Implementing the Scaled Agile Framework (SAFe) in scalenow AI
scalenow AI is a versatile transformation management platform that can be customised to fit a variety of frameworks and methodologies. For large organizations implementing the Scaled Agile Framework (SAFe), scalenow AI provides a robust set of tools to define, plan, and organize work, ensuring value delivery to customers.
Structure and Terminology
Preparing scalenow AI for SAFe requires configuration at both the project and global levels:
Individual Projects: These are independent entities consisting of modules, team members, work packages, and settings. Each project represents an Agile Release Train (ART).
Global Modules: These encompass content and settings that affect the entire platform, including all projects and ARTs. This global view corresponds to the Solution Train in SAFe.
Setting up Agile Release Trains
In scalenow AI, each Agile Release Train (ART) corresponds to a project.
A project in scalenow AI is made up of several elements:
Team Members: These can be added at the instance level and invited to individual projects. External users can also be invited directly.
Modules: Includes features such as Work packages, Gantt charts, Backlog, Team Planner, Wiki, Forums, and Meetings.
Work Packages: Different work package types include Epics, Features, Enablers, User Stories, and Bugs.
Member Groups: These can be created at the instance level and assigned to projects.
Project templates
In scalenow AI , simplify the creation of new Agile Release Trains (ARTs) by utilizing project templates. These templates allow you to replicate a consistent structure, enabled modules, project organization, and work package templates. Once an ART is created from a template, it can be customized as needed.
Agile teams within an ART can be managed as sub-projects of the ART or as saved custom views in tools such as the Team Planner or an Assignee-based Kanban board
Setting up Agile Release Trains
In scalenow AI, Project portfolios provide a comprehensive way to view, organize, sort, and filter all projects and their hierarchies. Since individual projects represent Agile Release Trains (ARTs), these portfolios offer access to information at the Solution Train level.
scalenow AI introduces a new Project List View, allowing users to create and save custom lists of projects based on their own filter criteria.
These lists can display custom project attributes, and individual projects can be marked as favorites for quicker access.
Project lists are an excellent tool for building precise and insightful dashboards at the Solution Train level.
Users can also create custom project lists or opt for a meta-project structure. A meta-project combines multiple sub-projects (representing ARTs and teams) into a unified view
This consolidated Work Package Table View can be filtered and customized to display specific attributes, custom fields, or columns.
It can also be grouped and sorted, providing a detailed overview. Additionally, this view can include epics, features, and user stories not only from sub-projects but also from entirely separate projects.
Create Versions for PIs, Iterations, and Sprints
In scalenow AI , a Program Increment (PI) or iteration is represented by a version.
Similar to other features in scalenow AI, a version is technically part of a project. This means a PI or iteration can exist within an Agile Release Train (ART). However, versions can also be shared across sub-projects, other projects, or even the entire system instance.
Sharing versions is particularly useful when Program Increments need to span multiple ARTs.
Versions are also integrated with the Backlog module. For more details, explore the sections on Backlogs, Kanban, and Team Planner below.
Epics, Features, Stories
Once the instance, ARTs (Agile Release Trains), and versions are configured, you can focus on setting up individual work initiatives.
In scalenow AI ,, all tasks and initiatives are managed as work packages categorized by various types. For SAFe, In scalenow AI , provides pre-defined types such as Epics, Features, User Stories, and Milestones. If needed, you can also define and customize additional types, such as Capabilities or Enablers.
While milestones are unique because they are tied to a single date, all other work package types can be freely adjusted or newly created to suit your specific requirements.
Each work package type includes several configuration elements:
- Fields: Standard and custom fields tailored to your needs.
- Workflows: Defined statuses and transitions between them.
- Access Settings: Specifies which ARTs or projects can utilize the type.
For SAFe implementations, it’s a good practice to configure the required work package types in advance. Since types can be shared across projects, the same structure can be reused across multiple ARTs when necessary.
Story points
In scalenow AI, Story points can be assigned to User Stories and even Features.
These points are visible for stories within a Feature, providing clarity on effort estimation.
Story points are especially useful as they are also displayed in the Backlog, enhancing planning and prioritization.
The Backlog,
In scalenow AI ,
Kanban
In scalenow AI, Kanban boards provide a clear, visual representation of work items, offering flexibility in how they are viewed. In scalenow AI, dynamic boards can be created based on various fields.
Kanban board of an ART organised by status
For each ART, we suggest creating dynamic Kanban boards for:
- Sprints (or PIs)
- Assignees
- Status
The boards in scalenow AI are highly customizable and can be filtered to display only the most relevant information.
Team Planner:
Team Planner
