The Conundrum

No artificial intelligence is practical without impactful data driving it. Research suggests that nearly 40% of enterprises have deployed some form of AI technology and that spending on the technology will double in just the next year and a half.

The potential for AI in a continuously evolving industry like HR or people management is immense. A.I. is disrupting the way organisation hire new talent, engage employees and even gives predictive visibility to the C-Suite, enabling them to strike the perfect balance of decision making that tethers on basing decisions on performance and cost.

The reality is that the marketed potential often does not live up to the hype. Sometimes people buy out-of-the-box A.I. solutions that don’t necessarily fit their specific use-case and, most importantly, isn’t connected to a consistent flow of clean and accurate data. 

The sinker

When the general public thinks of artificial intelligence or machine learning, they envision an all-powerful computer that knows the answer to all the questions posed to humanity and is the answer to world peace. 

A.I. isn’t magical at all; it’s a complex network of high-powered computing that execute sophisticated algorithms, allowing for trend prediction, inferring meaning in written and spoken communication and managing automation workflows. 

People fail to realise that AI isn’t autonomous; you simply can’t buy an out of the box solution and leave it to manage your organisation’s fate. 

AI needs data and tons of it, which presents unique challenges for enterprise organisations who often have tons of big data, whose potential is locked in siloed systems. 

Why it matters 

For AI to function intuitively, the data required to “train” the algorithms need to be robust, expansive and come from many sources to create a complete model possible and enable particular calculations and outcomes.

For this reason alone, every organisation should always have its unique algorithm because every organisation is different. HCM solutions that promise ready-to-launch AI on deployment can lead to heightened expectations and disappointments. This system can perpetuate bias because this solution was not tailored to your business, rather a generalised use case. To learn more about deploying AI to HCM use-cases, click here. 

The good news is that most Enterprise Resource Planning (ERP) and HCM platforms are data-rich environments that can facilitate AI’s unparalleled benefits when appropriately deployed. 

Quality + Quantity

Quantity of data isn’t as impactful in building practical AI algorithms if the data sources are dirty. Dirty information is laden with human error, typos, duplication, outdated, incorrect or is incomplete. This is particularly common in organisations without a centralised HCM database, where information floats between siloed and disparate legacy systems and is locked by function or department.

High-quality data is essential for creating AI models and functionality and removing inherent biases from the data. Inherent bias occurs when the available data is not representative of the data model’s population or subject. Unconscious bias can happen when information doesn’t consider variables that capture the situation you are trying to predict or can be rooted if the information is produced by humans, including latent biases against a particular group of people. 

While that might not seem like a big deal on the surface, it’s important to remember that partial data will produce biased models that can be discriminatory to your employees and generate incorrect conclusions that can harm your business. 

The Relevancy Quotient

Data is only impactful if it’s relevant. 

Having a lot of clean data is vital to effective AI modelling. Still, data appending takes cleansing several steps further, replacing outdated or incorrect data with new, up-to-date information such as swapping an employee’s title from their old job with their new title and direct report in their new department. 

The real value in data appending is that it takes the data points you already have and supplements them with correct and (often) new information. Ensuring your AI algorithm contextualises the report, it ingests to generate more accurate, precise, and correct outcomes.

Choosing the right AI provider for your HCM needs 

Next-generation HCM platforms like EVA.ai are infusing a range of robotic process automation (RPA), AI, and machine learning capabilities into their software to create higher-value, higher-performance solutions. 

A lot of competitors on the market are more inclined to deliver out-of-the-box solutions that launch immediately. Our approach is considerably different. We build AI algorithms to fit around your business and are designed specifically around your desired resolution. 

When looking for an HCM provider, it’s essential to investigate and fully understand their data governance and hygiene strategies. How is the data from disparate sources managed, in multiple databases or from a centralised data system? How often is the data updated, synchronised, and cleaned? And how experienced is the team in managing all those moving pieces and effectively building algorithms that can meet your specific needs instead of generic use cases?