What You Need to Know
Over the last five years, cloud migration took priority, leading to a misalignment between application development priorities. Cloud migration priorities – often driven by the desire for cost savings – led to
cloud consumption agreements that necessitated lift-and-shift migrations to hit minimum spend requirements. The migration failures in the last few years have spurred enterprises to embrace
SaaS-first approaches.
Why would enterprises go SaaS-first instead of doing more extensive modernization prior to application migration? The strong alignment between the data in these two studies suggests that the application development effort being prioritized is actually the cloud migration itself.
So application development efforts have been concentrating on cloud migration. Why does that matter?
The critical detail comes from the top two lines of the chart: AI and data products and BI, analytics, ML and AI tools. Two years ago, neither of these categories was on the enterprise priority list, let alone at the top (with a huge margin over the third choice). That rapid rise has led organizations to plan to
double their AI spend and double their number of AI-enabled applications.
This budget reprioritization means that anything not directly supporting AI is likely to be cost-optimized or reevaluated. Given most enterprises’ track record on cloud migration to date, it isn’t surprising to see them take up SaaS-first strategies to take pressure off development staff. With businesses prioritizing AI as much as they have, adopting a SaaS-first approach to lower-priority application categories keeps their limited development resources on AI.
The next question is, then, with so many resources and priorities going to support AI,
what is it that the AI is supposed to do?
