Top 10 Challenges in AI Projects

by | 2. May 2022 | Basics, Basics

In 2022, we are celebrating the 10th anniversary of [at] – Alexander Thamm.

10 years ago, we were the first consultancy in the German-speaking area to take up the cause of Data & AI. Today, we can say that artificial intelligence has the potential to make an important contribution to some of the major economic and social challenges of our time: AI plays a role in the energy transition and climate change, autonomous driving, the detection, and treatment of diseases and in pandemic control. AI increases the efficiency of production processes and makes companies more adaptable to market changes through delivering real-time information and predictions.

The economic significance of this technology is growing rapidly: More than two-thirds of German companies now use artificial intelligence and machine learning.

With #AITOP10, we show you what’s hot right now in the field of Data & AI. Our TOP 10 lists present podcast highlights, industry-specific AI trends, AI experts, tool recommendations, and much more. You get a broad cross-section of the Data & AI universe that has been driving us for 10 years now.

Enjoy the reading – and feel free to expand the list!

Artificial intelligence (AI) is finding its way into all industries and has the potential to improve businesses at all levels of the value chain. The technology is growing and gaining popularity fast and has urged several businesses to invest in the development and research of AI applications in nearly every business area. However, it is important to note that AI is still facing several challenges. Here are some of the common difficulties that most companies face when implementing Artificial Intelligence into their business processes.

#10 – The needed human skillset

A common challenge when implementing AI in businesses is the often-seen lack of skilled data science professionals. Creating a talented data science team can be costly and time-consuming due to skill shortages. Especially small and medium-sized enterprises often don’t have the capabilities to hire data science and data engineering professionals for tackling their adoption of AI. Without a properly trained team and business domain expertise, companies should not expect to accomplish much with AI. Businesses must analyse the costs and benefits of creating in-house data science teams or otherwise outsource data science and engineering tasks to domain experts.

#9 – Bias in AI

The ability to gain good data is the solution to unbiased AI systems in the future. However, the everyday data enterprises collect is poor and holds no significance of its own. Data is biased and often represents only a small fraction of the whole dataset and is based on user interactions or individual approaches to the dataset. This challenge can only be tackled by defining algorithms that can efficiently track these problems and make data less biased.

#8 – Data privacy and security concerns

The main factor on which all the deep and machine learning models are based on is the availability of data to train them. If this data is generated from millions of users around the globe, chances are it can be used for bad purposes. Some companies have already started working innovatively to bypass these barriers: Google, for example, has developed an approach to this problem named ‘federated learning’. It trains an ML-model with data from personal devices like phones on the device itself, and hence the data is not sent to the servers, only the trained model is sent back to the organization and therefore no personal user data is stored on servers.

#7 – Missing explainability and traceability of AI

In most cases, AI is a black box. While we may know the inputs and outputs of a model, in many cases we do not know what happens in between. Not even a Data Scientist can clearly explain why the AI made a certain decision. That’s why ‘Explainable AI’ is a broad research field with the goal of making the technology more transparent to humans – which will reduce any reservations and concerns about AI in the future. So far, the basis for decision-making can be explained, at least to some degree. But until there is full transparency, one must accept that AI will make decisions whose solution path may not be comprehensible to humans.

#6 – Large computing capacity is needed

The amount of energy these power-hungry algorithms use is a key factor keeping most developers away from implementing AI. Machine Learning and Deep Learning demand an ever-increasing number of cores and GPUs to work efficiently. There are many domains where deep learning could be implemented to gain valuable insights, but some algorithms require a supercomputer’s computing power. Although, due to the availability of Cloud Computing and parallel processing systems developers work on AI systems more effectively, they come at a price. Not everyone can afford the processing of unprecedented amounts of data and rapidly increasing complex algorithms.

#5 – Sourcing of external data

Incorporating external or third-party data is an important part of data analytics programs as companies look for strategic insight from outside their firms. With the volume of big data available, it is hard for organizations to know what type of external data to look for, and where to find it. Data marketplaces provide a platform to purchase data, but they usually don’t help buyers understand what type of data is needed for their use cases or business problems. It can be difficult to ensure good data quality and understand what impact datasets will have on predictive models prior to purchasing them.

#4 – Lack of guarantee of success

Another challenge when implementing and integrating AI into a business is the missing guarantee of success. Adopting AI and implementing ML projects in an enterprise is always connected to high efforts for the company. To initiate an AI project, the accessible data must be assessed, and experiments are made. Then, the prospects of success to the ML model meeting the considered business objective. In some cases, the desired outcome of the use case can’t be accomplished with the available data and further strategies to target the issue must be assessed.

#3 – The data must contain patterns

But what if the data does not fit? An often-faced challenge is that the data simply doesn’t contain any patterns. In some cases, data is changing mostly randomly and can’t be predicted or analysed well. Therefore, feeding an ML model with this data will not lead to good model performance metrics. Sometimes, the data sources can be further assessed, and the general issue of the use case must be questioned and further specified.

#2 – Sufficient amount of data

Data plays a key role in any use case. Therefore, having lots of data is more beneficial than not having enough of it – the more the merrier! ML-models need quite big amounts of data to create meaningful predictions. If the available data sets are too small, a trained ML model can be inaccurate or even not usable. Only if the data set represents all possible constellations and anomalies in training, they can then be detected in the application.

#1 – A unified understanding of AI

There is no single, universal definition for AI – everyone associates different concepts and ideas with the term. Exactly this circumstance can become a challenge in the project. For everybody involved, including the specialist departments, clarification should take place in advance. It must be discussed exactly what AI is capable of and what it is not capable of, myths and misunderstandings must be dispelled. Any concerns and worries employees may have, such as fear of losing their jobs or of critical situations arising from AI predictions, should also be taken seriously. This is the only way to make sure everybody is on the same page.

These are our Top 10 challenges we experienced when implementing AI into businesses in over 1300 use cases.

Here you can find our Use Case Database.

What challenges did you experience when implementing AI?

<a href="" target="_self">Lukas Lux</a>

Lukas Lux

Lukas Lux ist Werkstudent im Bereich Customer & Strategy bei der Alexander Thamm GmbH. Neben seinem Studium des Sales Engineering & Product Management mit dem Schwerpunkt IT-Engineering beschäftigt er sich mit den aktuellsten Trends und Technologien im Bereich Data & AI und stellt diese in Zusammenarbeit mit unseren [at]Experten für euch zusammen.



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