Operationalizing AI

Chapter 7. Conclusion

Many organizations have begun their journey toward adopting AI: they’ve built data science teams, launched projects, and so forth. Of course, not all of these initiatives have delivered. There’s a noticeable gap between the “haves” and “have nots” as AI becomes a matter of competitive advantage. In this report, we promised that you’d learn how to operationalize AI—in other words, learn the important steps to develop a comprehensive understanding across your organization of how to build AI solutions in a repeatable, timely manner to shorten TTM, lower overhead, and reduce risks. Think of this as a recipe, if you will. The end goal of operationalizing AI is to establish an AI Center of Excellence and move forward as a unified organization on that journey. Let’s recap these steps.

Summary of Key Points

In the current view of AI practices and challenges in industry, we’ve identified key priorities for business and technical stakeholders:

• Priority from a business stakeholder’s perspective: find a proper balance between abstracting another team’s contributions and obstructing the other teams.

• Priority from a technical stakeholder’s perspective: the metric that puts almost all of these points into context is time-to-market—the time required for a project to progress from proof of concept to production.

Other key points from the first few sections:

• Enumerate the personas—the descriptions of people in specific roles needed for operationalizing AI—that become ...