From AI pilot to production delivery model — that ships faster, has fewer defects, and costs less

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From AI pilot to production delivery model — that ships faster, has fewer defects, and costs less

September 23, 2026 | 15:00 - 15:45 CETOnline | 45-minute Executive Briefing

Most companies have run AI pilots. Far fewer have turned them into a delivery model with measurable business impact. We'll share our hands-on experience moving engineering teams from pilot purgatory to an operationalised AI SDLC — three accelerators, three case studies, and honest discussions of what worked and what did not.

Speakers

Two practitioners. Real deployments. No theory. The engineers behind the results — speaking directly to engineering leaders.

Pavel Azaletskiy

Head of Practices: Enterprise Software Delivery & Operational Efficiency

Maksym Kuznietsov

QE Architect & AI R&D Lead

Pavel Azaletskiy

Head of Practices: Enterprise Software Delivery & Operational Efficiency

Maksym Kuznietsov

QE Architect & AI R&D Lead

Who should attend

Senior engineering leaders responsible for engineering output, vendor strategy, and technology decisions — who need to break the cost-vs-speed trade-off without increasing headcount or budget. CTO, VP Engineering, Head of Engineering.

What we'll cover

Productivity & Speed

Engineering productivity — deliver more without growing your team or budget.

How AI-native development applied end-to-end across the SDLC enables teams to ship significantly more per engineer. We'll walk through a real case: a 400-person engineering organisation that achieved 2× output in 11 months — covering what changed in their workflow, where the gains came from, and what conditions made it possible.

Maintainability & Quality

How to avoid the AI technical debt trap. Faster AI-assisted development introduces a new class of quality risk — code that works today but becomes expensive to maintain tomorrow. We'll show how automated and AI-driven exploratory QA catches defects in parallel with development, and the guardrails that prevent quality degradation from silently compounding into production incidents.

Operations & Cost-to-serve

AIOps — cut incident response time and cost-to-serve in production. How AI agents that identify root causes and suggest fixes are transforming production operations — reducing MTTD and MTTR materially, and freeing senior engineers from reactive firefighting. Includes a real case study on measurable improvements in operational cost and incident resolution speed.

productivity gain

30-40%

reduction in development time per feature

~20%

more efficient delivery vs typical incumbent vendors

Hours→ minutes

Faster incident response resolving production issues in minutes, not hours

Which impact it may have on your business

Ship more features, faster

Reduce your time-to-market on new features and close the speed gap with AI-native competitors — without adding engineers or increasing burn rate.

Up to 2× delivery velocity.

Deliver more within your existing budget

Redirect engineering spend from repetitive, automatable tasks to high-value product work. Get roughly 20% more output from your current vendor or team investment.

~20% efficiency gain

Reduce defects and production risk

AI-driven QA running in parallel with development catches issues before they reach production — lowering the cost of defects and reducing the risk of incidents in regulated environments.

Fewer bugs in production

Cut the cost of production incidents

AI-assisted root cause analysis shortens the time between incident detection and resolution — reducing the operational cost and reputational risk of downtime in fintech and logistics systems.

Lower MTTD & MTTR

Retain and energise your best engineers

Automating repetitive coding, testing, and ops tasks frees your senior engineers for complex, high-impact work — reducing burnout and making your team a more attractive place to work.

Higher engineering satisfaction

Build a board-ready business case

Walk away with the metrics, frameworks, and real-world proof points needed to justify AI investment to your CFO or board — not just a vision, but a concrete ROI argument.

Measurable ROI framework

Why This Is Different

We aren't showing you how to write prompts. We are showing you how to re-engineer your entire Software Development Life Cycle (SDLC) for a world where AI is the primary engine, not just a passenger.

Ready to scale your business?

Let's discuss how we can help you grow with AI-enabled solutions and expert engineering

schedule a consultation

From AI pilot to production delivery model — that ships faster, has fewer defects, and costs less

September 23, 2026 | 15:00 - 15:45 CETOnline | 45-minute Executive Briefing

What we'll cover

Productivity & Speed

Engineering productivity — deliver more without growing your team or budget.

Maintainability & Quality

Operations & Cost-to-serve

Which impact it may have on your business

Ship more features, faster

Reduce your time-to-market on new features and close the speed gap with AI-native competitors — without adding engineers or increasing burn rate.

Up to 2× delivery velocity.

Deliver more within your existing budget

Redirect engineering spend from repetitive, automatable tasks to high-value product work. Get roughly 20% more output from your current vendor or team investment.

~20% efficiency gain

Reduce defects and production risk

AI-driven QA running in parallel with development catches issues before they reach production — lowering the cost of defects and reducing the risk of incidents in regulated environments.

Fewer bugs in production

Cut the cost of production incidents

AI-assisted root cause analysis shortens the time between incident detection and resolution — reducing the operational cost and reputational risk of downtime in fintech and logistics systems.

Lower MTTD & MTTR

Retain and energise your best engineers

Automating repetitive coding, testing, and ops tasks frees your senior engineers for complex, high-impact work — reducing burnout and making your team a more attractive place to work.

Higher engineering satisfaction

Build a board-ready business case

Walk away with the metrics, frameworks, and real-world proof points needed to justify AI investment to your CFO or board — not just a vision, but a concrete ROI argument.

Measurable ROI framework