Artificial Intelligence (AI) and machine learning in specific are attributed with tremendous economical and scientific relevance. However, it is still not enough for businesses to implement these technologies.
"Well, strictly speaking, we don’t invest in AI. We don’t invest in natural language processing. We don’t invest in image analytics. We’re always investing in a business problem."
This quote by Matthew Evans, vice president of digital transformation at Airbus, puts it in a nutshell: AI is a tool that should be utilized when the implementation costs are outweighed by the expected gain in value.
This said, there are three business areas, AI can address:
In practice, AI is applied in all three areas of business needs, with a little more emphasis on enhancing features, functions and performance of existing products. The question remains, why companies and executives are interested in AI in general.
Interestingly, a global survey by MIT Sloan Management Review of over 3,000 business practitioners found that, despite the hype around AI, more than the half of the respondents had no adoption of AI at all. Nevertheless, 83% of the participants perceive AI as a strategic opportunity for their organization, while 37% also recognize the risk of missing out. Most of them feel pressure to adopt AI from all dimensions. Customers and suppliers will ask for AI-driven products and services, incumbent competitor will use AI (e.g. for cost efficiency), and new market entrants will use AI and substitute them. In a nutshell, most of the respondents (84%) believe that AI will allow them to obtain or sustain a competitive advantage.
In summary, to obtain or sustain competitive advantage, most businesses perceive it as crucial to exploit the opportunities of AI. Of course, not everyone needs to be an AI expert, but for decision makers it is definitely an advantage to understand the basic principles. To put it in the words of Brynjolfsson, E. and McAfee (2017):
“Over the next decade, AI won’t replace managers, but managers who use AI will replace those who don’t.”
Companies want and should learn about AI, experiment with its possibilities, and deploy pilot projects. However, there are some specific technical, managerial, and organizational challenges for applying AI in a business context.
The Technical Perspective: AI, or specifically machine learning, is mostly based on statistical modelling, which again is based on mathematical theories grounded on precise and explicit premises. These premises often cannot be proven to hold in the real world but are pragmatically assumed to hold due to the lack of alternatives. For example, a fundamental assumption is that the sample data, on which the models are trained, is “representative”. That means, they assume this small sample shows similarities in terms of values, combinations, and their distributions, with the real population. Therefore, conclusions about the sample can be extrapolated to the whole population. The hope is that those insights can be generalized and used for predicting unseen samples in the future.
However, in business environments, circumstances are always dynamic, which implies the ever changing nature of the data over time. In those dynamic systems, fixed parameters can become invalid in the likely case of structural changes of the environment and thus of the data, resulting in a worse “fit” of the model and ultimately a loss of “predictive performance”. Consequently, to successfully implement AI in your business, you need technical experts who know the limitations and the implicit assumptions of the underlying statistical models. Furthermore, they also need a sufficient amount of domain expertise, to be able to assess the magnitude of the discrepancy between the model and the messy real world of the business.
The Managerial Perspective: When it comes to applying AI, most people focus on the difficulties of the implementation side. But besides these technical challenges, there are also managerial challenges regarding the application of AI in a business context. One of the most discussed issues with AI models is the lack of interpretability. Especially in the context of Deep Learning, those neural networks can be too complex to understand why this model comes up with its decisions. Even though this lack of interpretability is clearly a disadvantage in theory, the question arises, if this plays a role in practice.
Many practitioners would argue, it doesn’t matter how the system derives its decisions, as long as the black-box produces statistically validated and accurate predictions. But the reality is, there are three significant risks, that result from imprudent application of AI:
These risks can be addressed in two ways: during the technical implementation, you need technically-trained staff who know about the importance of good data selection, pre-processing, modelling, and monitoring mechanisms. From the interpretation side, you need decision makers, who understand the basic data science principles, and can differentiate between correlation and causation.
The Organizational Perspective: Even if you have specialized staff on the technical and managerial side, applying AI also brings a multitude of additional challenges to the organization as a whole. In fact, the barriers to the adoption of AI depend on to what degree the company has already adopted to AI. While for highly adopted companies the lack of AI talents clearly is the bottleneck, the novices with no AI adoption are struggling with identifying a business case for AI in the first place. However, identifying use-cases for AI in your own business require not only deep domain knowledge, but also innovative minds with a strong customer orientation. Those companies in the middle adoption level are rather challenged by deciding between competing investment priorities. In those cases, sound business cases can help to clarify between competing investment opportunities.
In general, there are three organisational challenges in introducing AI:
Business applications of AI naturally contain some inherent technical, managerial and organizational challenges. Therefore, you need trained staff with technical experience and domain expertise, who are aware of the technical challenges and know how to address these issues, as well as technologically experienced leaders and decision makers. Otherwise, the company is blind-folded, without knowing when AI should, could and why it would generate value and ultimately competitive advantages. AI does bring challenges but also many advantages to modern businesses, if applied smartly and according to the companies’ structure.
Keesiu Wong is Co-Founder & CEO of Design AI, a start-up focusing on agile AI development and use case identification through Design Thinking. He is a trained Data Scientist with an academic background in Mathematics, Management and Data Engineering at the Technical University of Munich. In addition to 5+ years of experience with AI projects, he has entrepreneurial experience in 4 start-ups, as well as experience in Design Thinking, top management consulting and as a start-up coach at UnternehmerTUM.Weitere Beiträge
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