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People Supply Chain

Make fulfilment AI-native to accelerate time to revenue, increase utilization and grow margins.

Our Impact

2–5 p.p.

increase in profit margin

10–15%

increase in utilization

5–10%

reduction in bench ageing

AI is opening a new path to margin growth across fulfilment. It is redefining how quickly and effectively IT services firms can turn workforce capacity into billable delivery.

In an AI-native people supply chain, employee skills stay current as work happens. Training is built around each person and role. Staffing moves beyond pure skill matching to reflect which suitable person the client is most likely to accept.

This is how firms train better, fulfil demand sooner, optimize staffing costs, increase utilization and grow margins.

How We Can Help

Each outcome may require a different combination of our three AI-native solutions.

Live delivery evidence revealing workforce capabilities.

Improve internal fulfilment

Avoid hiring expensive external talent.

Improve internal fulfilment

Challenge

Periodic skill records are out of date when demand must be filled. Suitable internal employees are missed, while poorly matched profiles are rejected by delivery or the client. Firms then turn to more expensive external talent.

How Aethilus helps

Use the Living Talent Graph to keep employee skills, proficiency, recency and project experience current from live delivery data. Add AI Role Simulation and Predictive Talent Fulfillment where role readiness or selection is limiting internal fulfilment.

Potential impact

+20–25% internal fulfilment
Role-specific simulation preparing an employee for billable work.

Increase utilization

Make more employees billable faster.

Increase utilization

Challenge

Bench resources cannot be deployed against open requisitions. Skills are not visible, micro-training is unavailable and most training takes days and costs billable time.

How Aethilus helps

Use the Living Talent Graph to identify suitable internal employees and AI Role Simulation to deliver personalized micro-training for the specific employee and role. Add Predictive Talent Fulfillment when suitable people are being rejected during selection.

Potential impact

+10–15% utilization5–10% lower bench ageing
Talent evidence aligned to a project and client decision.

Accelerate time to revenue

Reduce staffing delays. Start billing sooner.

Accelerate time to revenue

Challenge

Long, generic training delays role readiness. Candidates are not prepared for selection, pure skill matching misses stated and unstated client requirements, and rejection reasons are not captured. This leads to repeated rejection and slower fulfilment.

How Aethilus helps

Use AI Role Simulation to train, give feedback and assess each employee against the role. Use Predictive Talent Fulfillment to learn from previous selections, rejections and delivery feedback. Living Talent Graph keeps the skills evidence current when visibility is also part of the delay.

Potential impact

30–35% faster time to billability+10–15% on-time fulfilment

Client Results

An IT services provider improves on-time internal fulfilment by 15–25%

Live project data kept skill records current, helping staffing teams identify suitable internal employees while demand was being filled.

+15–25%on-time internal fulfilment
+20–25%delivery acceptance rate

An IT services provider improves on-time internal fulfilment by 15–25%

Challenge

An IT services provider relied on periodic skill records to fill project demand. By the time a role opened, those records no longer reflected the work employees had delivered. Suitable internal employees were missed, while poorly matched profiles were put forward and rejected by delivery or the client.

Solution

Aethilus created a Living Talent Graph using tickets, commits, merge requests, reviews, artifacts and project outcomes. The graph automatically updated each employee’s skills, proficiency, recency and project experience. Staffing teams could then see who had relevant exposure, whose readiness had been validated and who had already demonstrated the skill in delivery.

+15–25%on-time internal fulfilment
+20–25%delivery acceptance rate
5–10%lower bench ageing

A technology services firm cuts time to billability by 30–35%

One-to-one role simulations replaced generic training, preparing each employee for a specific assignment.

30–35%faster time to billability
10–15%lower training costs

A technology services firm cuts time to billability by 30–35%

Challenge

A technology services firm had capable employees on the bench, but its training was long, generic and trainer dependent. It was not built for a specific employee or role, so people took longer to become ready for billable work.

Solution

Aethilus created an AI Role Simulation for each employee and target role. It compared the employee’s delivery evidence with the role requirements, identified missing subskills and assigned the smallest applicable project or lesson. Employees practiced realistic feature work on the relevant technology stack while AI taught, tested, graded and provided feedback. Readiness was reassessed as they progressed.

30–35%faster time to billability
10–15%lower training costs
+20–25%client acceptance rate

An IT services firm increases client acceptance by 30–40%

The firm moved beyond pure skill matching, using account history and selection signals to identify which suitable employees each client was most likely to accept.

+30–40%client acceptance rate
+20–25%internal fulfilment

An IT services firm increases client acceptance by 30–40%

Challenge

An IT services firm staffed projects using skills and job descriptions alone. Candidates could meet the stated role requirements and still be rejected because the process did not account for how each client selected, including hidden requirements and blockers. Repeated rejection slowed fulfilment and kept suitable employees on the bench.

Solution

Aethilus combined account context, prior conversations, submissions, interviews, selections, rejections and delivery feedback to model how the client selected candidates. This created a client persona reflecting both stated requirements and the less-visible factors behind acceptance or rejection. Predictive Talent Fulfillment applied that persona to technically suitable candidates and estimated the likelihood of acceptance for the account. It then assembled a client-specific dossier using role-relevant delivery history, test results and proven performance.

+30–40%client acceptance rate
+20–25%internal fulfilment
+10–15%on-time fulfilment rate