The secret to getting AI on the fast track
What’s your CIO hiring for these days? Knowing the answer — and how to be the leader who has those skills — might just be the ticket to the next phase of your career. More than half of the CIOs surveyed by IT analyst firm Gartner in 2021 said that they plan to boost staffing to manage machine learning and AI initiatives.
The urgency is because AI workloads have traditionally required specialized IT infrastructure. That is, until now. AI and data science workloads can now run on accelerated mainstream servers in enterprise data centers, which makes it simple for IT professionals to support these new applications.
Running AI in the enterprise is now easier than ever, yet to understand project requirements IT still needs to know about the language spoken by AI experts. Luckily, the fastest way to learn the AI lingo is also the fastest way to find success in AI projects: include your company’s data scientists when you’re developing plans for your enterprise AI infrastructure.
When it comes to AI, an enterprise’s most important users are its data scientists. They’re serious AI experts, and they know that they need powerful servers with accelerated computing capabilities to get their work done on deadline. Data scientists crunch enormous data sets, so a lot of compute power is needed to iterate, refine and revise their AI models before they can be put into production.
Having data scientists at the table when you develop your AI infrastructure plans will help you define their use cases so you can prepare for your organization’s long-term AI ambitions. You’ll also get live feedback to help you eliminate options that don’t fit their requirements.
Inviting data science teams to join planning for AI might seem like an obvious step on the road to success, but since the domain is new for many organizations, it’s not always a common practice.
In fact, an IT manager at a leading healthcare organization recently mentioned how he was asked to invite three people he didn’t know to a meeting. Someone wise must have been planning the guest list, because it turned out that those three colleagues were on the data science team.
With their input, the IT team was able to jump past dead-end ideas that wouldn’t align with what the data science team needed to develop their AI project, and then get it running in production.
