ClearRoute finds AI has accelerated only the 20% of software delivery focussed on writing code, while the remaining 80% remains slow and constrained
7th July 2026, London, UK: AI may be accelerating software creation, but many enterprises are still unable to turn faster coding into faster business change, according to new research from ClearRoute, the AI-native platform engineering consultancy.
ClearRoute’s State of the Route to Live 2026 report, launched today, finds that AI is acting as a ‘Great Amplifier’ across enterprise software delivery: magnifying high-performing organisations while intensifying the dysfunctions of those constrained by manual processes, legacy systems and fragmented delivery models.
This is the ‘80% Problem’, where AI has accelerated the 20% of the software lifecycle focused on writing code but the remaining 80% - testing, security, compliance, governance and release orchestration - remains slow and under-optimised.
As a result, code is being written faster, but time to market remains unchanged.
ClearRoute’s analysis, based on four years of Route-to-Live assessments across financial services, retail, healthcare, media and technology organisations, reveals that many large organisations still measure lead time in months, and median lead time to production remains stubbornly high at 30–45 days. By contrast, elite teams deploy multiple times per day and achieve lead times as low as 24 hours.
Organisations with high levels of manual testing and release management also see change failure rates of 10–20%, compared with below 5% for elite teams.
The findings suggest the biggest barrier to AI-driven delivery is organisational friction: manual approvals, fragmented environments, slow governance processes and delivery models designed for stability rather than continuous change. And in an AI-enabled market where speed is the new competitive advantage, winners will be those that reduce the gap between decision and delivery, not those that simply generate more code.
The commercial impact is significant. At one global financial institution, a £2.76M annual engineering investment yielded only 5% of its value in delivered features. At another, it took new engineers between three and six months to make their first contribution. At one large firm, the average lead time for a business-critical feature was 266 days, while fixing a critical production bug took 158 days.
The report warns that, for organisations struggling with foundational weaknesses, AI adoption risks creating “more broken output, faster” unless they address the operational systems that determine whether software can actually reach customers.
James Jarvis, CEO of ClearRoute, says: “AI has changed the speed of software creation, but not the speed of enterprise delivery. For many organisations the bottleneck was never writing code, but the manual approvals, brittle test suites, fragmented environments, governance processes and release constraints surrounding them.
“The organisations that win from AI will not be the ones with the most coding assistants. Speed is the new competitive advantage, which means the next competitive frontier is operationalising AI safely across the full Route to Live and moving beyond fragmented tools to platform engineering as a core operational capability. This will close the gap between decision and delivery, getting change live faster, safer and at scale.”
The report also highlights a shift in enterprise AI maturity. While code completion and AI-assisted generation are becoming table stakes, more advanced organisations are moving toward environment-connected AI, agentic code reviews, self-correcting CI/CD and autonomous testing.
However, the move from AI experimentation to AI orchestration depends on platform maturity. Without a central control plane and platform-level capabilities for identity, access and discovery, enterprises risk simply replacing tool sprawl with ‘agent sprawl’ rather than solving the foundational challenge.
Sarndeep Nijjar, CTO, ClearRoute, adds: “The next phase of AI in software delivery will be defined by control. Enterprises are moving from individual copilots to AI agents that can interact with pipelines, environments and production systems. That creates huge opportunity, but only if those agents operate inside clear technical boundaries. Without that foundation, enterprises will not scale AI safely; they will simply create another layer of complexity.
“This is where platform engineering becomes critical. It gives organisations the governed pathways, automated guardrails and reusable delivery foundations needed to move from AI experimentation to AI operations.”
State of the Route to Live 2026: The Great AI Amplifier is available now.
ClearRoute finds AI has accelerated only the 20% of software delivery focussed on writing code, while the remaining 80% remains slow and constrained
7th July 2026, London, UK: AI may be accelerating software creation, but many enterprises are still unable to turn faster coding into faster business change, according to new research from ClearRoute, the AI-native platform engineering consultancy.
ClearRoute’s State of the Route to Live 2026 report, launched today, finds that AI is acting as a ‘Great Amplifier’ across enterprise software delivery: magnifying high-performing organisations while intensifying the dysfunctions of those constrained by manual processes, legacy systems and fragmented delivery models.
This is the ‘80% Problem’, where AI has accelerated the 20% of the software lifecycle focused on writing code but the remaining 80% - testing, security, compliance, governance and release orchestration - remains slow and under-optimised.
As a result, code is being written faster, but time to market remains unchanged.
ClearRoute’s analysis, based on four years of Route-to-Live assessments across financial services, retail, healthcare, media and technology organisations, reveals that many large organisations still measure lead time in months, and median lead time to production remains stubbornly high at 30–45 days. By contrast, elite teams deploy multiple times per day and achieve lead times as low as 24 hours.
Organisations with high levels of manual testing and release management also see change failure rates of 10–20%, compared with below 5% for elite teams.
The findings suggest the biggest barrier to AI-driven delivery is organisational friction: manual approvals, fragmented environments, slow governance processes and delivery models designed for stability rather than continuous change. And in an AI-enabled market where speed is the new competitive advantage, winners will be those that reduce the gap between decision and delivery, not those that simply generate more code.
The commercial impact is significant. At one global financial institution, a £2.76M annual engineering investment yielded only 5% of its value in delivered features. At another, it took new engineers between three and six months to make their first contribution. At one large firm, the average lead time for a business-critical feature was 266 days, while fixing a critical production bug took 158 days.
The report warns that, for organisations struggling with foundational weaknesses, AI adoption risks creating “more broken output, faster” unless they address the operational systems that determine whether software can actually reach customers.
James Jarvis, CEO of ClearRoute, says: “AI has changed the speed of software creation, but not the speed of enterprise delivery. For many organisations the bottleneck was never writing code, but the manual approvals, brittle test suites, fragmented environments, governance processes and release constraints surrounding them.
“The organisations that win from AI will not be the ones with the most coding assistants. Speed is the new competitive advantage, which means the next competitive frontier is operationalising AI safely across the full Route to Live and moving beyond fragmented tools to platform engineering as a core operational capability. This will close the gap between decision and delivery, getting change live faster, safer and at scale.”
The report also highlights a shift in enterprise AI maturity. While code completion and AI-assisted generation are becoming table stakes, more advanced organisations are moving toward environment-connected AI, agentic code reviews, self-correcting CI/CD and autonomous testing.
However, the move from AI experimentation to AI orchestration depends on platform maturity. Without a central control plane and platform-level capabilities for identity, access and discovery, enterprises risk simply replacing tool sprawl with ‘agent sprawl’ rather than solving the foundational challenge.
Sarndeep Nijjar, CTO, ClearRoute, adds: “The next phase of AI in software delivery will be defined by control. Enterprises are moving from individual copilots to AI agents that can interact with pipelines, environments and production systems. That creates huge opportunity, but only if those agents operate inside clear technical boundaries. Without that foundation, enterprises will not scale AI safely; they will simply create another layer of complexity.
“This is where platform engineering becomes critical. It gives organisations the governed pathways, automated guardrails and reusable delivery foundations needed to move from AI experimentation to AI operations.”
State of the Route to Live 2026: The Great AI Amplifier is available now.