Although AI is the hottest trend in technology, the pace of actual adoption and deployment is a different story. Most notably, a CIO survey in 2018, cited that only 1 in 3 AI projects are successful. This means that the greater majority of projects are not being completed on time, are exceeding expected budgets, and are not yielding the expected outcomes.
So what are the major obstacles keeping companies from successfully deploying AI in their business? In short, below are the three challenges highlighted in a webinar hosted by Databricks:
Challenge #1: Data is the key to success, but difficult to harness
Challenge #2: Data science and engineering silos, resulting in poor collaboration
Challenge #3: The explosion of ML frameworks and technologies adds complexity
Apart from the challenges, the webinar also mentioned and offered some tips to overcome these challenges. Below are some of the key best practices to help companies extract benefits from their work around AI. Below are the three key best practices shared by industry-leading companies:
- Leverage the cloud to simplify infrastructure, reduce on-prem costs and provide the elastic scale your teams need to meet the demands of modern analytics workflows.
- Make data and the output of the data (e.g. models) available to all teams to foster transparency, collaboration and productivity.
- Focus on providing the necessary architecture and scaffolding so your teams can leverage the best ML tools and frameworks to drive innovation.
A final takeaway from this survey result led to unified approac to analytics. By bringing together teams and data workflows the expected benefits include increased operational efficiency, accelerated time-to-market, increased innovation, and more.