Business leaders have discovered numerous use cases in which AI could solve a problem they have been battling for a long time. These problems could be related to reducing the development of flawed products in expensive manufacturing processes, rare disease identification at an early stage, or fleet optimization in your supply chain. For such challenges, AI solutions are like a magical wand.
Sometimes businesses feel they have all the data required to create an AI model, and projects are launched with great fanfare, with significant financial and time investments. However, after a few months, a bizarre event begins to happen! The AI models cannot handle real-time challenges, and the plan needs revision.
Most businesses that incorporate AI into their workforce follow a similar implementation pattern. They create a flawless proof of concept and work with an AI vendor who promises to launch the system on their behalf. Significant time, money, and effort are needed to ensure the project's success. Despite all this effort, many surveys have revealed that most AI projects fail to meet their goals. You're probably wondering why Codvo, an AI-focused company, is writing on failed AI projects. We hold the counterintuitive belief that it is preferential to confront challenges head-on.
According to a recent survey from Databricks, 90% of companies are working on AI projects, but only one in three AI projects is successful. We believe the cause is that organizations are bypassing some basic steps.
Before venturing into an AI project, your company must take the following steps:
Make it clear what business problems you hope to solve by incorporating AI techniques. Too many companies begin AI projects without a proper understanding of the problems they hope to solve. Sometimes the problem you're trying to solve is far too intricate for any AI solution. Don't be worried! The key to achievement is to target AI applications with attainable outcomes in line with current technological capabilities.
You should assess AI's potential return on investment. Additionally, before beginning an AI project, you should have the necessary modeling to understand the operational costs associated with implementing an AI model.
Numerous enterprises experiment with AI, but these experiments will fail. Data scientists spend months fine-tuning an AI model to make a good prediction, only to redo all that work whenever they must make a new prediction.
The challenge is to keep re-training the model over time (MLOPs). As the data changes and evolves, you should automatically feed accurate production data to your models. You should also train them regularly. Furthermore, validate the effectiveness of the models by constantly checking for bias. AI has to be approached by businesses as a tool to ramp up and last over time. According to the KGMP report, while 60% of enterprises use intelligent automation, only 11%use an inclusive solution approach, a strategy for scaling AI projects.
Additionally, some AI projects need some initial phase on R&D. You can start the project using the Kanban Approach during the model experimentation phase. You can switch to Scrum-based development once the ML model is mature and application development begins.
Artificial Intelligence (AI) is a cutting-edge technology widely regarded as the most effective way to address all business challenges. Everyone has a different take on artificial intelligence. Managers, users, developers, and all other stakeholders will all have different views about how AI solutions should be. However, before implementing it, ensure that all affected departments and stakeholders endorse the change.
Moving on, let's discuss the project's technical feasibility. To ensure that the technical feasibility is accurate, make sure you do not overlook the following points:
· Ensure quality data is available on time.
· Have a good understanding of potential users and application feature requirements.
· Keep an eye on the project completion timeline, which should be between 3-6 months.
· Lengthier projects are to be divided into phases, with a clear ROI for each phase
· Finally, begin with low-hanging fruit (easily implementable projects)
Your AI project may not be finished for a variety of reasons. Many projects fail because those in charge have no idea where they're going. Others struggle because their creators are unsure of how to move forward when there are more problems than solutions. The list below includes some of the most common reasons AI projects fail. Let's take a closer look at them.
· Data was poor, especially when working on speech-to-text/text-to-speech conversion
· The need for extensive data annotation and cleaning was not anticipated
· Real-time scenarios were missing
· Insufficient data to build a reliable AI model
· A lot of time and money is needed to train ML models
· Lack of defined business objectives
· Lack of leadership commitment and responsibility
· Inefficient management structure and level of expertise
Starting up in the AI world can be difficult; there are many aspects to consider, and a lot of uncertainty. Before embarking on any AI project, consider the preceding points mentioned in this blog. Also, understand that not every project requires an AI approach for an accurate result. Keep an eye out for situations where you can use/avoid ML models.
Finally, when using AI techniques for a project, have a clear vision of where things could go wrong. Don't let your project fall through the cracks!
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