Embarking on an AI project holds tremendous potential for businesses to revolutionize their operations and drive growth. However, it is crucial to acknowledge and navigate the common pitfalls that can lead to project failures. Drawing from my extensive experience as a Lead Data Scientist and AI consultant on real-world AI projects, I would like to shed light on the key reasons why AI projects often stumble and offer insights on how to overcome these challenges.
1. Nature of the problem and problem definition
A crucial aspect of successful AI consultancy lies in understanding the nature of the problem at hand and ensuring its clear definition. However, properly defining a problem, albeit it sounds easy, is actually quite challenging in practice. Here we explore why this is the case and the most common issues we encounter.
Lack of alignment with business goals
One of the primary causes leading to project failure is a lack of alignment between business goals and project objectives. To lay the groundwork for a successful AI project, it is vital to ensure that the problem being addressed is aligned with the organization's business goals and priorities. Misalignment between the problem and strategic objectives can lead to inefficiencies and hinder project success.
Another common obstacle to achieving success is ill-defined problems with unclear objectives. Without a clear understanding of what needs to be achieved, it becomes difficult to translate the business problem into an AI problem and define success criteria. By working closely with stakeholders to establish specific, measurable, achievable, relevant, and time-bound (SMART) objectives, it is easier for us AI consultants to provide a clear roadmap for the project.
Problem complexity, AI feasibility, and ethical considerations
Some problems may be inherently complex and not readily solvable using AI techniques. Although AI has made huge breakthroughs in the last year thanks to GenAI, it has its own limitations and can't solve every problem in the world, especially the more complex ones. A related but different issue is AI feasibility. During the early stages of client engagement, it is essential to assess the problem's complexity and determine if AI is the appropriate tool to tackle it. While AI has immense potential, there may be instances where alternative approaches or a combination of simpler techniques are more suitable. This evaluation ensures realistic expectations and avoids unnecessary investment in AI solutions that may not yield the desired outcomes. This is why our Accelerated AI Innovation program (AAII) is the ideal starting point for most AI initiatives.
Besides problem complexity, understanding and assessing the ethical implications of addressing a given problem is of utmost importance to avoid failure down the line.
Success cannot be objectively measured
In order to gauge progress and measure the success of an AI project, it is crucial to establish Key Performance Indicators (KPIs) or relevant business metrics that can be tied to the performance of the AI models. This is something that a well-defined problem usually includes. Without clear metrics, it becomes challenging to objectively assess the project's effectiveness eventually hindering its success. By defining measurable criteria early on, AI consultants can ensure alignment with business goals and provide tangible evidence of the AI solution's impact.
2. Data issues
Data serves as the cornerstone of AI projects, and effectively addressing data-related challenges is paramount for achieving successful outcomes. Some of the most relevant data challenges we came across are outlined below.
No data or insufficient data
The quantity of data available for training AI models plays a vital role in their performance. If no data is available from the client side, the client has to outsource data collection to a third party, thus losing control over how data is collected, captured, or generated. This lack of control and subsequent lack of knowledge of the data may compromise the value brought on by the AI project.
If, instead, the client has some data available, but this is found to be insufficient to achieve the desired outcomes, it can limit the AI model's ability to generalize and make accurate predictions. To mitigate this challenge, it is common to explore strategies such as data augmentation and synthesis, collection of additional data, or leveraging transfer learning techniques to enhance the model's learning capabilities. However, these may not be enough depending on the use case and industry (e.g., healthcare).
Poor data quality
Inadequate data quality can also hinder the success of AI projects. Issues such as inconsistent, inaccurate, or unrepresentative data have a direct impact on the effectiveness of AI models. Ensuring data quality problems are identified early on and strategies to overcome these problems are carefully considered and implemented are two essential steps for obtaining reliable insights and accurate predictions.
Lack of relevant patterns
Sometimes, even though more rarely, the provided data may be of high quality and available in sufficient quantities but it simply lacks patterns to be leveraged by the AI models. In these cases, there isn't much to be done besides considering other datasets.
3. Misaligned expectations
Another very common source of AI project failure is misaligned expectations. To ensure a smooth AI consultancy experience, it is crucial to address and manage expectations effectively. The most important ones are outlined here.
Lack of awareness of trade-offs
It is important to educate clients about the trade-offs inherent in AI solutions. AI is not a magical solution or an assembly line that churns out perfect outcomes. By setting realistic expectations about the limitations and complexities of AI, clients can gain a better understanding of the journey ahead.
Unrealistic time frame expectations
Accurately estimating the time required to develop an AI solution with a specific level of accuracy is challenging, if not impossible, especially for novel problems. AI projects are highly experimental in nature, often requiring iterations and refinements to achieve desired outcomes. Communicating the iterative nature of the process typically helps manage time expectations effectively.
Unrealistic model accuracy expectations
Unrealistic expectations of achieving 100% accuracy with AI solutions can hinder project success. AI systems are designed to learn from data and make predictions, but they are not infallible. Emphasizing the achievable accuracy levels and the potential trade-offs between accuracy and other factors, such as interpretability or computational resources, can foster a more realistic understanding. When accuracy is a concern and the clients seek to get a clearer idea of how accurate an AI solution will be at solving their problem, starting with proofs-of-concept (PoCs) is often a recommended approach to embark on the AI journey.
Unrealistic cost expectations
Building a successful AI product goes beyond creating a flashy demo. It involves complex processes, substantial resources, expensive infrastructure, and dedicated expertise. Unrealistic cost expectations can hurt progress and compromise the quality of the solution. Openly discussing the necessary investments, including data collection, infrastructure, and ongoing maintenance, helps align expectations and ensures a sustainable AI implementation.
By proactively managing expectations around trade-offs, timeframes, accuracy levels, and costs, AI consultants can establish a foundation of transparency and collaboration. This paves the way for realistic project planning, improved decision-making, and ultimately, successful AI solutions that align with the client's objectives.
4. Risk aversion
AI, being a data-driven and experimental field, inherently carries risks. It is important to acknowledge that success cannot be guaranteed, and the value it brings may be challenging to measure before conducting experiments. However, by implementing a robust risk assessment framework, we can identify potential risks, develop mitigation strategies, and communicate uncertainties to stakeholders. This enables a more informed decision-making process while embracing the transformative potential of AI.
5. Scope creep
In the dynamic landscape of AI projects, it is not uncommon for project scopes and requirements to evolve over time. However, that's a slightly different scenario from a common problem known as “scope creep”.
Project scope creep refers to the gradual expansion or addition of project requirements, features, or deliverables beyond the initially defined scope. It occurs when changes or additions to the project's goals, objectives, or deliverables are introduced without proper evaluation or control. Scope creep can result from various factors such as unclear requirements, evolving stakeholder expectations, insufficient planning, or ineffective change management processes. If not managed effectively, scope creep can lead to project delays, increased costs, resource overruns, and potential compromises in project quality.
Flexibility and adaptability are key attributes needed to navigate these changes successfully. Establishing effective communication channels and maintaining an iterative approach to project management can ensure that shifting needs are accommodated without compromising the overall project objectives.
AI projects hold immense promise for businesses seeking innovation and growth. By proactively addressing the factors that contribute to project failures, such as problem definition, data quality, expectations management, risk aversion, and significant changes to project scope, organizations can increase their chances of success. A collaborative and iterative approach, coupled with open communication and a realistic understanding of AI's capabilities, will pave the way for transformative AI solutions that drive truly meaningful business outcomes.