The Internal Platform That Powers Your AI Strategy

There is a growing sense of urgency around artificial intelligence in software development. Leaders want results, developers are experimenting with AI-assisted tools everywhere they can find them, and organizations worldwide are racing to prove their AI initiatives deliver measurable value. But according to the most comprehensive research available, success is rarely about which tool you adopt — it is about the foundation that surrounds it.

The 2025 DORA Report, drawing on survey data from nearly 5,000 technology professionals across the globe and over 100 hours of qualitative analysis, delivered a finding that should reframe how enterprises think about AI investment: platform engineering adoption has reached 90% among surveyed organizations, and there is a direct correlation between internal platform quality and an organizations ability to unlock the value of AI-assisted development.

This is not a small signal. In enterprise software circles, when one practice becomes nearly universal while simultaneously correlating with organizational success, it warrants serious attention from IT leaders who must decide where limited resources should flow.


The DORA Reports Core Messaging for Enterprise Teams

DORAs 2024 report had already sounded an alarm: more AI adoption correlated with reduced delivery throughput and increased instability. The concern was clear — when developers accelerate output without corresponding quality controls, problems compound.

The 2025 follow-up reveals progress on one front but reinforces the need for structural investment. Todays findings show a positive relationship between AI adoption and both software delivery throughput and product performance. Teams are learning where, when, and how AI is most useful.

But critically, AI adoption continues to show a negative relationship with software delivery stability.

What does this duality mean?

Teams working in loosely coupled architectures with fast feedback loops see genuine gains from AI integration. Those constrained by tightly coupled systems and slow release processes — still common in enterprise environments — see little benefit, or the benefits are consumed by increased defects and rollbacks.

It is the same dynamic we have observed in ERP implementations for years. Technology tools — whether an ERP system, a developer platform, or an AI assistant — do not create value on their own. Value emerges from how well the surrounding operational architecture supports them.

Seven Capabilities That Magnify AI Impact

DORAs new AI Capabilities Model identifies seven foundational practices that reliably amplify positive outcomes. For enterprise teams evaluating their readiness, understanding these dimensions reveals where investments will pay dividends and where they are likely to see disappointing results.

• Clarified AI policy — Teams with explicit guidelines on when, how, and what AI can be used for consistently outperform those experimenting without guardrails. This covers code generation, documentation assistance, testing automation, and customer-facing applications.
• AI aligned to internal context — The most effective deployments connect AI tool outputs directly to organizational standards, coding conventions, security policies, and domain-specific workflows rather than treating them as generic aids.
• Prioritized foundational practices — Source control discipline, code review culture, automated testing maturity — these basic engineering practices matter more now that change velocity has increased dramatically. AI accelerates output; foundational practices ensure the output is good output.
• Fortified safety nets — Automated testing pipelines, monitoring for production stability, rapid feedback loops between development and operations. These are not new recommendations, but they are essential prerequisites for safely scaling any AI-assisted workflow.
• Investment in the internal platform — Self-service developer portals, standardized tooling, streamlined deployment processes, internal documentation. DORAs data shows this as one of the strongest individual predictors across all measured outcomes.
• End-user focus — AI becomes most useful when applied to clearly defined problems with identifiable users. Teams that approach AI as a solution to a specific challenge perform better than those exploring it as a general capability enhancement.
• Talent investment and skill development — Gartners parallel research reinforces this: by 2028, 90% of enterprise software engineers will use AI code assistants. The role is shifting from implementation to orchestration. Organizations that invest in upskilling now will be positioned well beyond those waiting for tools to solve talent gaps.

The Gartner Perspective Supports the Same Conclusion

Gartners 2025 Technology Adoption Roadmap for Software Engineering, based on responses from more than 138 software engineering leaders, reaches a closely aligned conclusion. Two findings are particularly relevant.

First, GenAI platform engineering is advancing rapidly: Gartner predicts that by 2027, 70% of organizations with platform teams will include GenAI capabilities in their internal developer platforms. The integration is not optional — it is becoming the expected standard for how development teams access AI capabilities securely and consistently.

Second, technical incompatibility is cited as the primary deployment risk by nearly half of surveyed leaders. This matters for organizations evaluating new software investments of any kind, whether a developer platform, an ERP system, or a custom-built solution. Technology decisions must account not just for individual capability but for how everything works together.

What This Means for Canadian Businesses Planning Their AI Strategy

For organizations in Alberta and across Canada that are evaluating software modernization, the practical takeaway is straightforward. Before investing heavily in AI tools or external AI services, assess your internal platform maturity on these criteria:

1. Are developers able to find, access, and use standard tooling without assistance? If they rely on tribal knowledge or repeated manual setup, building an internal developer portal is the highest-return first step.
2. Is automated testing reliable enough to handle increased change volume? AI-assisted tools generate more code, faster. Test coverage that was good enough becomes inadequate quickly as throughput increases.
3. Do you have a clear policy governing AI use across the organization? Generic guidelines are insufficient. Teams need specific boundaries — approved models, acceptable use cases, review processes for AI-generated outputs, data handling requirements for sensitive information.
4. Are core systems up to date and well-documented? ERP systems with outdated configurations, legacy customizations, or unclear change histories create hidden friction that undermines any new initiative built on top of them.
5. Is there dedicated bandwidth for skill-building alongside implementation? The best tooling cannot compensate for a team that lacks the context to apply it effectively. Budget time and resources for structured training programs.

A Realistic Implementation Path

The organizations achieving measurable returns from AI-assisted development typically followed a sequenced approach rather than jumping toward capabilities-first deployment. While every environment is different, common patterns include:

• Starting with a defined scope — one team, one process, or one technology stack — rather than enterprise-wide rollout
• Establishing baselines for quality metrics before introducing AI tools so that results can be measured meaningfully
• Building quality gates into the platform itself (automated linting, policy enforcement, review workflows) alongside tool deployment
• Treating the internal platform as a product with its own roadmap, user feedback loops, and documented standards, rather than an infrastructure concern owned solely by IT operations
• Pairing any new technology investment with corresponding changes to team roles, expectations, and career development paths

The Bottom Line

The signal from the largest datasets available on AI-assisted software development is converging: the organizations that realize genuine value are those that have invested in their underlying operational architecture. The technology — AI or otherwise — functions as a force multiplier for whatever already exists.

For teams evaluating ERP modernization, custom software development, broader IT infrastructure investment, or specific AI tool adoption, the recommendation is consistent. Build the internal platform first. Measure your readiness honestly against the capabilities that research identifies as predictive of success. Then introduce new technology with confidence rather than hoping it will fill gaps that no tool can address on its own.

The companies winning at AI-assisted development in 2026 are not those with the biggest budgets or the most tools. They are the ones that understood this insight early and structured their engineering operations to support sustained improvement rather than temporary productivity spikes.