When SAP veteran Bill McDermott took over the CEO spot at digital workflow company ServiceNow in October, his mandate focused on growth. 

Though his strategy centers on organic expansion, acquisitions — a key part of SAP's expansion story — aren't off the table. 

"Should we choose to do 'tuck-ins' to compliment what our customers need, to get us somewhere faster, we'll do that very carefully," he told CNBC.

ServiceNow kicked off 2020 with one such "tuck-in": the acquisition of Israeli company Loom Systems, an AIOps company that uses artificial intelligence to give enterprise users insights into digital operations and fix IT issues.

The acquisition symbolizes a bigger trend in enterprise technology: Acquiring AI startups enables technology vendors to capitalize, enhance or expand their capabilities while bringing scarce talent aboard.

Last year, consolidation in the AI market hit record numbers. AI companies underwent 231 merger and acquisition (M&A) deals and 10 initial public offerings (IPO) in 2019, according to data from analyst firm CB Insights. In 2018, the total number of AI exits — the point at which company's backers capitalize on their investments — was 192.

In the year ahead, industry leaders can expect AI market consolidation to continue, as the race among corporations to amass talent and technology heats up. In fields such as healthcare, this trend will redefine core processes and impact customer expectations.

The playing field

Startups frequently mention competition from larger players as potential threats to the sustainability of their business in IPO documents. 

But in this context, acquisitions are the path of least resistance for rising AI startups heading for the exit. 

In healthcare, a field where Google is entering by leveraging its existing presence as a vendor, the presence of larger vendors will make it harder for smaller startups to compete, said Deepashri Varadharajan, lead AI analyst at CB Insights, in an interview with CIO Dive.

Despite the record-setting deal volume, a well-funded ecosystem of AI players remains under development. In the past five years, funding for AI companies went from $4.2 billion in 2014 to $26.6 billion in 2019.

The healthcare field leads the pack when it comes to startup funding. In 2019, AI startups in healthcare took in $4 billion, an amount that doubles the funding of finance startups ($2.2 billion) and leaves retail and consumer packaged goods ($1.5 billion) in the dust.

Mergers and acquisitions are an avenue for companies to integrate AI into their existing products and workflow without having to build them from scratch, according to Varadharajan.

"This is especially true given the limited AI talent that is now available," Varadharajan said.

One way companies are offsetting the talent crunch is by giving workers the opportunity to build skills through online training. Once training concludes, trainers Amazon, Microsoft and Google have deployed similar strategies to keep up with their hiring needs. 

But acquisitions for acquisition's' sake lead to disappointments and failed projects.

"It's important for companies to understand exactly what are they trying to solve for," said Varadharajan. "What is your unique bottleneck? And if you're looking at a specific technology or like application with an AI, how mature is it and is it able to deliver value today? Or are you okay with experimenting with something that could deliver value five years from now?"

Enterprise has learned from failed AI acquisitions, which means its focus in 2020 will be on providers that can help deploy a robust data strategy, said Gerti Dervishi​, Flybits co-founder and chief growth officer, in an email to CIO Dive. 

Market pressures

Corporate need for speed leads companies to thirst after new capabilities. Building internally isn't fast enough to respond to customer demands.

ServiceNow's acquisition of Loom Systems is one example of companies looking to augment their company's capabilities with AI, said Svetlana Sicular, research VP at Gartner, in an interview with CIO Dive.

"To the end user, there is value in finding AI inside the application they already use," Sicular said.

For vendors looking to acquire their way into AI capabilities, the road ahead poses some challenges.

To increase a deal's chance for success, companies have a clear understanding of a company's business case, thoroughly assess the technical challenges of the deal and use business outcomes as the guidance to determine redundancies in technology or staffing.