Research and Insights from the Center for Effective Organizations
Last updated November 5, 2025 As businesses continue to navigate the ever-evolving challenges of today’s dynamic landscape, we want to remind you of the valuable resources available to support you and your organization. The shift to remote and hybrid work models, the...
We are living through a tornado of change in organizations. When things change, our old practices often don’t work as well – or, we’re worried that they won’t work as well. So, we change them – often on pretty shaky evidence. But most of the time when we do this, we are looking at a symptom – not at the root cause. Like a Sisyphean game of whack-a-mole, that root cause keeps throwing up problems in new places.
As some astute commentators have noted, many features of what we today call “artificial intelligence” have been around in a number of forms for many years. Prior to the launch of ChatGPT and the global explosion of people using generative AI models, we had machine learning and other forms of highly sophisticated computer-based modelling that provided many of the features of genAI behind the scenes, invisible to us end users. So in one sense, the current genAI models are like a coming out – or coming of age – party for a technology that now is clear to everyone.
AI is going – and has already started – to transform how work is done within organizations. Anywhere people are asked to weigh in on decisions has the potential to be disrupted by AI. And in these early days, it’s really hard to say for certain what the limitations of AI will be in most cases.
The global economy is suffering through a particularly difficult time right now. The recent onslaught of sharply-changing tariffs, and uncertainty about the future of international trade and supply chains, has leaders turning to time-honored strategies for keeping costs down. And since labor costs often are the largest on a company’s balance sheet, cutting headcount, salaries and hourly compensation usually are at the top of the list. But should they be?
Organizations often give catchy names to transformation initiatives to create focus and signal their importance, but this practice can unintentionally reinforce the idea that change is temporary, leading to disillusionment and cynicism when further changes arise. Instead, aligning efforts with ongoing strategies and framing them as part of a continuous process, like “LRP Activation,” fosters clarity, engagement, and a culture where change becomes a normal and accepted part of organizational life.
This article is the third in a three-part series on applying systems diagnostics to understand the sources of organizational underperformance (Using systems diagnostics to drive process improvement and change), why transformations often fail to deliver (Designing transformations to avoid failure), and how to keep transformations from going off course (this one). It is based in part on my Strategic Analytics book. The issues addressed in the series will be covered in depth in the upcoming workshop Driving Organizational Change with Data.
This article is the second in a three-part series on applying systems diagnostics to understand the sources of organizational underperformance (Using systems diagnostics to drive process improvement and change), why transformations often fail to deliver (this one), and how to keep transformations from going off course (Diagnosing and adjusting transformations to ensure success)). It is based in part on my Strategic Analytics book. The issues addressed in the series will be covered in depth in the upcoming virtual workshop Driving Organizational Change with Data.
This article is the first in a two-part series on applying systems diagnostics to understand the sources of organizational underperformance (this article) and why transformations often fail to deliver (next article). It is based in part on Alec Levenson’s Strategic Analytics book. The issues addressed in the series will be covered in depth in the upcoming virtual workshop Driving Organizational Change with Data.
This SIOP White Paper Series examines the workplace that is being transformed by advancements in AI, emerging technologies, and changing cultural landscapes, reshaping organizational operations and employee interactions. In response, human resources teams have become more sophisticated and influential, leveraging expertise and data to enhance organizational effectiveness and gain a competitive edge.
Executing a (new) strategy means two things. You have to build new capability required for the strategy. And the organization has to change from the old capability to the new. Capability building and change. Simple enough, and easy to say. So why do most strategy executions fail?
The gig economy provides a novel setting that challenges many established ways of working. This paper unpacks the nature of talent identification in the gig economy through the role of three central actors; the online labor platform firm, the requester/customer and the gig worker. Talent identification in this context is especially novel as it emerges from tripartite relationships among independent economic actors, in contrast to traditional settings where talent identification is studied from a dyadic perspective (i.e., talented workers and the organization)