5. Findings
5.1. Bluefragments’ Perception of AI
5.1.2 Technology Strategy
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contextuality to human beings. You would most likely describe a person with the same
superintelligence, as an AI, as having some form of limited mental capacity - if they only displayed such a superintelligence in one specific area. We expect humans to have an average, but very broad intelligence. Therefore, according to Martinsen, we understand human and machine intelligence differently, as the two intelligences operate in different contexts.
However, Bluefragments expect that the context of intelligence might change. Martinsen specifically expects that the area of machine superintelligence will change in future:
“Yes! There is no doubt that over time it ((AI)) will get a smarter and smarter, of course. And we will see that in many aspects, we as humans will need to redefine ourselves as well, because of that. And ((whether)) it will happen in the next 5-10 years, I don’t know. It is totally impossible to predict. But what we can predict is that it's definitely going to be more intelligent, and even more intelligent than us in many areas.” (Appendix 2, l.
307-310).
It is clear, that Bluefragments expect that the reliability and availability of AI will only increase in the coming decades, and that this change will have a profound impact. Bluefragments expect that the change will force us to rethink what we expect from ourselves and everyone arounds us, since everyone will have unimagined computing power at their fingertips. This will allow everyone to undertake actions in a matter of hours, which would previously have required years. This technological development will negatively affect the relevance of a multitude of specialisations, which Martinsen is aware of:
“There is no doubt that we will see the involvement of AI as a threat for humans. Because many people will feel threatened by this, they will see that machines can do stuff we cannot do.” (Appendix 2, l. 315-316).
Evidently, Martinsen expects that his company will be part of an industry, which will have quite a
disruptive effect on many of the jobs traditionally done by humans. The machines will help to create,
and will simply be better at completing the task than any human being.
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“Since the cloud is now available to everybody and all companies. That is what makes it possible to push AI into all companies.” (Appendix 2, l. 498-499).
Despite that Bluefragments are seeing a rapid development of the availability of AI technology, they do not see the adoption of AI in an organisation as a strategic decision. Rather, Bluefragments understand the adoption of an AI technology as a practical solution to a specific problem. Martinsen emphasised the practical nature of many projects by illustrating a recent example from a client:
“No, it's actually not ((a strategic decision)). We've just started a new project for a customer, where they have people working with data and they just said ‘Well, at this point we as a human cannot solve this, because the data is too unstructured for us to do it, and then we can use a machine to do it instead.’.
So, that's actually not a strategic way of using AI. It is just, a tool in the toolbox. To say like ((In the same way as saying)), you have Excel, and now you have AI. Those two combined gives me the solution..”
(Appendix 2, l. 503-507).
AI is clearly understood to be a tool, which opens up new possibilities, but is not so revolutionary as to completely overhaul the entire organisation. Martinsen also acknowledges that, currently, there is also a lot of hype surrounding AI. According to Martinsen (
Appendix 2, l. 68-70), the hype should also be considered as a reason why a significant amount of companies now show an increased interest in acquiring AI technology, as they simply want to ‘ride the wave’ to ensure that they do not ‘miss out on anything’ as Martinsen puts it (
Appendix 2, l. 511-513).
There is room for interpretation in how Bluefragments understand the term strategic.
However, we understand the difference between a strategic decision and a strategic tool to mean that a strategic decision will have operational implications for a significant part of the organisation.
Whereas a strategic tool might not have any significant operational effects, the signal value is the main reason for acquiring AI solutions. That is not to say that it cannot have operational value, it is just not the reason for acquiring it.
However, as Martinsen subsequently pointed out, there might not be any strategic elements to acquiring AI technology at all. He emphasised this lack of strategic elements by illustrating an example from a client:
“No I guess not ((there is not always a strategic impact)). Because let's go back to the solution, where we just took AI as a tool and used it together with Excel. It actually didn't have any strategic impact. It was just a way
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to solve a problem, and we found a valid solution. And I actually doubt that even the managers of that department of the company knew that they were going to use AI.” (Appendix 2, l. 516-519).
We clearly have an example of AI not being used for any strategic purpose, as Martinsen states it is a way to solve a problem. However, you also have to ask the question ‘how can the technology be used to solve a problem, which was otherwise unsolvable and then not be understood to be strategic?’.
We asked this question during our second interview with Bluefragments, the results of which can be found in the section ‘Ready-Made-AI’ adjusted (5.4). Martinsen, first and foremost, perceives Bluefragments as a provider of technological solutions to a specific practical problem.
Solutions which are easier to facilitate if they do not require a significant restructuring of the client company’s strategies. Therefore, it could be understood to be better fitted for Bluefragments when companies do not consider adopting AI in strategic terms. The whole idea of Ready-Made-AI is to facilitate a smooth, straightforward, and most importantly, predictable process for the companies - from the point where they find a need to adopt AI to the point where they actually implement it.
However, once you step outside the ready-made framework, the time scale for projects becomes quite large:
“The smallest project, when talking about custom AI solutions, that is, after you talk about Ready-Made-AI concepts - that is between one and two months. That is the minimum you need to use.” (Appendix 2, l. 531-532).
It is clear that Bluefragments makes a distinction between Ready-Made-AI solutions, which will work well generally, and a custom AI solution, which will only work in a specific context. It is interesting that Martinsen does not see any strategic element to implementing a custom solution, which by Bluefragments themselves, is considered to be quite time-consuming. It would not be unreasonable for Bluefragments to ask their clients to consider the money, time and human resources they spend on the technology in strategic terms. That would allow the client companies to better understand and plan for the technology’s use in the future relative to the size of the initial investment in both money, time and HR. Martinsen also emphasised time as a continual challenge:
“The time, definitely the time! Accepting that things cannot be done super-fast” (Appendix 2, l. 568).
Evidently, there is still a consulting task in terms of explaining to the client companies that they
should change their traditional software development paradigm. After all, working with data and AI
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is more ‘organic’, as you need the AI model to work with the data and mould itself into something useful. Whereas conventional software development is still very complex, but in nature it is very functional - A leads to B which gives you C. AI modelling is not as straightforward as it is based on trial and error:
“We cannot do that with data, we cannot say ‘Well, we just upscale the machine learning model and make it better’. That is hard work, that is based on a good insight into the data. So, that is pretty hard to scale!”
(Appendix 2, l. 571-572).