GenAI , Agentic and More – Beyond Technology and Engineering

A Market Reflection from Industry & Customer Interactions

Business leaders across roles—including CDOs, COOs, CXOs, GCC Heads, Heads of Engineering, and Vertical & P&L Heads—face opportunities for transformation in areas such as People & Organization Architecture, Profitability and Productivity, Product Engineering and Development Lifecycle, Managed Services Agentification, and Deliver amazing impact to Quality of Life.

Decades Gone By:

Over the past two decades, as leaders in the software industry, we have focused on building high-performance teams, embracing a solution-oriented mindset, and prioritising engineering excellence. Our frameworks evolved to support profitable execution, global delivery models, quality engineering, scalable talent development, and innovation initiatives. Service offerings expanded from software engineering to infrastructure management, DEVSECFINOPS, data platforms, and global tech support.

Technological advancements shifted from client-server systems to cloud-native microservices architectures, enhancing user experiences through modern front-end technologies. The rise of data-driven enterprises transformed data ecosystems—from lakes and warehouses to oceans—and moved analytics from reactive reporting to predictive machine learning models.

 All these are known, so what has Changed? 

 Generative AI is unlocking significant opportunities to rethink, recalibrate, and transform various paradigms within the technology industry. Key areas requiring immediate focus and restructuring include People and Organizational architecture, Productivity and Profitability, Product Development Life Cycle, updating Tooling and Metrics, Transformation from Legacy Technologies to Services as Software models, and, importantly, enhancing quality of life for engineers, customers, and consumers.

 People and Organizational Architecture – The emphasis has shifted from prioritising individuals proficient in writing highly compliant code across multiple programming languages to those with strong abilities in Design Thinking, Systems Architecture, and Domain Expertise. While knowledge of syntax and semantics remains useful, the primary focus is now on understanding Domains, Developing Algorithms, articulating solutions through Pseudocode, and subsequently translating these into code.

 There is an urgent need to enhance our engineering talent by promoting design thinking, expertise in distributed systems, user experience awareness, and the capability to develop effective prompts that result in efficient code generation.

Organisational architecture today is defined less by structural Geometry (such as pyramids, hexagons, polygons, or circles) or size, and more by design efficiency and depth of domain knowledge, regardless of individual experience levels. Traditional models of organisational design based solely on team size are increasingly obsolete; the modern approach considers impact relative to scale. The leadership layer must be adept at driving transformative initiatives at the intersection of technology and domain expertise.

 Productivity and Profitability are increasingly focused on areas such as domain expertise, design, algorithms, and pseudocode. Code generation, test case development, automation scripting and execution, review, and deployment are managed by GenAI ecosystems.

Challenges such as inefficient iterative prompting can contribute to unsuccessful pilot projects. However, well-structured prompt templates that incorporate design, security, and quality considerations can increase downstream productivity by 40–50% and result in higher quality code from the outset. An agent-led maker-checker framework provides comprehensive testing for functionality, performance, security, and technical debt. These improvements can reduce project costs and timelines by an estimated 40–50% in many cases. Organizations may reconsider capital expenditure strategies and modernization plans given these efficiencies.

 Product Development Life Cycles – The product engineering lifecycle is experiencing significant acceleration and transformation. From backlog grooming and business case validation to engineering and release, the entire process has become more streamlined, resulting in a 30–40% reduction in time to market. Both ways of working and the tooling ecosystem are being redefined. Digital FTEs now participate actively at every stage of the lifecycle.

Agentic workflows—spanning from Product Owner Agent through Designer & Planner Agent, Coding & Testing Agent, Release Agent, and DevSecOps Agent—have fundamentally altered the Product Development Life Cycle (PDLC). The integration of agentic flow and intelligent ReACT agents has transformed traditional approaches. Agentic Software Engineering Agents (SWE) have expedited transformation initiatives in both greenfield and brownfield programs.

Managed services engagements, responsible for application and infrastructure management—including incident management, service requests, and code fix resolution—are now fully agent-driven. When combined with enterprise-wide observability, agentic implementations deliver substantial productivity and throughput improvements from triaging to resolution, resulting in enhanced customer experience regarding response times and issue closures. These solutions provide continuous, 24/7 availability without downtime.

 Transformation Journeys – Enterprises and ISVs are shifting from software products and SaaS to services delivered as software, requiring a reimagining of processes as end-to-end swim lanes (e.g., Procure to Pay, Lead to Cash) rather than separate systems. This goes beyond legacy tech updates, representing a new approach to enterprise blueprinting. Large Language Models accelerate the shift from process discovery to modernization and rollout, while natural language interfaces boost adoption and user experience.

Quality of Life – In Summary

GenAI has become a tool that can improve quality of life for both engineering and customer communities. However, it also presents challenges that require significant skill development in distributed software systems, design thinking, and domain expertise. Organizations may need to reconsider their architecture to minimise barriers, align with GenAI advancements, and identify talent to balance impact with scale. It is also necessary to reconsider opportunities in modernization, transformation, and support services, as well as to review how profit and loss structures are defined and managed. These considerations affect all areas of an organization, including HR, learning and development, engineering, quality, operations, and talent management.