AI is quickly becoming pervasive in software development and is changing the way developers build software. However, many enterprises have not invested in the key building blocks to sufficiently leverage this new technology. Software engineering leaders who fail to focus on the foundations of the AI-native era risk dooming their companies to irrelevance while faster, AI-enabled rivals seize innovation, revenue and market dominance.
According to a recent Gartner survey, software engineering leaders who equip their teams with the right AI technologies can achieve productivity improvements of more than 25%. They must establish a new foundation that enables their teams to effectively co-create software with AI.
To achieve this, software engineering leaders must invest in five foundational practices to set up their teams for AI-native engineering success.
Practice 1: Platform Engineering
Software engineering leaders should establish platform engineering teams to provision AI tools for software engineering, and provide the platforms necessary to enhance applications and software with AI capabilities
To achieve this, they should first build platforms that support AI software development tools in paved roads. Paved roads enable use of a set of common AI tools throughout the full software development life cycle (SDLC). This makes it easier for developers by not only removing the complexity of using the AI capability but also embedding guardrails to improve quality, costs, reliability and security.
Additionally, they should support the buildout of Model operationalization (ModelOps) and Agent Engineering and Operations (AgentOps). A key component of the platform is to facilitate the complete life cycle of ML models, offering deployment, management and operations of large language models (LLMs). These need to be curated and maintained according to enterprise security requirements as well as provided with various prompt injections to tailor results to the enterprise context.
Finally, software engineering leaders should build platforms that enable AI capabilities. Adding AI capabilities to existing and new enterprise software is necessary to remain viable. Leaders should also deliver internal developer platforms that securely and seamlessly assist developers to integrate AI capabilities like chatbots and AI agents into their software. Providing templates, Application Programming Interfaces (API)s, guidance and training will provide rapid innovation and risk-controlled rollout of AI capabilities.
Practice 2: Integration and Composability
As developers begin composing software instead of coding line by line, they will need API-enabled composable components and services to stitch together. Software engineering leaders should begin by defining a goal to achieve a composable architecture that is based on modern multiexperience composable applications, APIs and loosely coupled API-first services.
They should also set in place an integration strategy and tooling that implements well-defined API interfaces and creates rich metadata for APIs. Strong integration allows for easy composition when components follow commonly agreed patterns. Gartner predicts that APIs will become integral to the functionality of AI agents, providing these agents with the necessary interfaces to consume, analyze and act on data.
Practice 3: AI-Ready Data
The future of building software is dependent on AI-ready data. Data is everywhere, and it’s very messy.
Software engineering leaders should support AI-ready data by organizing enterprise data assets for AI use. Generative AI is most useful when the LLM is paired with context-specific data. Platform engineering and internal developer portals provide the vehicles by which this data can be packaged, found and integrated by developers.
The urgent demand for AI-ready data to support AI requires evolutionary changes to data management and upgrades to architecture, platforms, skills and processes. Critically, Model Context Protocol (MCP) needs to be considered. This emerging standard is designed to facilitate seamless integration between AI models, particularly LLMs, and external data sources, APIs and tools.
Software engineering leaders must also build out both data mesh and data fabric. They should work with data management leaders to combine these two approaches in a modern data architecture. Fabric serves as the foundational data management design pattern, and mesh for optimal data delivery using a federated model.
Practice 4: Rapid Software Development Practices
With the accelerating advancements in AI technology, software engineering leaders need to adopt newer, adaptive and iterative software development practices like agile, DevSecOps and the product-centric model. To realize productivity gains from AI, leaders must focus teams to optimize the whole SDLC with AI components.
To enable rapid development, software engineering leaders should revitalize agile and product-centric practices to respond to fast code generation and provide reliable, fast pathways to production. They should also expedite the shift to a product-centric operating model to strengthen product ownership and customer focus in engineering teams.
Additionally, software engineering leaders should challenge their teams to measure and improve idea lead time, which is the time from ideation to production working code, and thus to customer impressions and feedback.
Practice 5: Culture of Innovation
Software engineers can become risk-averse unless they are given the freedom, psychological safety and environment for risk taking and experimentation. Leaders must establish a culture of innovation where their teams are eager to experiment with AI technologies. This also applies in software product ownership, where experiments and innovation lead to greater optimization of the value delivered to customers.
To foster a cultural mindset that supports innovation, software engineering leaders should create a vision that inspires change, and ownership of the changes required by AI.
They should also foster an environment of psychological safety, where challenges are viewed as opportunities to learn, and team members can express ideas, voice concerns, ask questions and admit mistakes without the fear of negative consequences.
To incentivize behavior change, software engineering leaders should establish exploration teams to drive rapid innovation in key business areas using lean startup methodology and AI tools. They should also provide teams with dedicated innovation time and reward behavior that drives innovation. Software engineers will only spend time exploring innovation if it is emphasized by leadership as a core objective.
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from SD Times https://sdtimes.com/ai/software-engineering-foundations-for-the-ai-native-era/
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