Top Salesforce Customization Issues to Keep an Eye on in 2026

In 2026, adding features and automating repetitive processes are no longer the only aspects of Salesforce customisation. Businesses are pushing Salesforce well beyond conventional limits with AI-driven decision-making, Agentforce-based orchestration, and sophisticated workflow automation becoming commonplace. These developments increase speed and intelligence, but they also bring with them new dangers. These days, some of the most important issues facing Salesforce teams include execution errors, limit violations, and visibility gaps.

Organizations wishing to scale automation safely and sustainably must comprehend the main Salesforce customization problems in 2026.

1. Automation Growth Without Execution Control

One of the most common Salesforce customization challenges emerges when automation scales faster than governance. Enterprises often deploy multiple flows, triggers, and AI-driven actions simultaneously, without load management. This leads to overlapping executions, incomplete transactions, and frequent Salesforce Agentforce limit breaches.

What to watch:

Unstructured scaling increases concurrency and causes system slowdowns or shutdowns.

Best practice:

Segment automation into controlled phases, introduce checkpoints, and throttle high-volume executions to maintain system stability.

2. Workflow Sequencing Conflicts

As organizations layer automation, workflow collisions become inevitable. Multiple flows may act on the same records using different rules, creating conflicting updates and circular executions. These conflicts are difficult to detect without clear orchestration visibility.

What to watch:

Repeated triggers, unresolved record states, and unexplained automation failures.

Best practice:

Design a layered flow architecture with defined execution order and documented dependencies to prevent collisions.

3. AI Adoption Without Validation

AI-powered routing, approvals, and predictions are now central to Salesforce customization. However, many teams activate AI-driven decisions before validating input data or models. This results in inaccurate routing, failed approvals, and higher reprocessing loads.

What to watch:

AI decisions running on incomplete or inconsistent data.

Best practice:

Trigger AI-based actions only after data validation and introduce confirmation cycles before executing downstream workflows.

4. Limited Visibility Into Execution Failures

A major challenge in 2026 is invisible failure. Background flows and agent-based processes often fail silently, leaving users and admins unaware of the root cause. This increases support tickets and delays resolution.

What to watch:

Errors discovered late, long troubleshooting cycles, and unclear failure origins.

Best practice:

Implement monitoring dashboards, execution logs, and traceable workflows to gain real-time visibility into automation behavior.

5. Governance Gaps in AI-Driven Processes

AI introduces speed—but without governance, it also introduces risk. Many organizations lack accountability frameworks for AI decisions, making it difficult to explain outcomes or comply with regulatory expectations.

What to watch:

Untraceable AI decisions and inconsistent rule enforcement.

Best practice:

Document AI logic, track scoring criteria, and apply governance checkpoints to maintain transparency and control.

6. Deployment Fragility Across Environments

Workflows that perform perfectly in sandbox often fail in production due to data volume, integration differences, or execution timing. Deployment fragility remains a top Salesforce customization challenge.

What to watch:

Post-deployment failures and emergency rollbacks.

Best practice:

Run identical simulations across environments, deploy in stages, and monitor discrepancies before full production rollout.

7. Over-Automation and Workflow Density

Excessive automation on the same objects creates high workflow density. When too many policies execute simultaneously, processing windows shrink and system limits are reached quickly.

What to watch:

Performance degradation and frequent limit-based errors.

Best practice:

Consolidate redundant workflows, reduce parallel paths, and segment execution groups to balance load.

Concluding Remarks

AI-driven complexity, inadequate orchestration planning, and execution intensity are the main Salesforce customization issues in 2026. Automation needs to become more disciplined as it grows in intelligence. Strong governance, validated data inputs, controlled scale, and structured sequencing are now necessary rather than optional.

Organizations that invest in execution visibility and limit-safe design will scale Salesforce automation with confidence. With the right Salesforce development strategy, enterprises can reduce risk, improve reliability, and unlock the full potential of intelligent, agent-driven workflows in 2026 and beyond.


Source: https://www.anavcloudsoftwares.com/blog/top-salesforce-customization-challenges-to-watch/