This article covers the Resource Management AI inside KEBS. You will learn how the Smart AI Allocation engine scores and ranks candidates for open resource requests, how to read the suitability breakdown, how to switch between list and card views, and how to propose an allocation with soft or hard booking. It also covers how KAIS usage reporting gives managers visibility into AI adoption across the organisation.
Smart AI Allocation
Use AI-generated suitability scores and ranked candidate lists to allocate the right person to the right project request in seconds, without manual shortlisting.
When a resource request is raised in KEBS, the Smart AI Allocation engine automatically analyses every employee in the organisation and produces a ranked list of candidates. Each candidate receives an Overall Suitability Percentage based on three weighted signals: Skill Set match, Preference alignment, and Availability. The AI surfaces the top-ranked candidates at the top of the list so resource managers can act immediately.
How to open Smart AI Allocation for a request
- 1Navigate to People Allocation in the left sidebar and click Requests.
- 2Open an existing resource request. The request header shows the position, date range, and booking type (Onsite, Billable, Hard Booking).
- 3The Smart AI Allocation panel loads automatically beneath the request header, showing the total number of candidates evaluated (for example, “Showing 1031 Results”).
- 4The list is sorted by AI rank by default. Rank badges (#1, #2, #3 and so on) appear on the right side of each row, with the top three highlighted in gold, teal, and orange.
Understanding the list columns
| Column | What It Shows |
|---|---|
| Employee AID | The unique system identifier for the employee (for example, #10038). |
| Employee Name | Full name of the candidate as registered in KEBS. |
| Position | The employee’s current designated position. Displayed as a dash if no position has been assigned. |
| Suitability % | The AI-computed overall suitability score combining Skill Set, Preference, and Availability signals. Displayed as a donut chart indicator alongside the percentage value. |
| Work Location | The employee’s primary work location (for example, Chennai). |
| Employment Type | Whether the employee is Permanent, Contract, or another employment category. |
| Action | The Propose button. Click this to initiate a formal allocation proposal for the selected candidate. |
| Rank | The AI-assigned rank for this candidate relative to all others evaluated for the request. The top three ranks are colour-coded for quick identification. |
Card View: Comparing Ranked Candidates Side by Side
Get a clear, at-a-glance comparison of the top AI-ranked candidates and their individual Skills, Preference, and Availability scores before choosing who to propose.
The Smart AI Allocation panel offers two display modes: List View and Card View. Card view presents each candidate as an individual card, making it easier to compare suitability signal breakdowns side by side without reading across a dense table.
How to switch to card view
- 1In the Smart AI Allocation panel, locate the two view-toggle icons in the top-right corner, next to the search bar.
- 2Click the card view icon (the grid icon on the right). The list instantly switches to the card layout.
- 3To return to the list view, click the list view icon (the table icon on the left).
Reading a candidate card
Each card displays the following information:
- Rank banner: A coloured banner in the top-left corner identifies the candidate’s AI rank. Gold for #1, teal for #2, orange for #3, and grey for all subsequent ranks.
- Candidate name: The employee’s full name as registered in KEBS.
- Overall Percentage: The combined AI suitability score displayed prominently below the name (for example, 46.67%).
- Skills score: The percentage contribution from the Skill Set match signal. Shown in orange.
- Preference score: The percentage contribution from the Preference alignment signal. Shown in blue. A score of 40% means the candidate’s stated preferences partially align with the request parameters.
- Availability score: The percentage contribution from the Availability signal. A score of 100% means the candidate is fully available for the entire duration of the request window.
- View Details: Opens the employee profile for deeper review before proposing.
- Propose button: Initiates the Propose Allocation flow for the selected candidate directly from the card.
Understanding the Suitability Score Breakdown
Understand exactly why a candidate received their suitability score so you can make informed allocation decisions and identify gaps before proposing.
In list view, hovering over the suitability donut chart next to any candidate opens a Suitability tooltip. This tooltip displays a full breakdown of the three signals that make up the overall score, giving you the specific contribution of each factor rather than just the combined total.
The three suitability signals
| Signal | What It Measures | Colour |
|---|---|---|
| Skill Set | How closely the employee’s registered skills match the skills required by the resource request. A 0% score means no skill tags have been matched, typically because the employee profile does not have skills entered or the request has no skill requirements defined. | Orange |
| Preference | How well the employee’s stated work preferences (such as location, project type, or domain) align with the parameters of the request. A 40% score indicates partial alignment. | Blue (teal) |
| Availability | Whether the employee is free during the full date range of the request, with no conflicting allocations. A 100% score means the employee is completely available for the entire request period with no booking conflicts. | Green |
Proposing an Allocation from Smart AI Allocation
Submit a formal allocation proposal for a candidate directly from the AI-ranked results, choosing between a soft or hard booking type based on the certainty of the project commitment.
Once you have reviewed the AI-ranked candidates and identified a suitable match, clicking Propose opens the Propose Allocation modal. This form captures all the details needed to formally commit the employee to the project request, including the booking period, utilization capacity, and whether an interview is required before the allocation is confirmed.
How to submit a proposal
- 1In the Smart AI Allocation panel (list or card view), click the Propose button next to the candidate you want to allocate.
- 2The Propose Allocation modal opens. The candidate’s name, Employee ID, department, and location are pre-filled at the top of the modal.
- 3Set the Start Date and End Date for the allocation. These default to the request window but can be adjusted if the actual deployment period differs.
- 4Enter the Utilization Capacity as a percentage (for example, 100 for full allocation or 50 for a part-time split across two projects).
- 5Select the Booking Type: choose Hard Booking to lock the allocation, or Soft Booking to hold the candidate provisionally while the project is still being confirmed.
- 6Add any relevant Notes in the free-text field, such as onboarding instructions or client-specific requirements.
- 7Under Schedule Interview, select Yes if the candidate must be interviewed before the allocation is finalised, or No to proceed without an interview step.
- 8Attach any supporting documents using the Attachments paperclip icon if required.
- 9Click Propose to submit. The allocation proposal is sent to the relevant approvers and the request status updates accordingly.
Proposal form field reference
| Field | Required | Description |
|---|---|---|
| Start Date | Yes | The date from which the employee will be allocated to the project. Defaults to the request start date. |
| End Date | Yes | The date on which the allocation ends. Defaults to the request end date. |
| Utilization Capacity | Yes | The percentage of the employee’s working time dedicated to this project. Enter 100 for full-time or a lower value for a shared/split allocation. |
| Booking Type | Yes | Hard Booking locks the resource to the project and blocks availability for other requests. Soft Booking holds the resource provisionally without fully blocking availability, useful for pipeline projects that are not yet confirmed. |
| Notes | No | Free-text field for any additional instructions, context, or comments relevant to the allocation. |
| Schedule Interview | No | Toggle Yes to trigger an interview workflow before the allocation is finalised. Toggle No to proceed directly to approval without an interview step. |
| Acceptance Criteria | No | Criteria defined at the request level that the candidate must meet. This is pulled from the original request and displayed here for reference. |
| Attachments | No | Supporting files such as project briefs, scope documents, or candidate assessments relevant to the allocation. |
KAIS Usage Statistics and Reporting
Track how frequently KAIS is being used across teams, identify which modules and prompt categories are driving the most AI activity, and use this data to guide training and adoption efforts.
KEBS tracks all KAIS interactions and surfaces usage data through the Reports module. Administrators can view query volumes, active user counts, most-used prompt categories, and AI agent usage patterns over any selected time period. This reporting capability helps organisations understand where the AI is adding value and where adoption may need encouragement.
Key usage metrics available in KAIS reporting
| Metric | What It Tells You |
|---|---|
| Total Queries | The total number of prompts and questions submitted to KAIS across all users during the selected period. |
| Active Users | The number of distinct KEBS users who have interacted with KAIS at least once in the period. |
| Queries by Category | A breakdown of query volume by Prompt Library category (Reports, People Allocation, Timesheet, Projects), showing which modules are generating the most AI activity. |
| Agent Usage | How frequently each default AI agent (Expense Assistant, Timesheet Assistant, Leave Assistant) has been triggered. |
| Smart AI Allocation Usage | The number of resource requests that triggered the Smart AI Allocation engine, and how many proposals were submitted from AI-generated ranked results. |
