Meta director who used to work for Elon Musk says companies without an internal AI tool are 'already behind the curve'

• Meta product director Esther Crawford talked about Meta's internal AI tool for employees, Metamate.
• The executive said companies that don't use internal AI tools are "already behind the curve."
• Other companies like Shopify and EY are also investing heavily in internal AI tools.

In addition to its suite of consumer-facing AI offerings like Meta AI, Facebook's parent company also has its an internal tool for employees.

The AI tool was first previewed to employees a little over a year ago and tested with a small group, The Verge's Alex Heath reported last June. The tool is called Metamate, a name that may be derived from Mark Zuckerberg's brief attempt at nicknaming the company's employees. Heath reported that it uses company data to help employees with tasks like summarizing meetings and debugging features.

More than a year after its existence became known, we're seeing Meta employees talk publicly about it.

Meta product director Esther Crawford , known for sleeping on the floor during Elon Musk's Twitter takeover, touted the AI tool in a post on X Saturday. She said she uses the tool "all the time for efficiency gains," — and companies that don't have their own internal AI tool are "already behind the curve."

Crawford added in a response to the post that the tool "has a ton of capabilities," including summarizing documents, exploring metrics and visual data, creating queries, and getting project status updates.

Meta did not immediately respond to Business Insider's request for comment ahead of publication.

Crawford's post about Metamate seems to have sparked an online conversation about the usage of internal AI tools at Meta and other companies.

One user, whose Threads profile lists her as an AR design prototyper at Meta, replied to Crawford's comments with ways the tool has improved her performance.

"Metamate pointed out achievements I overlooked to improve my performance review," the user said. "And it's also written code for me faster than I could type it out myself."

Shopify COO Kaz Nejatian quoted Crawford's post on X and said he "agreed. 100%." The company's internal AI tool, which is called VaultBot, answers "around 32% of all engineering questions," according to a company announcement from January.

Big banks and consulting firms have implemented similar offerings for employees over the last couple of years as companies look to improve collective performance with personalized AI tools.

Last September, consulting firm EY announced it invested $1.4 billion in AI and created its own large-language model, EY.ai EYQ, to power an in-house chatbot. PwC also announced last April it would invest $1 billion into AI offerings over the next three years and KPMG said a few months later it would spend $2 billion on AI and cloud services for the workplace over a five-year span.

Other consulting firms, like McKinsey, have developed entire AI arms for their business. McKinsey acquired QuantumBlack in 2015 , where it now employs around 2,000 data scientists and functions as McKinsey's AI powerhouse.

Banks are following the same path. Goldman Sachs recently announced plans to roll out a series of generative-AI tools to its workforce as soon as next year and JPMorgan recently rolled out its own internal AI assistant powered by OpenAI.

Other big tech giants are also developing internal AI tools alongside consumer-facing products. For example, Google's internal AI model "Goose" helps employees write code faster. One technical program manager at the company said various AI tools have transformed his daily workflow , helping with risk management and administrative tasks.

While organizations are taking different approaches, it's clear that big companies are making moves to integrate AI. A recent survey by consulting firm Bain & Company found that out of 200 US companies with at least $5 million in revenue, 85% said adopting AI was in their top five list of priorities.

The companies also reported an average spending of $5 million a year on generative AI, with a fifth of those surveyed saying they spent over $50 million annually on the technology.