Organization structure layer of an ADM operating model

The organization structure layer encompasses people, process, and technology. This layer is where the most visible and profound changes occur when organizations introduce generative AI in the ADM operating model. Roles evolve, organizations reimagine processes, and technology stacks expand to include generative AI tools.

This section provides insights into the practical implementation of generative AI in your organization's ADM transformation, covering changes in organizational structure, individual roles, and core processes. By embracing these strategic shifts, you can position your organization to integrate generative AI in the ADM operating model effectively. This transformation can improve development speed, software quality, and innovation capacity, potentially enhancing your competitive edge. The actual impact will vary based on your organization's specific context and implementation.

Platform management services, technology and tools, and partnerships

Platform management services provide a core set of shared capabilities and standardized services for application teams, including:

• Codified reference architectures and design patterns

• Self-service mechanisms for deploying approved architectures and configurations

• Standardized development, observability, and operational tools

• Support for setting up environments, continuous integration and continuous deployment (CI/CD) pipelines, and management processes

• Centralized governance and security standards

Typically, platform engineering and cloud operations teams manage these services, collaborating to support application teams and drive continuous improvement.

Generative AI is transforming platform management services in the following ways:

• An AI assistant for architecture recommendations suggests optimal reference architectures based on project requirements, recommended design patterns and organizational standards.

• Intelligent self-service provisioning uses AI to automate and optimize the deployment of resources and services addressing complex workflows.

• AI-powered observability provides deeper insights and automates anomaly detection across the platform.

• AIOps agents handle multiple automated remediation workflows using approved standard operating procedures (SOPs).

• Automated compliance checking continuously verifies and enforces governance and security standards using AI.

These AI-powered enhancements allow infrastructure teams to focus on resolving complex time-consuming issues and improving an application's reliability, enhancing the efficiency and effectiveness of platform management.

Integrate generative AI capabilities into your managed services partners' existing platform offerings. With this strategy, you can achieve the following benefits:

• Harness advanced AI technologies and make use of your partners' expertise and proven processes.

• Enhance your platform engineering and cloud operations with integrated AI capabilities.

• Maintain the benefits of your established managed services partner relationships while advancing your AI capabilities.

Organization structure and roles

Generative AI integration necessitates a reimagining of ADM organizational structure. Adapting the responsibilities of key roles within your organization structure is crucial. These AI-driven changes can help your teams to work more efficiently and deliver higher value.

The organization structure depends on several factors:

• Engagement size – Examples include the scope and complexity of applications such as trading systems, drug discovery, and enterprise resource planning (ERP).

• Specific customer needs – Examples include Payment Card Industry Data Security Standard (PCI DSS) compliance for payment systems and Good Practice (GxP) compliance for pharmaceutical industries.

• Methodology used – Examples include agile and waterfall methodologies.

Some roles combine or expand based on project requirements. Projects involving advanced technologies or strict compliance needs often include specialized roles such as data scientists, machine learning (ML) specialists, Advanced Business Application Programming (ABAP) developers, and compliance officers.

The following sections spotlight common roles in ADM that are evolving with generative AI integration. These roles are expanding and adapting to use AI capabilities, which can enhance their value and impact within the organization. This evolution represents opportunities for skill development and career growth across many roles. The following aspects provide insights into how each role evolves as it integrates with generative AI:

• Current focus – The primary tasks that the person in the role performs currently

• AI-driven shift – The ways in which generative AI can be incorporated into the role

• Key benefits – The benefits gained by incorporating generative AI into the role

• Key considerations – The considerations when considering an AI-driven shift for the role

• Key steps – The primary steps that the person in the role can take to help them adapt to AI

This comprehensive view can help you understand the current state, direction of change, and steps needed to navigate the AI-driven transformation for each role successfully. You can gain insights into how AI is enhancing existing roles, and how to prepare your organization structure for these advancements.

Product owner or business analyst

The following table provides an overview of how the product owner or business analyst roles can adapt to use generative AI capabilities.

