Imagine the world’s best workplace contributors — they have been at the company for 40 years, have access to all the right people, ask the right questions, and know all the answers. They’re at your door and ready to get work, automating administrative and routine processes, freeing up your team’s capacity to focus on strategic, complex, and human-focused work, and making your people better and faster. Their name is artificial intelligence (AI).
The possibilities and the current realities of using AI and machine learning (ML) to tackle everything from customer engagement to running complex “what if” scenarios in this macro environment continue to grow. Based on the number of organizations that have already implemented AI, it is not a passing trend. It’s a major business disruptor that is rapidly being adopted by both large and small companies.
In a recent survey of 1,000 business decision-makers from around the globe, 73% said they are feeling pressure to implement AI in their organizations, as leadership seeks to increase productivity and gross domestic product.
According to a report, AI could drive a 7% (or almost $7 trillion) increase in global gross domestic product and lift productivity growth by 1.5 percentage points over 10 years. In every sector, executives are asking: What can AI do for us? And how can we leverage it to stay ahead of or catch up to the competition? The answer lies in the data.
Harnessing the power of AI is reliant upon quality data at the ready. As much as AI-driven solutions like large language models (LLMs) appear to mimic human intelligence, they are only as good as the data on which they’re trained and the people who make decisions based on the tool’s output.
Recent headlines may lead you to believe AI is upending everything and going to cost jobs. However, the combination of AI-powered efficiency and the ever-important human touch creates a powerful synergy to build better business relationships. That’s why it’s essential to combine human judgment with a pipeline of reliable, unified data to make the most of AI and ML. Unfortunately, more than three-quarters (77%) of respondents to the Workday survey said they were concerned about the timeliness or reliability of their data.
Empowering Human Judgment
Much of the media coverage around AI has been focused on predictions that millions of people could lose their jobs to the technology. Fortunately, cooler heads have prevailed and organizations like the Society for Human Resource Management (SHRM) have begun talking about the more likely scenario in which AI aids employees instead of replacing them, The world’s largest HR association, boasting nearly 325,000 members, SHRM points to a Microsoft report envisioning “a future where AI saves time, enables smarter work, stops information overload, improves data searches, eliminates busywork and unleashes creativity.”
I view AI and ML as talented new co-workers, not replacements for people. These advancing technologies can help provide sharper insights, free up the capacity to partner with business teams in new and exciting ways and enable faster business decisions, 20% to 30% faster, according to recent Workday research. By automating certain processes and transforming the way we can look at data, AI and ML empower humans to make more precise decisions and drive more effective behaviors.
Understandably, AI and ML are causing anxiety in the workplace. As they are deployed, teams will continue to have to navigate learning curves as they become comfortable working with new technologies. AI and ML will be deployed in process-specific ways that will reshape people’s jobs, but not eliminate them. For example, finance teams will not simply hand over decisions to AI. Instead, AI and ML will help remove some of the risk from predictive modeling.
Such advancements are poised to revolutionize businesses, yet organizations may find their expectations dashed if they do not set the stage to seize that potential. Companies saddled with data silos will find their AI and ML journey slow-going.
AI automation requires careful testing and deployment, and that process is far more time-consuming if teams must pull internal and external data from multiple systems. Data is fueling the emerging AI age — build the right data infrastructure and you will find yourself moving forward much faster.
Katie Rooney is CFO and chief operating officer of Alight, which provides cloud-based benefits and human capital solutions.