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Join Elisabeth Hendrickson and Joel Tosi for a discussion of chapter 10 of Signals & Levers as part of Bredemeyer’s Papers in Systems series. Registered attendees receive a link to the chapter to read before the session. The Zoom room will remain open until 2:30 PM Eastern for informal discussion.
Your organization is a socio-technical adaptive system. You can shape and influence the system, but you can’t control it. That is: You poke the system and the system pokes back. These days, AI is poking systems HARD. Some organizations are seeing massive gains, others are pouring money into tokens and seeing underwhelming results. The difference isn’t necessarily underlying capability. Rather, AI is amplifying output inside the parts of the system unevenly and in doing so is pressure testing your entire delivery chain. Drawing on lessons from the new book Signals & Levers: Systems Thinking Tools to Unblock Software Delivery, this talk shows you how to use systems thinking tools to reason about the impact of AI and find the right levers to take advantage of what it can give you.
Signals - signs that something is happening. Levers - ways to influence the system. These are everywhere in software delivery. 'No meetings Friday' is a signal, and the problem isn't that there are so many meetings. 'Testing is hard / not really automated / flaky' - is a signal. Planning for months across lots of teams...signal. The challenge is in finding the appropriate lever - ways to shift the way work works that address where the signal is caused, and not just where you see it. This session helps you see signals and levers.
Mob Programming: All the brilliant people working on the same thing, at the same time, in the same place, and on the same computer. Mob Programming is a cost-effective, collaborative and fun way to get work done together. It's a whole-team approach to development, where coding, designing, testing, and working with the "customer" (partner, Product Owner, User, etc.) is all done as a team. Participants in this workshop experience a day of learning and doing Mob Programming. We cover the mechanics of how to work together as a Mob as well as the techniques that make this form of development so effective. We’ll learn how a Mob performs sample project work, including user stories, prioritization, test-driven development, refactoring, and retrospectives.
To shift a system, you have to learn how to read the signals that tell you what’s really going on, then find the right points of leverage. This is harder than it sounds. The signals that are easy to see, like metrics dashboards, are usually convenient proxies, unreliable and full of noise. The most obvious levers are often local optimizations that are likely to backfire. In this masterclass, you’ll learn how to read signals, especially the less obvious ones. You’ll use foundational system thinking tools, especially causal models, to understand and reason about the system as a whole. Finally, you’ll learn how to find the right levers: the interventions that can shift the system while avoiding unintended consequences. Along the way you’ll discover why decisions made with the best of intentions all too often backfire, how to break out of doom loops, and how even the smallest actions can have the biggest impact.
Have you ever seen an attempt at process improvement backfire? If so, you’re not alone. Back in 2001 Elisabeth wrote about a case where better testing led to worse quality but that example is hardly unique. Examples abound. An organization trying to tighten up release timelines ended up with ever-increasing planning horizons. Yet another organization that rolled AI tooling to speed up development ended up slowing down. Best intentions go astray because software development organizations are non-linear adaptive systems. Attempting to control outcomes as a straightforward chain of cause and effect doesn’t work. When you face a big challenge—poor quality releases, slow delivery, or missed commitments—there is no single dial to turn. You can influence the system, but first you have to understand how it works. In this talk we show you how to see your context as a system, and reason about it so you can find the possibly counter-intuitive approaches to solve hard problems.
Joel and Elisabeth talked to Charles Humble about signals, levers, and why systems thinking is having a moment right now.
Watch on YouTube →Joel and Elisabeth talked to Henry Suryawirawan about Signals & Levers, the persistent illusions in software delivery, and implications of AI.
Watch on YouTube →Explore our YouTube playlist →
Joel talked to host Hunter Harris about high-functioning product teams, integration, customer feedback, and the importance of focusing on strategy over reaction.
Elisabeth talked to Hunter Harris about systems thinking and feedback loops.
Elisabeth talked to Dave Mangot and Dr. Paul Karner about quality as a strategic advantage for PE-backed companies.
Elisabeth and Joel talked to Bob Fabien "BZ" Zinga (Coach BZ) about building better systems.
Elisabeth and Joel talked to Kim Harrison, Heidi Waterhouse, Adam Zimman, and James Governor about Signals & Levers and seeing software delivery as a system.
