3 AI Project Challenges To Know Before Starting

In this blogpost we will outline the top 3 challenges faced when setting up a new AI initiative; 1) Data Issues  2) Use Case Definition  3) Integration Challenges  Through understanding and preparing for these difficu...

3 AI Project Challenges To Know Before Starting

In this blogpost we will outline the top 3 challenges faced when setting up a new AI initiative;

1) Data Issues 

2) Use Case Definition 

3) Integration Challenges 

Through understanding and preparing for these difficulties, you can ensure that your new AI systems will run efficiently within your business.

It is becoming increasingly common for Artificial Intelligence (AI) to be incorporated into the projects and initiatives of businesses. See our previous blogpost that gives insights into why today’s leading businesses are adopting AI.

AI can have a diverse range of applications within businesses, from the use of chat boxes on a website to facial recognition on a phone app. Consequently, there is no ‘one size fits all’ solution to adopting an AI into your business. Many new AI initiatives often fail due to the business being unprepared for the intricacies which may be involved when setting up such a project. In order to avoid this, it is important that businesses are prepared for the most common difficulties faced when implementing a new AI initiative. 

  1. Data Issues

The most common issues which arise when setting up a new AI initiative are data related. All predictions and decisions made by your AI system will be dependent on the data which is used to train the system. If there are complications with the inputted data, there undoubtedly will be complications with the outputted information. Therefore, having high quality data available for your AI initiative is paramount. 

First things first, you should ensure that they have enough data available for the project. If not enough useful data is provided, the results produced by the project can have low complexity, include bias’s or be inaccurate. The more useful data which is available for the project, the more accurate the algorithm’s output will become. 

However, it is also important not to overload your project with excess data which is not relevant. Irrelevant, inaccurate or biased data can confuse the algorithms causing them to learn from variances in the data rather than the overall trends. This typically leads to a system delivering incorrect results which can waste a business’s time and money. In order to avoid this, it is important to organize, categorize and clean data before introducing it into your AI project. 

Prior to beginning your project, it is prudent to carefully consider whether your business has the capabilities to manage the available data. Obtaining and maintaining high quality data is not easy and can be a daunting or time consuming task for businesses. If you do not think your business currently has the means to do this, you should consider hiring a data engineer or third party to clean and organize the data for the duration of the project. This will allow for the data to be monitored regularly and to always be kept as accurate and reliable as possible.  

It is also extremely important that you ensure that the data they are using in their AI project complies with ethical standards and GDPR. This article gives a quick rundown on Data Ethics for businesses: If your business does not obey these standards, you can run into many issues including lawsuits, drops in share price and a fall in customer loyalty. Errors in this area can lead to a major loss for your business, with a prime example being Amazon paying a €746 million euro GDPR fine over a cookie consent issue. To avoid challenges in this area, you should begin your project only once you have a full understanding of these rules, regulations and standards. 

If you understand and prepare for all these possible data related complications before implementing your AI project you will ensure it runs smoothly and that your business’s money has been allocated effectively. 

  1. Use Case Definition

Poorly defined use cases often lead to challenges and wasted potential within an AI project Use cases are used to identify how to best use AI to create substantial value for the overall business.  Your business should always carefully define these use cases in order to achieve maximum potential from your project.

It may sound like a simple task but far too often businesses begin AI projects without a clear understanding of the problems that they wish to solve. It is important to identify use cases which encompass the company’s goal prior to beginning this project. Businesses should ask themselves : How critical to the overall business strategy is implementing this project? If the main use cases stray from the overall aims of the business, it is often likely the resources used for the AI project could be more effectively allocated to an alternative business project. 

For example, let’s say a food chain wants to implement an AI project with its top use case being to automate it’s manufacturing processes.  However, if the overall mission of the business is to offer a ‘home-cooked’ style of food, then the use case could cause the project to be misled from the businesses goals. To avoid wasted money and time, it is important that the use cases prioritize a strategic business goal which suits the overall business. 

The AI project can have many use cases with different levels of priority. It is also essential to  be mindful when prioritizing your use cases. Use cases which offer better long term value for the business should always hold higher priority to those which offer short term value. 

Defining and evaluating the use cases can also allow the business to gain perspective on the level of value that the project will give to the business. AI can cause large changes in the organization and running of a business. If your business is not properly equipped for these changes, this project can become disruptive, time consuming and expensive. Correctly defined use cases will allow your business to understand the exact aim and goal of the project. 

Overall, having organized and clearly defined use cases will help avoid misunderstandings of what exactly your business needs the AI project to achieve. This ensures that maximum value can be created from the AI initiative for the business as a whole.

  1. Integration Challenges

Challenges also commonly arise when a business begins integrating the AI project with the rest of its operations. Although there is a lot of awe and excitement surrounding AI today, it is important to remember that an AI project should be treated like any other IT project within a business. The project will not magically begin running smoothly in parallel with all previous business systems. Constant management and monitoring of the AI project will be needed. It is important that you undergo appropriate preparations to ensure the smooth integration of this project. 

Sometimes businesses fall into a trap of believing that they outsource one or two specialists to implement their whole AI project. This is often unsustainable and can lead to challenges soon after the project begins. If the specialists are not in communication regularly  with the general businesses team, they may misunderstand the overall business needs. Therefore, it is important that your project and it’s team are kept in the loop with general business operations so that the integration of your project does not come as a shock to the rest of your business systems.

Furthermore, keeping a specialist team on hand to manage and monitor the project can be quite expensive. Good data scientists are hard to find in today’s labour market and they often charge high rates for their work. It is important that the business prepares for the costs of the upkeep and monitoring of the project after it is released. Businesses should weigh up whether these costs will be available and worthwhile prior to beginning their AI initiative. If your business does not prepare for these costs, your business may not be able to sustain the integration of the AI project.  

In order for the AI project to last, it is also important that your general employees understand how to incorporate the new technology into their work. It can be quite complicated for some employees to wrap their heads around. Businesses should invest in adequate training for employees and managers to understand the uses and key factors of the new AI system. Without this training and regular communication on the project, your employees and managers will not be able to fully utilize the new project. This can often result in a waste of project potential and even failure of the project. 

It is best to undergo this training and preparation within your business prior to the integration of the AI project. This will allow for businesses to avoid integration challenges and setbacks. The end goal is to prepare your business so that the AI project is not just a new part of the production environment, but is also able to help your business achieve its wider goals.

Final Thoughts

It is well known that AI can help to increase productivity of processes and provide valuable insights which can help your business to advance and grow. However, if not implemented correctly, a new project can alternatively end up being a colossal waste of your businesses time, effort and money. If a business jumps into the deep end of AI without learning how to swim, it will not be able to stay afloat.  That is why it is essential that these 3 common challenges faced by AI projects are understood prior to beginning a new AI initiative. With these challenges in mind, your business is now prepared to run the new project in a sustainable and valuable way. 

Special thank you to Rian Spillane who researched and wrote this blogpost!

If you have any further questions or worries relating to setting up your new AI project, be sure to get in contact us at Sparkd AI by using the chat box below.


  • Understand the ethical issues cause by super-intelligence – Learn more
  • Read one of Prof. Bryson’s seminal articles – Read More
  • Check out the paperclip example exploring how AI may be an existential threat.
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