Aspect of the role	Description
Current focus	Requirements gathering Feature prioritization Stakeholder communication
AI-driven shift	Make use of AI for: Data-driven decision-making process and accelerated market insights Business requirements document (BRD) creation, prioritizing the features based on customer feedback and demands
Key benefits	Faster requirements gathering and analysis Improved feature alignment with market needs More comprehensive user stories and use cases
Key considerations	Ensuring that AI comprehends complex business contexts Maintaining meaningful stakeholder relationships
Key steps	Implement AI-powered market analysis and requirements tools. Develop prompt engineering skills for effective AI interaction. Establish stakeholder processes to validate AI-generated insights

Project manager

The following table provides an overview of how the project manager role can adapt to use generative AI capabilities.

Aspect of the role	Description
Current focus	Activity planning Resource allocation Risk management
AI-driven shift	Adopt AI for enhanced predictive planning and real-time project intelligence.
Key benefits	Improved resource allocation accuracy Enhanced risk identification and mitigation Real-time project health monitoring and predictive analytics
Key considerations	Balancing AI recommendations with human judgment Ensuring team adoption of AI-driven methodologies
Key steps	Integrate AI-driven project planning and risk assessment tools. Develop protocols for AI-human collaborative decision making. Upskill team in AI-augmented project management practices.

UI/UX designer

The following table provides an overview of how the user interface/user experience (UI/UX) designer role can adapt to use generative AI capabilities.

Aspect of the role	Description
Current focus	Creating user interface designs and prototypes Conducting user research and usability testing Ensuring optimal user experience across applications
AI-driven shift	Use AI for rapid design iteration, data-driven user insights, and automated usability testing.
Key benefits	Faster generation of UI design alternatives Enhanced user research analysis and persona creation Automated usability testing and feedback analysis
Key considerations	Balancing AI-generated designs with brand guidelines and user needs Maintaining creativity and innovation in an AI-assisted design process
Key steps	Integrate AI-driven project planning and risk assessment tools. Develop protocols and process for AI-human collaborative decision making. Upskill team in AI-augmented project management practices.

Full-stack developer

The following table provides an overview of how the full-stack developer role can adapt to use generative AI capabilities.

Aspect of the role	Description
Current focus	Creating user interface designs and prototypes Conducting user research and usability testing Ensuring optimal user experience across applications
AI-driven shift	Embrace AI for comprehensive full-stack development assistance and optimization.
Key benefits	Accelerated full-stack code generation and optimization AI-driven API design and integration Automated performance tuning across the stack
Key considerations	Maintaining proficiency across multiple technologies alongside AI tools Ensuring consistency and integration between AI and manually developed components
Key steps	Develop expertise in AI-assisted development across the full stack. Establish process and guidelines for integrating AI-generated and manual code. Implement continuous learning programs for emerging AI tools in full-stack development.

Solutions architect

The following table provides an overview of how the solutions architect role can adapt to use generative AI capabilities.

Aspect of the role	Description
Current focus	Designing comprehensive enterprise-wide solutions Aligning technology solutions with business goals Ensuring integration and interoperability across systems Creating detailed design documents
AI-driven shift	Use AI for rapid solution prototyping, data-driven architecture decisions, automated integration analysis, and design document generation.
Key benefits	Faster generation and evaluation of solution alternatives Enhanced alignment of technology solutions with business objectives Improved assessment of system integration and interoperability Accelerated creation of comprehensive design documentation
Key considerations	Ensuring that AI-generated solutions address complex business requirements Maintaining a holistic view of enterprise architecture in AI-augmented design processes Validating the accuracy and completeness of AI-generated design documents
Key steps	Develop expertise in AI-powered solution design tools and methodologies. Establish processes for validating AI-generated solution proposals against business needs. Implement AI-driven tools for continuous solution optimization and integration assessment. Adopt AI-assisted documentation tools for creating and maintaining design documents.

Software developer

The following table provides an overview of how the software developer role can adapt to use generative AI capabilities.

Aspect of the role	Description
Current focus	Code writing Debugging Maintenance
AI-driven shift	Embrace AI as a coding companion for enhanced productivity and quality.
Key benefits	Accelerated code generation and completion Improved code quality and consistency Faster bug detection and resolution
Key considerations	Maintaining code readability and performance in AI-generated code Balancing AI tool reliance with core programming skills
Key steps	Improve the use of AI-assisted coding and pair programming techniques. Establish guidelines for reviewing and optimizing AI-generated code. Implement continuous learning programs for emerging AI dev tools.

