Software development rarely stands still. In recent years, technologies once confined to science fiction — cloud computing, artificial intelligence, augmented reality — have become production realities. But where does the field go next?
In an article originally sourced from Forbes, members of the Forbes Technology Council — an invitation-only community for senior technology executives — shared their predictions for the next wave of software development trends. What follows is a structured breakdown of those predictions, with context added for a DevOps engineering audience.
1. Market Consolidation
With so many software vendors competing across every category — from project management to developer tooling — consolidation through mergers and acquisitions is a natural next step. Market leaders looking to extend their moats will absorb competitors, particularly in productivity and collaboration software categories. For engineering teams, this means vendor lock-in decisions carry more long-term risk than ever.
— Toni Horn, FJORD & MBJ LONDON
2. AI-Powered Business Processes
Routine business and technical workflows are ripe for AI-driven automation. Whether it is automated triaging of support tickets, anomaly detection in monitoring pipelines, or intelligent code review suggestions, AI is being layered on top of existing processes to improve throughput and reduce human error. The opportunity is large: any repetitive, rule-bound task is a candidate for augmentation.
— Linda Apsley, Capital One
3. Interactive Group Feedback and Human-Centered Design
Agile methods introduced the concept of tight feedback loops with users. The next evolution moves this into richer, multi-user social contexts — think usability testing conducted inside virtual or augmented reality environments, or AI-driven analysis of how groups of users interact with a shared platform. Multi-user interaction data surfaces insights that single-user testing simply cannot capture.
— Marlyne Pierce, Modern Mogul Ventures
4. DevSecOps: Shifting Security Left
Security bolted on at the end of the software development lifecycle is increasingly untenable — both from a cost and a risk perspective. DevSecOps integrates security tooling and practices directly into the CI/CD pipeline, so vulnerabilities are caught at the point of introduction rather than after deployment. Automated threat modeling is one concrete example: tools can analyze architectural design decisions and flag attack vectors before a single line of implementation code is written.
Key practices in a mature DevSecOps workflow include:
- Static application security testing (SAST) integrated into pull request checks
- Software composition analysis (SCA) to flag vulnerable dependencies
- Automated threat modeling during design reviews
- Dynamic application security testing (DAST) in staging environments
— Archie Agarwal, ThreatModeler Software, Inc.
5. Edge Computing and Functions-as-a-Service (FaaS)
Traditional cloud deployments still often isolate workloads to a single geographic region. As latency requirements tighten — driven by IoT, real-time analytics, and globally distributed user bases — the compute model shifts toward the edge. FaaS platforms deployed at CDN edge nodes allow teams to run logic geographically close to users with built-in redundancy, without managing the underlying infrastructure. For DevOps engineers, this means rethinking deployment targets and observability strategies for stateless, distributed function execution.
— Frank Orozco, Slickdeals, LLC
6. Continuous Integration and Continuous Delivery (CI/CD)
CI/CD remains one of the most impactful engineering practices a team can adopt. By automatically building, testing, and validating code changes as they are committed, CI surfaces integration problems early. Paired with continuous delivery, teams can ship smaller, safer increments more frequently — enabling faster feedback from real users and reducing the risk profile of any individual release.
CI/CD is not a single tool but a set of practices that typically includes:
- Automated build triggers on every commit or pull request
- Unit, integration, and end-to-end test suites run in the pipeline
- Artifact versioning and promotion through environments (dev → staging → production)
- Automated rollback or feature flag controls for failed deployments
— Amy Czuchlewski, Bottle Rocket
7. Low-Code and No-Code Platforms
Low-code and no-code tools allow business users to build and iterate on internal applications and workflows without requiring full engineering involvement for every change. For DevOps teams, this has a practical implication: engineering capacity can be redirected toward platform and infrastructure work while domain experts handle front-end automation for their own processes. The risk to manage is governance — ensuring that no-code-built integrations still meet security and compliance requirements.
— Katherine Kostereva, Bpm’online
8. Delta Testing: Beyond Beta
Traditional beta testing — releasing a near-final build to a limited group before general availability — struggles to keep pace with continuous delivery cadences. Delta testing evolves this model by maintaining a continuously active group of real customers who validate specific changes incrementally, in their own environments, before full rollout. This aligns more naturally with feature-flag-based deployment strategies and canary releases already common in DevOps practice.
— Luke Freiler, Centercode
9. Automated Code Quality Evaluation
Code review is a bottleneck at scale. Static analysis tools have long flagged style issues and obvious bugs, but evaluating deeper qualities — readability, appropriate abstractions, maintainability — has historically required an experienced human reviewer. AI-assisted code analysis tools are beginning to address this gap, providing automated feedback on code structure and quality patterns. For DevOps teams this intersects with pipeline quality gates: automated quality scores can block merges that fall below acceptable thresholds.
— Tigran Sloyan, CodeSignal
10. Cross-Language Development and Interoperability
The boundaries between programming languages are softening. Language features migrate across ecosystems — async/await, pattern matching, type inference — and multi-language projects are increasingly common, particularly in mobile development (Swift/Kotlin interoperability), WebAssembly targets, and polyglot microservices architectures. Tools like language servers, shared build systems, and container-based environments make it more practical for developers to contribute across language boundaries without losing productivity.
— Waije Coler, InfoTracer
11. Redefining Success Metrics
Feature velocity and deployment frequency are useful engineering metrics, but they do not tell the full story of whether software is succeeding. Metrics like Net Promoter Score, customer effort score, retention rates, and qualitative user feedback increasingly sit alongside traditional engineering KPIs. Teams that instrument for user experience outcomes — not just system performance — are better positioned to build software that earns long-term adoption.
— Ben Lee, Rootstrap
12. Access to the Global Development Community
Remote collaboration tooling, globally distributed teams, and crowdsourced quality assurance platforms have compressed the software development lifecycle. Access to a worldwide talent pool — combined with bug bounty programs, open-source contribution models, and on-demand QA crowdsourcing — lets smaller teams move at a pace previously available only to large engineering organizations.
— Alyssa Simpson Rochwerger, Figure Eight
13. Motion UI and Dynamic Interfaces
Static interfaces are giving way to motion-driven design. Thoughtfully applied animation — transitions, micro-interactions, loading states — can communicate system state, guide user attention, and make interfaces feel more responsive and intuitive. Motion UI frameworks allow front-end developers to implement these patterns without hand-crafting every animation. The challenge is restraint: motion used gratuitously degrades both performance and usability.
— Thomas Griffin, OptinMonster
Key Takeaways for DevOps Engineers
Stepping back across all thirteen predictions, a few themes emerge that are directly actionable for DevOps practitioners:
- Security is not optional or deferrable. DevSecOps integration into the pipeline is becoming a baseline expectation, not a differentiator.
- The delivery model is shifting. Edge computing and FaaS demand new approaches to deployment, observability, and cost management.
- Automation extends beyond build and deploy. Code quality, security scanning, and user feedback collection are all becoming automated pipeline concerns.
- Measure outcomes, not just throughput. Engineering metrics are maturing to include user-facing signals alongside DORA-style delivery metrics.
- Tooling empowers non-engineers. Low-code platforms change the relationship between platform teams and business stakeholders — governance strategies need to evolve accordingly.
Original predictions sourced from Forbes Technology Council contributors, via Forbes.com (2019).