Growth Reinvented: Turn your data and artificial intelligence into money by Mika Ruokonen

Author:Mika Ruokonen [Ruokonen, Mika]
Language: eng
Format: epub
Published: 2020-12-01T23:00:00+00:00

turned into new business models and

growth opportunities. Simply bringing

data dashboards to the office and teaching

infrastructure so that it enables new business

people to demand up-to-date data before

they make decisions, is an easy step that

could potentially spark innovation in terms

of creating new business models.

Figure 27 shows our interpretation of the

In digital-by-definition companies, by

companies’ data and AI maturity status (as

contrast, the situation is different. As

discussed in Chapter 4.2).

the business has been built around data

and algorithms, it is very easy to start

In many of the co-creation sessions, we also

commercialising them, for example by

talked about the maturity of the customer

finding new customers, creating new

purchasing data and AI-enabled product/

product/service offerings and building new

service offerings. In the case of digital

revenue models. Basically, nothing stops

media Company B, the customers wanted to

these companies from making data and AI a

buy raw data and had data science teams

significant business opportunity.

in place that could enrich and analyse the

data after it was purchased. In other co-

In our sample, there were numerous

creation sessions, it was evident that the

examples of firms that represented

customers were nowhere near this level of

the middle ground. That is, they were

maturity. This meant there was plenty of

not digital-by-definition but they were

work to do for the businesses selling data.

not laggards either. What this shows is

They had to educate their customers on the

that data and AI maturity unfolds over

value of data, then develop product/service

a spectrum of stages that companies

offerings that were simple enough for

progress through as they gradually improve

customers to grasp, and finally implement

their capabilities.

revenue models that were very easy to

adopt. Having said all that, customers’

For many companies, a simple way to

low level of data/AI maturity could also be

increase the level of data and AI maturity

turned into an opportunity. As one company

in the workplace is by making data and

leader put it:

algorithm-induced insights more easily

available to employees. If data literacy in

“As our various stakeholders have a low level of the company is low, people will not be used

to viewing and using data dashboards or

making day-to-day data-enabled decisions.

It is hard for them to understand how data

and AI could help them in their current

7.4 CLOSING THE LOOP

It is important to note that in our sample

revolutionary new data and AI business

there was very little correlation between

models. They are doing their best to make

a company’s data and AI ambitions, its

most out of the assets they have. But

strategic approach and its level of data and

companies should resist getting too stuck

AI maturity. The case study of construction

in their industry-focused thinking or ways

services Company H in Appendix 6 is a good

of working. As one business leader said:

example of this. Participants from this

company were very ambitious about their

aims to revolutionise the industry using

data and AI, and their related initiatives

control mechanisms for decisions. Because of that, were highly strategic. Yet the starting

point and the maturity in the industry was

We often fail to get things done and miss out on data comparatively low. On the other hand,

telecoms provider Company D and digital

media Company B were good examples of

Going forward, it might be beneficial to

the opposite scenario. These companies

invite companies from various sectors to

had achieved

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