Tech Trends 2023: AI-Driven Organisations

Dávid Fekete
Data & Machine Learning Engineer David Fekete discusses some of the most common challenges organisations face when becoming AI-Driven.

Becoming an AI-driven organization is a significant undertaking that requires a clear vision, strong leadership, and the right resources. While the potential benefits of adopting artificial intelligence (AI) and machine learning (ML) technologies are numerous, integrating these technologies into an organization’s operations can also present several challenges. In this blog post, we’ll explore some of the key challenges that organizations may face when becoming AI-driven, as well as propose strategies to overcome them.

Every organisation that tries to implement AI solutions faces some typical issues, such as:

  • Data quantity and quality problems
  • Lack of expertise
  • Financial issues

For example, in publishing, the latest TrendWatching report showed that although 77% of executives think AI is important for their business, almost half of them don’t do anything about it.

Think about it: Your organization’s future depends on how you make the decisions to transform it into an AI-driven corporation.

 

DATA QUANTITY AND QUALITY PROBLEMS

One of the first challenges organisations may face when becoming AI-driven is the need to secure and manage large amounts of data. AI and ML technologies rely on the availability of large, high-quality data sets to train and refine their models. This can be an issue for organizations that do not have a strong foundation in data management or that have limited access to data sources. Although you can develop an AI solution without taking too much care with the data, this approach could be expensive and requires highly educated data scientists.

To overcome this challenge, organisations often use general data-driven AI models.

A general AI model is the cheapest solution (effectively addressing the financial challenges), thanks to the researchers who made state-of-the-art (language or image processing) models. You can still personalize those, but remember, you need data to retrain them. 

Another option is to use open-source data. We’ve had projects where our clients didn’t own data, but thanks to the nature of the task being natural language processing, we could still provide excellent solutions.

Source: Kovair

 

LACK OF EXPERTISE

Another common challenge is the lack of internal expertise and understanding of AI and ML technologies. Many organizations do not have a strong foundation in these technologies and may struggle to understand how to effectively implement and utilize them. This can be particularly problematic for organizations that are looking to adopt AI and ML technologies for the first time, as they may not have the necessary in-house expertise to properly design, build, and deploy such systems.

To address this challenge, organizations may need to invest in training and development programs to help their employees gain the necessary skills and knowledge or hire external experts.

This raises another question: can you share your data with external organisations? Don’t fret: involving data scientists and other experts to develop data strategies and plans means they are also implementing data security and privacy measures to protect sensitive data. Even though some executives are still afraid of looking for help externally, a Deloitte report shows that if you really want to implement your AI solution, you need to start with finding expert support.

 

 

External support

If you decide to hire experts for your AI project, you need to keep your eyes open. Many consulting companies simply want to get the product done, which could be acceptable in some fields, but it is different for AI-driven applications. After all, you deal with a lot of data and often big investments.

Another difference from the usual process of finding a suitable external partner is that it doesn’t matter if the company has experience with projects in your industry. That’s what it’s all about in AI development. You fill the project-related gaps through a close collaboration of your expert team and the AI team. A best practice here is to select your most experienced employee in the field you’re operating. They don’t need to have a tech background; they just need to be able to communicate with tech experts.

 

What’s next

While the process of integrating AI and ML technologies into an organization’s operations can present a number of challenges, these difficulties can be overcome with careful planning and investment in the right tools and resources. By addressing these challenges and building a strong foundation in AI and ML technologies, organizations can unlock the full potential of these technologies and drive significant improvements in efficiency, effectiveness, and competitiveness.

Nevertheless, Gartner’s AI report says at least 85% of AI projects will fail by 2022, primarily because of the previously mentioned problems. If you don’t want to be part of this failing group, feel free to reach out to us for advice. 

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