Enterprise software promises to solve talent management challenges, but getting real results from these systems remains frustratingly difficult for many organizations. Herbert Roy George has built his career helping companies bridge this gap between expensive technology and practical outcomes. After two decades working with major HCM platforms, he founded his own consulting firm in 2020 to tackle these persistent problems.
Turning HCM Systems into Practical Tools
George’s background gives him an unusual perspective on talent management. “Every other Indian kid grew up in the IT boom of the 90s and then eventually moved here in 2008,” he says. His MBA focused on HR and systems, which led him into enterprise resource planning. “I had a combination of HR and IT knowledge and systems and processes, so I was able to get into enterprise resource planning.” What started as corporate talent management work has expanded into unexpected areas. George now applies these principles to schools, families, and early-stage companies. “I’ve also been looking closely at talent management for startups, talent management for children, for schools, talent management for parents, families,” he explains. This broad application has shaped his understanding of how talent systems can work across different contexts.
The Streamlining Effect
He has watched technology transform talent management throughout his career. He’s seen multiple waves of change, from basic internet connectivity to cloud computing and mobile devices. “Over the years I find that it’s become increasingly streamlined with the enterprise systems,” George notes. “What was not manageable earlier has become super manageable now.” The shift goes beyond simple convenience. He puts it bluntly: “What cannot be managed on software cannot be operationalized.” Companies have moved away from Excel spreadsheets and Outlook calendars because growth demands better systems. “As they expand and grow, becoming distributed while setting up operations and marketing across different regions and cities, you have no choice but to use software.”
The Data Collection Problem
While software has made measurement possible, George identifies a critical weakness in most implementations. Companies can gather market data and run analytics, but struggle with customer and employee input. “The typical problem that today’s situation has is many companies have all these systems, they get whatever data they can and they have analytics. And they do not have the last mile,” George explains. Getting information from busy employees or customers proves challenging. “They need to collect the data from customers and employees. And staff members are super busy. They’re not getting paid to hand out the data to you,” he says. “So they don’t have time. And you have to find a way to get the data, that information off them.”
Using AI to Capture Unstructured Data
He sees artificial intelligence solving this data collection bottleneck. The technology can now capture unstructured information from everyday interactions. “AI is reaching the point where people are on your consumer devices, on your mobile phones—what you talk, what you see, what you record—all that can now be converted into data,” he explains. This represents a fundamental shift from previous approaches. “That ability had not been there with us so far. All the unstructured data we see and use now can be converted into points at scale which can be effectively used for understanding compensation, motivation rewards, where talent is, what skills are coming in, what’s going out.”
Ensuring Data Accuracy in AI Systems
With AI’s power comes responsibility for data quality. George warns about the importance of proper data governance and weighting. Using business units as an example, he explains: “If you have 10 business units and you’re getting data only for three of them and you’re making a decision based on that, you’re going to be skewed in decision making.” The challenge becomes more complex with AI systems. “AI is just a ginormous machine that will take whatever garbage it’s fed and churn it, then put it in systems and give it back to you,” George warns. “If I change the data being input, AI will give me a different output. So then now you’re playing God and you have to figure out how you want to play God.”
He think a shift from information scarcity to information overload has already happened. “The problem I think that the next few years will have is not lack of information, but over-information,” he says. “Now you got 100 different models, all data based, that claim to give you the facts about a matter. How do you now decide which one is accurate?” This creates new challenges for organizations trying to make sense of their talent data. The companies that figure out how to filter and act on the right information will gain significant advantages in managing their workforce. George continues working with organizations through his consulting firm, helping them navigate these evolving challenges while maximizing their HCM technology investments.
Connect with Herbert Roy George on LinkedIn to explore more about talent management innovation.
