based on the team's velocity history and proposes a realistic sprint scope.

AI project estimation: end the pattern of triple time overruns

“It took twice as long” is the norm, not the exception. Studies show that 70 % of IT and implementation projects exceed their original time plan. The reason: estimates are made optimistically, without accounting for history and actual team availability.

Why manual estimates fail

Optimism bias: we estimate the best-case scenario, not the realistic one
Forgotten risks: we don’t factor in typical “waiting periods” (approvals, testing)
Team capacity: we ignore that people are engaged on other projects
Ignoring history: every project is “different,” even when it isn’t

How AI estimates projects

Input: project template

When creating a new project you enter:

Project type (software implementation, construction, product development…)
Scope (large / medium / small)
Team and availability

AI analyzes history

The system reviews all past projects of a similar type and identifies:

Average delivery duration
Typical “slow” phases (where delays always occur)
Most common risks and their schedule impact
Speed of specific team members

Output: realistic schedule

AI generates a schedule with:

Pessimistic and optimistic scenarios
Probability of on-time delivery (e.g. “72 % chance to finish by June 15”)
Identified risks with probability and impact
Recommended buffer for each phase

Project: ERP Implementation for Client XY
Estimated duration: 12 weeks (AI: realistic 14–16 weeks)

⚠️ Risk phases:
• Data migration (2 weeks) — historically 40 % longer
• Customer acceptance testing — always add 1-week buffer
• Pohoda integration — depends on customer IT availability

Recommended commitment to customer: 17 weeks
(at 85 % probability of on-time delivery)

Ongoing tracking vs. plan

During the project AI monitors:

Actual progress vs. plan (burndown)
Where the team is falling behind and by how much
Updated predicted completion date
Alert when the project deviates more than 20 % from plan

The manager receives a weekly report: “Project is 1.5 weeks behind schedule. Probability of delivery on original date: 35 %. We recommend escalating or adjusting scope.”

AI project estimation — a realistic schedule instead of optimistic wishful thinking