Why Forecast for Engineering Managers?
Engineering Managers responsible for resource planning across multiple squads or projects will find Forecast's AI-driven approach to allocation uniquely valuable. Instead of manually balancing engineers across projects in spreadsheets, Forecast's auto-scheduling engine distributes work based on team members' skills, availability, and project deadlines — proposing optimal allocations that an EM can review and adjust rather than building from scratch each planning cycle.
Where Forecast stands out for engineering leadership is the tight integration of project planning, time tracking, and budget management. Every hour tracked feeds into both delivery progress and project financials, giving Engineering Managers a single system that answers "are we on schedule?" and "are we on budget?" simultaneously. Utilization reports show which engineers are over-committed and which have capacity, enabling data-driven staffing decisions during quarterly planning or when new projects compete for limited engineering bandwidth.
Engineering Managers Workflow with Forecast
Here's how Engineering Managers can integrate Forecast into their daily workflow:
- Step 1: Define your engineering projects with milestones, required skill profiles (frontend, backend, DevOps, QA), and effort estimates, then let Forecast's AI propose team allocations based on each engineer's skills, current commitments, and availability.
- Step 2: Review and adjust the AI-proposed resource plan, locking in confirmed allocations and flagging flexible assignments, then use the capacity heatmap to identify weeks where engineering bandwidth is over-committed and needs scope adjustment or additional staffing.
- Step 3: Enable time tracking so engineers log hours against tasks, feeding real-time data into both project progress dashboards and utilization reports — giving you accurate velocity data without maintaining a separate tracking system.
- Step 4: Build utilization and budget dashboards for engineering leadership meetings, showing planned vs. actual effort by project and team, cost performance against budget, and forecasted completion dates based on current velocity rather than original estimates.
Key Features for Engineering Managers
- AI auto-scheduling proposes engineer-to-project assignments based on skill matching, availability, and deadline priority — transforming resource planning from a multi-hour spreadsheet exercise into a review-and-approve workflow.
- Capacity management provides a visual heatmap of team utilization by week, showing exactly which engineers are over-committed and which have bandwidth, so staffing conversations are driven by data rather than gut feel.
- Integrated time tracking captures actual effort at the task level, automatically calculating project velocity, cost performance, and utilization rates without requiring engineers to update separate timesheets or status reports.
- Budget tracking ties engineer hours directly to project costs, showing real-time burn rate and forecasted spend-at-completion — enabling Engineering Managers to flag budget risks early and negotiate scope adjustments before overruns materialize.
Pricing Quick Look
| Model | per-seat |
|---|---|
| Free Tier | No |
| Lite | $29 /user/month |
| Pro | Custom /user/month |
| Plus | Custom /user/month |
For complete pricing details, see our full Forecast review.
Methodology Fit
Bottom Line
Forecast is the strongest choice for Engineering Managers whose primary challenge is resource allocation across multiple projects — particularly in consulting, agency, or matrix organizations where engineers frequently shift between initiatives.
Frequently Asked Questions
How does Forecast's AI resource allocation handle engineers with specialized skills?
Forecast allows you to tag team members with skills (e.g., React, Python, Kubernetes, security). When the AI proposes allocations, it matches project skill requirements to engineer profiles, so your Kubernetes specialist is not proposed for a frontend-only project. You can also set preferred assignments and lock allocations for critical resources, with the AI working around those constraints for remaining assignments.
Can Forecast replace Jira or Linear for engineering sprint management?
No. Forecast is a resource planning and project management tool, not an agile engineering tool. It lacks Scrum/Kanban boards, backlog management, Git integration, and the developer-centric workflows that Jira and Linear provide. Most engineering organizations use Forecast alongside Jira or Linear — Forecast handles resource allocation, capacity planning, and project financials, while the engineering tool manages sprints and technical workflows.
Is Forecast worth the investment for a single engineering team of 10-15 people?
For a single team, Forecast's value is limited — you likely know your team's capacity intuitively, and the AI allocation engine adds less value when there is only one project to staff. Forecast shines when an Engineering Manager oversees 3+ squads or manages engineers allocated across multiple concurrent projects. If you are managing resource allocation in spreadsheets and spending hours per week on capacity planning, Forecast's ROI becomes clear quickly.
Other Tools for Engineering Managers
Looking at alternatives? Here are other top-rated tools for Engineering Managers: