Table of Contents
Interactive Demo
AI-Powered Resourcing: Use Cases and Benefits
Enabling and Disabling AI-Powered Resourcing
Required Permissions
AI-Powered Resourcing Location
Understanding Matching Rules
How AI-Resourcing Finds the Best Match
Color-Logic in AI-Resourcing
Scenario: AI Scoring in Action
Completing Staffer Data to Ensure the Best AI-Resourcing Results
Glossary
FAQ
Is my data used to train AI models?
Interactive Demo
Explore Foresight Connect AI-Powered Resourcing below with an interactive demo. Click Get Started to navigate seamlessly through the interface and discover a feature. You can restart the demo at any time.
AI-Powered Resourcing: Use Cases and Benefits
Foresight AI-Powered Resourcing optimizes workforce allocation by ensuring the right people are assigned to the right projects. With just one click, you can generate a comprehensive match analysis of your staffers to a given demand based on criteria that consider availability, skill matching, seniority level, and budget—significantly accelerating the resource planning process.
Enabling and Disabling AI-Powered Resourcing
The feature can be enabled or disabled for the entire organization in the admin panel. To perform one of these actions, log in to your BigTime Foresight administrator account or ask someone in your organization for the necessary permissions.
| Log in to the Administrator Role Account ➡ Go to the Settings Tab ➡ Stay in the General subsection ➡ Scroll down to the Additional Settings section ➡ Switch the toggle: Enable Foresight Connect AI – AI-Powered Resourcing ➡ Click Save button |
Please watch the walkthrough below to learn how to enable and disable Foresight AI:
Required Permissions
Users must have the appropriate financial permissions to fully utilize AI-Powered Resourcing and its profitability matching feature. Without the necessary access rights, specific features will be restricted.
Permission Requirements:
- Financial Access for Managers + View All Staffer Costs provides full access to the Profitability ($) section using AI-Powered Resourcing.
- Financial Access for Managers without View All Staffer Costs → The Profitability ($) icon will not be visible on the left panel, and the Profitability section inside the modal will be hidden.
- If a manager only has Project Manager Read-Only access, they won’t be able to access Foresight AI for that project.
User permissions can be managed in Administrator Role Account. Learn more about user rights in Foresight: Lesson 5 - Initial Account Setup. Security Groups and User Rights in Foresight Connect
| Permission Level | AI-Resourcing Feature Access | Access to Profitability ($) Icon | Access to Profitability Section in Modal | Notes |
| Foresight for Managers + Full Projects Access (View any Project + Manage any Project) and Financial Access (Financial Access for Managers + View All Staffer Costs) |
✅Yes | ✅ Visible | ✅ Full access | Full functionality enabled |
| Foresight for Managers + Financial Access (Financial Access for Managers + View All Staffer Costs) and be assigned as a project manager to a specific project |
✅Yes | ✅ Visible | ✅ Full access | Full functionality enabled |
| Foresight for Managers + Financal Access for Managers |
✅Yes | ❌ Hidden | ❌ Not visible | Profitability data is completely restricted |
| Foresight for Managers + View/Manage any Projects or Project Manager | ✅Yes | ❌ Hidden | ❌ Not visible | Profitability data is completely restricted |
| Foresight for Managers + Project Manager Read Only | ❌No | ❌Hidden | ❌Not visible | If managers have Read-Only permissions for a project, they can’t make any changes to it and therefore can’t access AI-Resourcing either. |
AI-Powered Resourcing Location
You can use AI-Powered Resourcing in four places in Foresight:
1. In the Project Profile, under the Team subtab:
| Log in to the Manager Role Account ➡ Go to the Projects List Tab ➡ Click your project ➡ Remain in the Team subtab ➡ Scroll down to the Demand section ➡ Click the AI icon |
2. In the Project Profile, under the Demand Calendar subtab:
|
Log in to the Manager Role Account ➡ Go to the Projects List Tab ➡ Click your project ➡ Go to the Demand subtab ➡Go to the Demand Calendar section ➡ Click the AI icon |
3. In the Demand Calendar, under the Calendar tab:
|
Log in to the Manager Role Account ➡ Go to the Calendar Tab ➡ Select Demand Calendar ➡ Click the AI icon |
Understanding Matching Rules
Foresight AI analyzes several key criteria to find the best fit between available team members and project demand:
- Seniority Level – Ensures employees have the right level of experience.
- Skills Match – Verifies that employees have the required technical skills.
- Availability Score – Checks if employees have enough time for the project.
- Continuous Availability – Ensures employees are consistently available.
- Financial Considerations – Evaluate cost-effectiveness and profitability.
Each factor is given a score from 0% to 100%, with higher scores meaning a better match.
How AI-Resourcing Finds the Best Match
- Higher scores mean better matches → The best candidates get scores closer to 100%.
- Experience matters → If someone has less experience, their score drops, but small gaps don’t hurt much.
- Skills must match → Missing skills lower the score, but small gaps aren’t a dealbreaker.
- Availability is key → If someone has enough time, their score stays high. If not, it goes down.
- Consistency helps → People available throughout the project score better than those with gaps.
- Budget matters → If hiring someone fits the budget, their score stays high. If not, it drops.
- The AI adds everything up → The final score combines experience, skills, availability, and cost.
- Some factors don’t always count → If a job doesn’t require certain factors, that part isn’t used.
- The best matches appear first → The AI sorts candidates from best to worst based on their score.
- You can improve scores → Updating skills, availability, or budget can boost a person’s match next time!
| Factor | How It Affects the Score | How It's Calculated |
| Seniority Score | Measures experience level compared to project needs. | If below required level, score drops using a logarithmic penalty (small gaps = small penalty, large gaps = bigger penalty). |
| Skills Score | Evaluates if the person has the right skills. | Perfect score (100%) if skills match or exceed. If below, -25% per missing level is applied. |
| Availability Score | Ensures the person has enough time for the project. | If hours meet or exceed the need, score = 100%. If below, score is reduced proportionally. |
| Continuous Availability Score | Checks if the person is available throughout the project. | Score is based on how many required days they are available (more days = higher score). |
| Financial Score | Determines if the person’s cost fits the project budget. | If cost meets budget, score is high. If cost is too high, score drops based on how far off they are. |
| Final Score | Combines all factors into a single number. | AI calculates a weighted average of all criteria. |
| Missing Criteria | Some factors may not be considered in certain cases. | If a project doesn’t require a skill or financial check, that factor is ignored in scoring. |
| Sorting Candidates | The best matches appear at the top of the list. | AI ranks candidates from best to worst based on scores. |
| Improving Scores | Updating details can boost future matches. | Adjusting skills, availability, or budget fit can improve scores. |
Color-Logic in AI-Resourcing
AI-Resourcing uses a color-coded system to indicate how well a staffer matches the demand based on Seniority, Skills, Availability, and Profitability. The colors provide a quick visual representation of alignment with project needs.
1. Color Coding for Matching Levels
Each factor (Availability, Profitability, Skills Level, Seniority Level) is assigned a color based on how well it meets the demand:
🟢 Green (90% to 100% match) → The staffer fully meets or exceeds the demand requirement.
🟡 Yellow (60% to 90% match) → The staffer partially meets the demand, but there are some gaps.
🔴 Red (60% or lower match) → The staffer does not meet the demand requirement significantly.
2. Displaying Missing Data
If the staffer is missing Seniority or Skills but the demand requires them, these sections won’t be visible, but you can still find out why by checking the Insights section.
For example, if a staffer doesn’t have their skills filled out, the Insights panel will explain the reason and provide a message like:
“The specific skill of ‘Data Conversion’ at a proficiency level of 1 is required, but there are no skill details provided for the staffer, making it difficult to assess a direct match in this area.”
Scenario: AI Scoring in Action
The table below illustrates how AI scoring is calculated for a staffer matched to a projected demand. The AI considers seniority, skills, availability, and financial viability to determine the final match score.
| Scenario: As a resource planner, I need to find a mid-level Python developer for a high-priority software project requiring at least 160 hours of availability over 20 days while ensuring financial feasibility. |
| Criteria | Project Requirement | Staffer Profile | Score Calculation | Final Score |
| Seniority Level | 800 | 750 (50 below requirement) | The AI applies a logarithmic penalty: the larger the gap, the bigger the penalty, but it’s more forgiving for smaller gaps. The difference is 50 levels, leading to a score of 43%. | 43% |
| Skills Match (Python) | Level 3 | Level 2 (1 below requirement) | The candidate is one level below the requirement, so a penalty of 25% per missing level is applied. | 75% |
| Availability Score | 160 | 200 (exceeds requirement) | Since the candidate has more available hours than required, they receive the maximum score of 100%. | 100% |
| Continuous Availability | 20 days | 15 full days | The AI calculates the ratio of available days (15/20) and compares it to the target, leading to 94%. | 94% |
| Financial Margin | 50% target | 40% actual | The margin is 10% below the target, so the score is adjusted proportionally to 80%. | 80% |
| Final Match Score | Weighted average of all scores | 78% | ||
| Outcome: Despite their lower seniority and skill level, the developers score high in availability and continuous engagement, making them a strong match for the role with a final score of 78%. The AI suggests this candidate as a good fit, with minor considerations for upskilling or senior mentorship. |
Completing Staffer Data to Ensure the Best AI-Resourcing Results
Correctly filling out staffer details ensures AI-Resourcing functions correctly and provides accurate recommendations. Missing or incomplete data can lead to empty sections in AI-Resourcing and impact matching accuracy.
1. Seniority and Skills
To achieve the best AI-driven matches, ensure seniority and skills data are as detailed and up-to-date as possible for each staffer. These details determine how well an employee fits a project in terms of experience and expertise.
- Seniority
| Go to the Staff List Tab ➡ Click on the staffer’s name ➡ Go to the Profile subtab ➡ Remain in the General section ➡ Configure Seniority |
- Skills
| Go to the Staff List Tab ➡ Click on the staffer’s name ➡ Go to the Profile subtab ➡ Go to the Technical Skills and/or Soft Skills section to configure skillset |
Learn more:
- For Manager: Lesson 3 - Creating Staffers’ Profiles in Foresight Connect
- For Staffer: Lesson 1 - Your Staffer Profile
2. Financial Data and Capacity
These details impact how AI-Resourcing evaluates availability and profitability.
| Go to the Staff List Tab ➡ Click on the staffer’s name ➡ Go to the Contracts subtab ➡ Configure Cost Rate and working hours |
Learn more:
Glossary
Assignment Matching – AI-driven process for finding the best available employee for a demand request.
Availability Score – Measures how many hours an employee is available compared to project needs.
Continuous Availability – Percentage of days an employee is fully available during a project (e.g., 5 out of 14 days = 36%).
Demand Form – A request form defining staffing needs, including skills, experience, and availability.
Demand Planning – Forecasting and preparing for resource needs across projects.
Margin Score – Percentage of profit made on an employee’s work based on billing and cost rates.
Profit Score – Measures a company's absolute profit by assigning an employee to a project.
Profitability – Revenue relative to costs.
Seniority Score – Measures how well an employee’s experience level aligns with project requirements.
Skills Score – Evaluates how well an employee’s skills match project needs.
FAQ
Is my data used to train AI models?
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