While AI has been around for decades, it has only recently begun to revolutionize the business world. The technical potential is enormous: AI can be used to analyze massive amounts of data and make decisions in a way that was not possible before. However, the successful implementation of an AI project is not as simple as acquiring a powerful machine and slapping some code on it. There are many pitfalls along the way, and if any one of these pitfalls is underestimated or ignored, the project could fail.

Why Some AI Projects Fail?

While AI has been around for decades, it has only recently begun to revolutionize the business world. The technical potential is enormous: AI can be used to analyze massive amounts of data and make decisions in a way that was not possible before.

However, the successful implementation of an AI project is not as simple as acquiring a powerful machine and slapping some code on it. There are many pitfalls along the way, and if any one of these pitfalls is underestimated or ignored, the project could fail.

Factors Contributing to the Failure of AI Projects

One could question why we don’t have the ideal guide for adequately implementing AI yet. In practice, many variables go into creating great AI, making it challenging to recommend prescriptive measures that will always be successful for every case.

Even so, progress is being made in gathering best practices (through lessons learned from successes and failures). As a result, common patterns regarding what frequently causes failure is beginning to emerge.

Here are some instances when businesses could misstep:

Factor #1: Haphazard Planning

Some companies undermine the difficulty of AI projects and launch initiatives without proper planning. In such cases, teams may rush to build products without fully comprehending business requirements or technical capabilities. These teams build products quickly to meet aggressive deadlines, which creates technical debt and leads to implementation issues down the line.

To take full advantage of AI, companies must align all software development life cycle aspects with business strategy, goals, and objectives. The best way to ensure this alignment is by creating a roadmap for AI projects.

These roadmaps consist of three major steps: planning, building and incorporating into business operations, and that can be a lot to keep track of.

To make sure your project stays on track and achieves desired results, here are some key things to remember when planning your AI project:

  • Identify who will be involved in the project
  • Establish clear goals and objectives that tie in with your overall business strategy
  • Define success criteria for each step along the way
  • Develop realistic schedules and budgets for each phase
  • Use an agile software development methodology that streamlines communication between all stakeholders

Factor #2: Data Issues

Data can be thought of as the fuel that powers AI projects. It’s often not enough to just have a data set that includes a reasonably large sample—you need lots of data that’s been curated and arranged in a way that makes it easy for computers to pick out specific aspects, or patterns, of the information.

The larger and more diverse the dataset, the easier it will be for an AI system to learn how to correctly identify specific characteristics, such as determining what makes a demography buy a particular product. But even with all this data, there are still some fundamental issues with how data is handled that can contribute to the failure of an AI project. They include:

  • Improper data labeling
  • Poor feature selection
  • Improper train/test/validation split
  • Underfitting and overfitting data

Factor #3: Unbalanced Teams

While the importance of machine learning experts cannot be overstated, tech leaders often undermine the importance of professionals with other backgrounds.

It is vital to have a group with complementary skills assigned to an AI project, with the team including:

  • IT people with the required technical skills (ML, backend, and frontend) to provide model development and integration expertise.
  • Data scientists who can assist with data management needs
  • Domain specialists in the business and operations domain who understand the technology at the basic level

When project owners rely on their ML engineers’ basic data preparation skills, they often overlook the importance of data quality. This oversight can lead to problems with their products.

Factor #4: Inadequate Resources

Depending on the complexity of the AI project, you may need more computing power, personnel, or data.

Many organizations are making large datasets publicly available. Examples of these models include Open AI’s GPT3 engine and Google’s Imagenet. This makes it possible for project teams to get started with less data and computing power.

Additionally, the proliferation of remote work enables companies to hire qualified candidates regardless of where they live. This widens the talent pool to a global level, creating better teams and robust products.

Takeaway: Practical Steps to Create a Successful AI Solution

Even though many companies are on the brink of adding AI-powered solutions to their product. Yet, many of these organizations underestimate the complexity of AI projects and overestimate their ability to implement them effectively. This leads to failure to deliver business value on time or at all.

In general, there are five stages of deployment that lead up to full value realization:

  • Business case development: Understand the business problem and identify potential data sources that can be used to understand the problem.
  • Data preparation: Clean and organize data, so it’s ready for analysis.
  • Training model(s): Develop and test machine learning models to improve performance and optimize accuracy.
  • Deployment: Deploy the model(s) into production where the business uses them.
  • Feedback loop: Monitor and measure how well models perform in production and make adjustments when needed.

Achieving full value from an AI project requires comprehensive understanding and consideration of each stage, with particular attention paid to areas where projects often struggle, such as data cleaning and model accuracy assessment.

To ensure a seamless approach to these steps, especially those related to data preparation, feature engineering, modelling and deployment, your team can take a look at our ML Studio where we provide an easy drag and drop tool to cover these steps in a solid way.

Founder, CEO

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