Public Sector Modernization: GenAI changing the game in Digital Transformation

Introduction

Public sector organizations face unprecedented pressure to modernize their legacy IT infrastructure. Traditional drivers for modernization have become more urgent than ever: technology obsolescence threatens operational stability, the "silver tsunami" of retiring skilled mainframe professionals creates knowledge gaps, and high support costs strain already tight budgets. A brighter light has been shed on these challenges by the recent surge in Generative AI technologies and the DOGE agenda pushing for greater cost efficiency.

Today, approximately 80% of agency resources are consumed by "keeping the lights on" rather than innovating new business capabilities. This imbalance severely hampers an organization's ability to meet its core mission and serve constituents cost-effectively. As mainframe systems become increasingly difficult and costly to maintain, the urgency for digital transformation has never been greater.

Modernization Approaches: The 4Rs

Four primary approaches define the landscape of mainframe exit strategies:

1. Rehost: Lift and shift of legacy code onto emulation software, preserving existing functionality with minimal changes.
2. Refactor: Automated code conversion that maintains functional equivalence while transforming to modern languages and architectures.
3. Rewrite: Complete redevelopment of applications using modern frameworks and development practices.
4. Replace: Implementation of Commercial-Off-The-Shelf (COTS) or Low-Code/No-Code solutions to replace legacy functionality.

While each approach has merit depending on specific circumstances, automated code refactoring has emerged as the most pragmatic strategy for many public sector organizations. It strikes an optimal balance between risk and reward, offering a realistic path to modernization without the extreme costs and risks associated with complete rewrites or replacements. However, the recent emergence of GenAI is changing the game as it tips this balance between risk and reward.

The Transformative Impact of GenAI

While Generative AI has become a catalyst for modernization efforts, its impact varies across different modernization approaches:

1. Rehost: Low impact – GenAI offers limited benefits for straightforward emulation approaches.
2. Refactor: Medium impact – GenAI can enhance pattern recognition in legacy code and assist in generating equivalent modern code structures.
3. Rewrite: High impact – GenAI has shown the greatest influence here, dramatically accelerating code generation and offering "Gen AI Enabled Rewrite" as a new capability for accelerated greenfield development.
4. Replace: Medium impact – GenAI can assist in requirements analysis and configuration of COTS solutions.

Despite its significant potential, it's crucial to recognize that GenAI is not a complete solution for modernization challenges. It serves as a powerful accelerator but must be integrated within a comprehensive modernization strategy that includes human expertise, rigorous quality control, and proper governance.

Modernization Factory powered by Agentic AI

To truly transform legacy infrastructure, public sector organizations should establish modernization as a strategic capability rather than treating it as a one-time project. This approach involves creating a "Modernization Factory" – a tightly integrated combination of people, processes, and GenAI equipped tools that serve as a platform to modernize and maintain legacy applications consistently and predictably over time.

The key components of this approach include:

1. Modernization Blueprint: An initial assessment that shapes subsequent work, progressing from proof-of-concept to sustainable modernization competency.
2. Application Portfolio Management (APM): Screening the application portfolio to identify modernization scope and potential.
3. Business Case Development: Creating a high-level case for change and a strategic modernization plan.
4. Discovery and Planning: Documenting the current state and developing an implementation roadmap.
5. Proof-of-Concept: Executing a pilot to demonstrate feasibility and value.
6. Modernize and Migrate: Building upon the POC to transform legacy applications at scale.
7. Maintain and Operate: Ongoing optimization and evolution of modernized applications.
8. Agentic AI Enablement: Embedding Agentic AI selectively across all key components to accelerate and enhance outcomes.

As the Modernization Factory is established and integrated as a Business-As-Usual (BAU) capability, it helps address one of the most significant challenges for public sector organizations: securing capital funding for large-scale modernization initiatives. The Modernization Factory allows for a progressive approach to modernization where operational expenditure (OPEX) savings can be leveraged to fund modernization over time as a business-as-usual function.

This approach involves three key elements as the Modernization Factory is built up:

1. Offload: Implement optimized support models to achieve cost savings of 30-40% in OPEX.
2. Re-invest: Redirect a portion of these savings to fund transformation initiatives.
3. Sustain: Create a self-sustaining funding model through net new savings from modernization initiatives.

Progressive modernization via the Modernization Factory activates different cost-saving levers:

• Infrastructure optimization (20% savings potential)
• Labor arbitrage (10% savings potential)
• Staff productivity improvements (5% savings potential)
• Agency financial performance enhancements (5% savings potential)
• Technology standardization (5% savings potential)

These combined savings can deliver 30-50% OPEX reduction that can be channeled into a Modernization & Digital Transformation Fund to support ongoing projects.

Best Practices for Implementation

Successful modernization requires a "best-of-breeds" approach that leverages the core competencies of various modernization players: global system integrators, hyperscalers (like AWS, Google Cloud, and Microsoft Azure), and modernization tool ISVs. This collaborative ecosystem ensures access to the full spectrum of capabilities needed for complex transformation initiatives.

When implementing modernization strategies, public sector organizations should:

1. Start with the "Maintain & Operate" phase to establish modernization as an ongoing, sustainable Business-As-Usual capability.
2. Develop comprehensive functional and technical documentation during the modernization process.
3. Consider the state of the art in architecture and design, particularly for high-volatility systems with evolving business needs.
4. Assess organizational impact and prepare change management strategies accordingly.
5. Evaluate the appropriate level of GenAI integration based on the chosen modernization approach.

Conclusion and Call to Action

The modernization of legacy applications and infrastructure is not merely a technical necessity but a strategic imperative for public sector organizations. Although automated code refactoring, enhanced by GenAI capabilities, offers a pragmatic path forward that balances risk, cost, and speed of transformation, the business case for other modernization approaches has dramatically improved with the help of GenAI automation.

Public sector organizations should tap the proven capabilities of GenAI to:

1. Conduct an initial application portfolio assessment to identify modernization candidates.
2. Develop a business case that quantifies both the costs of inaction and the benefits of modernization.
3. Implement a proof-of-concept to validate the technical approach and demonstrate value.
4. Establish a modernization factory iteratively over time as a sustainable capability within the organization.
5. Adopt a progressive modernization approach that leverages OPEX savings to fund ongoing transformation.

By taking these steps, public sector organizations can transform their legacy infrastructure into modern, agile platforms strategically and progressively to better achieve their core mission while significantly reducing operational costs and technical debt.