Accelerating Software Development without sacrificing quality

In this short point of view Wiktor explains how strategic initiatives drive real business value through practical use cases.

The problem statement

Even the best engineering teams hit bottlenecks—prototyping, planning, requirements analysis, architecture design, security reviews, edge-case testing. These upstream stages decide how fast everything else moves. Yet many organizations still treat AI as a coding shortcut rather than an end-to-end accelerator for the software delivery lifecycle. The result? Dazzling demos, slow delivery, and value that fails to materialize at scale. If adoption isn’t anchored to business outcomes and disciplined execution, projects risk joining that 30% that stall after POC.

The author’s perspective and expertise

Observations and learnings from recent projects

For example one of the biggest productivity gains we have observed in software delivery come from automating aspects of requirements gathering, and designing the proposed architecture as part of a Software-Development-Lifecycle (SDLC), with a focus on:

• Identifying bottlenecks: for each key stage, including requirement synthesis, architectural review or security sign-off we can use agents to define desired outcomes and flag risks, issues and inter-dependencies. 
• Tying improvements to business metrics: when using DORA metrics we can use agents to track lead time for changes, deployment frequency, change failure rates, mean time to restore.
• Gathering requirements: when gathering stakeholder inputs we can use agents that convert stakeholder inputs into structured user stories, trace requirements to tests, and propose design alternatives.
• Conducting architecture reviews: when performing architectural reviews we can use agents that generate diagrams, map dependencies, run risk checks, and propose patterns aligned to standards.
• Performing testing and quality appraisals: when performing testing and quality appraisals we can use agents to create tests cases and scripts, spin up sandboxes, assess change requests, detect outliers, create synthetic sample data.
• Aligning with security & compliance policies: when conducting security and compliance reviews we can use agents that flag policy violations, scan for license issues, and produce evidence packs for audits.
• Enabling iterative validation: when validating and refining outputs we can use agents that request input from architects, engineers, analysts and subject matter experts.

Consider a practical example of a financial services client striving to achieve rapid development but consistently stalled at architecture design. By introducing AI agents to support their architects with scanning code repositories, generating architecture diagrams, mapping dependencies across services, and flagging risks against security and compliance policies, the client was able to reduce the duration of design cycle by more than 10 times. Furthermore, this was achieved without compromising quality because reviews remained an integral part of the cycle, architects still owned decisions but this time agents helped with accelerating the analysis.

A word of caution

• Not all acceleration adds value: requirements which have to be refined over and over again due to variation in scope will propagate into agent re-deployment.
• Data and context matter: unstructured poor quality data will require a wider selection of agents and more extensive configuration and training.
• Beware of hidden costs: integration complexity due to a multitude of systems, applications, dashboards and reports will increase agent deployment costs.
• Quality is non-negotiable: lack of user acceptance criteria as part of definition of done will lower the threshold for successful deployment.
• People expertise is critical: agents cannot be deployed without the buy-in and input from data owners, system custodians, business process owners, heads of departments to name a few.

Concluding point(s)

The integration of AI agents promises to reshape software development, by delivering exponentially faster outcomes and automating manual processes whilst removing common pain points. The path to 10x requires a strategy, well though through roadmap and well defined set of use cases across the Software Development Lifecycle. Target the stages that have the greatest degree of complexity, assign agents to focus on high impact activities, measure results against business-relevant KPIs, and build in human oversight.

Supporting perspectives

Looking for how these ideas translate into broader business impact? 

• Adam Rogalewicz’s point of view on applying GenAI to strategic initiatives like Revenue Optimization shows how to turn pricing, offers, and customer engagement into measurable growth. Read Adam’s perspective.

• Ernest Orlowski’s value-first approach outlines how to select use cases, define KPIs, and scale responsibly so your AI initiatives don’t stall after POC. Read Ernest’s perspective.