About Vanity Metrics...

 Recent news about Amazon shutting down a token AI Leaderboard ("don't use AI just to use AI") reminds me of this old chart from Jennifer Laycock, formerly of Search Engine Guide.

Vanity metrics don't necessarily = desirable outcomes.

 𝐂𝐨𝐥𝐥𝐢𝐬𝐢𝐨𝐧 𝐒𝐩𝐚𝐜𝐞𝐬: 𝐑𝐞𝐝𝐮𝐜𝐢𝐧𝐠 𝐒𝐨𝐜𝐢𝐚𝐥 𝐅𝐫𝐢𝐜𝐭𝐢𝐨𝐧 𝐢𝐧 𝐃𝐢𝐬𝐭𝐫𝐢𝐛𝐮𝐭𝐞𝐝 𝐖𝐨𝐫𝐤

A casualty of the shift to remote and hybrid work was the removal of many natural collision spaces. It's less common now to see places workers used to just run into each other, as with the literal water cooler, break room, or even stairwell. These moments helped people see what was happening elsewhere, meet new colleagues, discover unexpected expertise, and build a sense of “we’re in this together.” Such "collisions" can go a long way toward providing a psychologically safe environment, let people connect as peers, and help support a healthy culture.

While onsite workers may have now have access to high-traffic spaces intentionally designed to break down silos and encourage casual interaction -- like coffee bars or lounge areas with whiteboards -- distributed workers, don't encounter those collisions, and most digital environments don’t replace them.

𝐒𝐮𝐠𝐠𝐞𝐬𝐭𝐢𝐨𝐧𝐬 𝐟𝐨𝐫 𝐜𝐫𝐞𝐚𝐭𝐢𝐧𝐠 𝐚𝐧𝐝 𝐬𝐮𝐩𝐩𝐨𝐫𝐭𝐢𝐧𝐠 𝐦𝐨𝐫𝐞 𝐝𝐢𝐠𝐢𝐭𝐚𝐥 𝐜𝐨𝐥𝐥𝐢𝐬𝐢𝐨𝐧 𝐬𝐩𝐚𝐜𝐞𝐬:
Think about virtual spaces that mimic the casual interaction of the office,such as Slack channels for non-work topics (e.g., pets, hobbies), virtual coffee chats, or digital, interest-based groups.

Create or enhance these opportunities to support performance and the work culture and to alleviate the feelings of disconnection experienced by many remote workers.

𝐀𝐧𝐝 𝐠𝐞𝐭 𝐀𝐈 𝐭𝐨 𝐡𝐞𝐥𝐩 𝐲𝐨𝐮:
AI can help compensate for the absence of natural collision spaces. It can:
--suggest relevant conversations or communities based on work and interests
--surface shared challenges, complementary skills, or overlapping goals
--summarize meetings and threads so people can step into ongoing conversations
--create continuity across asynchronous exchanges
--create dynamic "office maps" where presence signals invite drop-ins
--house persistent digital whiteboards that connect ideas across sessions and teams
--summarize asynchronous conversations so people can more comfortably jump in mid-conversation

It's important to understand that this is not some sort of scheduled collaboration but, rather, a matter of restoring the conditions for more serendipitous, spontaneous connection, as with that water cooler. Giving space and places for safe opt-in, asynchronous (across time zones) silo-spanning connection that requires little activation/energy can help workers feel supported as well as provide catalysts for ideas and innovation, increase tacit knowledge sharing, and provide a peripheral awareness of sharing a physical space within the organization.

Extra credit: If you're interested in a more academic view, read up on "relational density" and its relationship to trust.

What ideas do you have for creating or supporting collision spaces for your remote and hybrid workers? What are you seeing work to create these kinds of collisions in distributed teams?

 What do humans bring? (6)

Social infrastructure to continuous learning to human meaning-making: AI is an AMPLIFIER, not a driver, of these things. It can find patterns, uncover redundancies, and speed up access, output, and experimentation.

But in many organizations it can't do that without the humans.

https://youtu.be/YtUw6lAZ9Xg

 𝐖𝐡𝐚𝐭 𝐝𝐨 𝐡𝐮𝐦𝐚𝐧𝐬 𝐛𝐫𝐢𝐧𝐠?(5) 𝐌𝐨𝐫𝐞 𝐨𝐧 𝐡𝐮𝐦𝐚𝐧 𝐦𝐞𝐚𝐧𝐢𝐧𝐠-𝐦𝐚𝐤𝐢𝐧𝐠

I posted about this on Friday. Social infrastructure and continuous learning are brought to bear in human meaning-making: How people interpret, question, and apply what they know. This demands contextual, rather than procedural, decision-making, involves weighing competing "goods" (like efficiency vs. safety) rather than a clear right or wrong, and often requires social awareness. And: it's frequently invisible work, where decisions are hard to see but impact can be huge.

