For CIOs, 2024 looks a lot like 2023 in many ways. AI remains top of mind, security and privacy are looming concerns, and everyone's gunning for a smarter tech stack while balancing existing and evolving efforts.
Here’s my take on how CIOs will solve problems and tackle opportunities in the year ahead.
Generative AI changed the entire game in 2023, but we’re not exactly there yet in terms of AI being closely tied to business objectives. Plenty of companies are waiting for the dust to settle, so to speak, before they make a move. They’re keeping a close eye on ChatGTP, with each rollout materially and exponentially better than the last. Still, leaders who (primarily for reasons of regulatory concern) have hesitated to institute AI are facing the reality that they’ll have to start using it.
At the same time, companies that focused their model and approach on an earlier version of ChatGPT are likely in jeopardy each time a new version is released. It’s a frothy environment, and we’ll probably see a lot of consolidation in the space over time.
For now, smart CIOs are being deliberate in how they apply AI. We all know that AI is particularly good at replacing mundane tasks to enhance personal productivity, but AI has also helped to:
- Accelerate code development and reviews, a la GitHub CoPilot
- Enhance customer service across chat interactions and self-service tools
Where the true long-term value of AI lies is up for debate. End-to-end automation is still difficult to achieve, and AI doesn’t replace quality human effort and review. But it can make a good employee — say, a developer — that much more efficient.
One thing’s for sure: Box AI is a no-brainer for executives in general, because they know their data stays secure and they can control access. All the capabilities built into the Content Cloud mean that Box AI is architected specifically for the enterprise, so right out of the gate, that’s a differentiator.
Tangential to the greater conversion about AI is the issue of data security and privacy. In the midst of a fast-paced rollout of AI, CIOs have to ask themselves, “Is my data secure? How can I be sure it’s not being leveraged by competitors to train their own models?” Right or wrong, there’s a lot of consternation about that topic, and who has access to data in general.
It will be interesting, as this space continues to evolve and develop, to see if public AI models become more tailored to specific industries and verticals, more focused in their areas of discipline. As they do, security and data privacy will have to evolve as quickly as the AI space itself.
Globally and regionally, there are nuances (and sometimes stark differences) in data privacy and residency rules. This is a very complex environment for a global company to navigate, because it can so easily go sideways. The movement of data as it relates to personal privacy is also incredibly important as we see more regulatory response in reaction to public concerns about how data is being used within AI models and in general
The volume and sophistication of threats evolves every day, and it keeps CIOs and CISOs up at night. The more data you collect, the more risk you’re exposing yourself to, whether that’s legislative, regulatory, or security-based. This year, I predict that data collection will get increasingly scrutinized and be something companies need to think hard about. We’re already starting to see a pushback on data collection, and that will absolutely continue.
The best-of-breed approach to technology that’s evolved over time has created application sprawl. Every application represents a door into your environment. The more applications you have, the more attack surfaces, and the higher your risk. The massive wave of applications suddenly looks less like “problem solving” and more like too many paths to the data pot of gold.
For this reason (and others), the concept of simplification and consolidation in the tech stack continues to gain momentum — almost like a backlash against the abundance of SaaS. Still, you’re going to have some degree of application diversity. There are things that happen within competitive markets that the biggest software companies don’t have the ability to respond fast enough to, making it necessary for enterprise organizations to leverage disruptors and startups to ensure competitive advantage.
The trend I’m seeing is technology leaders analyzing where they need agility (the latest and greatest) versus where they need stability (core products that you know you’ll use long term). Consolidating and standardizing where you can, while maintaining or even gaining a competitive edge in your market, is the goal - this dynamic is probably here to stay.
The advent of GenAI has made faster innovation possible and has driven a lot of excitement around what’s to come. But conversations around cost and consolidation are still very relevant. So I’m curious to hear from other CIOs on where they lie on the continuum from “strategic innovation” to “buckled-down data security.” I suspect that, for most CIOs, 2024 will bring the need to get better at balancing both.