10 Reflections

‘Follow effective action with quiet reflection. From the quiet reflection will come even more effective action.’  Peter Drucker

When producing the Pressbook, my goal was to make it a collaborative, living document through incorporating ‘user feedback’ in the form of reflections from students and alumni.

This chapter is currently structured into two sections:

1. Thematic feedback gathered through cohort collaborative de-brief on different core aspects of the module. This is non-attributable
2. Specific reflections offered by named students or alumni. In this case the person offering the feedback is named as collaborator and their name included in the Pressbook ‘front matter’ as a contributor.

This structure may evolve as the collaboration process gains momentum.

10.1 Generic Feedback

10.1.1 Impact of Lego Serious Play on Team Dynamics

The cohort of 2024/25 were invited to a social debrief session where we used Lego Serious Play to consider the learnings about the impact of Workshop [1] Lego Serious Play sessions on team dynamics. Yes, I know that sounds recursive(!), but it did work. The team presentations were videoed (with the students’ permission), and the transcript ingested into NotebookLM to generate a short report of the five main learning points. These were:

1. Lego facilitates the analysis and representation of a team’s internal state and external challenges. The teams utilised their Lego models as a powerful tool to metaphorically depict and analyse their internal landscape, such as strengths and weaknesses, as well as external factors like threats and opportunities. This tangible representation allowed them to better understand their team’s position and the environment it operated within.
2. The models help teams visualise and articulate their journey, including challenges and overcoming barriers. The process of building allowed teams to create a narrative for their shared experience, representing their evolution from potentially scattered individuals to a strong, cohesive unit. Their models depicted the ‘ups and downs’ of their journey, significant challenges encountered (likened to sharks, snakes, or barriers), and how they collectively navigated these obstacles, such as by ‘bridging everything together’ or choosing a path that builds a ‘stronger foundation’.
3. Lego Serious Play highlights the critical role of relational elements like empathy, trust, and understanding in successful teamwork. A prominent recurring symbol across models was the ‘heart’. This represented core relational aspects such as empathy, which was seen as crucial for overcoming challenges together and trust, necessary for facing difficulties and bridging gaps. Speakers emphasised that understanding differences and showing kindness are key to effective teamwork and a positive process.
4. The exercise provides a platform for teams to reflect on and demonstrate how they handle diversity, bridge differences, and build cohesion. The models were used to show how teams composed of individuals with different foundations or from different groups learned to work together despite their differences. They illustrated the process of consolidating diverse ideas, making decisions collaboratively, and coordinating efforts to resolve issues and build a unified solution.
5. Building with Lego helps teams connect abstract strategic concepts and learned methodologies to their practical experiences in overcoming team challenges. The models incorporated representations of concepts discussed in their learning, such as SWOT analysis and strategic analysis. The builds allowed teams to show how the methodologies, tools, and guidance they acquired helped them gain perspective, overcome ‘barriers’, and navigate difficulties to achieve their goals.

10.1.2 Principles of responsibly GenAI usage

The same cohort were also asked to model the principles for responsible and ethical use of Generative AI that they had learnt through the module, again using Lego Serious Play. Following the same process as described above, these are the five main learning points:

1. Use AI Mindfully and Verify its Output: It is crucial not to use AI blindly or go forward without looking at what it provides. AI output is not always accurate, and it can give misleading or incorrect information. You need to be mindful when using AI, double-check the information, and sort or choose the data selectively. Just copying and pasting what AI generates is not ethical.
2. Master and Control the AI, Don’t Let it Control You: The relationship with AI should be one where you have the upper hand; you are the ‘boss’. The goal is to train the AI so well that it doesn’t manipulate you but rather guides you in the right direction. Responsible use involves mastering the use of AI, which means filtering and training it, not just asking questions and getting answers. It’s like “taming the lion” to get the right answer.
3. Understand the Ethical Implications and Context: Responsible use includes an ethical approach. Ethical considerations are important, such as understanding that simply copying and pasting AI-generated text is not ethical. Responsible use also extends to considering the environmental impact, as mindful use can lead to less carbon impact from servers.
4. Be Aware of AI’s Limitations and Potential Risks: It is vital to be aware of the limitations of generative AI. GenAI is not always accurate. One significant risk is uploading client information into open AI platforms without knowing where the data is going. Understanding these implications is a top priority for industry professionals.
5. Adopt a Holistic Approach with Oversight: Responsible use involves taking a holistic look at using AI. There should be oversight in the process. This implies not just focusing on getting immediate answers but considering the broader context, potential issues, and ensuring proper management of the AI tools being used.