Test engineer

The following table provides an overview of how the test engineer role can adapt to use generative AI capabilities.

Aspect of the role	Description
Current focus	Test case design Defect identification Quality assurance
AI-driven shift	Implement AI for comprehensive, automated testing strategies.
Key benefits	Increased automation in test case generation and execution Improved test data quality and coverage Earlier issue detection through predictive defect analysis
Key considerations	Ensuring thorough coverage beyond AI-generated test cases Balancing automated testing with exploratory methods
Key steps	Develop skills in AI test strategy design and data modeling. Establish processes for continuous refinement of AI testing models. Implement AI-augmented exploratory testing processes and techniques.

Release manager

The following table provides an overview of how the release manager role can adapt to use generative AI capabilities.

Aspect of the role	Description
Current focus	Planning and coordinating software releases Managing release schedules and dependencies Ensuring smooth deployment and post-release stability
AI-driven shift	Adopt AI for intelligent release planning, automated deployment, and predictive stability management.
Key benefits	AI-driven release planning and risk assessment Automated deployment and rollback strategies Predictive post-release monitoring and issue detection
Key considerations	Balancing AI recommendations with business priorities and constraints Maintaining control and oversight in automated deployment scenarios
Key steps	Develop skills in AI-powered release management tools and predictive analytics. Establish processes for human validation of AI-generated release plans. Implement AI-driven post-release monitoring and rapid response standard operating procedures (SOP).

Technical lead

The following table provides an overview of how the technical lead role can adapt to use generative AI capabilities.

Aspect of the role	Description
Current focus	Overseeing application development and operational processes Ensuring alignment between development teams and operational requirements Managing the application lifecycle from development to production Driving continuous improvement in development and operational efficiency
AI-driven shift	Make use of AI for enhanced application lifecycle management, automated operational analytics, and predictive resource optimization.
Key benefits	Improved coordination between development and operations teams Enhanced application performance monitoring and predictive maintenance Automated resource allocation and scaling based on operational analytics Frequent number of changes Accelerated issue resolution and reduced downtime
Key considerations	Balancing AI-driven automation with human oversight in critical operations Ensuring seamless integration of AI tools across the application lifecycle Managing the cultural shift towards AI-augmented DevOps practices
Key steps	Develop expertise in AI-powered application lifecycle management tools. Establish processes for integrating AI insights into development and operational decision making. Implement AI-driven monitoring and predictive maintenance systems. Create training programs to upskill teams in AI-augmented DevOps practices.

DevOps engineer

The following table provides an overview of how the DevOps engineer role can adapt to use generative AI capabilities.

Aspect of the role	Description
Current focus	Implementing and maintaining continuous integration and continuous deployment (CI/CD) pipelines Automating infrastructure provisioning and management Ensuring seamless integration between development and operations
AI-driven shift	Use AI for enhanced automation, predictive analytics, and intelligent infrastructure management.
Key benefits	Accelerated deployment cycles Improved system reliability and performance Proactive issue detection and resolution
Key considerations	Integrating AI tools with existing DevOps processes Balancing automation with necessary human oversight
Key steps	Implement AI-powered CI/CD pipeline optimization. Adopt AI-assisted infrastructure as code (IaC) generation tools. Develop skills in AIOps for predictive maintenance and automated remediation.

Support engineer

The following table provides an overview of how the support engineer role can adapt to use generative AI capabilities.

Aspect of the role	Description
Current focus	Resolving user issues and incidents Maintaining the system's reliability Providing technical assistance to end -users
AI-driven shift	Adopt AI for intelligent issue triage, automated problem resolution, and predictive support.
Key benefits	Faster issue resolution times Improved first-call resolution rates Proactive identification of potential system issues
Key considerations	Ensuring that AI systems accurately understand and categorize complex technical issues Maintaining the human touch in customer interactions
Key steps	Implement AI-powered knowledge bases for faster issue resolution. Adopt AI-driven ticket classification and routing systems. Develop process and skills in working alongside AI chatbots and virtual assistants for customer support.