/ Artificial Intelligence

An Overview of A.I. in HR – Part One

The Current Challenging State of Affairs in Human Resources

The competition for top talent among industries undergoing rapid transformation is fierce. In the world of IT, finding experienced data scientists and engineers is difficult and expensive. The struggle extends to other industries as well, e.g. financial services, healthcare, and (increasingly) manufacturing. The blockchain sector is being nearly crippled by this shortage, and it will only get worse as the technology becomes more widely adopted.

A robot named Pepper holding an iPad

At the same time that the tech industry is experiencing a “talent crunch”, their Human Resources divisions have additional struggles. They need diverse workforces from varied backgrounds for interdisciplinary teams in order to meet market demands. They also need to ensure pay equity. While salary history bans have been widely introduced, they put pressure on successfully predicting an applicant’s value, without which it is difficult to offer an appropriate salary.

Keeping a workforce engaged and healthy has been an ongoing challenge for years. The focus has been toward ensuring mental and emotional wellbeing, and both public policy and demanding work environments are calling attention to this. Expanding leave laws and company practices have created a need for careful scheduling to avoid critical gaps in the workforce.

Increasingly strict privacy regulations have created issues with professional social media and the “data-fication” of the workplace, requiring robust data security and confidentiality.

Increased use of personal information on social media for recruiting purposes in HR’s pre-selection process has become a critical issue. Data ownership and use of an employee’s personal data throughout their contract is shifting toward needing to be monitored, reported, and justified.

The reduced “shelf-life” of qualifications in modern, fast-moving markets often makes it difficult to find and retain the required workforce locally, calling for flexibility and more permissive ways of incorporating both a remote and on-site workforce.

Due to all of the above, combined with the evolution of the workforce and shifting demographics, workforce planning has become a top challenge for employers.

Enter “People Analytics”

People Analytics is the use of behavioral data to understand how people work and aid in their management. It has become a major field of study, and courses are currently being taught at top business universities.

People analytics can aid in:

  • Identifying the critical features/variables for jobs and employees
  • Matching those critical features/variables with a pool of applicants/recruits
  • Predicting whether workers will stay on the job or “churn”
  • Providing salary recommendations and other compensation to bolster longevity
  • Measure a team and individual productivity levels, aligning qualifications with management’s goals
  • Monitor sentiment, feedback, and worker engagement to positively drive core talent retention

Current adoption of A.I. in HR processes by C-Level execs:

Forty percent of international companies’ HR functions are currently using A.I. applications, most of these are US-based, with European and Asian organizations lagging. Worldwide, 50% will invest in data analytics to find, develop and retain talent. 39% are considering its impact on future skill needs. 63% are re-thinking the role of their human resources department.

A recent survey by IBM found that 50% of Chief Human Resources Officers already recognize and anticipate the technology’s potential to completely reinvent critical HR responsibilities, including operations, talent acquisition, and talent development. Suffice it to say, A.I. is on the cusp of transforming many aspects of HR to better equip it for a dynamic, ever-evolving workplace.

Why NOW is the time to integrate A.I. with HR:

HR has been investing heavily in preparing the ground for A.I.

Over the last several years, companies have been building, implementing, and improving A.I. frameworks. This has allowed managers and HR professionals to streamline and automate a multitude of tasks, including on-boarding, identifying, selecting, and developing top talent. With the wealth of data collected on job applicants/recruitment prospects (gathered through applications forms, pre-employment assessments, and correspondence), we now have the ability to train A.I. systems to improve the candidate’s experience, identify high-potential candidates, and predict who is most likely to succeed in a given position.

Investment in A.I.-related Products in HR has steadily increased

The need for high quality data is urgent, as CEOs and CHROs are being asked to report on pay equity, diversity, and skill gaps by their Board of Directors. The new generation of integrated cloud HCM systems now require a company to implement a more consistent system of records. Approximately 40% of companies now have a cloud-based HCM system.

The problems of data quality/integrity and integration are demanding attention

The average company now has six or more separate record systems: Payroll, learning, recruiting, performance, engagement, wellbeing, and more. Data Integration has become easier thanks to a wide set of tools, and with the adoption of Hadoop Based Clusters and Data Lake Architectures, scalable infrastructures are now available to integrate data from diverse sources, breaking up former Data Silos.

This leads to a more integrated use of data throughout the organization, and increases the overall demand for quality data and consistent definitions.

Companies are greatly expanding the type, nature, and level of data needing analysis

The widespread use of software solutions has eased various data source integrations into HR’s workflow. Data Silos in payment, performance metrics, employee education, professional development, recruiting, talent mobility, and organizational compliance are broken down and integrated into company-wide analysis and reporting. We can now see the impact from adopting solutions derived from monitoring and analyzing new data sources over the last three to four years.

Examples of newly available, analytically-rich data include:

  • Near real-time data about employee engagement (via pulse surveys or continuous performance management tools)
  • Employee recognition (from social recognition systems)
  • Employee communications and teams (through organizational network analysis and email metadata-analysis systems)
  • Travel and location (through time and expense reports, employee badge readers, or company phone location data)
  • Employee wellbeing (via wellbeing applications and voluntary data shared about exercise, fitness, and recreational activities)
  • Trust and employee sentiment (via “mood analysis” through survey responses and emails)

In our Part 2 Article in the series “An Overview of A.I. in HR”, we will explore: Application use cases, talent sourcing, attracting talent, current use cases, and recruitment automation.

Thanks for your time and interest! We look forward to hearing from you.

-Frank Fichtenmueller, CTO


About DREAM

The DREAM platform isn’t just another untested beta program on a white paper… It’s live and being used right now to hire blockchain professionals. The token sale will enable DREAM’s innovative team to take DREAM to the next level by integrating A.I. and incorporating our platform token.

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