Constellation Research: AI Piques Marketers’ Interest, But Overall Adoption Is Slow

Artificial intelligence (AI) adoption is relatively modest across large enterprises, but marketing and sales organizations are embracing the technology most quickly.

Forty-six percent of companies are investing in AI for sales and marketing purposes, and 50% are deploying AI projects for commerce and customer service, according to a report released Thursday by Constellation Research. By contrast, 48% of firms said they do not plan to invest in AI for finance, legal and administration.

It’s much easier for marketers and sales teams to adopt AI because of the breadth of plug-and-play options in the market, said Courtney Sato, director of research development at Constellation and co-author of the report.

“They’re already using a CRM platform or some enterprise software that has an automated tool set built in,” she said. “All they have to do is turn it on and pay an extra fee.”

Despite marketer uptake, the report, which surveyed 50 C-level executives at large companies across 12 verticals, found enterprise investment in AI is modest across the board. Most companies (51%) are investing less than $1 million in AI, although 60% plan to grow their budgets by at least half this year.

“Even early adopters are not spending a lot on AI yet,” Sato said. “The budgets are pretty modest compared to everything we’ve been hearing about AI.”

Most companies are in the early stages of adopting AI. Forty percent of respondents said they’re investing in building a data lake and big data analysis capabilities, which can take up to two years. Just 30% are investing in predictive analytics and machine learning, while 22% are working on natural language processing and image recognition. And only 8% are building deep learning neural networks.

“Companies are investing in the foundational technologies,” Sato said. “They need data lakes and statistical analytics, so they're going to invest in those first. They don’t have the appetite for the more cutting-edge technologies.”

How they’re doing it

Depending on what the AI is being used for, companies have three options for deploying it: build their own infrastructure, leverage computing power from providers like Google or Amazon or license off-the-shelf tools from platforms such as Salesforce or Oracle.

Each option has its pros and cons, Sato said. Building AI infrastructure is a huge and long-term investment, whereas working with a vendor or cloud provider can allow companies to start using AI more quickly. But organizations choosing to leverage or license should be wary of getting locked in with a cloud provider and aware that out-of-the-box AI tactics aren’t very flexible.

“Contracting with a vendor is helpful if you don’t want to build the back-end infrastructure,” Sato said. “The drawback is it’s not as customizable, and then you face lock-in.”

Companies that want to own their AI technology need to source from a limited and competitive talent pool. Eighty percent of respondents told Constellation they need to bring on more employees to implement AI solutions, and 40% said they’ll need to hire a “significant” amount of new talent.

Attracting talent will be tough, especially when competing with large tech and consulting firms with deep pockets, Sato said.

“If you have someone good, big tech companies are going to come after them,” she said. “It’s going to be a difficult decision for the employee not to go there.”

Because of these challenges, most companies are taking a hybrid approach to adopting AI. Twenty percent of respondents said they are both building and working with a cloud provider or vendor, while 26% said they’re doing all three.

“This is the fastest way to dip your toe in the water and then decide which strategy works for which project,” said R “Ray” Wang, principal analyst at Constellation.

Friction and oversight

Companies are cautious about deploying AI because it will fundamentally change the way their organizations function. It will also demand new roles and skills and affect roles beyond manual jobs.

Fifty-four percent of executives told Constellation they’ll need to understand how to restructure their business to accommodate AI, while 50% say they’ll need to acquire data expertise. Forty-eight percent said they need to learn how to manage an AI-augmented team.

Just 6% of executives said they don’t expect their roles to change as their organization adopts AI.

“Most executives are going to have to understand how an algorithm works and how to identify false outcomes,” Sato said. “After a while, I’m not sure how much value an executive that doesn’t have those competencies will be.”

Many executives are anxious that AI will replace their jobs and automated technology won’t be able to do tasks as well as humans, Sato said.

“There is fundamental distrust of the technology and what it’s going to do to people’s livelihoods,” she said.

There are also looming questions about how AI will be regulated. The General Data Protection Regulation in Europe and the Facebook-Cambridge Analytica debacle mean US data privacy regulation could be around the corner – and companies should put it high among their priorities.

“AI can create personal data,” Sato said. “You might give consent to share information A and B, but when those two things come together, they make information C that you didn’t consent to share.”

AI, however, gets smarter by consuming as much data as possible. Tech companies may now be gathering as much data as possible before potential regulation, Sato said.

“It’s against their interest to curb data collection,” she said. “They can’t help themselves.”

 

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