• The original AI (intelligent automation, conversational AI, visual AI, etc.)
  We’ve reached the point of talking about "traditional" AI capabilities—ones you’re likely familiar with and may use already. Some Finance organizations have broadly used capabilities like robotic process automation (RPA) and natural language processing (NLP) to automate tasks, streamline individual tasks, and uncover patterns and correlations. These tools have shown they can automate routine activities and reduce human workloads, but they cannot replace human workers when it comes to addressing complexity, ambiguity, or surprises.
• Machine learning (ML)
  Machine learning is a data analysis method that uses algorithms and historical data to identify patterns and make predictions. The “learning” in its name comes from real-world contexts, not programmed rules. High-volume finance activities that require judgment-based decisions can benefit from ML—in chatbot interactions, predictive forecasting, and tax compliance, for example. But ML can’t work past anomalies or substandard data the way humans can. At least, not yet. For those curious about generative AI, ML could be a start, with similar use cases, but implemented generally faster and less expensively (for now).
• Generative AI
  Behind the current buzz is a powerful new kind of AI in which machines can create new content that mimics human work—text, code, voices, images, videos, and more. For Finance, the technology’s full potential may still be in the (near) future. For now, though, generative AI can function like an always-on, automated analyst: It can prepare a budget or write a report, but you wouldn’t take its work to the board without checking it yourself. Still, because it can "team up" with humans so quickly and efficiently, generative AI appears likely to spur dramatic changes in finance work in the years ahead.

Augmented reality (AR)
By superimposing digital images over a view of the physical world, AR can build immersive virtual experiences in real time. In finance, the same technology may offer benefits such as experiential learning. When combined with digital twin technology, AR may help simulate production lines, facilities that haven’t been built yet, or even finance workflows. Imagine touring a factory and "seeing" information about production costs and operational expenses superimposed over each part of the line. For now, though, most of the likely uses of AR appear to remain consumer-facing.

Quantum computing
Not a faster or bigger computer, but rather an entirely new kind, quantum machines use deep physics to perform complex tasks at speeds that transcend math itself. The possibilities appear immense, but even the people creating quantum computers are still learning what they’re capable of. For Finance leaders, this could be a technology to keep abreast of, but it’s not one you’re likely to use very soon. Someday, though, it may take AI technologies to new heights—with sophisticated modeling, valuation simulations, and other uses. When quantum comes of age, it may be a disruptive business force.