Enterprise leaders are not short on ambition when it comes to AI across the SDLC. They are short on results.
Despite massive investment and enthusiastic adoption, most AI initiatives never deliver meaningful business value. According to recent research by Boston Consulting Group (BCG), only a small fraction of companies globally are capturing significant AI value, while the majority see minimal impact despite large technology spend.
This pattern is most acute in technology functions, where organizations deploy AI tools widely but fail to link them to standardized workflows, clear metrics, and human supervision.
The problem is not AI itself.
The problem is how AI is introduced into software delivery.
At Gorilla Logic, we have seen a clear distinction between teams that use AI and teams that are AI-enabled. The difference comes down to whether AI is treated as a collection of actors or as an integrated, governed delivery capability embedded across the SDLC.
Why most AI initiatives fail before they reach production
BCG’s research highlights a persistent “AI impact gap”: while many companies invest heavily in AI, only a minority (around 5%) are meaningfully generating value from it. Another roughly 35% are beginning to scale and see value, but most remain stuck at smaller efficiency improvements or pilots.
This mirrors what we see in the market:
- AI is introduced without a clear business problem
- Disconnected tools with no shared metrics
- Lack of governance, trust, and human oversight
- AI confined to local tasks rather than integrated workflows
In software delivery specifically, organizations often stop at code-generation copilots. The result is isolated gains but no scalable impact.
True AI impact only appears when AI is woven into how software is planned, built, tested, reviewed, and released.
AI Across the SDLC: The shift from AI assistance to AI orchestration
Most engineering teams start with AI assistance. Developers use copilots to generate code. Product teams experiment with AI for documentation or backlog grooming. QA teams explore test generation.
This stage can deliver local productivity gains, but it rarely compounds. BCG’s research shows that leading adopters focus investment and effort on reshaping core processes and embedding AI into end-to-end value chains rather than small-scale pilots.
The real value emerges when AI moves from assistance to orchestration across delivery workflows:
- AI supports the full SDLC, not just development
- Workflows connect planning, engineering, QA, and delivery
- Humans remain firmly in the loop for judgment and oversight
- Metrics are tied to productivity, quality, and adoption, not novelty
This is where AI stops being a tool and becomes a delivery engine.
What AI in production actually looks like
AI in production does not mean removing humans from the process. It means redefining their role.
In mature AI-enabled teams:
- Engineers focus on design, oversight, and refinement rather than repetitive coding
- QA teams validate quality at scale rather than manually authoring every test
- Product teams move from backlog maintenance to outcome-driven planning
- Delivery leaders gain earlier insight into risk, velocity, and readiness
Critically, trust is earned through measurable results and strong human-in-the-loop controls, not blind automation. The BCG insight on the value gap confirms that organizations that embed AI into core functions and measure real business outcomes outperform others still experimenting with fragmented implementations.
A real-world example of AI-enabled delivery
We recently partnered with a medical affairs and compliance software client as they continue building their next-generation SaaS platform for medical affairs.
Rather than introducing AI as a side experiment, we embedded AI across their delivery model through a dedicated AI-enabled engineering Pod. AI was applied consistently across planning, development, QA, and delivery workflows.
The results were not theoretical.
- Development cycles accelerated by 40 to 50 percent
- Sprint velocity increased while defect counts dropped
- Backlog preparation time was reduced from days to hours
- Engineers trusted and accepted AI-generated code at extremely high rates
- QA coverage improved while manual effort declined significantly
These outcomes were achieved while maintaining strong governance and human oversight throughout the SDLC.
What enterprises should do next
For organizations serious about moving AI into production, the path forward is clear:
- Assess AI maturity honestly
Understand where AI is used today and where it creates friction rather than value. - Standardize tools and workflows
Fewer tools, better integrated, with shared guardrails and metrics. - Embed AI across the SDLC
Planning, development, QA, release, and monitoring must evolve together. - Keep humans firmly in the loop
Trust, oversight, and accountability are non-negotiable. - Measure what matters
Productivity, quality, delivery speed, and adoption, not tool usage.
These steps align with patterns seen in both leading adopters identified by BCG and successful engagements we have delivered.
AI success is not about experimentation anymore
The era of AI pilots is ending. The organizations pulling ahead are those that treat AI as a core delivery capability, not an add-on.
AI in production is achievable today.
But only when it is embedded into how software is delivered, not layered on top of it.
Click here to learn more about how our AI-enabled Pods integrate automation, metrics, and continuous learning across the entire SDLC.