Identifying AI talent
As much as you can, survey your organization to map existing analytics talent or teams with an analytics orientation. Though analytics and AI are not the same, there are many overlapping baseline skills. Existing analytics knowledge can grow into AI knowledge.
If there are already people in the organization with AI skills, where do they sit and who do they report to? Are they in IT, in one of the business functions, or part of the Office of the Chief Experience Officer (CXO)?
How do you know if your existing talent has AI skills? Start looking for people who exhibit some of these qualities:
- Support their decisions and arguments with data
- Are comfortable with statistics and math
- Make their own macros in excel
- Have expressed an interest in or started to learn computer programming
- Recognize that technology can make a process faster, easier or more efficient
- Know the data your organization uses well
- Follow the latest technology trends closely
An important part of assessing an organization’s existing talent is acknowledging that some people may already be leveraging defined AI and ML skills. Others, however, may work in technical roles or have skills that are not directly AI related, but could easily be supplemented to become AI skills.
Your organization employs intelligent and skilled people who may already be working in AI and ML. It may also have an even broader pool of people with skills related to AI. These people may not even realize that they already have many of the skills and capabilities to help use AI and data science to advance the objectives. Your agency can train these people so they can become AI professionals.
Augment talent when needed
Certainly, many agencies want to increase the AI know-how of their internal staff. However, much of the innovation emerging in the AI field comes from private industry. Public-private partnerships are often an excellent way to get more support for AI projects.
When to bring in outside talent or vendors:
- The agency has had difficulty attracting, training, and retaining data science talent to achieve some of its objectives.
- The use cases in question are limited and require niche skills that may not be worth hiring for and developing over the long term. These niche skills are needed for the long-term solution’s maintenance, not only for the build.
- The agency needs to quickly test the potential benefits of an AI solution before deciding whether to invest in developing internal capabilities. However, the reverse may also be true. An agency may wish to use internal talent to quickly test a new capability being pitched by a vendor before deciding to invest in the outside resources. We discuss this further in Chapter 7, Module 4: Starting an AI project.