By 2026, writing code is no longer the hard part.
AI has made software output fast and abundant, but it has also exposed a new constraint: confidence in what gets shipped.

This creates a quiet but serious tension for CTOs and software project managers. Delivery looks faster on paper, yet risk accumulates beneath the surface—across quality, security, compliance, and operations. Some teams will discover this only after incidents force the issue. Others will redesign how custom software is built before that happens.

Let's look at where those pressure points emerge—and what custom software development trends and changes in 2026 will separate resilient teams from fragile ones.

3 main forces driving change in custom software development in 2026

Software development stops being primarily about building features and becomes equally about building decision systems around those features: how code is produced (more AI), how it is verified (more rigor), and how it is governed (more formally). The teams that win won’t be the ones that generate the most code; they’ll be the ones that can reliably ship in a world where code generation is cheap and trust is expensive.

Three forces are converging here:

1. AI assistance is now mainstream in day-to-day development. Stack Overflow’s Developer Survey 2025 (AI section) reports 84% of respondents are using or plan to use AI tools in their development process, and 51% of professional developers use them daily.
   
2. The EU regulatory calendar is no longer “future tense” for AI. The European Commission’s AI Act and Digital Strategy timeline states the Act entered into force 1 Aug 2024, GPAI obligations applied 2 Aug 2025, and full applicability is 2 Aug 2026 (with longer transition for certain high-risk rules until 2 Aug 2027).
   
3. Delivery performance does not automatically improve with AI. Google’s DORA report highlights AI’s impact and discusses both benefits and risks; Google Cloud’s summary notes measured negative associations with throughput and stability as AI adoption rises.

Together, these forces set the conditions for a structural shift in how custom software is designed, delivered, and governed.

The following trends describe how this shift materializes in practice for CTOs and software project managers in 2026.

1) “AI in the IDE” becomes table stakes; “AI in the pipeline” becomes the differentiator

By now, most organizations have some mix of assistants in editors and chat tools. In 2026, the separation appears between teams that generate and teams that verify. The durable advantage comes from putting AI into the engineering system (requirements → design → code → test → release → ops), with explicit checkpoints.

What changes in practice:

• Specs become first-class artifacts again, because AI is only as useful as the constraints you provide. PRDs and ADRs stop being documentation and become “control surfaces” for automated work.
• Teams standardize “definition of done for AI-assisted changes”: provenance, tests, security review, and measurable acceptance criteria—before merge, not after incident.

A grounded signal: enterprise adoption and frequent use are already visible. For example, GitHub’s Accenture study reports high adoption and frequent usage patterns for Copilot among participants.

2) The new role: engineering “verification loops” (and funding them)

AI raises output volume; that pressure shifts cost into:

• code review bandwidth,
• test coverage and quality gates,
• security review,
• incident response when something slips.

DORA’s research emphasis is a warning here: you can add AI and still risk reduced delivery stability if you don’t adapt the system around it.

Google Cloud’s own summary states that increasing AI adoption was accompanied by an estimated 1.5% decrease in throughput and 7.2% decrease in stability (in their analysis).

The 2026 budgeting will bend towards verification:

• More spend moves from “feature teams” into platform engineering, QA automation, security engineering, and observability.
  
• CTOs start measuring success less by “lines shipped” and more by escape rate, rollback rate, mean time to restore, and the percent of AI-assisted changes that pass gates without rework.
  

3) Platform engineering becomes the default operating model for mid-size and larger orgs

Once AI makes it easy to produce many changes, internal friction becomes the bottleneck: environments, permissions, deployments, and fragmented tooling. That’s why internal developer platforms (IDPs) keep gaining mindshare.

CNCF describes platform engineering as building self-service development platforms so provisioning, deployment, rollback, and the broader flow can happen through developer self-service.

The 2024 DORA report explicitly calls out platform engineering as a major theme alongside AI.

What you can expect to be common in 2026:

• A thin “golden path” for most services (templates, opinionated CI/CD, standard observability, default security controls).
• Stronger product thinking applied internally: platform teams run roadmaps, usability testing, and SLAs.

4) LLM and agent security becomes standard AppSec, not a specialty

In 2026, it becomes normal for custom apps to include LLM features (support, analytics, content generation, workflow triage). That means common AppSec programs must absorb new failure modes: prompt injection, insecure output handling, data leakage through retrieval, and supply-chain issues inside model and tool integrations.

OWASP’s Top 10 for LLM Applications provides a shared vocabulary for these risks (prompt injection, insecure output handling, training data poisoning, etc.).

Practical expectation:

• Threat modeling explicitly includes LLM interaction points, tool permissions, retrieval sources, and logging/redaction rules.
• “What did the model see, and what did it do?” becomes an audit requirement internally even before regulators demand it.

5) AI governance becomes an engineering requirement, not just a legal one

The EU Digital Strategy and AI Act timeline already matters operationally: prohibited practices and AI literacy obligations applied 2 Feb 2025, GPAI obligations 2 Aug 2025, and broader applicability 2 Aug 2026.

Meanwhile, reported policy discussions suggest the EU has considered delaying some high-risk AI provisions, which adds uncertainty to planning and procurement cycles. Reuters

In 2026, serious teams will treat governance like reliability:

• model and dataset registries,
• documented intended use and limitations,
• evaluation reports and change logs,
• clear accountability for approvals.

Two widely used anchors here are:

• NIST AI RMF as a practical structure for mapping AI risks across the lifecycle.
• ISO/IEC 42001 as a management-system standard for an AI management system (AIMS).

6) Supply-chain security moves from “best practice” to procurement reality

As more code is generated and more dependencies enter through tools, plug-ins, models, and build pipelines, provenance becomes harder to ignore.

SLSA frames this directly as a checklist of controls to prevent tampering and improve integrity across the supply chain.

In the EU, product requirements also tighten: the Commission notes the Cyber Resilience Act entered into force 10 Dec 2024; reporting obligations apply 11 Sep 2026, with main obligations later, as per Digital Strategy.

For custom builds, in practice this means:

• SBOM + build provenance become standard deliverables in B2B deals.
• “We can reproduce this artifact” becomes a contractual expectation, not a nice-to-have.

7) Data portability and vendor lock-in become architecture topics again

Many AI-enabled systems rely on data access patterns that easily drift into lock-in (specific clouds, proprietary vector stores, closed telemetry, or platform-bound identity).

So in 2026, expect more custom projects to include:

• explicit exit plans (data export formats, migration runbooks),
• modular retrieval layers (swap vector DB / search backends),
• contracts that address data access, access logs, and switching costs up front.

How to plan 2026 software development roadmaps

As a practical frame for CTO-level prioritisation, consider using three parallel tracks:

1. Capability track: where AI genuinely improves throughput (scaffolding, refactors, test generation, internal search, support automation).
   
2. Control track: quality gates, evals, red-teaming, prompt/tool permissioning, provenance, security reviews. (This is where many orgs are underinvested.)
   
3. Platform track: self-service paths, paved roads, standard observability, default security controls, and measurable developer experience.

The competitive edge comes from balancing all three—because in 2026 the market will penalize teams that can build quickly but can’t prove correctness, safety, or compliance when something goes wrong.

How Blocshop supports AI-ready software delivery

Blocshop helps engineering teams adapt custom software delivery to AI-driven realities, with a focus on verification, platform engineering, and AI governance.

Schedule a free consultation with Blocshop to review your 2026 delivery risks and define a practical rollout plan.

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