5 Ways Companies Succeed with A.I. Projects

Many organizations have begun tiptoeing into artificial intelligence (A.I.) and machine learning, figuring out what to spend and who to hire in order to make apps, services, and internal processes “smarter.” It’s a difficult and often confusing journey, and some organizations are better at it than others.

What differentiates the organizations getting A.I. “right”? McKinsey and MIT’s Machine Intelligence for Manufacturing and Operations (MIMO) recently studied 100 businesses in sectors from automotive to mining, and used the data to determine best practices for adopting A.I. The Harvard Business Review breaks down the report in exhaustive detail, but here are some of the ways that certain organizations manage to stand out in their A.I. and machine-learning work: 

  • Governance: Smart companies continually refine and adjust their A.I. and machine-learning process. In addition, they also put a lot of internal emphasis on resourcing and guiding A.I. projects to success.
  • Deployment: Broad deployment of A.I. and machine-learning processes is key; narrowing the scope of these technologies means that fewer parts of the company will ultimately benefit from them.
  • Partnerships: Partnerships with academics, third-party vendors, and consultants can help accelerate the adoption and iteration of A.I. and machine learning.
  • People: It’s not just about hiring the right A.I. and machine-learning specialists; successful organizations will train as many employees as possible in A.I. tools and processes.
  • Data availability: There’s no A.I. without data. “Leading companies are almost twice as likely as others to enable remote access to data and to store a significant fraction of their data in the cloud,” Harvard Business Review adds. “In short, the democratization of data is a critical aspect to the effective use of analytics.”

For technologists interested in mastering machine learning and A.I., there’s some good news on the job front. Thanks to all this demand from organizations for better A.I. processes, the number of job postings requesting A.I. skills will increase 297 percent over the next two years, according to Emsi Burning Glass, which collects and analyzes job postings from across the country. The median salary for jobs with a heavy A.I. component stands at $103,168. 

Data scientists and machine-learning specialists interested in helping companies build out A.I. and machine-learning processes should also pay attention to SlashData’s recent analysis of companies’ data science/machine learning (DS/ML) workflow. Many of these highly specialized technologists choose to focus on specific aspects of that workflow, such as data exploration or model performance optimization; but as the HBR study makes clear, having a holistic grasp of the organization’s approach to A.I. and machine learning can make you a far more valuable player. 

Getting to that “master of all” stage can take quite a bit of work, however. “Each stage of the DS/ML workflow is becoming more complex—it’s difficult for developers to stay up-to-date with the newest backend technologies and cutting edge machine learning algorithms, and so we’re seeing fewer people involved end-to-end,” added SlashData’s note accompanying its data. “Here, developers are increasing the number of individual stages that they’re involved in, while still remaining somewhat specialized within a section of the overall process.”

Whether you choose to specialize in a specific aspect of A.I./machine learning, or want to adopt a more managerial/oversight approach to it, rising demand may well mean a lot of opportunities in the future.