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4AI Maturity Model: Process Model with Roadmap

4AI Maturity Model: Process Model with Roadmap

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P. Gentsch

semi-automated to the automated enterprise. The super intelligence enterprise represents the highest level of maturity. This is where the autonomous

and self-learning AI systems described in Sect. 3.1 are used. This highest

degree of maturity is difficult to forecast due to the uncertainty of the time

of occurrence of the singularity and it is not of relevance in the short or

medium term. According to the various expert opinions, this highest degree

of maturity of AI is to be expected between 2040 and 2090. The individual

degrees of maturity are described in the following:

Data, algorithms and AI do not play a business-critical role when it comes

to the non-algorithmic enterprise (Fig. 3.4). The topics are ascribed rather

an operative and transactional significance. The strategy and organisation

are rather classical and less analytical and data-driven. Upon the transition

to a semi-automated enterprise, the crucial value of algorithmics and AI is

increasingly recognised. Accordingly, there are corresponding data and analytics structures. Characteristic is the increased degree of automation of data

collection and analysis as well as the decision-making and implementation

(Fig. 3.5).

This is made possible by a holistic integration of data sources, analyses

and process chains. Data, analytics and AI facilitate the creation and implementation of new business processes and models in this maturity level. The

data- and analytics-driven real-time company obtains systematic competitive

advantages this way (Fig. 3.6).

Fig. 3.3  Algorithmic maturity model (Gentsch)

3  AI Business: Framework and Maturity Model    


Whilst with the automated enterprise the approaches of narrow AI

described in Chapter 2 are applied, the super intelligence enterprise concludes the potential of autonomy and self-learning of companies by way

of general and super intelligence. This scenario currently appearing to be

hardly realistic has two types of manifestation. In the positive version, we

as humans control the framework conditions and rules of the autonomous

AI systems. We can intervene and rectify via regulative and corrective measures at any time. Productivity and well-being are increased further by the

performance, scalability and innovations of these super intelligences. In the

negative version, we as humans have lost control over the framework conditions and rules of the autonomous systems. There is no longer the last

Fig. 3.4  Non-algorithmic enterprise (Gentsch)


P. Gentsch

Fig. 3.5  Semi-automated enterprise (Gentsch)

call for man. AI systems further develop uncontrolled without the possibility of human intervention—permanently and with an open-ended result

(Fig. 3.7).

Even if the super intelligence enterprise seems to be a long way away,

there are some businesses today with an extremely high level of automation.

3  AI Business: Framework and Maturity Model    


Fig. 3.6  Automated enterprise (Gentsch)


P. Gentsch

Guests at the Henn-na Hotel (http://www.h-n-h.jp/en) in Japan, for example, are greeted by a multilingual robot who helps the guests to check in and

out. Artificial butlers take the luggage to the rooms and there is a room for

the storage of luggage which is put away by a mechanical arm. The devices

are not gimmicks for the company but a serious effort to be more efficient.

The hotel is keyless and uses facial recognition technology instead of normal

electronic key cards. A guest’s photograph is taken digitally at check-in. In

the rooms themselves, a computer globe with a stylised face caters for the

comfort of the guests. The computer globe can be used on the basis of digital butler technology (Sect. 4.1) to switch the light on and off, to enquire

about the weather or a suitable restaurant.

Amazon can be quoted as a company with a high maturity model. It has a

high level of maturity across all dimensions (Fig. 3.8).

DAO (decentralised autonomous organisation) is a highly automated and

virtual organisational construct. This is a virtual company without a business

domicile, CEO or staff, which organises itself with the help of codes.

Fig. 3.7  Super intelligence enterprise (Gentsch)

3  AI Business: Framework and Maturity Model    


DAO broke all crowdfunding records in as early as 2016 and collected

160 million US$. DAO works like an investment fund, whereby the collected capital is invested into start-ups and products to yield a profit for the

members of the organisation. The so-called crowdfunders vote on the direction in which the organisation is to develop.

So-called smart contracts regulate the investments of the DAO members. These are algorithms added to the software, which automatically and

permanently review the terms of a contract and take corresponding measures. These rules are stored in a decentral managed database—the so-called


When the defined goal has been achieved, the smart contract automatically executes the transfer. The DAO members receive tokens for the voting, which are used for voting, in line with the money paid in. In addition,

the members can also submit their own ideas for projects and ideas to be

financed by the DAO.

