Artificial intelligence needs and boundary subjects

The director would ask AI, what sales leads are saying since it could help forecasting the production faster. He/she would like to know more about the availability of some compo- nents, and he/she thinks most the whole budget could be done with the help of AI if it would ask inputs and then iterate it further. The controller would like to have an auto- mated budgeting tool, which would enable him/her to be more professional and efficient at work. He/she argues time could be saved especially from revenue and payroll calcu- lations. The controller says automated budgeting tools are not in use in HardwareCo since they are so expensive. The procurement manager would ask AI how accurate the forecasts have been and whether a big monthly deviation should be ignored or not.

‘Budgeting could mostly be done with the help of AI. […] It could ask for certain inputs and then iterate it further.’ – Product Development Director

‘[…] the kind of budgeting system we would like to have is make […] all the cal- culations automatic […] to really save time to be more professional and efficient on the work. I think that’s a difference.’ – Controller

‘So, I would say for that kind of a system it can make like the revenue calculation, and the payroll is more automatic calculated, that can save a lot of time.’ – Con- troller

‘[I would ask AI] how accurately sales department have forecasted […] and of course my own accuracy. […] Is a big monthly deviation part of a trend or should it be ignored?’ – Procurement Manager

Outside of this forecasting process, the director would like to get more knowledge about the market situation including changes in the competitive environment and behavior of their customers and potential customers in e.g. social media. The director thinks it would be beneficial to know when a potential customer starts a development program so they could offer their services right away. The head of product would like to know what are the most loved and hated features of the products as it would help with prioritization of R&D. Since there are so much engagement data available, the head of product would like to have an algorithm going through all the data and to pinpoint unusual behaviors in order to recognize new customer values.

‘I would ask what is the most-loved feature of my product.’ – Head of Product

‘For example in my job, it would make it so so much easier, because for example the prioritization, of what do we do in R&D would become so much easier, if I know what customers love, not what they use, what they love then I can press the pedal more there. If I know what they hate, I know what I need to go and fix. So,

at the moment again all these decisions they are not really based on data […].’ –

Head of Product

‘For me for better decisions, […] I would need exactly this kind of thing, an algo-

rithm to comb through all that engagement data how our customers are using the product and give me these nuggets that hey, you have, 50 users which are using this thing but they are using it 30 times day, at least. This kind of stuff. Because

then I can go pick up my phone and call that guy and say “Hey, why are you doing this? Why do you find so interesting that you do this 30 times a day where majority

of my user, does it once every second day? What value do you get out of it?”’ –

The above-mentioned AI needs are presented in Table 10. I clarified the needs to be more compact. These needs can be understood also as new boundary objects in their future decision-making processes, if the AI needs are fulfilled.

AI needs/objects in HardwareCo

Identified AI needs/objects Requesting informant(s)

What our leads are saying? Product Development Director Component availability Product Development Director Market analysis tool (competitive environment,

behavior of existing and potential customers)

Product Development Director Notification when customer starts a develop-

ment program Product Development Director

Automated budgeting tool Product Development Director, Controller Forecasting accuracy feedback tool Procurement Manager

What are the most loved and most hated fea-

tures of our products? Head of Product

Unusual use cases of our products Head of Product

In order to utilize AI in operations, the director says they would need more data from different industries. For analyzing customers, they would need to meet the right com- pany, which does that kind of AI product development. In addition, the value of the data would be needed to demonstrate, for example if it would somehow generate more sales. By contrast, the head of product, who has a math degree, thinks that implementing ma- chine learning is just a matter of resources, as the data for the information he needs is already available. In addition, the head of product argues that a good algorithm plus some behavioral data is critical for service companies. The reason behind this is that it enables better investment decisions.

‘[For implementing machine learning] we just need resources. That’s it. Because data we have available […] so that’s not an issue. It’s just that, we have couple of

data scientists, but again they are busy doing, other stuff.’ – Head of Product

‘I believe machine learning for decision-making will, especially around this be- havioral data for company like ours, because we sell a service, I think this is crit-

ical. Because […] when you have a good algorithm, it allows you to take the cor-

rect investment decisions saying okay I’m going to, invest a bit more here, invest a bit less here. This is what we are trying to do now. But yeah, we’ll get there slowly. It’s going to make life so easy.’ – Head of Product

There seems to be several needs for AI and/or machine learning in HardwareCo. All the informants would like to have some sort of intelligent system to help them with their work.

For some knowledge desires, there is already sufficient data available and the imple- mentation is just a matter of resource commitment. As discussed in the methodology section, new expected boundary subjects were not part of the initial research interest. The topic did not emerge in the interviews within HardwareCo. Thus, there is no data on new expected boundary subjects with AI in this case company. Now I will move on to the next case company, AnalyticsCo.

4.2 Case AnalyticsCo