Strategic evolution toward intelligent resource, capacity and multi-project planning
The Rillsoft roadmap shows the strategic evolution of Rillsoft Project, Rillsoft Integration Server and Rillsoft Cloud. On a proven planning core, the focus going forward is on AI-supported resource optimization, automated scheduling, scenario simulation and forecasts for realistic project planning with limited resources.
The roadmap is deliberately phrased as a directional picture. It shows which planning problems Rillsoft aims to solve even better in the future – for project managers, resource managers and executives. It is not a technical to-do list and contains no binding delivery dates.
Last updated: June 2026. This roadmap is non-binding. Content, order and timing may change; only the released versions and their official release information are binding.
The foundation is in place
Rillsoft already plans realistically with limited resources today: resource planning, qualification-based capacity planning, automatic scheduling, multi-project planning and multi-user collaboration across server and cloud are established.
The roadmap deliberately does not describe these basics again. It shows how Rillsoft will next extend this foundation with AI-supported methods and stronger automation.
Where is Rillsoft heading?
The market for project and resource management is visibly moving toward forecasts, scenario planning and intelligent resource optimization. For Rillsoft, the central question is not “How do we build yet another AI assistant?” but:
Which planning problems should Rillsoft solve even better in the future?
The development directions follow directly from this:
- detect resource bottlenecks earlier,
- make capacity conflicts more transparent,
- assess schedule risks more reliably,
- prepare suggestions for resource allocation and schedule shifts,
- support multi-project decisions with more data,
- connect operational feedback and central planning more closely.
AI roadmap
This is where the actual focus of the roadmap lies: Rillsoft extends the existing planning core with AI-supported methods and stronger automation. Rillsoft pursues several separate development directions.
Own AI modules in the Rillsoft planning logic
These modules are to be embedded directly in the planning logic and work with project structures, tasks, resources, qualifications, capacities, dependencies and priorities.
A strategic priority is an algorithmically supported method for intelligent resource planning and automated scheduling that analyzes and optimizes resource allocations across several projects. Planned topics:
- AI-supported conflict detection for resource overloads,
- automatic suggestions for resource allocation,
- schedule optimization respecting capacities and priorities,
- adaptive planning based on historical project and feedback data,
- scenario simulation and what-if analyses for alternative planning variants,
- forecasts of schedule risks, bottlenecks and plan deviations.
To this end, Rillsoft also evaluates actor-critic approaches (reinforcement learning) for large project plans – in two separate trainings: one for optimal staff assignment and one for even resource utilization. In both, a good target state is created first, then deliberately degraded, and a model is to learn to gradually move the plan back toward a lower-conflict, more resource-realistic state. The page AI Research for Planning Optimization describes in detail what these research scenarios, the self-generated training data and the evaluation look like.
Integration with existing AI systems via MCP and APIs
This direction describes external AI integration. The focus is on controlled access to Rillsoft data and functions by connected AI assistants – not on replacing the Rillsoft planning logic.
The integration builds on the REST interface introduced with Rillsoft Project 10. Under review:
- MCP connection for AI assistants and agentic workflows,
- querying project, resource, capacity and status data via defined interfaces,
- AI-supported summaries of project status, bottlenecks and risks,
- integration with cloud and enterprise AI platforms,
- controlled automation of analysis and reporting tasks.
The functional planning logic and data sovereignty remain in the Rillsoft platform in every case.
AI support for API integrations
This direction aims at AI-supported integration assistance based on defined rules and the REST API introduced with Rillsoft Project 10. The AI assists in connecting external systems by generating integration suggestions, simplifying configurations and accelerating the implementation of standardized interfaces.
Planned focus areas:
- rule-based integration suggestions based on the Rillsoft REST API,
- simplified configuration of recurring connection scenarios,
- accelerated implementation of standardized interfaces,
- faster connection of third-party applications and partner solutions.
The goal is significantly faster and easier integration of Rillsoft with third-party applications and partner solutions – from the first interface idea to a working connection.
AI-supported quality assurance and test automation
This direction does not concern planning itself, but the stability and quality of Rillsoft Project. Rillsoft already uses automated tests that execute random user and project commands and log their order to reproduce crashes reliably. AI is intended to make this quality assurance more targeted.
Planned is the use of AI for intelligent crash analysis and for targeted control of the test scenarios: based on stack traces and the order of the most recently executed functions, the AI determines probabilities for possible error causes and shifts test generation specifically toward risky command groups.
Planned flow:
- derive the likely error causes from stack traces and the last command sequence,
- generate targeted test runs focused on risky command groups,
- run these tests automatically to reproduce crashes and identify their causes,
- after successful reproduction, re-validate the critical function sequences and assess their impact on system stability.
The goal is significantly faster error analysis, higher test coverage and more efficient quality assurance for complex projects.
Version roadmap
The following overview groups the topics roughly by phase. It refers back to the detailed descriptions above and does not repeat them. The phases indicate the approximate order and are not a delivery commitment.
| Phase | Focus | Example topics | Status |
|---|---|---|---|
| Short term | analysis, transparency, planning support | better detection of overloads, clearer capacity evaluation by qualifications, conflict and bottleneck display, prioritization logic | planned |
| Medium term | automation and intelligent optimization | suggestions for schedule shifts, AI-supported resource allocation, optimization across several projects, forecasts, scenario simulation of alternative planning variants | under review |
| Long term | adaptive multi-project planning | continuous re-planning on changes, risk indicators, automatic recommendations under resource scarcity, deeper platform integration of own and externally connected AI functions | under review |
For an overview of the changes already released, see What’s new in version 10.0.
Missing a feature?
The roadmap thrives on the exchange with our users. If a particular development direction is especially important to you, or if you are missing a feature, we look forward to your feedback. Get in touch – your input feeds into the prioritization of the further development.