DAO automates company processes on the basis of blockchain technologies. The governance rules are executed by the “algorithmic CEO” and not,

as is customary, by the Board of Directors. A company organisation is formed

that is fully digitalised.

Fig. 3.8  Maturity model for Amazon (Gentsch)


P. Gentsch

If we follow the definition of contract theory, according to which a company is nothing other than a network of contracts in which objectives,

authorisations and terms are laid down, the high level of automation of

company processes and decisions seems realistic. Employment contracts,

for example, regulate and control the actions of the employees. Employees

“execute” tasks laid down in the contract. The title CEO—Chief Executive

Officer—is derived from this execution rationale. Contracts thus regulate

everything in a company, why not be executed by algorithms instead of


Algorithmic technology has the potential to fundamentally change the

way we do business, and has been flagged as the most prominent sweeping change since the industrial revolution (Charmaine Glavas, Queensland

University of Technology, 2016).

3.4.2Benefit and Purpose

The concept of a maturity-level model not only has the aim of classifying

companies into individual levels but moreover indicates a road that companies have to take in competition. Before companies occupy themselves with

AI, they should digitalise and structure their processes systematically. Benefit

and purpose can in principle be subdivided into three types:

Descriptive is a maturity-level model to the extent that a descriptive classification takes place. This helps to obtain a better understanding of the current situation. This allows companies, for example, to recognise the status

quo regarding a certain topic.

In addition, a maturity-level model provides the possibility of a normative

character. The recognition of the current state is obtained by the constructive

maturity levels of the model. The maturity-level model is ground-breaking

if it indicates what is necessary to achieve future or higher degrees of


A further benefit of a maturity-level model is that it can be applied in a

comparative way. The position or the maturity level within a model can be

compared. This facilitates the execution of an internal and external analysis. On the one hand, this facilitates the comparison of company-internal

departments; on the other hand, the company can be measured with the

competitors in competition.

All in all, companies can locate their current status with regard to big

data, algorithmics and AI. This positioning is a vital starting point on the

systematic path to becoming an algorithmic business. On the basis of the

3  AI Business: Framework and Maturity Model    


positioning, targeted measures can be derived for the next highest maturity

level. Furthermore, benchmarking helps in and beyond sectors (Fig. 3.9).

3.5Algorithmic Business—On the Way

Towards Self-Driven Companies

The effects and implications of algorithmics and AI affect the entire corporate value added chain. According to the focus of the book, the “business

layer” of the AI business framework has foregrounded the “customer facing” processes and functions. In this chapter, the potentials for the entire

corporate value creation are briefly described. It will be shown that artificial intelligence can change the way of working in classical company areas

both sustainably and radically: By using artificial intelligence, companies can

not only exploit efficiency and productivity potentials but also cater better

to customers and thus create added value. In addition, the significance of the

ideas and potentials of so-called Conversational Commerce (Sect. 4.2) for

internal company functions and processes will be illustrated and explained

(Conversational Office). Finally, the areas of marketing, market research

and controlling (as relevant cross-sectional function) will be described and

explained in more detail. Furthermore, algorithmics and AI also have the

Fig. 3.9  The benefit of the algorithmic business maturity model (Gentsch)


P. Gentsch

Fig. 3.10  The business layer for the AI business framework (Gentsch)

potential of reinventing business models; these topics will also be treated in

this chapter. Finally, it will be investigated whether it makes sense to install

the position of a chief artificial intelligence officer in companies.

3.5.1Classical Company Areas

The fact that artificial intelligence will change the way of working sustainably and radically can be demonstrated in the following fields of application.

By using artificial intelligence, companies can not only exploit efficiency

and productivity potentials but also, as described above, cater better to

customers and thus create added value. This issue is frequently underestimated in the discussion about AI in the corporate world. Employees in

companies will have to learn to work together with smart technologies.

Whilst well-structured and standardised areas of artificial intelligence can be

adopted, there will be a continued necessity for human staff in areas where

empathy or the collaboration with humans is involved. There is thus more

than only competitive advantages when reducing staff and increasing productivity. Further, it is not necessarily a given that the use of AI is more

efficient than a conventional employee. The development of artificial intelligence has indeed become more affordable than a few years ago due to open

source frameworks, yet statements on the economic feasibility of AI cannot

be made across the board (Fig. 3.10).

3.5.2Inbound Logistics

Inbound logistics are the first primary activity of a company’s value added

chain. The most important tasks of logistics include accepting goods, controlling stocks and warehousing. Companies are working on optimising the

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