{"product_id":"9798868834189","title":"The Predictability Loop A Control Layer for Enterprise Software Delivery in the Age of AI","description":"\u003ch1\u003eThe Predictability Loop\u003c\/h1\u003e\u003ch2\u003eA Control Layer for Enterprise Software Delivery in the Age of AI\u003c\/h2\u003e\u003ch3\u003eRoman Lobus\u003c\/h3\u003e\u003cdiv\u003e\u003cb\u003eComputers \/ General\u003c\/b\u003e\u003c\/div\u003e\u003cbr\u003e\u003cdiv\u003e\n\u003cp\u003eKeep software delivery predictable in the age of AI with a practical operating model built for engineering leaders. AI coding assistants have made development faster, but speed alone does not finish work; it shifts the constraint downstream into review, integration, and testing, where rising output quietly hides commitments becoming unsafe.\u003c\/p\u003e\n\u003cp\u003eThe Predictability Loop is the control layer for that problem: a closed practice you run to observe the signals the delivery system actually emits, interpret what changed, choose the least-harmful intervention, validate whether the system improved, and communicate the remaining uncertainty honestly to the people making commitments. It runs on established flow measurement, but its real subject is the layer above the instruments: which signal to act on, which fix to try first, how to prove it worked, and when to distrust the number. That is the difference between a dashboard and an operating model.\u003c\/p\u003e\n\u003cp\u003eAround that loop, the book builds the enterprise layer the AI era demands. It shows you how to diagnose where AI has shifted the constraint, build an early-warning view of delivery risk, set service-level expectations based on real history, and defend a forecast to the board in language a non-statistician can follow. It tackles the questions that decide whether AI adoption pays off: governing AI use across many teams, managing the verification burden it creates, telling real productivity gains from theatre, and reasoning about the economics and cost of delay at portfolio scale. The methods come from running AI-accelerated delivery in practice, including guarded use of generative AI under human review - built, not theorised.\u003c\/p\u003e\n\u003cp\u003eYou Will:\u003c\/p\u003e\n\u003cp\u003e· Apply the Predictability Loop - observe, interpret, intervene, validate, communicate to keep delivery predictable as AI accelerates development.\u003c\/p\u003e\n\u003cp\u003e· Use the four delivery signals - work in progress, throughput, cycle time and work item age with service-level expectations and probabilistic forecasting to make delivery decisions you can defend.\u003c\/p\u003e\n\u003cp\u003e· Identify and address AI-shifted bottlenecks across code review, integration, testing, and deployment pipelines before they cost you a commitment.\u003c\/p\u003e\n\u003cp\u003e· Distinguish genuine productivity gains from misleading metrics and develop reliable approaches for measuring the impact of AI-assisted development.\u003c\/p\u003e\n\u003cp\u003e· Run the four gates - data quality, stability, forecast-readiness and distrust, so a number has to earn its way into a promise.\u003c\/p\u003e\n\u003cp\u003eThis book is for: VPs of engineering, engineering directors, CTOs and heads of platform and delivery who own delivery predictability. Delivery leads and engineering-effectiveness practitioners who operate the instruments will find every artefact in fillable form in the appendices.\u003c\/p\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003cp\u003eRoman Lobus is VP of Agile Product and Portfolio Management at Singapore Airlines, where he redesigned the end-to-end software delivery lifecycle across more than 120 teams. He replaced velocity-based reporting with a delivery-signal system — queue monitoring, work-in-progress aging, bottleneck detection, and structured intervention reviews — now extended with guarded GenAI analysis under human review. His work focuses on how engineering organisations stay predictable as AI accelerates development and shifts the constraint downstream into review, integration, and testing.\u003c\/p\u003e\n\u003cp\u003eHe has spent more than 20 years in enterprise technology, leading delivery and transformation across telecommunications, finance, oil and gas, pharmaceuticals, logistics, and government. That cross-industry experience, much of it in regulated environments, informs the operating model at the centre of this book. He writes and speaks on flow metrics, delivery predictability, and AI-accelerated software delivery. He is the author of The Art of Creating Self-Organizing Teams: Agile Team Coaching from a Journeyman to an Expert. More at lobus.works and linkedin.com\/in\/lobus.\u003c\/p\u003e\n\u003c\/div\u003e\u003cbr\u003e\u003ctable\u003e\n\u003ctr\u003e\n\u003ctd\u003ePublication Date: \u003c\/td\u003e\n\u003ctd\u003e30 March 2027\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePublisher: \u003c\/td\u003e\n\u003ctd\u003eApress\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eImprint: \u003c\/td\u003e\n\u003ctd\u003eApress\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eISBN-13: \u003c\/td\u003e\n\u003ctd\u003e9798868834189\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eFormat: \u003c\/td\u003e\n\u003ctd\u003ePaperback softback\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e","brand":"Apress","offers":[{"title":"Default Title","offer_id":55562827268236,"sku":"9798868834189","price":58.49,"currency_code":"USD","in_stock":true}],"url":"https:\/\/lateknightbooks.com\/products\/9798868834189","provider":"Late Knight Books and Services, LLC","version":"1.0","type":"link"}