10.2 Specific Feedback

10.2.1 Digital Transformation in Chinese Manufacturer

These reflections were kindly submitted by Xinyi Wang in March 2026. Note that formal consent to use this reflection was granted by Xinyi to the Pressbook owner by email.

Digital Transformation Practice

After completing my dissertation, I began to actively participate in the digital transformation process within the company. Through this practical experience, I have gained a deeper and more concrete understanding of digital transformation and encountered many challenges that differ from those discussed in academic research. The following reflections summarise some of the insights I have developed during this process.

Current Stage of Digital Transformation

The company is currently at the early stage of digital system implementation. Although the system now covers several functional areas such as production, warehouse management, and procurement, the main focus of the current work lies in two areas:

1. Testing whether the system truly aligns with the company’s operational needs
2. Establishing and standardising foundational data structures, particularly the material master data

At this stage, the most time-consuming tasks are not technical implementation, but rule-setting and data organisation, including the design of material coding systems, historical data cleaning, and the standardisation of data entry formats.

Key Challenges Encountered in Practice

Through the implementation process, I gradually realised that the main challenges of digital transformation are concentrated in several areas.

Material master data disorder
Historically, the company lacked unified naming and classification rules for materials. The same material could appear under multiple names, while the same name could refer to different materials. In addition, specification parameters were often filled in based on individual habits rather than consistent standards.

Once the system was introduced, these issues quickly became visible, making data migration and coding system construction extremely challenging.

Large-scale historical data cleaning
Due to the company’s scale, the historical material database contains tens of thousands of records. When establishing a unified coding system, each entry needs to be reviewed, merged, and supplemented with missing specifications, often requiring repeated confirmation with procurement and production teams.This process is extremely time-consuming and requires sustained cross-departmental collaboration.

Lack of clear decision-making standards

Another realisation during the process is that digital transformation rarely has a single “correct answer”. For many key design decisions, such as material classification structures, coding rules, or data field definitions, it is difficult to determine which solution is objectively optimal.
Many decisions must therefore be made under uncertainty and are often influenced by the company’s existing management capabilities and operational habits.

Key Insights from Practice

Several important insights emerged during the implementation process.

1️⃣ First, I realised that digital transformation is not merely a technological issue, but fundamentally a governance issue. Digital systems do not automatically resolve organisational problems; instead, they often expose previously hidden management weaknesses.

For instance, after the system was introduced, it became clear that many internal processes relied heavily on experience and informal communication rather than clearly defined procedures. Apart from the production line itself, many roles lacked clearly defined responsibilities or standardised workflows.

Therefore, the first problems revealed by digital systems are often not technical limitations, but the absence of structured management processes.

2️⃣ Second, I came to understand that process optimisation is a fundamental prerequisite for digital transformation. If existing workflows are already disorganised, implementing digital systems may amplify inefficiencies rather than resolve them. As a result, much of our effort has shifted toward process clarification and rule-setting, such as material coding standards, data entry rules, and clearer responsibility allocation.

3️⃣ Third, I observed a common phenomenon: many senior managers strongly aspire to pursue digital transformation, yet lack a clear roadmap for how to implement it. Because digital transformation does not have a universally correct path, decision-makers often struggle to determine what the “right approach” should be.

Ultimately, I realised that what companies truly lack is not software systems, but two core capabilities:

1. the capability to design and structure business processes
2. the capability to continuously drive organisational change

Without these capabilities, digital transformation initiatives often struggle to progress sustainably.

Re-evaluating the Value of Digital Transformation

Before becoming involved in practice, I was somewhat sceptical about the cost-benefit balance of digital transformation. However, through the implementation process, I gradually realised that the company’s previous lack of digitalisation had created substantial hidden inefficiencies.
For example, production data was not systematically collected or analysed, making it difficult for management to make evidence-based decisions. This often resulted in significant waste of labour, materials, and time. In addition, the lack of transparency and standardised processes could also lead to quality problems.
I therefore now strongly believe that although digital transformation is extremely challenging, its potential value for operational efficiency and organisational improvement is substantial.

Personal Reflection

On a personal level, the most significant change for me during this process has been developing greater patience and a stronger ability to accept uncertainty and failure.

At the beginning, I hoped to find a clear “correct answer” for many problems. However, through practice I gradually realised that digital transformation is inherently a process of continuous experimentation and adjustment.

As a result, I am no longer as afraid of failure as before, because I have come to understand that failure is simply part of the exploration process. Digital transformation is not a one-time project, but an ongoing journey of learning and iteration.

This perspective has also made me more patient in driving change and more willing to test ideas on a small scale in order to gradually identify solutions that fit the company’s real context.