A CASE STUDY ON EVALUATING ROI THROUGH OPTIMIZED TECH STAFFING STRATEGIES
OPTIMIZED TECH STAFFING STRATEGIES
Challenge
As the digital revolution accelerates, companies are constantly pressured to gain a competitive edge through innovative technologies. Yet maintaining a talented tech workforce remains a significant cost. This case study examines how one global firm optimized its staffing model to preserve capabilities while lowering costs.
Analyzing Tech Talent Archetypes
As a first step, leadership conducted a thorough analysis to understand how these employees were structured. They categorized talent into four archetypes based on value proposition:
- Core Play
- Value Play
- External Play
- Flex Play
Evaluating Sourcing Locations
The leadership examined how talent was sourced across four main categories based on location and employment type:
- High-cost internal staff in major financial centers.
- Low-cost internal staff located in developing countries.
- High-cost external consultants.
- Low-cost external providers with offshore delivery centers.
Reframing Functional Domains
Each core functional domain within the IT organization was then reviewed:
- IT Governance: Strategy, planning, portfolio management
- Information Security: Risk management, compliance
- Business Relationship Management: Partnering with lines of business
- Application Development & Maintenance
- Infrastructure Services: Data centers, end-user support
- IT Operations: Service level management, reporting
Conclusion
The Optimization Strategy
Leveraging findings from the initial analyses, senior leadership collaborated with division managers to craft a multi-faceted optimization approach. The overarching goal was to reduce IT function costs by at least 15% while modernizing supporting infrastructure and tooling to advance the digital customer experience.
Transition non-core, non-differentiating flex play resources support internal teams
Renegotiate contracts with external vendors to demand discounts
Gradually shift external staff and consultants to optimized delivery models