expensive and risky transformation effort, FOC is in no sense a “final” operating capability. Quality It is difficult for IT to gain a seat at the table when IT is always failing, but on the other hand face the consequences. In the plan-driven model, quality was easier to understand.  We specified what the system should do, and then measured quality as adherence to that specification.  The opposite Probably not that, either.  We are constantly making quality decisions, especially in a Continuous Delivery model, as we decide whether the quality of each individual feature is adequate for the feature

framework includes the concept of “Done,” which constitutes a critical aspect of ensuring high-quality software. To be considered potentially releasable, a piece of functionality must adhere to a common team of software developers, including software and security engineers, data specialists, testers, quality assurance, and configuration managers. Ideally these participants are co-located in the same also write user stories to cover underlying characteristics of security, technical performance, or quality. Interfaces with other systems are usually captured as user stories. User stories require little

manufacturing lead time required to convert raw materials into finished goods was the best predictor of quality, customer satisfaction, and employee happiness 2. one of the best predictors of short lead times flow that cascade through the entire manufacturing plant. The result is long lead times and poor quality 3. Consider when we have an annual schedule for software releases, where an entire year’s worth of WIP and massive disruptions to all downstream work centers, resulting in poor flow and poor quality outcomes. This validates our common experience that the larger the change going into production

measurable improvements on three fronts: 01 Workforce performance Helping people deliver higher-quality outputs in shorter 
 time frames. 02 Automating routine operations Freeing people from repetitive examples. 5 AI in the EnterpriseLesson 1 Start with evals How Morgan Stanley iterated to ensure quality and safety As a global leader in financial services, Morgan Stanley is a relationship business. clients.
 They started with three model evals: 01 Language translation Measuring the accuracy and quality of translations produced 
 by a model. 02 Summarization Evaluating how a model condenses information

Employed USA Adults AI User + Usage + CapEx Growth = Unprecedented 0% 25% 50% 75% 100% Improving the Quality of Their Work Allowing Them to Do Things More Quickly Extremely / Very Somewhat Not Too / Not general-purpose models may be accelerating commoditization and driving diminishing returns, as output quality converges across players and differentiation becomes harder to sustain. At the same time, the cost general-purpose models may be accelerating commoditization and driving diminishing returns, as output quality converges across players and differentiation becomes harder to sustain. At the same time, the

purchasing? Employing good people? High technology? Producing products? Producing high quality products? Selling quality products? Capturing market share? After much reflection, Alex realizes: The goal bottleneck produces in that time. And that makes an enormously expensive lunch break” Story - Quality control Jonah: “Make sure the bottleneck works only on good parts by weeding out the ones that

monitoring, and management tested continuously. Steve Barr steve.barr@csgi.com @srbarr1 Overall Quality improvements, “it” goes in better, and with less issues.  Maintenance window changed from once recurring maintenance.  Do define a primary purpose and measure it.  Do process map.  Do focus on Quality first.  Do start a book club. Book List  Scaling Lean & Agile Development: Thinking & Organizational

Perspective 1 Kyle Baardson Scrum - Doing Twice the Work, in Half the Time 1 Nick Tuck Assuring Quality 3 Toby Flint Lean Startup Book Review 0 Mark Staroska Story Points 4 Jason Smith Empowering Teams SAFe: Scaled Agile Framework 3 Josh Sagucio Collaborative Work Environments 3 Nick Tuck Assuring Quality 3 Josh Wade Cyber Security through Agile 2 Nick Tuck Maximizing Retrospectives 2 Nick Wenner

struggle with: Data Quality, Integration & Silos – Analytics, search, and AI require seamless integration of structured, semi-structured, and unstructured data with high-quality indexing. Yet, fragmented