Where will tomorrow’s judgement be built as AI changes work?
Where will tomorrow’s judgement be built as AI changes work?
As organisations integrate AI into daily operations, it changes not only which tasks people perform. It also changes how experience is built, how judgement is developed and how trust grows. When AI takes over research, analysis, administration and first drafts, parts of everyday learning may disappear unless organisations consciously replace them. For leadership and HR, the question is therefore not only how AI should be implemented, but how work should be organised so that people continue to develop the experience and judgement the organisation needs.
Key Takeaways
AI is changing not only tasks, but also how experience, judgement and trust are built within organisations.
When routine tasks are automated, important learning opportunities may disappear, such as observing experienced colleagues, testing lines of reasoning and learning from mistakes.
Real value is created not by AI-generated material, but by people’s ability to interpret, question, prioritise and make well-informed decisions.
Organisations need to move from allocating work to designing experiences, deliberately creating new learning environments through mentoring, rotation, simulations and joint decision reviews.
Leadership and HR need to ensure that AI strengthens the organisation’s long-term capabilities, so that more efficient processes do not simultaneously weaken learning, succession and future leadership.
When efficiency also changes learning
Organisations have long functioned as learning environments, often without this being explicitly recognised. Through doing, observing, discussing, making mistakes and receiving feedback, people have gradually built experience, developed judgement, grown into greater responsibility and earned trust.
The point is not to return to previous organisational models. Many traditional learning paths were slow, unevenly distributed and dependent on informal networks. But they also contained situations in which people could see how experienced colleagues thought, prioritised and took responsibility. As work changes, organisations therefore need to be more deliberate about which of these experiences still need to be built, and how.
A leadership team that previously worked its way through a strategic decision paper did not only learn the facts of the matter. In the process, assumptions were tested, risks were weighed against opportunities, and different perspectives were set against one another. If AI can summarise material, identify patterns and propose recommendations more quickly, the organisation must therefore ensure that people are still trained in what happens before the decision: interpreting, questioning and weighing things together.
AI can create value quickly in clearly defined tasks, such as summarising texts, structuring information or producing first drafts. But the closer the technology comes to complex questions, analysis, prioritisation, recommendations and decision support, the more critical business knowledge becomes. This requires an understanding of the organisation’s logic, history, risks, relationships and trade-offs, not just access to information.
This is the dividing line between efficiency and real value creation. AI can often help us arrive more quickly at a basis for action. But the quality of the next step, the interpretation, prioritisation and decision, still depends on how well people understand the business.
The problem is that much of the knowledge that determines the quality of these assessments is “silent” and experience-based. It is rarely gathered in systems or process descriptions, but becomes visible in how people interpret situations, weigh risk and understand consequences.
How do we make decisions?
How do we assess risk?
What characterises a good client dialogue?
How have we historically solved complex problems?
“As more everyday work is automated, some of the situations in which people previously learned from one another also disappear. An important question for the future is therefore how AI can help organisations preserve, share and further develop human experience.”
Companies risk digitalising processes without first capturing the knowledge that has made them successful. As more everyday work is automated, some of the situations in which people previously learned from one another also disappear. An important question for the future is therefore how AI can help organisations preserve, share and further develop human experience.
AI does not have to weaken learning if the technology is used well. On the contrary, it can spread knowledge, provide faster feedback, simulate complex situations and allow more employees to work on qualified, complex issues. The risk arises when organisations automate work activities without simultaneously creating new ways to build experience.
From allocation of work to experience design
I believe we need to broaden the conversation about AI. It is not enough to ask which tasks can be automated. We also need to ask: what did employees previously learn by doing these tasks?
If AI produces a first analytical draft, the employee must still be able to challenge assumptions, identify weaknesses, understand what is missing and decide whether the conclusion holds.
Learning does not therefore have to disappear, but it does need to change. Employees may need to engage with more complex questions earlier, and have greater opportunities to review, prioritise, reason and stand behind their recommendations. Here it is important to distinguish between knowledge, experience and judgement. Knowledge: understanding facts and information. Experience: built when knowledge is tested in different situations. Judgement: ability to use experience wisely when the situation is uncertain, information is incomplete and different interests need to be weighed against one another.
In practice, judgement becomes visible in the decisions where data is not enough: when risks must be weighed against opportunities, when the client’s needs are not clear-cut, when a business decision has consequences for people, or when the organisation needs to choose between rapid efficiency gains and long-term capability building.
Organisations may also need to create more structured ways of building experience: rotation between projects and functions, mentoring, joint reviews of decisions, simulations and more time together with senior colleagues.
For HR, this means that learning, performance management, succession and leadership development need to be linked more closely to how work is actually designed. It is about understanding which tasks are being automated, which experiences may therefore disappear, and which new training environments need to be created.
The aim is not to recreate the manual tasks that AI can perform better. The aim is to ensure that we do not make the learning itself more efficient out of existence.
“Leaders need to create more opportunities for colleagues to show how they think, not just what they deliver to keep building judgement and trust.”
Trust is not built through results alone
Trust has traditionally grown through working together over time. We see how a colleague reasons, handles uncertainty, takes responsibility and responds when something goes wrong. When more work is carried out with the support of AI, organisations therefore need not only to follow up on the result, but also to create visibility into the thinking behind it.
When AI helps us deliver faster and more complete results, it can become harder to see the process behind them. Leaders therefore need to create more opportunities for colleagues to show how they think, not just what they deliver. Can the employee explain why a recommendation is reasonable, which assumptions it is based on and what could change the assessment? It is through these conversations that both judgement and trust can continue to develop.
Building tomorrow’s capability
For organisations, this raises a broader question. Flat hierarchies, high trust and participation create good conditions for learning and knowledge sharing. But trust does not automatically create development.
“The organisations that succeed will not only use AI to make today’s work more efficient. They will also use the technology to strengthen tomorrow’s competence. ”
Coaching, mentoring and knowledge transfer need time, structure and recognition. Senior employees who develop others’ ability to make good decisions are not only building individuals; they are building the organisation’s future capability.
This also makes succession planning more important. Organisations need to be more deliberate about creating the experiences that the next generation of specialists and leaders require in order to develop and take on greater responsibility.
The organisations that succeed will not only use AI to make today’s work more efficient. They will also use the technology to strengthen tomorrow’s competence. Those that miss this dimension may gain faster processes but weaken the learning systems that future capability depends on.
For leadership and HR, AI is not only about technology choices or efficiency gains. It is about shaping work so that people can continue to build experience, judgement and trust, while the organisation captures the opportunities the technology offers.
Questions to bring to the leadership team
Which important learning opportunities disappear when we automate parts of the work?
Where should that experience be built instead?
How do we give people the opportunity to practise judgement, responsibility and complex trade-offs?
How do we assess people’s thinking and judgement, not only the quality of the finished delivery?
Do we give our most experienced employees the time and recognition to develop the next generation?
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Director People & Culture, Alumni Global
Jenny is part of the Alumni Global management team, responsible for the firm’s internal approach to talent, culture and organisational development. In this role she partners with senior leadership to shape strategic HR priorities, foster an inclusive and high‑performance culture, and support the growth and wellbeing of our people across markets. —> Read the full bio here
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