Colm Farrell

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On November 15, 2018, Colm Farrell commented on Volkswagen Hits the Road with the Adoption of 3D Printing :

Thank you for the great article – super interesting topic!

On your questions; I agree with Thomas that AM has limited potential in car customisation. However, I believe AM has significant potential to be applied in other areas of the automotive industry.

Specifically, I believe that AM can also be used to create support tools for automotive production lines. This is not about using AM to produce the input parts as mentioned above, but instead about creating printed tools to support production and quality assurance on the factory floor. One case study of this I found stood out to me; Stratsys, a 3D printer manufacturer, helped to design a multi-functional tool to combine several production steps and check the quality of a taillight, via measurement, in one step [1]. While this is a simple application, it illustrates that the use of AM in the automotive industry should not be limited to producing input parts.

[1] Stratasys, “Five Ways 3D Printing is Transforming the Automotive Industry”, 2016,, accessed November 2018.

On November 15, 2018, Colm Farrell commented on Adidas and Additive Manufacturing :

Thank you for writing about this! It’s a very interesting topic.

To your question, I believe there is sustainable demand for higher-priced customized shoes. My dream scenario is I walk into a store, have my feet scanned, and have a customized shoe printed right then that conforms perfectly to the way I walk or run. I would be definitely be willing to pay a premium for this service.

It’s not just me – according to a recent Deloitte study, 1 in 5 consumers would be willing to pay a 20% premium for customized products [1]. That said, the current $300 price point for Futurecraft 4D isn’t sustainable, but a smaller price premium could still be used to absorb the capital cost of AM in the future.

This yields an important question; when will AM technology be cheap enough to allow sustainable demand for customized shoes? It’s hard to say when this will happen, but it’s estimated that the price of 3D printing machines will continue to drop ~6% annually over the coming years [2]. This, coupled with falling filament and other input costs, could mean that the goal of on-demand, customized shoes is not that far away.

[1] Deloitte, “The Deloitte Consumer Review. Made-to-order: The rise of mass personalisation,” 2016,, accessed November 2018.

[2] Greg Nichols, “Promising trend for innovators: 3D printer prices are falling,” ZD Net, February 2016,, accessed November 2018.

Super interesting post! I’ve seen first-hand how slow moving many traditional UK banks are, so I’m glad Barclays are attempting to innovate in this way.

To your question; I believe full-scale acquisitions are a way of supplementing the product development funnel. Fundamentally, by acquiring a business Barclays are signalling that they failed to develop that product via the Barclays Accelerator Program (or other internal innovation methods). As a result, the acquisition is a way of ‘plugging’ an innovation gap. For example, Societe Generale acquired Treezor, a payments platform, earlier this year as a way of reducing the time to market of payment products for clients [1]. This capability gap had not been addressed by their own open innovation process, so they were forced to acquire.

However, given a choice, I believe a business should choose to use its open innovation process to develop capabilities in an organic way. This allows you to achieve the ‘reverse mentorship’ you mentioned above, alongside influencing the direction of technological development and developing lasting partnerships with top talent. Indeed, 75% of FinTech start-ups noted they want to collaborate with banks – so the demand is there [2]. Conversely, by acquiring, you expose yourself to the classic risks of integration; that processes won’t integrate effectively and talent will leave.

[1] Societe Generale, “Societe Generale announces the acquisition of Treezor and accelerates its open innovation strategy,” September 2018, accessed November 2018.

[2] Capgemini, “World Fintech Report,” 2018,, accessed 2018.

On November 14, 2018, Colm Farrell commented on Google Duplex: Does it Pass the Turing Test? :

Thank you for the interesting read! I too remember watching the original launch of Duplex and being amazed it its potential.

I believe the questions you posed are important. In my opinion, I do not believe there are many current Google services, aside from a phone or Google Home ‘assistant’, where this natural language processing technology can be implemented. However, I do see a real potential for Google to commercialize this technology and apply it to customer service process improvement. For example, if I were to call my bank for a simple query, I see no reason why this can’t be handled by a machine.

If Google were to commercialize this technology into a customer service product, I believe they would need to position it as a win-win for the business and end-consumer. By doing so, they could minimise backlash against the business for eliminating human jobs. For businesses, the benefit is clearly a reduction in customer service headcount. For end-consumers, I would argue it leads to improved customer service levels, as machine-agents waste no time searching for information or transferring calls to other departments, leading to shorter call times and higher customer satisfaction. Indeed, a recent Oliver Wyman report noted that use of virtual agents could reduce call times by up to 20% [1].

However, I agree, for the sake of our sanity, it should not be applied to telemarketing.

[1] Oliver Wyman, “Customer Service in the Age of Siri and Alexa”, Dec 2017,, accessed November 2018.

On November 14, 2018, Colm Farrell commented on Resurging Queen: Singapore Airlines and Its Open Innovation Scheme :

Thank you for the interesting read! With my response, I wanted delve into your question of ‘how open is too open’ and how this can impact SIA’s competitive advantage.

I would argue that SIA should go even further in their open innovation strategy and enter into ‘open innovation networks’ with other airlines. In such networks, not only does each company run their own open innovation initiatives, they share the resulting ideas/technologies with one another [1]. While I recognise this may concern you from a competitive advantage standpoint, I would propose that this ‘network’ comprise of non-direct competitors. For example, SIA could engage with other companies that they rarely compete with, but who are also struggling with the expansion of Middle Eastern airlines (e.g., British Airways).

By taking this network approach, SIA could both expand the number of ideas entering their product development funnel, and also gain access to additional resources to narrow down these ideas. At the same time, they would maintain a competitive advantage in the markets they operate. While industry experts agree these networks are liable to ‘gaming’, whereby some partners may try get out more than they put in, I believe the benefits outweigh the risks [2].

[1] Deloitte, “Executing an open innovation model: Cooperation is key to competition for biopharmaceutical companies”, 2015,, accessed November 2018.

[2] Torbjørn Netland, “The future of competitiveness is open,”, World Economic Forum, January 2018,, accessed November 2018.

On November 14, 2018, Colm Farrell commented on Enhanced Learning Through Machine Learning :

Patricia – thank you for the interesting read!

To your first question; I am of the opinion we will always need teachers in the classroom. It is the responsibility of teachers to provide a well-rounded education to students. While this education should cover teaching traditional hard math and language skills, it also involves inspiring students and helping to diagnose social and emotional causes of learning difficulties. To take an example, for a child whose parents are going through a divorce, a human-teacher can recognise this and provide support and inspiration that a machine can’t. Furthermore, I worry that the lack of human-contact resulting from machine-only teaching could curtail the development of children. Such concerns have also been raised relating to the use of ‘robot nannies’, with early research suggesting it could lead to developmental impairments among younger children [1].

As a result, I agree that machine learning has application in education, but I believe this is limited to acting as a support-tool for teachers (e.g., for grading, teaching of simple concepts and record keeping). Given what we know about machine potential, McKinsey estimates only 27% of education activities could be automated in the future [2]. While this number may increase as technology develops, for the reasons mentioned above, I don’t see it ever hitting 100%.

[1] N. Sharkey, A. Sharkey, “The crying shame of robot nannies: an ethical appraisal,” Interaction Studies: Social Behaviour and Communication in Biological and Artificial System, 11(2) (2010): 161-190.

[2] M. Chui, J. Manyika, M. Miremadi, “Where machines could replace humans—and where they can’t (yet),” July 2016,, accessed November 2018.