The job of application architects is getting harder as the integration of generative artificial intelligence, adoption of platform engineering, and heightened security demands are providing change at a breakneck pace. As organizations strive to meet increasing automation needs and address cybersecurity threats, understanding these trends and planning effectively becomes crucial. Technology leaders must adjust for these three pillars or risk becoming obsolete within the enterprise.

Gen AI: moving beyond the hype

The potential of gen AI is enormous. AI-augmented integration, for example, can streamline the creation of workflows through chat-based integration, optimize workflows automatically, and automate both data transformation and testing, minimizing manual tasks. Organizations are leveraging gen AI to optimize operations, streamline repetitive processes, improve agility and responsiveness to demand, and deliver better customer experiences.

Other use cases to consider include AI-enhanced automation that consolidates traditional methods with intelligent document processing and gen AI, automating complex tasks that involve unstructured data or require human intervention, such as defect detection and medical billing.

Autonomous AI agents further disrupt the landscape by utilizing large language models to operate independently, enhancing efficiency and scalability, and simplifying integration between disparate systems with minimal human input, particularly for tasks that defy predefined rules.

However, organizations embarking on the gen AI journey face challenges that prevent them from getting gen AI projects off the ground or from achieving the expected cost savings and benefits. These challenges include heterogeneous vendor solutions, siloed organizational functions and flawed and inefficient processes.

Partly because of these challenges, gen AI has transitioned from a phase of excitement to one of disillusionment as organizations demand tangible results from their investments. Despite the initial hype, the result of implementing gen AI tools in integration and automation workflows and the software development life cycle has brought mixed results. Though vendors boast productivity gains of up to 55%, real-world outcomes often fall short, ranging from 0% to 20% improvements.

Selecting the right automation use cases for AI requires careful consideration of efficiency, suitability, and business outcomes. Use of gen AI should be aimed at solving existing business problems or delivering needed capabilities, rather than finding a way to incorporate gen AI for buzzword compliance. Gen AI excels in areas like natural language processing and pattern recognition, making it suitable for tasks such as chatbots and document assessment.

Platform engineering: optimizing value delivery

Platform engineering is a key discipline for optimizing developer experience and accelerating the delivery of customer value. Unlike traditional approaches, platform engineering focuses on building self-service internal developer platforms that enhance productivity and streamline workflows.

Adoption of platform engineering is a growing trend, and it is predicted that by 2026, 80% of large software engineering organizations will establish platform engineering teams as internal providers of reusable services, components and tools for application delivery.

Application technical professionals should consider the following when it comes to platform engineering adoption:

• Transitioning to product-centric DevOps delivery and adopting Team Topologies are essential steps in enabling platform engineering.
• Organizations must look beyond portals to identify opportunities for maximizing productivity, addressing bottlenecks, and integrating modern self-service platform features.
• Supporting dedicated cross-functional platform teams ensures scalability and fosters collaboration between software delivery and infrastructure organizations.

Architecting for security: meeting cybersecurity challenges

Security by design is becoming a cornerstone of risk management strategies. Integrating security requirements from the outset and adopting secure coding practices are crucial for mitigating potential risks. Industry regulations like GDPR and PCI DSS demand stringent security measures, while the rise of APIs expands the attack surface of the enterprise.

Proactive integration of security requirements ensures that vulnerabilities are addressed early. The increasing emphasis on architecting for security not only aligns with regulatory requirements, but also fortifies the software development process, leading to more secure and resilient applications.

To develop secure software products, application architects must implement a secure-by-design approach, collaborating with security teams to define standards and integrate threat modeling into application design.

Application architects must embrace risk-based prioritization, bridging the gap between software engineering and security teams. Validating secure coding practices with application security testing tools ensures adherence to coding standards and enhances productivity.

APIs are crucial to modern software development, but the increasing number of APIs, coupled with a lack of organizational awareness, has created an expansive attack surface ripe for exploitation by malicious actors. API management is just one piece of the puzzle to achieve security by design. To counter this, organizations must adopt specialized API security tools for enhanced visibility and threat protection, and architects must apply a secure-by-design approach to APIs.

As application technical professionals navigate the rapidly evolving technological landscape, embracing generative AI, platform engineering, and cybersecurity becomes crucial. By managing expectations, selecting suitable use cases, optimizing developer experience, and embedding security into design, organizations can harness the potential of these technologies to drive innovation and deliver measurable business results.

Kevin Matheny is a VP analyst and KI Leader in Gartner’s Software Engineering practice for Technical Professionals. He covers software architecture patterns and practices, focusing on application architecture documentation and decision-making, microservices architecture, API design and API management. He wrote this article for SiliconANGLE. He and other Gartner analysts will provide additional insights into application innovation and software engineering strategies at the Gartner Application Innovation & Business Solutions Summits taking place June 3-5 in Las Vegas, June 18-19 in Tokyo, and Sept. 8-9 in London. Follow news and updates from the conferences on X using #GartnerAPPS.

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