Top 3 Challenges for an AI Project
1) AI Data Issues
2) Use Case Definition
3) AI Integration Challenges
By understanding and preparing for these difficulties, you can ensure your new AI systems will run efficiently.
It is becoming increasingly common for Artificial Intelligence (AI) to be incorporated into the projects and initiatives of businesses.
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. New initiatives often fail due to the business being unprepared for the intricacies involved when setting up a project. To avoid this, it is important that businesses are prepared for the most common difficulties faced in implementation of projects.
AI Data Issues
The most common issues that arise are data related. All predictions and decisions made by the system are dependent on the data used to train the system. If there are complications with the inputted data, there will be complications with the output. Therefore, having high quality data available is paramount. 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, be biased or inaccurate. The more useful data 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. 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 results in wastage of a business’ time and money. To avoid this, organize, categorize and clean data before introducing it into your AI project.
Prior to beginning, 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 task for businesses. If you do not think your business currently has the means to do this, 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 extremely important to ensure that the data used in the AI project complies with ethical standards and GDPR. The following 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.
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 AI 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 the AI 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 AI 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 project can have many use cases with different levels of priority. It is 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 value 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, the AI project will be 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 avoid misunderstandings. This ensures that maximum value can be created from the AI initiative for the business as a whole.
AI Integration Challenges
Challenges also commonly arise when a business begins integrating the AI project with the rest of its operations. Despite the 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 is 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 can outsource to one or two specialists to implement the entire 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’s important that your AI 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.
Importance of a Good Team
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 labor market and often charge high rates for their work. It’s important that the business prepares for the costs of the upkeep and monitoring of the project. Businesses should weigh up these costs prior to beginning their AI initiative. If your business fails to prepare for these costs, it may not be able to sustain the integration of the AI project.
For an AI project to last, it’s also important that your general employees understand how to incorporate the new technology into their work. AI can be 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 wasted potential and even, failure of the project.
It is best to undergo this training and preparation within your business prior to the integration. It 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.
AI can increase productivity of processes and provide valuable insights that can help your business to advance and grow. However, if implemented incorrectly, it can alternatively end up being a colossal waste of time, effort and money. If a business jumps into the deep end without learning how to swim, it won’t stay afloat. It is essential that these 3 common challenges faced by AI projects are understood before a new AI initiative.
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