Editor’s note: This article draws on insights from an Oct. 30 CIO Dive and CFO Dive live virtual event. You can watch the sessions on-demand.
Enterprise technology leaders have been issued a clear mandate from their organizations: leverage the best technologies available in support of broader business goals. To deliver, CIOs keep an eye on two critical factors: costs and risks.
Alongside their finance and security counterparts, CIOs are working to buttress cyber defenses and ensure adequate ROI from technology spend. All the while, tech chiefs must ensure that competitors don't outpace their innovation efforts and employees have the tools they need.
Executives are also grappling with the impact of SEC provisions that require them to measure and disclose cyber risks in the face of mounting financial and cybersecurity pressures.
CIO Dive and CFO Dive held a live virtual event in October to discuss the challenges surrounding enterprise technology. Here are six takeaways from our speakers:
(Comments below were lightly edited for length and clarity.)
Ryan Downing, VP and CIO of Enterprise Business Solutions at Principal Financial Group
With any platform or capability that we build, we always take a very use-case driven approach. It's not that we're funded to build a new generative AI platform; what we're funding is use cases that can add value for the business.
We recognize that, in order to get that value, we have to build platform capabilities along with it. That means the platform is really being built with those use cases in mind, but also with a long-term future in mind.
Jaime Montemayor, chief digital and technology officer at General Mills
How we're measuring the value from generative AI investments is very important. In some cases the value question has a straightforward answer: we're saving money or increasing the top line or improving the margin.
What's harder is when you are improving agility, reducing time-to-market or just driving efficiency. Sometimes it's harder to put a dollar sign next to that. That's where the partnership with the finance team is important: they can help us put a business lens on the value equation and we can keep our technical teams focused on the solution.
John Pearce, principal, cyber risk, risk advisory services at Grant Thornton
One factor that drives up cyber spending is technology debt. If an organization has 25% to 30% of the environment at an end of life or unsupported stance, that adds a significant amount of compensating and other controls to protect the organization.
Cyber budgets are very much under scrutiny, but what many organizations are having to deal with is finding where they have control versus visibility and oversight. It's sometimes particularly challenging, because some of these systems have been around for so long and are very hard and costly to replace.
Jonathan Fairtlough, principal at KPMG cyber response
The purpose of the SEC regulations and the NIS2 regulations in Europe is transparency but also management oversight. That means you have to look at how you're using your technology, not only for the business gain it's providing, but the regulatory effect that the use of that technology can have on your business.
We're seeing boards and C-suites really trying to grapple with the methods they can use to understand and quantify cyber risk without having to understand each and every piece of the technology.
Mark Partin, CFO at BlackLine
In the public markets, there is a thesis that AI has stalled software spending, or at least crowded it out in the short term while CIOs and CFOs determine where their investments lie. Leaders are maybe going from best-of-breed application players to platform players that can represent a longer term partnership.
The main goal is digital transformation and finding a partner that can grow with these companies, and so the application of technology and the partnership between CIOs and CFOs is, I think, starting to improve.
Bhadresh Patel, COO at RGP
If you don't have good data, anything you do with AI is going to be relatively useless. We are looking at where our data is the cleanest, and where we can create some efficiencies. That also creates good organizational change management and readiness.
People are so afraid of AI as it relates to job loss that you actually have to show them how you can evolve the business and actually become more productive.
