Enterprise talent systems have grown sophisticated, but they still struggle to answer a basic question: how are skills actually applied in day-to-day work?
Most platforms rely on indirect signals. Learning systems confirm that someone completed a course. Performance tools summarize outcomes months after the fact. Engagement surveys capture sentiment, not contribution. Even modern credentialing platforms validate preparation rather than execution.
As organizations move toward skills-based operating models, this gap becomes material. Leaders need to understand who is doing the work, which skills are being applied, and where risk or opportunity is emerging—while the work is happening, not after.
Good4Work was built to address this problem by treating recognition not as a cultural gesture, but as structured talent data.
The starting point is simple: recognition happens where work happens. By embedding directly into Slack and Microsoft Teams, Good4Work captures peer recognition in real time, tied to specific contributions and outcomes. This shifts recognition from an occasional managerial task to a continuous signal generated by the people closest to the work.
What makes this data different is that it reflects skills in action. Instead of recording that someone holds a certification or completed training, recognition records that a skill was applied to ship a project, resolve a critical issue, or lead a team through a milestone. Each recognition event is time-stamped, contextual, and validated by another contributor whose own credibility evolves over time.
This creates a dataset most talent systems do not have access to. It cannot be recreated retroactively, and it becomes more valuable the longer it runs. Over time, it forms a living record of capability based on execution rather than self-reporting.
The role of AI in this system is not to replace judgment, but to make recognition habitual and consistent. Managers are often supportive of recognition but inconsistent in practice. By analyzing collaboration signals in Slack and Teams, Good4Work’s AI surfaces likely moments of contribution and prompts recognition when it matters. The result is higher participation without adding workflow friction.
As the dataset matures, the same recognition signals begin to support higher-order use cases. Patterns in recognition activity reveal contributors who are consistently undervalued, teams at risk of attrition, and individuals ready for expanded responsibility. Because the data is continuous rather than episodic, these insights emerge earlier and with greater confidence than traditional review cycles allow.
Over time, the system extends beyond individual organizations. Recognition validates not only the recipient, but also the recognizer. As multiple companies participate, skills gain external credibility and portability. This allows talent data to travel with individuals across roles, employers, and work arrangements, including contract and AI-augmented teams.
This network effect is structural. Each new organization increases the reliability and usefulness of the system for all participants. Unlike profile-based platforms, value is created through validation and repeated interaction, not self-curated narratives.
Distribution plays a central role in making this possible. Good4Work integrates directly with enterprise systems of record, including Workday, and is available through Microsoft AppSource and the Slack App Directory. Adoption does not require new logins or behavioral change. Recognition becomes part of existing workflows, which materially improves participation and data quality.
The timing for this category is not accidental. Enterprises are under pressure to improve retention, internal mobility, and workforce planning across increasingly hybrid environments. At the same time, AI is changing how work is produced and how teams are composed. Static role definitions and annual reviews are no longer sufficient to manage this complexity.
In this context, recognition becomes infrastructure. When captured continuously and structured correctly, it turns everyday work into verifiable skill data that supports better decisions across hiring, development, and retention.
Good4Work is not positioned as a badge system or an engagement layer. It functions as a talent data platform that converts recognition into intelligence and intelligence into action. The outcome is a clearer, more current view of capability—grounded in what people actually do.