Consider: If these decisions are left to AI, who is accountable if something goes wrong? AI will not be fined, fired, go to jail, feel guilt, or stay up at night worrying.

Here are some examples of human meaning-making. What others can you think of?

𝐒𝐚𝐟𝐞𝐭𝐲
When to shut down operations due to risk vs. continue under pressure to meet deadlines
Interpreting near-misses: anomaly or warning sign?
Adapting rules to real-world conditions (weather, fatigue, equipment variability)
Reporting safety issues that could delay projects or cost jobs
Enforcing rules consistently vs. making exceptions for experienced workers
Balancing productivity metrics with human well-being
Recognizing when a “compliant” situation is still unsafe

𝐂𝐨𝐧𝐬𝐭𝐫𝐮𝐜𝐭𝐢𝐨𝐧
Adjusting plans when site conditions don’t match designs
Deciding whether to proceed with imperfect materials or wait (cost vs. quality)
Pressure to cut corners vs. long-term structural integrity
Navigating “this is how we’ve always done it” vs. safer/better methods
Making tradeoffs between craftsmanship and speed

𝐇𝐞𝐚𝐥𝐭𝐡𝐜𝐚𝐫𝐞
Choosing between treatment options with different risks
Prioritizing patients when resources are limited
Using AI recommendations vs. overriding them
Managing uncertainty—when evidence is inconclusive
Balancing protocol adherence with individualized care
Communicating difficult news with empathy

𝐋&𝐃
Choosing when to push standardized training vs. allowing informal/social learning to emerge
Interpreting incomplete data from learning analytics (for example: low completion ≠ low capability)
Using AI to personalize learning vs. protecting employee privacy
Deciding if a “good enough” solution is acceptable under time pressure

What Do Humans Bring? (4): Human Meaning-Making

I submit that the next layer of "what do humans bring to organizations?" is meaning-making. What do they do with what happens in the social infrastructure and continuous learning layers?

This includes things like:
How people interpret, question, and decide
How they determine what matters, what to trust, and what to act on
Getting beyond information and "content" to judgment, context, and ethics shaping outcomes

(Note that the word "layer" is intentional. I am not thinking of a hierarchy, steps, or stack, but a dynamic, interdependent, reinforcing system. I am trying, in part, to get beyond the ages-old problem of "learning" being viewed as an event.)

 What Do Humans Bring (3)?  Continuous Learning

Connie Malamed told me to keep going...so I am. So far this week, in pondering what humans bring to the workplace, I've talked about my ideas around social infrastructure (made up of networks, Communities of Practice, working out loud/showing your work, and culture), and the ways L&D can support the humans in those spaces.

As I see it, the larger picture of organizational learning can be fleshed out into what I think of as the "Continuous Learning" layer, where the activities and learning in the social infrastructure are supported by formal, more structured things: LMSs, LRSs, traditional formal instruction, and the like. (I can't decide where to put performance management systems, but I'm thinking here, maybe?). It's not another tier --this isn't a hierarchy-- but another piece that completes the idea of "continuous learning" that happens in the organization.

And social learning happens here as well, as people -- we hope -- engage in robust, 𝘴𝘬𝘪𝘭𝘭𝘧𝘶𝘭𝘭𝘺 𝘧𝘢𝘤𝘪𝘭𝘪𝘵𝘢𝘵𝘦𝘥 (I repeat: skillfully facilitated) learning experiences built on realistic scenarios, real-world practice, and applicable takeaways, in settings that allow them to make meaningful connections.

In short:
Continuous learning (formal layer) = designed, structured, intentional
Social infrastructure = emergent, relational, adaptive

What Do Humans Bring? (2) More on Social Infrastructure

More:  The pillars of Social Infrastructure are networks, communities of practice, working out loud/showing your work, and organizational culture & its micro-cultures.

Where does L&D fit in here?

L&D’s role here isn’t to push out more content, but to shape how work, learning, and decision-making actually happen across the organization. That means
-helping communities of practice (CoPs) stay active, in a space where people working to improve their practice can share real work, compare approaches, and learn from each other in context.
-making it easier to surface what people are trying, what’s working (and what isn’t) so knowledge grows instead of getting lost or duplicated.
-introducing simple habits like debriefs so groups pause, reflect, and improve over time.
-helping managers create space for transparency, honest conversation, and experimentation.
-helping connect people across teams and networks so ideas don’t stay siloed.

In short, L&D supports the social infrastructure by strengthening the everyday patterns through which people learn, share, and get better at their work together.