4 Ways to Overcome AI Obstacles

Four out of five organizations haven’t scaled their AI. Here are some ways to change that.

Source: 4 Ways to Overcome AI Obstacles

It’s clear that all companies investing in AI are hoping to maximize its success and capabilities, but other factors are holding them back. Here are four ways organizations can overcome the obstacles that prevent them from scaling their AI programs:

1. Buy-in from leadership
Building AI models is one thing but getting them into production is another. It requires additional resources, including the right people and architecture to support it (more on that in a bit). One thing working against AI deployments is that there is a lack of support among executive leadership given the number of steps and investment needed to execute effectively to achieve the highly beneficial end results. AI teams must prioritize demonstrating the value of their programs and showing accurate forecasts for the future benefits to get buy-in from leadership to keep pushing forward and scaling these initiatives.

2. The right people and skillsets
For companies to successfully get their AI models into production, they’ll need more than just data scientists on staff. Data engineers must build the pipelines, and machine learning (ML) engineers are needed to get models in production. Companies also will need business analysts to capture the insights from the data and translate the numbers into relevant takeaways for the business. Organizations that only invest in bringing data scientists on board will have a difficult time getting their AI programs to scale.

3. Technology
To get AI models into production and start running operations, companies will need the technology and architecture to support them. This includes everything from setting up environments to develop models that easily integrate with code repositories, to creating docker containers and setting up continuous integration (CI) triggers to rebuild docker images of ML steps. Then, teams can execute the pipelines to deploy the models to production (CD).

4. Operating model
In many cases, data scientists and engineers are scattered throughout an organization, aligning with specific IT or business functions. This is practical in theory, but it also creates silos, with these AI employees lacking visibility and connection with their counterparts across the company, creating a ‘my model culture’. Organizations must create an AI-centric operating model. In our organization, we refer to it as the AI Center of Excellence. The Center of Excellence takes care of the end-to-end life cycle of AI projects, ensuring that they get from concept to completion — or in AI terms, from pilot to production to scale. Most companies lack an operating model that is structured for AI program success.

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