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.

 What Do Humans Bring (1)? Social Infrastructure

Cogitating, ruminating, showing my work. What are your thoughts about "Social Infrastructure" and what humans bring to the workplace?

https://youtu.be/g--T7k1GT74

 𝐘𝐨𝐮 𝐂𝐚𝐧’𝐭 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐞 𝐓𝐫𝐮𝐬𝐭

I’ve written a lot over the past few weeks about ways AI can support Networks, CoPs, and Showing Your Work/Working Out Loud. It can do a lot of things to amplify our efforts, corral our messy work habits, and generate rivers of content.

But AI can’t:

♦create trust
♦generate reciprocity
♦produce psychological safety
♦substitute for shared experience

𝘊𝘶𝘭𝘵𝘶𝘳𝘦 is where those things happen, in organizations where people are allowed to talk with each other, given opportunities to get together, enabled to voice concerns and objections, and can admit problems or mistakes without fear of the sky falling. That's a culture that fosters trust, reciprocity, inclusion, and psychological safety, values shared experience, and provides “collision spaces” like break rooms and water-cooler corners, both literal and virtual. Culture is about transparency. Culture permits the existence and effective use of the things I’ve been talking about for the past 20 years: strong networks, communities, CoPs, and showing your work/working out loud.

One—ok, two-- of my lasting concerns with conversations regarding “culture” is that the word itself is devilishly hard to define, and often it feels like the onus for creating an effective/productive/supportive culture is on management alone. Workers may not create enterprise-wide culture, but we absolutely help shape local culture. I mean, every team and community and department has its own culture, right? Its own norms and ways of working? (They’re called micro-cultures, btw.)

What culture(s) are you a part of? What can you, as an individual, do to help shape it? As an L&D practitioner, what influence might you have over it?

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