AI Projects Pitfalls and How Consulting Can Help to Overcome Them
Artificial Intelligence revolutionized how industries and businesses operate with hundreds of innovative use cases. However, implementing an AI project can be complex and challenging despite its potential benefits. According to studies, 70% of companies report minimal or no impact from AI, while 87% of data science projects never make it into production. It highlights the significant obstacles companies face in developing Artificial Intelligence solutions.
In this article, we will explore the common pitfalls of AI projects and how Artificial Intelligence consulting services can help to overcome them. By understanding these challenges, companies can take proactive steps to ensure the successful implementation of AI solutions and realize their full potential.
Unclear Business Objectives
Unfortunately, companies often jump into AI simply because they think they should be doing it, without fully understanding what problem they want AI to solve. Even when a company has a clear idea of its needs, it may choose the wrong problem to solve first, or the scope of the problem may be too large to be fully solved in a reasonable amount of time.
The key to avoiding these pitfalls is looking at the most urgent business priorities and then assessing the resources required to solve them. AI projects should not start with the data at hand and then try to find a problem to solve, as this often leads to science experiments that may not address business challenges.
Instead, it’s important to ask critical questions before launching a project, such as: Is this problem urgent? Why is AI the right solution? How will success be defined? AI strategy consulting company can help clarify business objectives and ensure that AI projects are aligned with the most pressing needs.
Cold starts are a significant challenge in AI projects, as the performance of an AI system is mainly dependent on the availability and quality of data. The infamous “cold start problem” refers to the situation where an AI system doesn’t have enough data on its users, resulting in poor performance and ineffective recommendations. It can be a major issue for organizations, leading to a poor user experience.
One solution to the cold start problem is to find relevant data that can be used to train the AI system. It can include anonymized data from public sources, which can be structured in a way that the AI can leverage a pre-existing understanding of user characteristics and capabilities. As Artificial Intelligence consultants, according to our DIET framework for AI project development, we define and prepare the necessary amount of data to make effective recommendations without having to wait for the system to collect enough data over time.
Poor Data Quality
Poor data quality is another one of the challenges in AI projects, as it can result in dangerous outcomes if used in decision-making. Before embarking on an AI project, ensuring that the data collected is sufficient, relevant, and comes from reliable sources is crucial. The data should also be labeled correctly and suitable for the AI tool deployed.
One way to address the data quality problem is to know what data you already have and compare that to what data the model requires. It requires knowledge of the types and categories of data you have, whether it is structured or unstructured, and whether you have collected data about your customers’ demographics, purchase history, and on-site interactions, among others. AI technology consulting firms can help companies understand what types of data they need, how to acquire it, and how to integrate it into their AI system.
One of the biggest pitfalls in implementing AI is the need for proper infrastructure for the development and deployment of AI/ML models, software, and applications. The transition from an academic setting, where machine learning is primarily research-focused and solves problems under controlled conditions, to a business setting requires a more complex infrastructure.
This is where Artificial Intelligence consulting services comes into play. As consultants with expertise in AI and machine learning, we can provide crucial support in overcoming the challenges of AI implementation. We help businesses to evaluate their current systems, assess their needs, and design an infrastructure that supports the development, deployment, and operation of models.
Unrealistic expectations about AI
Despite its many advantages, AI is not a magic solution to all problems. One of the greatest misconceptions about AI is that it can take any data and produce results. However, the results’ quality depends on the quality of the data fed into the system. Just like a human, AI works based on the input it receives. Therefore, manual data processing is necessary before starting any AI project.
It is also essential to remember that AI is not a perfect solution and that no neural network works with 100% accuracy. AI is just an assistant that needs to be monitored and corrected if it makes mistakes. Therefore, in IT consulting, we help businesses understand AI’s real applicability in their operations and how much it can help.
Implementing AI projects can be challenging due to various pitfalls, such as unclear business objectives, cold starts, poor data quality, implementation challenges, and unrealistic expectations. At Exposit, we provide AI strategy consulting to help you overcome these challenges, successfully implement your idea, and transform your business. Contact us today to learn how we can support your AI journey.