A large number of firms are still reporting a 50% failure rate for AI projects, pointing to the lack of unrealistic expectations and AI skills as the two main roadblocks. In such a rapidly growing market, the adoption of AI needs a clear plan to overcome these challenges.  

Whether it’s through the growing AI investment – which is expected to skyrocket to $98bn by 2023 – or the fact that AI projects are set to double over the upcoming year, organizations are improving efficiency, performance, and analytics capabilities with the support of AI to solve real-world problems faster. McKinsey found that in two-thirds of the use cases, AI – specifically deep learning – improved performance beyond that provided by other technologies.

Below are the steps to ensure the smooth and successful implementation of AI.

Step 1- Identifying a clear and appropriate use case

One of the most common slip-ups while adopting AI is rushing into deployment, which turns to be costly, wasting time and money on ill-planned projects. Ernst & Young (EY) found that 48% of C-suite think and acknowledge a lack of managerial understanding and sponsorship as the main reason behind slowing the AI adoption. Businesses should identify the areas benefitting from the efficiency, analytics, and performance that AI has to offer, setting clear targets for the project from the offset stage itself.

Step 2: Making the company data AI-ready

Without high-quality data, the risk of failure is much higher, and organizations could end up strategizing based on biased, inaccurate, or unintelligible insights.

Organizations need to review the relevant data sets for each project, ensuring that everything from tabular data to image, audio, and unstructured text can be processed, stored, categorized, and labeled effectively and securely.

Additionally, IT needs to ensure the required data can be continually collected and fed into AI models so that data sets remain relevant, with trends and models continuing to improve their outputs as time passes. If the business doesn’t have these capabilities and can’t store and categorize data effectively, this needs to be fixed before starting an AI project.

Step 3: Offset the talent gap by training and empowering the workforce

LinkedIn reported a shortage of 151,717 data scientists in the U.S alone in 2018, indicating that the demand continues to rise as skills gaps widen. Not only is this a problem for businesses, but it also indicates a slowing down of the overall progress of AI development.

To get more people using AI, organizations must find ways to reduce the learning curve. This can be simplified through partnerships with AI experts, or by using a platform to remove the underlying complexity for more challenging types of AI like deep learning. By automating workflows and creating a visual development environment, organizations can reduce the skills gap by enabling more developers, junior data scientists, and domain experts to run and operationalize highly complex projects. This will mitigate the pressure and move AI projects from concept to production much faster, increasing their chances of success.

Step 4: Operationalize AI to accelerate the AI goals

Even once organizations have a working AI model, the project needs to be operational before it gets deployed in the business. This pushes the need for AI projects to be auditable, scalable and efficient enough to function in today’s business environment, where regulatory requirements, cross-departmental collaboration, and scalability remain vital. Creating custom algorithms requires a massive amount of time, money, and resource to overcome departmental IT silos.

Instead, organizations need to seek out the right partnerships so that the technology can be deployed smartly, easing common pain points and increasing the chance of project success. The AI industry is booming, and firms are set on reaping the benefits, but success isn’t assured until the implementation process is managed smartly. If they don’t, a golden opportunity may pass them by.