1 Student Role and Contribution
My contribution to the AI Strategy was primarily centred on the governance, failure analysis, and future-market test. While these components of the project were positioned more towards the end of the project, their development needed thorough understanding of the decisions made in the components prior to these. Therefore, our team supported a strong collaborative environment, where we discussed and reviewed many preceding stages as a group. The individual responsibility remained central, but within this we asked, brainstormed, and solved together.
For the governance and failure analysis, I examined the potential failures of AI-based visual inspection. This included false acceptances and rejections, manipulated evidence, performance variation, and the limitations inherent to photographic assessment. I aimed to consider not only the identification of these potential failures, but additionally their consequences and the measures through which we could possibly mitigate them. Furthermore, I concerned myself with the future -market test. Here I examined how the proposed solution would remain strategically viable in an environment where other stakeholders, such as customers, suppliers, competitors and AI providers would also possess advanced AI capabilities. This required me to look beyond the immediate operational improvements, and consider how the technology might alter behaviour of other parties.
2 Important Decisions and Iterations
If I could identify one of the major decisions we had to make during this project, it would be where the photographs used for inspection would originate. Initially, we considered photographing returned items in the warehouse, which would allow for relatively standardized photographic conditions and greater control over the evidence provided to our model. However, this also introduced additional operational costs, potentially undermining the very efficiency gains our solution intended to achieve.
To mitigate this problem, we moved towards customer-submitted photographs. While this would solve the additional operation cost issue, it presented its own, new, problem: the consumer providing evidence also has an interest in the outcome of the assessment. The resulting information asymmetry became prominent in my failure analysis. Customers could potentially selectively photograph an item, conceal damages, or submit manipulated images. This led to the introduction of safeguards, such as live in-app photography, meta checks, and duplicate-image detection. However, this remains a limitation and can not entirely eliminate the underlying information asymmetry. Therefore, after iterating through the projects several stages we decided to implement the customer photography element and the decision was made to retain human oversight for uncertain or high-risk cases.
3 Surprises & Failures
One of the most interesting realization was that the technological capabilities of the model did not necessarily constitute the primary obstacle to implementation. The difficulties often emerged when we attempted to translate those capabilities into a reliable and economically viable business process.
Our evaluation demonstrated that the model could assess returned items considerably faster than human graders, averaging 5.9 seconds compared to approximately 38-48 seconds. Initially, this appeared promising. However, 50% of the evaluated cases were escalated for human review, whereas our economic analysis assumed an escalation rate of 20%. Although the evaluation sample deliberately included difficult cases and therefore hindered us from establishing the escalation rate under normal operating conditions, it raised an important concern. The economic viability of the system depends not merely on how quickly the AI can assess an item, but on how frequently its assessment actually replaces human labour.
Another limitation became visible through the governance and failure analysis. Certain defects, such as changes in texture or internal damage cannot necessarily be observed through photographs. No matter how capable the model becomes, it cannot reliably identify information that the provided evidence does not contain. I found this distinction particularly important, as it demonstrates that not every limitation can be overcome through improved model performance.
4 The Impact of feedback on Project Development
Feedback and discussion throughout the project were particularly useful in forcing us to reconsider assumptions that initially appeared relatively straightforward. Within my own contribution, this became apparent when examining the proposed mitigation measures. For example, requiring customers to take photographs directly through the application initially seemed like a reasonable response to manipulated evidence. However, further consideration revealed that photographs made live did not necessarily provide an accurate representation of an item’s condition. A customer could still deliberately omit particular angles without technically manipulating the image. Similarly, the discussion surrounding the visibility constraint made us reconsider what it actually meant if the model would make an error. We identified a distinction between incorrectly identifying an observable defect and being unable to assess a defect and being unable to assess a defect in the first place. The questions and feedback that the group provided on each member’s part, pushed the failure analysis away from general AI risks and towards more specific limitations.
5 What was learned from the collaboration
Working in a group of four made the interdependence of the different project components particularly apparent. While the responsibilities could be divided relatively easily, the underlying assumptions and decisions could not be treated independently. For example, the economic analysis was dependent on assumptions regarding the proportion of cases requiring human intervention. The governance analysis introduced those safeguards that could then increase this proportion. A stricter approach to human oversight might improve decision reliability yet simultaneously reduce the expected cost savings.
I found this particularly educational in the aspect of integrating different perspectives into one coherent business proposal. The collaboration therefore required more than combining individually completed sections. It demanded that we reconsidered how decisions made in one part of the project affected the arguments developed elsewhere. Looking back at it, I think this interdependence could have been addressed more explicitly throughout the development process, rather than primarily when bringing the separate components together.
6 The Projects Influence on the Understanding of AI and Business Strategy
Before this project, I primarily associated the strategic value of AI with its ability to automate existing activities, thereby reducing costs and improving efficiency. This idea remains relevant, however, the project made me increasingly aware that the actual value of AI depends considerably more than the performance of the underlying model. Our model might predict that a returned item is in satisfactory condition, but it does not automatically mean that the retailer should approve the refund without further verification. And the decision also depends on how reliable the evidence is and what the consequences of an incorrect assessment are. I additionally came to realize that governance is not just an external constraint on technological innovation, but it also directly shapes its economic potential. Human oversight reduces the risk of incorrect decisions, but it also adds costs. So the strategic question isn’t just how much human involvement can be cut, but where it creates more value than if it were removed. My future-market analysis complicated things further. If Zara successfully implements AI-based return inspection, its competitors may introduce similar systems eventually. Meanwhile, customers equipped with their own AI tools may become better at navigating return policies, potentially creating new opportunities for strategic behaviour. So the competitive advantage cannot be assumed to come from just having an AI model. Instead, having proprietary return data, accumulated feedback, and integration into existing operations becomes more important.
