Why Moving Platforms Won't Make You AI Ready

While companies now ship AI features in weeks, a recent survey found that 42% of enterprises abandoned most AI initiatives in 2025—up from 17% in 2024. Organizations often point to a lack of AI readiness as the core issue, though many aren't even sure what that entails in practice.

The conventional response to this AI execution gap has been predictable: modernize the platform first, then add AI capabilities. CIOs are told that their legacy infrastructure is the bottleneck, that fragmented toolchains are preventing AI adoption, and that consolidating onto "AI-ready" platforms will unlock their organization's potential.

But new research reveals a counterintuitive truth. Platform migrations, often pursued as the fastest path to AI readiness, actually make organizations more AI-fragile.

Based on surveys of over 300 enterprise IT and technology leaders conducted by independent research agency TrendCandy, our inaugural DevOps Migration Index report shows a clear pattern. Organizations pursuing integration-first modernization consistently outperform those attempting wholesale platform replacements. The advantages show up across every metric that matters for AI success: delivery efficiency, security governance, and innovation velocity.

The conventional wisdom around platform-first modernization isn't entirely wrong. It addresses real enterprise pain points that genuinely complicate AI adoption.

Legacy infrastructure does limit AI experimentation:

• Decades-old systems weren't designed for the computational demands or data flows required by modern AI.

• Fragmented toolchains create data silos that prevent the cross-system insights AI needs to be effective.

• Compliance requirements seem more manageable when centralized through unified platforms.

Major cloud providers and DevOps platform vendors capitalize on these concerns, marketing their solutions as addressing each of these limitations while offering the fastest route to enterprise AI success.

The appeal is understandable. Platform consolidation offers the promise of simplicity: one vendor relationship, one security model, one set of APIs for AI integrations.

For executives under pressure to demonstrate AI progress, replatforming feels like decisive action—a clear before-and-after moment that boards can understand.

Despite the logical appeal of platform-first modernization, research reveals that replatforming often disrupts the foundational elements AI systems need most.

AI systems require deep situational awareness across code, tests, compliance frameworks, and team dynamics. Platform migrations often disrupt this accumulated organizational context that AI systems depend on to be effective.

Data from the Migration Index report demonstrates an immediate impact of platform migration on innovation capacity:

• 61% of organizations delayed new initiatives for over six months post-migration.

• 70% experienced increased developer burnout during forced platform transitions.

Teams focused on migration survival have no bandwidth for AI experimentation.

When you migrate platforms, you lose years of accumulated insights about how your systems actually work—which code patterns cause problems, how your teams collaborate most effectively, and where your security vulnerabilities typically emerge.

Migration forces you to rebuild this contextual understanding from scratch.

The AI landscape evolves too rapidly for monolithic platform strategies to keep pace. What looks like an “AI-ready” platform today may lack tomorrow’s breakthrough capabilities. Organizations often adopt a platform, believing it will consolidate their data and eliminate tool silos, only to discover functional gaps that force them to add new point solutions to compensate. As a result, 74% of leaders report more tool sprawl post-migration.

Research also found that 85% of enterprise leaders still see platform lock-in as a top concern after migration. This concern becomes critical in the AI era, where best-of-breed AI tools often can't integrate with proprietary platform ecosystems.

Platform lock-in becomes a competitive liability. Teams lose the autonomy to adopt AI incrementally with their preferred tools, and organizations become dependent on their platform vendor's AI roadmap rather than market innovation.

At a time when AI tools are processing increasingly sensitive enterprise data, migrations are systematically weakening the security controls organizations need most.

The research reveals how migrations compromise security precisely when it's most critical:

• 75% found security integrations harder to maintain post-migration.

• 40% discovered new security blind spots in their replatformed environments.

• 70% admit business leaders push AI tools into pipelines without security review.

The compliance risks become existential when AI systems have access to sensitive enterprise data through weakened security controls.

The Migration Index data reveals a compelling alternative to the platform-first approach: 92% of organizations achieved greater delivery efficiency by integrating tools rather than replacing them.

This integration approach helps organizations avoid the innovation paralysis that plagues platform migrations. Rather than forcing teams into months of migration survival mode, integration preserves the momentum that AI experimentation demands.

Integration also sidesteps the security disruption that weakens governance frameworks precisely when AI adoption requires the strongest controls. Instead of struggling to maintain security integrations in unfamiliar platforms, organizations can build AI capabilities on top of their existing, mature security foundations.

Platform migrations are expensive gambles that often don't pay off. Among the companies surveyed, migrations exceeded their original budgets by an average of 18%, resulting in cost overruns averaging $315K per organization.

Even more telling, only 25% of platform migrations delivered expected value within a year, while 38% delivered less ROI than promised to executive sponsors and boards.

Integration-first modernization enables organizations to focus on innovation initiatives by immediately allocating resources to AI tools, training, and experimentation.

AI readiness is a capability you cultivate by strengthening the systems, processes, and context your teams already rely on. For some organizations, replatforming may still be the right long-term move. But for many, a quieter, less disruptive path deserves serious consideration.

Taking the time to explore integration-first options gives organizations a clearer picture of what they truly need for sustainable AI adoption—whether that's better governance, stronger observability, or simply the space for teams to experiment and focus on innovation.

If you're evaluating a platform migration in the name of AI readiness, it’s worth understanding the trade-offs and hidden costs before committing. Our 2025 DevOps Migration Index goes deeper into the data behind these decisions.

Download the full report to gain a clearer, data-driven perspective on where migration efforts succeed, where they stall, and how integration compares as a modernization strategy in the AI era.