Our Insights

A simple framework for interrupting our biases at every stage of learning.

Here's something that's both fascinating and deeply human. We don't experience reality objectively. We notice the evidence that stands out. We interpret it through our existing beliefs. We share the perspectives that make the most sense to us. And we build systems that reinforce what we already think is true.

None of this is intentional. It's simply how our brains work.

Which means one of the most important questions we can ask isn't: "How do we gather more evidence?" It's: "How do we make sure we're seeing the evidence we're naturally inclined to overlook?"

Every learning leader eventually hears the same question: "Should we make learning mandatory?"

It's a fair question.

If people don't attend learning sessions, should leaders require participation? Should attendance be tied to performance? Should calendars simply be blocked and everyone told to show up? After all, learning matters. So shouldn't it be required?

There is a particular kind of spaciousness that comes with standing inside a centuries-old European attic after everything has been cleared out, but it is fleeting. My imagination is quickly stifled by the deep sense of responsibility I feel to protect the layers of labor carried out long before my time, and the worry that I am in over my head.

Over the last decade, I've seen systems change become an increasingly prominent part of the evaluation and programming landscape. There have long been practitioners and champions for a systems approach, but lately, it seems like more funders want to understand it, more grantees are trying to do it, and more evaluators are being asked to measure it. And yet, a common thread in many conversations I have is the same: systems change can be hard to see, hard to measure, and hard to communicate.

At The Convive Collective, everything starts with the learner. We believe learning experiences are exponentially more effective when they are designed around how people actually prefer to receive, process, and apply information, rather than how we assume they should.

In part two of this blog series, I share my findings on leveraging AI as a design tool, specifically in the phase that comes after you’ve analyzed the data and are deciding how to present it for sensemaking. I was curious to see if AI is ready to be the new intermediary - either by helping non-designers produce these learning artefacts or by assisting experienced designers in doing their work more efficiently.

At Convive, we have been experimenting with AI tools across a range of tasks: as thought partners, research assistants, and of course, notetakers. On a recent assignment with a European Foundation, we tested ChatGPT, Perplexity, and Gemini to support a large-scale qualitative systems change analysis.

Our actions, decisions, collaborations, and even our frustrations shape, and are shaped by, the systems we’re part of. Systems thinking isn’t an abstract academic exercise; it’s the water we swim in every day.

At Convive, we believe a little levity goes a long way! When you give people permission to laugh, play, and explore, you’re making space for agency and delight in the midst of the pressure to demonstrate impact.

This is the fourth blog of a four-part blog series. The post outlines the "BRIDGE Method," a conversational tool for organizational learning specialists working in philanthropy.

This is the third blog of a four-part blog series. The post outlines the "BRIDGE Method," a conversational tool for organizational learning specialists working in philanthropy.

This is the second blog of a four-part blog series. The post outlines the "BRIDGE Method," a conversational tool for organizational learning specialists working in philanthropy.

This is the first blog of a four-part blog series. The post outlines the "BRIDGE Method," a conversational tool for organizational learning specialists working in philanthropy.

Our classic approaches to evaluation methodologies and designs leave little to no time for learning and digestion (often leaving it for the last stage when resources are tight and energy is low). Not to mention, evaluators often do not have the skills to effectively facilitate learning and therefore are approaching it in the least effective ways (which undermines the whole purpose of the evaluation - urgh!).

We think that sending people reports, infographics or two-pagers is enough to help them learn. But we know from the learning sciences that this simply isn’t true.

So, for us professionals who are trying to build organizational learning cultures and practices rooted in iterative evaluation and research, we’ve got to ask ourselves: how do people actually learn? And how can we use these learning sciences principles to design organizational processes that actually work?

Maybe you’re a new team lead and you want to establish clear norms for communicating with your team. Or you may be a team lead that has been working with your team for some while, but you’ve had some changes or new team members recently and the norms about how you communicate with each other are worth exploring. In either case, you’ve come to the right blog! 

As organizational learning professionals, we have received a lot of questions over the years about what makes for a good organizational learning plan.

This blog is part two in a three-part series and focuses on how to actually create an organizational learning plan.

This is the third blog of a three-part blog series. It details how to actually put an organizational learning plan into action and common challenges that arise.