Eight common barriers hindering the adoption of AI

Artificial intelligence has become a key priority for corporations. However, the more they deal with the integration of AI, the clearer it becomes which barriers they must overcome on the path to its successful integration and adoption. Based on ten in-depth interviews and discussions and interactions with dozens of companies we have identified eight barriers that need to be mastered to successfully apply AI.

 

Artificial intelligence (AI) as the next logical step in digitalization has become increasingly important in recent years as it enables companies to further optimize and automate their processes and develop new products and business models to make their businesses future-proof. However, the development and implementation, as well as the necessary adoption of AI systems, hold more obstacles and risks than some may expect. Resource procurement is complex and arduous: the demand for relevant skills on the job market far exceeds the supply. The data required are often not available, not accessible or not of sufficient quality. With the drive to acquire more date, data handling issues arise, along with concerns on the ethical implications of widespread adoption of data based decision systems. Governments are responding with stricter regulatory frameworks (such as the european GDPR), creating a complex and rapidly changing environment. 

Based on 10 in-depth interviews with company representatives from various industries, the following eight main barriers to AI implementation and adoption were identified:

                                              Figure 1: Overview of the main barriers to AI implementation and adoption

 

1. Organizational aspects

So far, many companies lack the understanding of how and where AI competencies should be anchored in their organizational structure. This is accompanied by the unclear distribution of responsibilities and tasks, such as the development of prototypes or the management of data, as well as the need for the definition of collaboration models.

2. Culture & Change 

In times of uncertain and fast-moving market changes, organizations feel the pressure to open their businesses in order to be more innovative and collaborate with their ecosystem, and this also applies to the development of AI applications. Especially in large companies, cultural changes require a lot of time and strong management.

In addition to the cultural aspect, the importance of strategic change management is (still) underestimated in many companies. Instead of an integrated change support along the implementation process, isolated (communication) measures are often the only ones applied.

3. Competence & Capabilities

For many companies, the slow AI progress can be attributed to a talent shortage of relevant AI experts. All companies included in the study see significant challenges in attracting and retaining new talent. The internal upskilling of employees may be one way to reduce the problem, but it does not replace AI experts’ years of specialized education and experience. 

In order to retain new hires in the long term, companies must ensure that they develop attractive and transparent career paths and meet further upcoming requirements, such as . 

But even with the necessary AI resources on board, which primarily include AI experts (from the field of mathematics, statistics or equivalent studies), data scientists and engineers, and software engineers, companies face the challenge of handling interdisciplinary teams which need well-structured management due to their different backgrounds and working methods. 

4. AI application 

Considering the application of AI , it is known that perceived high compatibility and perceived low complexity of a system contribute to faster user adoption. This is especially true of AI systems, because lack of predictability and explainability and therefore often unintuitive results of the AI system result in a perceived higher complexity imposing a barrier to adoption. This means that AI applications must be carefully designed, as this is central to ensuring that the end user will trust and cooperate with it.

5. Data & Infrastructure

One major obstacle mentioned in every interview is the lack of quality data. The key questions arising are about what data is needed, whether it is available, how it can be obtained, who has access to it and whether its quality is sufficient.

6. Competitive pressure 

From a traditional market perspective, the AI pioneers participating in our study do not have much to fear and are far ahead of their competitors. However, these days we must expand our definition of the competitive field: many technology companies are now also perceived as relevant competitors and bring AI solutions to the market, which increase expectations on the supplier and customer side putting higher pressure on companies.

7. Partners & Ecosystem

The scarcity of talent and data resources in particular raises a big question for many companies: How can we successfully implement AI under these circumstances? Many of the interviewees confirm that they use their ecosystem to drive AI progress forward. The evolving challenges at this point are the question of the right partners and the agreement on joint targets.

8. Regulations

AI developments depend, among other things, on the country in which the company operates. Stricter data protection regulations fostering a more ethical handling of data, for example, can slow down the AI progress dramatically. Whether this is an advantage or disadvantage for companies opens up a big discussion. The fact is, however, that without data no training of AI systems can be conducted.

Many companies share the same challenges in AI implementation and adoption. Therefore, it is a good option for many companies to become part of a network in order to profit from the exchange with AI experts and other companies.

 

This article is based on the research for the master thesis “Success factors for the implementation and adoption of applied artificial intelligence within established companies” by Laura A. Solvie (2019) that was developed in cooperation with appliedAI and the Dr. Theo Schöller – Endowed Chair for Technology and Innovation Management, TUM.