The AI pressure cooker: missing ingredients and failed dishes

AI surely is hot, and many companies are racing against each other and themselves to implement AI for any and all tasks. Success stories around increased output, more lines of code written, optimized pipelines and faster deliveries are all around the corner. One would assume AI is reshaping the Software Development Lifecycle (SDLC), and without it, you’ll miss out on engineering excellence, improved quality and efficient and streamlined developer productivity. But does this assumption hold up in the actual world? In this talk, we’ll examine whether greater productivity automatically translates into better business outcomes and what the “missing ingredient” is in the current business landscape. Hint: AI amplifies existing engineering capabilities, but it can’t compensate for poor processes or organizational inefficiencies. Using actual examples from the field, we’ll discover why adopting AI alone isn’t a shortcut to engineering excellence, what bottlenecks can be encountered and ultimately need to be considered and how to deal with pitfalls and downsides. Yes, AI can really be that game-changer for many companies, but not automatically so. Organizations that blindly introduce AI into inefficient development processes expecting expert results risk accelerating existing bottlenecks, increasing technical debt and driving up costs without realizing meaningful benefits. But if AI is viewed as an accelerator of well-designed engineering practices, and not as a replacement for them, true leveraging and increasing of efficiency and output can be expected. Ultimately, the true strength of AI is less about the tools themselves and more about creating the operational foundation that enables AI to generate measurable business impact throughout the organization.