AI in HR: Charting the course to a brighter future
A guide to integrating artificial intelligence solutions into Human Resources by bridging the gaps between ideation, innovation and implementation
AI solutions without a foundation of clearly thought-out business use cases and rigorous data management, are just castles in the air. HR digital transformation lies in prioritising data integrity and analytical rigour before any investment in AI. In this whitepaper, we set out to (a) debunk common marketing claims related to AI, (b) provide clarity on what is and is not AI and (c) to outline a process by which HR leaders can successfully embed AI into people management best practice.
As a company with deep-rooted experience in data engineering and data science, we have been delighted to see the emergence of AI as a headline discussion topic at board level but simultaneously dismayed by the cloud of uncertainty created in the industry by the surge in ‘AI’ products without due care for process.
It seems that for now we find ourselves in a quandary whereby the continued growth and interest in AI goes hand in hand with the associated confusion of how to embed genuine AI into business decision-making.
As AI covers many domains, including robotics, data and LLM’s, for the purposes of clarity, this whitepaper will focus on that aspect of AI that relates to data and the analytical applications that primarily drive predictive and optimised outcomes for businesses.
3 misconceptions about AI in HR
AI offers guaranteed accuracy
AI systems can make errors, particularly if they are trained on biased or incomplete data. This is a viable risk presently as data science is still quite a new discipline and many data scientists lack experience and correct technical knowledge to build AI systems for organisations. When modelled outcomes relate to people decisions, including development or talent profiling, the stakes are very high. There is little margin for error.
Implementing AI is quick
We live in a consumer culture, whereby we expect to acquire solutions and outcomes fast, without considering if this consumption is truly right for us.
This endemic has plagued organisations and contrary to media portrayals, deploying AI requires careful planning, data preparation, and ongoing maintenance. It is a complex process that involves investment in time and resources.
AI is a magic solution
AI is not an off the shelf software. Anyone marketing as such is not disclosing the truth to clients or business prospects.
Successful AI solution implementation includes setting clear business objectives, regimented approaches to data acquisition, curated and creative approaches to data cleaning and transformation, all with continuous expert human oversight.
" DATA + LEARNER + AUTOMATION = AI "
What AI is and is not in the Context of HR
In HR, AI refers to the use of machine learning algorithms and data analytics to enhance various processes and decisions. It involves systems that can learn from data, recognise patterns, and make decisions with minimal human intervention. True AI in HR can compute and predict outcomes based on the right inputs, providing valuable insights for decision-making. One must also be vary of wrong inputs, computations and subsequent consequences. The choice isn’t just if something is AI but also the selection of “right” AI.
Contrary to popular belief, not all technology solutions in HR are AI-driven. Many tools marketed as “AI” are actually data visualisation or reporting tools. These systems can present data in an easy-to-understand formats but do not have the capability to learn or make decisions.
Differentiating: Genuine & Bogus AI solutions
Learning Capability: Genuine AI systems continuously learn from data and improve their performance over time. Bogus AI tools do not adapt or evolve.
Decision-Making: True AI can make autonomous decisions based on underlying algorithmic application. In contrast, non-AI tools rely on human input to interpret data and make decisions.
Predictive Analytics: AI solutions offer predictive insights and recommendations, while bogus AI tools can only provide historical data analysis. A classic example being extensive dashboard-driven solutions that condense and report historic data with multiple chart views.
Complex Problem-Solving: Authentic AI can handle complex, multi-dimensional problems, unlike reporting tools that only present data visually.
2 Business Use Cases for AI integration in HR
Talent Profiling & Succession Planning
AI solutions can accurately analyse various aspects of an employees’ contribution and value to an organisation in terms of attributes such as learning, skills development, performance, competence progression and feedback.
This can provide a robust and accurate prediction of where each employee sits in the organisational hierarchy in terms of talent and value, offering HR leaders a more objective and dynamic view of an employee versus conventional assessments such as 9 box.
Solutions that can provide a diversely modelled perspective of an employee can be used to assess employees that sit in your top and bottom talent tiers on multiple features and can be considered for succession planning, reward adjustments or performance management interventions.
Learning and Development
With AI, organisations can create value from their data to reinforce investment in professional development.
Data points related to employee performance, skills and competence can be connected to data points on training such as training format, supplier, content and training feedback.
Learning experiences can be personalised by way of a recommendation engine to essentially create an initial organisation-wide training plan and predict the:
- type of training best fit for an employee
- employees where training investment should be focused
- expected outcome of training in terms of employee performance impact and ROI.
Setting a clear path to integrating AI in HR
