A message from MindBridge CFO, Matthias Steinberg
The Gartner CFO conference is an important industry event for my company MindBridge. It gives me the opportunity to join and support the MindBridge team and I get to participate in many of the keynotes, presentations, and round tables. Naturally, I am keen to understand where AI adoption in finance stands and I enjoyed participating in the discussions.
Here are my takeaways on AI in finance:
1. AI is top of mind, but for most the journey is just starting.
AI is clearly top of mind for many CFOs and finance professionals. Accordingly, many presentations and discussion forums concerned AI, as did the opening Gartner Keynote. What became clear is that AI adoption in finance is still at the beginning. Access to ChatGPT and/or Microsoft Copilot for example were in many cases the first steps that CFOs worked on in adopting (generative) AI. It also transpired that many CFOs are still unclear about their roadmap and next steps. Gaining clarity on prerequisites, use cases, cost, ROI, and governance were at the forefront of the discussions.
2. AI use cases in finance are just beginning to come into focus.
AI based products and applications that deliver on clear, valuable use cases are only just beginning to come into focus. Today there are few gen-AI applications capable of delivering value at the core of finance functions. Gartner did its best to provide guidance. I am convinced this is because generative AI, which sparked recent attention to AI, is neither built nor capable of numerical (i.e., financial) data. This leads to many CFOs taking a cautious approach and focusing on learning and planning rather than executing at this point.
3. Cost and ROI is at the center of the discussion
The ROI of AI implementations is important, and in many cases difficult to quantify. Gartner warned in its opening keynote that significant cost overruns are prevalent among first movers. In addition, the value of AI does often not (only) lie in an increased efficiency (providing clear cost cases) but rather in increasing effectiveness and competitiveness, the benefits of which are often hard or not possible to be quantified. I agree with that. My belief is that developing the knowledge of how to draw value from AI is highly strategic at the same time as individual to the specific company and department. This makes it an imperative to drive the process of adopting, learning, and building even if when the “efficiency case” alone does not provide a positive ROI.
4. Cost overruns, loss of trust and misuses in decisions making are key risks
Gartner opened the conference with a Keynote highlighting potential pitfalls that can derail AI deployments. Misuse in decision making, loss of external trust, rigid mindsets and cost overruns are the 4 “AI stalls” CFOs face when deploying AI. CFOs need to consciously manage and pre-empt these risks.
5. AI governance is being defined right now, and CFOs have a key role to play
AI governance, i.e., taking decisions on which technologies and tools to purchase, deploy and maintain and how to use it safely is a collaborative, cross-functional team effort. Data access and quality, security, and privacy, use case and features and cost and ROI are a few of the dimensions to be clarified. CFOs and their teams were deemed to be in a good position to drive this process and by doing so to take an important strategic and tactical role in the company-wide deployment of AI.
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About the author: Matthias Steinberg is CFO of MindBridge.ai, a leader in AI driven financial risk discovery. Previously he was CFO of IONOS.com, Europe’s largest mass market hosting provider where he oversaw a multi-year effort to ready the company for its IPO. In the process, he developed an interest in AI technologies to transform risk management and assurance in the Enterprise.
