Is AI stopping your team from “working out loud”?
- 1 day ago
- 5 min read
It’s clear that AI is changing how work gets done. What’s less visible is how it’s changing the way work is shared, discussed and improved inside organisations.
The concept of working out loud was first described by Bryce Williams in 2010 and later developed into a structured practice by John Stepper.
At its heart, it is about making work visible as it happens. This includes sharing ideas, talking through approaches, showing work in progress, challenging assumptions and refining outputs together. Importantly, rather than waiting to present a finished product, people make their thinking and decision-making visible along the way.
When work is visible like this, it becomes a powerful driver of capability. People learn about how work gets done, not just what a great final product looks like. They have the chance to see how other people in their team approach problems, how decisions are made and how outputs are improved over time. They can also see that people don't always get things right the first time and need to refine and update to get a good end result. If you think about it, most of us have learnt important skills in the workplace this way.
When this process is made deliberate, it becomes a consistent and scalable way to build capability across a team.
Heads up, right now this practice is eroding across many organisations.
As AI is adopted across businesses (or used quietly by individuals), more work is happening in isolation. People are using these tools to draft, analyse and refine outputs privately before sharing a final version. There can be hesitation about being fully transparent about how that work was created. In some cases, this leads to AI being used “in the background”, with only polished outputs shared openly.
Let's face it, most of us are scared of looking like we don't have all the answers. For people in your team, delivering a polished end result can really reduce the discomfort of being seen to get things ‘wrong’ or needing to explain how the work is developed.
Over time, this can change something really fundamental. The process people go through to create work becomes less visible, and only the outcome is shared. We collectively stop ‘working out loud’.
Leaders, in turn, are left to interpret outputs without visibility of how they were produced. Work may appear strong, but the thinking behind it, and how AI has influenced it, is less clear.
When this happens, capability development becomes more isolated and less consistent. Some individuals rapidly build capability, while others struggle without support or visibility of good practice. The organisation’s ability to improve collectively reduces.
In an AI-enabled environment, it is even more important to ensure that work is not only efficient, but also high quality, responsible and adding real value. One practical way to do this is by deliberately creating structures that support ‘working out loud’.
A framework for 'working out loud'
The process doesn’t need to be complex to be valuable. In its simplest form, it is about making thinking, decisions and approaches visible.
Working Out Loud = Share the context → Explain the approach → Show the output → Reflect and improve
1. Show the Starting Point
What was the problem or task? What were you trying to achieve?
In an AI context:
Share the brief, context or objective
Define the expected outcome or value (ideally with a client or customer-centred lens)
Why this matters: it helps others understand intent, not just output, and ensures AI is aligned to the right problem and outcome.
2. Share the Approach
How did you approach the work? Where did AI support the process?
In practice:
Describe how you used AI at a high level
Explain key steps, inputs or decisions
Highlight where your judgement was applied
Note any checks applied to ensure appropriate use
Why this matters: It keeps thinking visible, supports learning, and helps ensure AI is used appropriately and responsibly.
3. Show the Output (and Refinement)
What was produced? What changed along the way?
In practice:
Share progressive outputs or examples
Explain how outputs were refined
Highlight what steps or actions improved quality
Identify what was adjusted to ensure accuracy and relevance
Why this matters: it builds a shared understanding of how judgement and insight are applied, and whether AI is improving quality, not just speed.
4. Reflect and Improve
What worked well? What would you do differently next time?
In practice:
Capture lessons learned
Share practical tips
Listen to feedback
Identify improvements
Consider whether AI added value or created additional effort
Reflect on whether its use aligned with expectations and guidelines
Why this matters: It supports continuous improvement and helps validate whether AI is adding real value, being used effectively and supporting sustainable ways of working.
A useful question to consider is: 'does your organisation understands how work is being done, or only what is being produced?'
If thinking and process are no longer visible, capability becomes harder to build, sustain and scale. AI has the potential to streamline and even improve how work gets done, but your team’s capability is still developed through shared experience and visible thinking. The challenge to make sure that as work becomes more faster, it doesn’t become less transparent, thoughtful or focused on customer outcomes.
How Capability Alliance can support you
At Capability Alliance, we focus on ensuring AI adoption strengthens how people work, how capability develops and how outcomes are delivered.
Our Capability Alliance AI Transformation Model™ is built on a simple principle: AI should improve person-centred outcomes, quality of work and shared capability, not just increase activity and outputs.
We work with organisations to understand how AI is being used in practice, not just whether it is being used. This includes identifying where work has become less visible, where capability is developing unevenly, and where AI is adding value or creating additional effort.
Using our model, we help you to:
assess real capability at an individual level, validating what people can consistently demonstrate
identify where visibility has reduced and where “working out loud” practices are being lost
understand whether AI is improving quality and outcomes, or simply shifting effort into less visible activities
embed practical ways to make thinking and decision-making visible again in an AI-enabled environment
support sustainable and ethical use of AI, so that it is applied with appropriate judgement, transparency and accountability
A key part of this approach is helping teams reconnect how work gets done with how capability is built. This means creating environments where people not only use AI effectively, but also share how they are using it, reflect on what works, and learn from each other.
If your organisation is starting to see less visibility in how work is developed, or you want to ensure AI adoption leads to stronger capability, better outcomes and consistent, ethical practice, we can help you assess where you are today and define a clear, practical way forward. Contact us.
References and Further Reading
Williams, B. (2010). Working out loud: Observable work + narrating your work. Retrieved fromhttps://ipma.world/working-out-loud-an-idea-that-became-a-movement/
Stepper, J. (2015). Working out loud: For a better career and life. Ikigai Press.
Stepper, J. (2020). Working out loud: A 12-week method to build connections, capability and career development. Page Two Books.
Gustafson, K. (2023). Rehumanise your workplace by working out loud. Retrieved fromhttps://www.reworked.co/collaboration-productivity/rehumanize-your-workplace-by-working-out-loud/
Augner, T., Schermuly, C. C., & Jungmann, F. (2024). Working out loud: An intervention study to test an agile learning method. Journal of Workplace Learning. Retrieved fromhttps://eric.ed.gov/?id=EJ1407526
