Emerj Launches Report on Getting Started with AI in Business

Dylan Azulay

Dylan is Senior Analyst of Financial Services at Emerj, conducting research on AI use-cases across banking, insurance, and wealth management.

Emerj Launches Report on Getting Started with AI in Business

We set out to create a report that would be particularly useful for executives and business leaders who are looking to get started with an AI initiative, as well as IT and management consultants who want to competently and effectively guide their clients through AI adoption.

A big part of our editorial focus and our one-to-one work with government and business clients is around the successful adoption of AI.

What Happened

Daniel searched though 50 of our best interviews with very agile unicorns that are adept with AI⁠—companies like Facebook, AirBnB, and Salesforce⁠—and interviews with AI expert who work in older more established industries, such as healthcare, banking, and finance, to find the critical best practices for getting started with AI. 

We set out to explore and answer three major questions:

  1. How do the best AI-savvy business leaders determine an AI strategy and a plan to begin adopting AI at their companies?
  2. What are the simple frameworks for deciding where to adopt AI and deciding whether to build or buy an AI solution?
  3. What are the proven best practices for avoiding risk and finding profitable AI initiatives?

What We Learned

  • A set of critical principles executives should understand to fully grasp the difference between adopting traditional software and adopting AI, including specific details around project timelines, data requirements, and the construction of effective teams.
  • A number of extremely useful criteria to help executives make decisions about which AI applications they absolutely must build in-house and which they can buy from a vendor. These strategic insights are not just about saving money and being efficient; they’re also geared toward companies that want to carve out a strategic advantage in their market with AI.
  • Across all of our interviews, we found a pattern of different phases of AI adoption, and it’s important to understand that these phases are different from adopting other software. These phases will help enterprise leaders truly implement an AI solution successfully without getting hung up on the usual hurdles and surprises that cause so many AI projects to fail.

If you’d like to see an outline of the product itself and a full description of this report to see if it might be a fit for your organization, please see the report page: Getting Started with AI: Proven Best Practices of Adoption.


Header Image Credit: Bank of Hope