The enterprise software landscape is undergoing a profound transformation. AI-augmented software acceleration isn’t just about building software faster; it’s about reimagining how value is deployed across the organization. Enterprises that strategically integrate AI into their development frameworks are seeing unprecedented gains in speed, agility and cost efficiency—all while unlocking new opportunities for customization, governance and security.
To fully capitalize on AI’s potential, however, enterprises must adopt a structured approach to software development—one that recognizes the different tiers of software, the role of AI across them, and the operational shifts required to support this transformation.
A three-tiered approach: Aligning AI with software longevity
AI-augmented software development isn’t a one-size-fits-all endeavor. Instead, enterprises should think in terms of a three-tiered software system, where each tier serves a distinct purpose.
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QuickGenAI-generated, fast-deploy and throw-away software QuickGen is about speed and agility. This tier represents software that is predominantly AI-generated, requiring minimal human input. It’s ideal for rapid prototyping, internal tooling and short-lived applications where speed outweighs longevity. In this space, “vibe coding” emerges—where developers interact with AI agents to generate functional code almost instantaneously, reducing effort and cost. |
SmartAssistAI-augmented software with human oversight SmartAssist blends AI-augmented acceleration with human curation, balancing efficiency and quality. This tier is optimal for business-critical applications that require moderate governance, reliability and support. AI assists in code generation, testing and optimization, while human developers refine and validate the software to meet compliance and operational standards. |
PrimeCraftHigh-reliability, long-term software Representing the highest level of enterprise software, PrimeCraft is where AI and human expertise collaborate to build mission-critical, keep-forever applications. These systems demand rigorous governance, deep security integration and extensive human craftsmanship. AI enhances efficiency through automated testing, predictive maintenance and intelligent debugging, but the final product remains meticulously engineered to meet enterprise-grade expectations. |
AI as the new standard for software acceleration
AI is no longer a tool that speeds up coding; it’s becoming the primary driver of modern software development. In this paradigm, AI is the foundation upon which human expertise refines and governs outputs. This shift calls for new operational strategies, such as the following:
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• DevOps reinvented: Traditional DevOps pipelines are evolving to accommodate AI-assisted coding, automated testing and continuous AI-driven code reviews. |
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• AI-empowered governance: With generative AI writing a significant portion of code, enterprises must rethink compliance and quality control to ensure responsible AI development. CGI’s Human-Agent Partnership Management Framework addresses the increasingly complicated aspects of AI-empowered governance. |
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• Security at the core: AI-powered security frameworks must be embedded into software development, leveraging multi-agent AI ecosystems to detect real-time vulnerabilities. We have discovered multiple security issues when letting agent-modes take over the IDE keyboard. |
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• Legal at the core: As AI-generated software becomes integral to enterprise operations, legal and compliance considerations must be embedded from the outset. Organizations need to evaluate the impact of AI on copyrights, ensuring that generated code aligns with intellectual property laws and does not inadvertently introduce liabilities. AI agents must be instructed to avoid specific open-source dependencies that could conflict with enterprise licensing requirements. Additionally, AI-driven development must align with contractual obligations, ensuring generated outputs adhere to regulatory standards, corporate policies and industry best practices. This proactive approach mitigates legal risks while fostering responsible AI adoption. |
The business case: AI-augmented software acceleration in action
Let's use the example of a client engaging CGI to develop a customer engagement chatbot. Through a structured approach, incorporating organizational change management best practices, AI-powered solutions and responsible acceleration layers, the engagement would focus on helping the client meet its aggressive modernization deadlines without compromising governance or quality.