Software Delivery is full of illusions. In this session, we will look at 3 of them - Illusion of Progress, Illusion of Predictability, and Illusion of Control. Want to understand why all of those meetings aren't helping? What about all of that planning? How about why all that agile stuff we are doing (our own special way) just seems to make things slower? We have you covered.
Strategy requires choice. No choice? No strategy. In this session we will explore how to find choices - in architecture, leadership, improvement initiatives, products, and more. Along the way, we will look at how context effects our potential choices and, occassionally, can make an outcome inevitable. Leave this session understanding how to find more choices, leading you to better strategy.
In this live coding session, Joel will demonstrate four techniques for better testing: leveraging wiremock for API dependencies; acceptance testing for legacy code; refactoring for readability (of course); in memory / container based databases for DB dependencies (mainframe, db2, etc).
AI is reshaping how we build software, but to get the greatest benefits you have to understand your system. That makes systems thinking more relevant now than ever. We show you how to use causal models to find the best leverage points, and how to measure variability to detect if AI is just amplifying the bottlenecks and chaos. This session will show you how to get the most from your AI investment.
Let’s say you want to change something about software delivery in your organization. Maybe you want to improve testing, raise quality, speed up delivery, or leverage AI more effectively. You make a change: introduce a new practice, restructure work or teams, or roll out new tools. Now what? Maybe you got what you hoped for but with a side order of unintended consequences: delivery is faster but costs went up, or quality is better but morale is down. Or maybe the changes backfired spectacularly and the original problem is worse than ever. That’s what happened at a startup where Elisabeth was a Director of Quality Engineering during the dotcom bubble in the late 1990s. She wrote about it in her paper “Better Testing, Worse Quality.” Why does that happen? Software development is a non-linear adaptive system. Any given property of the system arises from a web of influences. There is no single root cause, and no one weird trick that can fix everything. You poke the system; the system pokes back. Pulling ideas from the book Signals and Levers, this hands-on session uses a simulation of a software delivery system to explore systems thinking tools to reason about that web of influences. Unlike the real world, you’ll be able to see the effects of your policy decisions in seconds rather than months, enabling you to run experiment after experiment. You’ll learn to see the telltale signs of reinforcing loops—both virtuous and vicious—and how to identify possible interventions. By the end of this session you’ll have better tools in your toolbox to reason about the system, watch for surprises, and help others see what you see.
The Association of Software Testing is explicitly Context-Driven. It’s right there in the mission statement: “The Association for Software Testing is dedicated to advancing the understanding of the science and practice of software testing according to Context-Driven principles.” Bret Pettichord coined the term in his seminal 2003 presentation “Four Schools of Software Testing.” But what exactly IS context? In the book Signals & Levers, Elisabeth Hendrickson and Joel Tosi offer a definition: “context” is a shorthand term for the cumulative effect of all past decisions. All of them. Foundational decisions like what business the company is in and what clients it serves. Compromises or tradeoffs made to get to this point. External decisions like regulatory requirements. Current policy or process decisions. It all adds up, and the context is constantly shifting. The book introduces the CREATE framework—capacity, risk, execution, adaptability, trust, and economics—as a way to compare and contrast contexts. This is important because software development is an adaptive system. You poke the system, and the system pokes back. The context shifts and the system changes, often in unpredictable ways. You can’t reason about it with linear cause-effect thinking. These days AI is reshaping everything about how technology organizations operate. Chances are, your system is being poked hard. You need a concrete way to describe your context and discuss the implications in order to navigate this change. In this talk, Elisabeth demonstrates how to reason about context through the lens of systems thinking. In doing so, she highlights the role that all of us play in shaping the systems in which we live.
Most organizations are in a constant state of change, yet few see meaningful results. Initiatives promise improvement but often create the illusion of progress while outcomes stay the same or get worse. In this keynote, Elisabeth Hendrickson and Joel Tosi challenged how we think about change. Drawing on systems thinking and real-world experience, they reveal why well-intentioned efforts often fail – and what it actually takes to shift outcomes at scale. Instead of focusing on frameworks or surface-level fixes, this session explored how to read the signals that matter, understand the system you’re operating in, and identify the levers that drive real impact. For practitioners and leaders alike, this is a new way to approach change – not as a rollout, but as a system to be understood and shaped.