AI · Workforce planning · Executive analytics
Applying AI to workforce strategy and commercial planning
Executive summary
I built a workforce planning model connecting individual qualifications with labor categories, delivery roles, organizational structure, and phased resource needs. I used AI to organize resume evidence and generate role-alignment recommendations for evaluation by the pursuit team. An executive dashboard brought staffing coverage, gaps, and resource-allocation tradeoffs into a consistent view, giving leaders a clearer basis for shaping the delivery approach and assessing its commercial implications.
- EvidenceIndividual qualifications from resumes
- OrganizeAI sorts evidence and recommends role alignment
- ReviewPeople evaluate every recommendation
- DecideDashboard: coverage, gaps, staffing mix, and price together
The workforce model links roles, labor categories, structure, and delivery phases
The challenge
A pursuit for a technology delivery engagement called for a proposed team of 250 people. To shape a credible approach, leaders needed evidence at a granular level, connecting:
- individual qualifications,
- labor categories,
- delivery roles, and
- the proposed organizational structure.
They also needed to see how those pieces moved together. Seniority, technical coverage, and price pulled against each other, and resource needs changed from one delivery phase to the next. The evidence existed, but it sat in individual resumes and couldn’t be compared at the level leaders needed.
My role and scope
I designed and built the planning approach: the workforce model, the AI-assisted process for organizing resume evidence, and the executive dashboard. This was planning for a proposed team during pursuit shaping.
Decisions and operating mechanisms
A workforce planning model as the single frame. The model tied each delivery role to its labor category, qualification requirements, and place in the proposed structure, across delivery phases. That gave leaders one consistent frame instead of several partial views.
AI to organize evidence, with people making the calls. I applied AI to sort resume evidence and recommend how candidates might align to roles. Every recommendation went to human evaluation. The AI made the evidence faster to review, and people decided who fit which role. No hiring decisions were automated.
An executive dashboard built around tradeoffs. The dashboard showed coverage, staffing gaps, where hiring or reassignment would be needed, and how different staffing mixes affected the shape of the team. It answered leaders’ actual questions, not just how many people were in each box.
The key tradeoff was speed against judgment. AI could compress the time spent reading and sorting evidence. Keeping role alignment as a recommendation for human review protected the quality of the decisions and the accountability for them.
Outcomes
- Leaders could assess staffing feasibility and pricing implications together during pursuit shaping, instead of answering those questions in separate passes.
- Coverage, gaps, and hiring or reassignment needs were visible in one place for executive review.
This case describes the planning approach and how leaders used it. It makes no claim about the pursuit’s result.
Leadership takeaway
AI was most useful here as an organizer of evidence and input to decision-making. It put the right facts in front of people faster, so leaders could spend their time on the tradeoffs only they could make.