Through our tiered acceleration approach, the client would:
QuickGenUse QuickGen, an AI agent that generates a functional chatbot within minutes, enabling teams to test real-time interactions and refine their engagement strategy. Our CGI PulseAI solution and data analytic accelerators generate responsible chat-based solutions in minutes. However, strengthening capabilities for production-grade deployments requires additional operational and technical layering. |
SmartAssistMove into SmartAssist to enhance the chatbot with custom workflows and compliance-based logic, ensuring business and regulatory requirements are met. Our CGI Accel360 solution based on robotic process automation solutions such as UIPath enables us to accelerate the deployment of more automated workflows. However, balancing execution speed with quality, robustness and security requires time and capability investments for both humans and AI. |
PrimeCraftReach the PrimeCraft level, where the chatbot becomes an enterprise-grade digital assistant, integrated with back-end systems, continuously learning from interactions, and governed by strict security protocols. We are achieving this level with solutions such as CGI Advantage, which has a substantial human workforce empowered by an ever-increasing AI capability in the form of software acceleration, DevOps acceleration, quality control and AI integrated functionality. |
This tiered approach enables our clients to reduce development cycles, significantly improving their speed-to-market while maintaining security, compliance and operational integrity. Enterprises that integrate AI acceleration effectively can realize similar gains, ensuring that modernization efforts meet business agility and risk management goals. AI acceleration also optimizes investment, enabling clients to spend lightly on disposable software while prioritizing durability where it matters.
Contracting for AI-led software development
AI-augmented software acceleration demands a shift in how enterprises structure their contractual agreements. The traditional approach to software procurement and vendor management must evolve to account for the role of AI in generating, testing and maintaining software. The following should be kept in mind:
- Tiered contracting models: Contracts should align with the three-tiered software framework described above. QuickGen solutions may require flexible, outcome-based contracts with minimal or no SLAs, while SmartAssist agreements should specify a balance between AI automation and human oversight. PrimeCraft-level solutions necessitate robust, long-term contracts that define security, compliance and performance guarantees with outcome-based terms.
- AI accountability in contracts: Contracts must define AI accountability, ensuring enterprises maintain control over AI-generated outputs. This includes specifying liability in cases where AI-generated code introduces security vulnerabilities, compliance risks or unintended consequences.
- Continuous AI governance clauses: Given that AI-generated software is continuously evolving, contracts should include clauses for ongoing AI governance, auditing and compliance reviews to ensure the software remains aligned with regulatory and business needs.
Risk mitigation: Managing AI-human collaboration effectively
As enterprises scale AI-augmented software acceleration, it’s important to integrate structured risk management frameworks to ensure responsible AI adoption. CGI has developed two critical frameworks to manage AI and human collaboration effectively:
- Human-Agent Partnership Management Framework: This framework ensures seamless collaboration between AI and human developers, defining clear roles, oversight mechanisms and intervention points for governance. By embedding structured handoff points, enterprises can mitigate risks associated with AI-generated code while maintaining efficiency.
- Responsible Use of AI Risk Management Framework: AI must be deployed responsibly, with risk mitigation strategies addressing unintended consequences. This includes AI model transparency, explainability, bias detection and ongoing risk assessments to prevent compliance and ethical pitfalls.
By implementing these frameworks, enterprises can confidently deploy AI-powered software without compromising governance, security or ethical responsibility.
Future-proofing enterprise applications with AI
Enterprises adopting AI-augmented software acceleration are not just optimizing today’s processes; they are future-proofing their entire application ecosystem. AI’s role in software engineering will only grow, with enterprises shifting from AI-assisted development to AI-first development models. The challenge is ensuring governance, strategic integration and a thoughtful approach to how AI, humans and automation co-create enterprise value.
By embracing a structured, tiered model and recognizing AI as the backbone of modern software acceleration, enterprises can build software faster and deploy value in previously unimaginable ways.
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About this author
Frederic Miskawi
Vice-President - AI Innovation Expert Services Lead, Global AI Enablement Center of Expertise
Fred Miskawi leads the AI Innovation Expert Services in CGI’s AI Enablement Center of Excellence. He is responsible for connecting CGI’s coalition of applied AI experts in CGI Innovation Centers and emerging technology practices.
