June 5, 2025
AI in software delivery (or “AI in the SDLC”) is rapidly becoming a game-changer. We’re not talking about AI as the feature inside your product, but AI as the accelerator that helps teams plan, code, test and ship better, faster, and more reliably (whether the products/services they are delivering utilise AI or not). Organisations (led by product companies and scale ups) across industries are racing to integrate AI into every step of their delivery pipelines, driven by promises of boosted productivity, reduced cycle times, and fewer defects.
But wrapped in the hype are a host of misconceptions – five big myths – that hold teams back from realising AI’s full potential. By debunking these myths, you’ll be better equipped to weave AI into your delivery practice, turning it from a buzzword into a powerful multiplier.
Myth: These models “think,” understand context like a human, and can make sound judgment calls on their own.
Truth: LLMs are extraordinarily advanced pattern-matchers, not intelligences. They automate repetitive tasks – but they don’t possess true understanding or common sense. Treating them as autonomous experts sets you up for errors and overreach.
In the rush of AGI hype, teams often waste cycles probing whether an LLM can mirror human reasoning – testing edge cases, debating its “understanding.” When it inevitably stumbles, they deride AI as broken rather than recognising it for what it is: a sophisticated tool.
This even extends to how frequently people talk about “hallucination.” LLMs, by design, are ‘hallucination engines’. They are eager pattern-matchers (to the point of people coining the term “Sycophant AI”) – and hallucinations are an undesirable artefact of this. The better the prompting, context, and rules, the less that hallucination is a problem.
In reality, these LLMs excel as companions to human expertise, augmenting our knowledge with rapid recall of patterns from vast codebases and documentation. It’s this partnership, not autonomy, that delivers real value in the SDLC.
This myth creates false ceilings: teams either expect too much from AI, only to be disappointed, or underestimate the effort needed to integrate and guide the tool. By acknowledging LLMs as powerful assistants rather than independent thinkers, you set realistic goals and invest in the human–AI collaboration that drives success.
Myth: AI should be used solely for churning out code, treating it as a developer replacement and measuring success in “developer productivity”.
Truth: Developer productivity is notoriously hard to quantify and even when you boost code output, it rarely translates into faster time-to-value for the whole team. Drawing on the Theory of Constraints, the real leverage comes from identifying and speeding up the biggest bottleneck in your delivery pipeline – whether that’s backlog refinement, automated testing, documentation, or deployment.
In our work, we’ve seen organisations fixate on code‑gen as the low‑hanging fruit, only to hit a “faster horses” ceiling, boosting snippet counts without improving release cadence or business outcomes (see “From faster horses to motor cars”.) True AI-enabled software delivery targets cross‑functional flows end-to-end, not just developer desks.
By broadening AI’s remit beyond code into areas like test-case design, user-story validation and CI/CD optimisation, you unlock step‑changes in efficiency and value realisation.
Myth: “If it can’t give the same answer twice, it can’t be relied on for mission-critical software.”
Truth: Inherent non‑determinism is baked into LLMs, but that doesn’t render them unreliable for delivery. While you can tune parameters like temperature and top‑k/top‑p to reduce variability – an approach more critical for in‑product AI scenarios – most delivery tasks don’t require these tweaks.
We don’t ship human code without clear validation and verification practices, so why treat AI any differently? Humans are non‑deterministic too: ask two developers (or the same developer twice) to solve the same problem, and you’ll get different implementations every time. We embed code reviews, automated tests, monitoring, and staged rollouts into every delivery pipeline so that we have confidence over what is delivered to Production.
Applying these proven delivery safeguards to AI-generated artifacts – clear standards/guardrails, enforcing robust test architecture, separating deployment from release, and monitoring in production – ensures AI contributions meet the same standards of reliability and auditability as any other code.
Myth: Introduce AI, and every squad, no matter their maturity, will instantly outperform previous delivery metrics.
Truth: AI acts as an amplifier. It magnifies both strengths and weaknesses. High-performing teams that are pragmatic, disciplined, cross-functional, and relentlessly value-focused will see their capabilities supercharged. But teams lacking solid engineering practices, clear roles, or a value-oriented mindset risk accelerating flawed processes, leading to failures rather than wins.
We expect to see disasters this year where organisations, eager for rapid gains, rush AI into delivery without demonstrable experience or governance. Without shared understanding of why practices exist, or the discipline to uphold quality gates, these teams will compound issues leading to chaotic deployments, hidden defects, and lost stakeholder trust.
Who succeeds with AI:
By embedding AI within these proven team characteristics, organisations unlock transformative benefits, inverse those traits, and you risk magnifying chaos.
Myth: Giving existing teams new AI tools will automatically boost delivery performance without changing anything else.
Truth: In the first phase of AI adoption, injecting tools into current workflows can deliver incremental gains in areas like code completion or defect detection – but it rarely shifts the needle on team-wide metrics like cycle time or time-to-value. The real transformative impact emerges in the second phase when organisations rethink team structures, cross-functional roles, and processes around AI capabilities.
Phase 2 of AI-enabled delivery represents a paradigm shift on par with the leap from waterfall to agile. Teams become dynamic learning engines, rapidly absorbing customer feedback, competitive insights and stakeholder signals into each iteration. By collapsing hand‑offs and embracing end‑to‑end ownership – from product definition and design through engineering and operations – you accelerate feedback loops, drive down time‑to‑value and cost‑per‑feature, all without compromising quality or maintainability.
Achieving these gains demands more than new tooling: it requires redesigning team structures, incentive models, skills development programmes and governance frameworks around AI’s unique capabilities – transforming how people collaborate and processes flow rather than simply layering on technology.
AI in software delivery only fulfils its promise when teams dispel these five myths and focus on true collaboration, robust delivery practices and transformative team design. LLMs aren’t junior developers or human stand-ins. They’re process catalysts: reference points for knowledge, accelerators for routine tasks and enablers of new workflows. Embed standard safeguards – reviews, tests and monitoring – and work pragmatically across functions to deliver measurable value. Organisations embracing this integrated AI approach will not only accelerate performance but redefine what high‑velocity, high‑quality delivery looks like today.
All business models have their pros and cons. But, when you consider the type of problems we help our clients to solve at Equal Experts, it’s worth thinking about the level of experience and the best consultancy approach to solve them.
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