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The NExT frontiers in technology transformation

Generative AI, language models, machine learning, augmented reality, quantum computing: These hot topics are everywhere, and they likely aren’t going away. Everywhere you turn, it seems like someone is testing the abilities of consumer-facing generative AI applications, such as ChatGPT, AlphaCode, DALL·E, and Bard, and learning their limitations … and potential. And everyone—including Finance leaders—could be wondering what these technologies mean for their work and for the future.

It’s no secret that these leading-edge solutions will likely play an outsized role in Finance’s evolution. Self-service, finance cycles, and enterprise resource planning (ERP) all stand to potentially change because of them. But the question remains: How? The answers will be up to each organization and its leaders. Forward thinking can yield opportunity, but there’s also room for skepticism: about the technology’s limits, about return on investment, and about the ethical downsides that loom amid equity and bias concerns, plagiarism, intellectual property theft, and socioeconomic challenges. These are likely to be new items on every Finance leader’s existing stack of concerns. It’s no wonder that in the face of so much change, another risk can be inertia itself.
With that in mind, we’ve designed a pragmatic guide to the technologies that are likely to disrupt your organization over the next few years. We’ll show you what you should know, what to watch out for, and where to focus. (It might not be where you think.)
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 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).
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.

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.
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.
An evolution is based on DNA—and no matter what technologies you incorporate into your Finance function, your tech evolution should rest on a no-regrets foundation of a clean core, data, and security. Key investments in your people, processes, and core technology (which includes the ways you deal with your data) mean you could be better off when you decide to implement leading-edge tech. If Finance can’t trust and scale new tech because the building blocks aren’t there, then the investment likely isn’t worth it.
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If you’ve taken the steps above, you have a foundational road map. Stay true to it. If a new shiny object comes along—and don’t worry, one’s likely already on the way—you should take a step back and consider the fundamentals. How would this technology really create value for my Finance organization and strengthen our position as a business partner? Is this technology really that different from what we’ve already implemented, or is it truly a game changer? Only you and your organization can answer those questions. However, as you move forward, keep in mind: evolution, not revolution. It’s crunch time.
Our Finance Labs explore the “art of the possible” and define your Finance Transformation strategy, bringing to life potential use cases, road map priorities, and future-state benefits. Contact us to learn more.
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Jonathan Englert is a Principal in Deloitte’s Finance & Enterprise Performance consulting practice. He has 18+ years of experience working with CFOs and finance organizations on their most pressing ch… More
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Soumen is a partner at Deloitte Consulting Netherlands specializing in digital finance transformation. Soumen has more than 25 years of experience in the finance domain where he has discharged the rol… More
+1 973 602 5771
Omosede is a partner at Deloitte with 20 years of experience. She is a trusted advisor to CFO’s and other senior executives at Deloitte’s top-tier clients and has a proven track record of leading tran… More
909 979 7212
Adrian is a managing director in Deloitte Consulting’s Finance Transformation practice. He consistently innovates and helps his clients produce deeper business insights through the application of anal… More
+1 214 840 1475
Robyn Peters is a Senior Manager in the Finance and Performance within Deloitte Consulting LLP. Focused primarily in Consumer and Retail industries, Robyn helps CFOs define their vision for the future… More

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