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How AI Empowers HR Teams

Human Resources teams need AI to support collaborative teams and employee retention in addition to keeping job listings competitive.

There is nothing more critical to meeting business goals than hiring—and keeping—the right people. Organizations that leverage data-driven and advanced analytics approaches are able to recruit more effectively. In addition to reducing recruiting bottlenecks, the strategic use of AI in the recruiting process demonstrates to potential applicants and future coworkers that an organization is invested in supporting recruiting efforts and building the best possible teams. 

High-Value Use Cases

Recruiting with greater precision: All organizations, regardless of size or industry, can use marketing techniques to reach ideal job candidates with outbound recruiting. AI is able to target these candidates and see how they engage with recruiting material. When recruiters can visualize engagement, they can adjust their messaging to accommodate a particular candidate’s interests without much additional effort. 

Attracting higher-quality applicants: Natural Language Processing (NLP) can equip HR professionals with the ability to see how adjective and attribute selections have impacted applications for previous postings and target future openings accordingly. Additionally, teams can search other postings for similar roles within their industry to make sure their vacancy is positioned competitively.

AI for Human Resources

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Reducing bias: As organizations seek to reduce the effect of conscious and unconscious bias in their hiring and promotion processes, AI can be a boon, as it offers the chance to correct for human errors that are embedded in a company’s hiring processes. For large organizations, machine learning models can help identify employees who may qualify for promotion. 

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Understanding employee satisfaction: By leveraging machine learning, AI, and predictive analytics, companies can gauge their current employees’ attitudes about their work and their place in the organization. With these tools, it is easier for employers to identify pain points and potential sources of job dissatisfaction and explore ways to improve the situation.

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Dataiku for HR

Dataiku is the platform democratizing access to data and enabling enterprises to build their own path to AI. More than 250 customers across retail, e-commerce, health care, finance, transportation, the public sector, manufacturing, pharmaceuticals, and more use Dataiku to massively scale AI efforts.

While Dataiku has a variety of robust production-ready machine learning capabilities, features that offer special value to HR include:

  • Subpopulation analysis equips recruiters and managers with the ability to see how internal machine learning models respond to specific populations (based on age, gender, race, physical ability, etc.). This is particularly useful when exploring biases or anomalies within a system and can highlight areas for improvement in future iterations.
  • Clustering and unsupervised machine learning enable recruiters to search a wide variety of profiles for potentially successful candidates without targeting for specific constraints that might induce bias into the process. With robust visual AutoML features, Dataiku makes unsupervised machine learning easy.
  • Regulatory compliance and auditability help HR teams leverage sensitive employee information without compromising their privacy.  Dataiku offers robust user permission tracking plus built in version history and usage justification information to ensure full auditability.

Responsible AI for a Sustainable Data Future

Dataiku is the basis of a responsible AI strategy, ensuring that models are accountable, architecture and infrastructure is sustainable, and data processes are governable.

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Go Further

The Regulatory Future of AI

Though Europe has certainly been the leader, several states in the United States are also considering their own data privacy regulations.

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Use Cases for AI in HR

Get the white paper that uncovers challenges and roadblocks as well as top use cases and a path forward to AI in HR.

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Responsible AI on the Ground

It’s easy to talk about data ethics in science-fiction hypotheticals, but what does it mean practically, day-to-day for data scientists?

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Why Teams Need AI Tools

Enterprise AI is at peak hype, yet AI has yet to fundamentally change most businesses. Enter: AI platforms.

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