Talent intelligence is software that works out which skills each person has, and which jobs they could do next, from the jobs they have already held. It learns this from a model of careers, built from millions of CVs and online profiles. Large employers use it for three jobs: hiring, moving their own staff into new roles, and planning which skills they will need in future.
The model learns from patterns. Suppose that, across millions of profiles, people who spent three years as an "MIS executive" usually list Excel, SQL and reporting dashboards as their skills. Many of them became data analysts two years later. So when the software reads a new profile with the title "MIS executive", it infers those skills, even if the person never wrote them down. To infer means to work out something that is not stated from what is stated. The software also suggests "data analyst" as a likely next role.
Take, for example, a general insurance company in Gurugram with 9,000 staff. It needs 40 cloud engineers in the next six months for a new claims system. First, the software reads the records of its own staff. It takes job titles and dates from the HRIS, the system that holds every employee's record, and past ratings from performance management. It finds 60 employees who have run Linux servers and databases, skills that are close to cloud work. It ranks them by how close they are. Then it lists the two skills that most of them lack, Kubernetes and cloud security. The learning management system assigns courses for exactly those two gaps. For the roles that the company still cannot fill from inside, the same model ranks outside applicants in the applicant tracking system (ATS) by their skills rather than by their job titles.
To compare people by meaning rather than by exact words, the software turns each skill and each job into a list of numbers called a vector embedding. The numbers for "PL/SQL developer" and "Oracle database engineer" come out close to each other, so the software treats them as similar. Newer products also use a large language model to read CVs and project notes.
How is this different from AI recruiting? AI recruiting searches outside the company for candidates and contacts them. Talent intelligence reasons about the whole workforce, inside and outside. Its matching of staff to open roles is the internal talent marketplace. Its view of other employers' hiring comes from labour market analytics.
Two risks need care. A model trained on past careers can repeat past unfairness, for example if few women held a role in the past. And the data is personal, so India's data protection law governs how it is collected and used. Most of that law's duties for companies apply from 13 May 2027.