Article
Crunch time series for CFOs: Novel and exponential technologies in Finance
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.)
A DNA of people, technology, data, and controls
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.
Expand
How to get started
Discussions of new technologies often end with the advice to pilot, experiment, and “fail forward fast.” That spirit may help inform the implementation of emerging technologies like these, but some of these tools may require too large an investment to move forward without a comprehensive plan. Finance can lead the way in creating that road map by identifying parts of the enterprise that can benefit, crafting the relevant business cases, and subjecting any pilot efforts to careful bottom-line analysis.
Here's a list to consider when getting started:
- Frame and communicate your vision for an AI-enabled Finance function.
- Come to grips with your data standards and governance.
- Evaluate and execute a pilot for leading-edge technology in a controlled manner; scale once successful.
- Make strategic choices about your talent—what do you need that’s different from what you have today, and how do you make sure you have what you need for tomorrow?
- Create an ecosystem for interoperable tech solutions that work together to deliver end-to-end outcomes that can make AI-enabled Finance a reality.
It's Crunch time.
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.
Ihre Anprechpartner
full
