The AI application startup, which was founded in 2016 and was valued at more than $2.1 billion in 2021, uses a reasoning engine to help employees search for information across the enterprise.

Since its inception, a key ingredient in the company's success has been AI and generative AI technology.

"We were the first company after Google to deploy BERT in production," said co-founder and president Varun Singh on the latest episode of Informa TechTarget's Targeting AI podcast.

BERT was Google's first model with bidirectional encoding that enabled computers to understand large text spans. It was pretrained, so Moveworks did not have to train it from the ground up. It also did not require a lot of data.

After using BERT to train its automation platform, Moveworks started using GPT-2 from OpenAI in 2020. This is two years before the mass popularization of the generative AI vendor's ChatGPT chatbot, mostly to generate synthetic data.

Singh added that he and his team had failed to realize right away that the model could also be used for reasoning tasks.

"It's not so much a mistake that was made or not, but it was just sort of as technology evolved, the moment a paradigm shift actually comes into full focus, you look back and you're like, 'We could have done that sooner because we had access to the models, but we didn't see how powerful they could be,'" he said.

Since the shift, Moveworks has evolved from a platform with a reasoning engine to a platform for building AI agents.

On Oct. 1, Moveworks launched Agentic Automation as part of its Creator Studio offering. The system enables developers to build AI agents.

Throughout the evolution of its business, Moveworks has differentiated itself with its use of AI technology, Singh said.

"Without AI, there's nothing Moveworks has to offer to the world," he said. "There's only value from Moveworks because of AI."

Esther Ajao is a TechTarget Editorial news writer and podcast host covering artificial intelligence software and systems. Shaun Sutner is senior news director for TechTarget Editorial's information management team, driving coverage of artificial intelligence, unified communications, analytics and data management technologies. Together, they host the Targeting AI podcast series.

When generative AI became the next big thing in tech, enterprise software giant Oracle bet heavily on a startup to provide it with foundation and large language models rather than scramble to develop its own.

That then-fledgling company was Cohere. Founded in 2019, the generative AI vendor raised $270 million in a Series C round, and its investors included Oracle, Nvidia, Salesforce Ventures, and some private equity firms. In July, Cohere raised another $500 million and reached a market valuation of $5.5 billion.

Cohere's open generative AI technology is now infused in many of Oracle's databases, a fixture among large enterprises. The tech giant has also tapped Cohere's powerful and scalable Control-R model for Oracle's popular vertical market applications, including those for finance, supply chain and human capital management.

But while Oracle has put Cohere at the center of its generative AI and agentic AI strategy, the tech giant is also working closely with Meta.

The social media colossus has gained a foothold in the enterprise AI market with its Llama family of open foundation models. Oracle is customizing Llama for its Oracle Cloud Infrastructure platform, along with Cohere's models.

"We have made a decision to really partner deeply around the foundation models," said Greg Pavlik, executive vice president, AI and data management services at Oracle Cloud Infrastructure, on the Targeting AI podcast from TechTarget Editorial.

"What we're looking for are companies that are experienced with creating high-quality generative AI models," he continued. "But more importantly … companies that are interested in enterprise and specifically business solutions."

Pavlik said Oracle values the open architecture of the models from both Cohere and Meta, which makes it easier for Oracle to customize and fine-tune them for enterprise applications.

"The advantage really of having a deep partnership is that we're able to sit down with the foundation model providers and look at the evolution of the models themselves, because they're not really static," he said. "A company will create a model and then they'll continually retrain it.

"We see our role as to come in and proxy for the enterprise user, proxy for a number of verticals," Pavlik continued. "And then try to move the state of the art in the technology base closer and closer to the kinds of patterns and the kinds of scenarios that are important for enterprise users."

Oracle also uses generative AI technology from other vendors and enables its customers to use other third-party models, he noted.

Shaun Sutner is senior news director for TechTarget Editorial's information management team, driving coverage of artificial intelligence, analytics and data management technologies. Esther Ajao is a TechTarget Editorial news writer and podcast host covering AI software and systems. Together, they host the Targeting AI podcast.

At the beginning of the wave of generative AI hype, many feared that generative models would replace the jobs of creatives like artists and photographers.

With generative AI models such as Dall-E and Midjourney seemingly creating unique works of art and images, some artists found themselves at a disadvantage. Some say the generative systems took their artwork, copied it and used it to produce their own images. In some cases, the generative systems allegedly outright stole the creative work.

Two years later, artists have to some extent been reassured by the support of stock vendors like Getty Images.

Instead of trailing behind generative AI tools such as Stable Diffusion, Getty created its own image-generating tool: Generative AI by Getty Images.

Compared with other image generators, Getty has taken great lengths to restrict its model through the data set. The stock photography company maintains what it calls a clean data set.

"A clean data set is really a training data set that a model is trained on that can lead to a commercially safe or responsible model," said Andrea Gagliano, senior director of AI and machine learning at Getty Images, on the latest episode of TechTarget Editorial's Targeting AI podcast.

Getty's clean data set does not contain brands or intellectual property products, Gagliano said. The model's data set also does not include images of well-known people or likenesses of celebrities like Taylor Swift or presidential candidates.

"We have taken the very cautious approach where our generator will not generate any known person or any celebrity," Gagliano said.

"It will not generate Donald Trump," she said, referring to the President-elect. "And it will not generate Kamala Harris," referring to the vice president and former presidential candidate.

"It has never seen a picture of Donald Trump," she continued. "The model has never seen a picture of Kamala Harris."

Gagliano added that removing this possibility also guards against those who want to misuse the technology to create deepfakes. Therefore, any generated output is labeled synthetic or AI-generated.

"We don't want any situation where we start to undermine the value of a real image," Gagliano said.

Finally, the data set that Getty uses produces images with licenses on them, ensuring that creators get compensated. Thus, a portion of every dollar made by Generative AI by Getty Images is given to the creator who contributed to the data set.

"The reason for that is the more unique imagery that we bring into the training data set, the more additive it is," Gagliano said.

Getty updated its generative AI tools Tuesday. The new capabilities include Product Placement, which lets users upload their own product images and generate backgrounds, and Reference Image, which enables users to upload sample images to guide the color and composition of the AI-generated output.

Esther Ajao is a TechTarget Editorial news writer and podcast host covering artificial intelligence software and systems. Shaun Sutner is senior news director for TechTarget Editorial's information management team, driving coverage of artificial intelligence, unified communications, analytics and data management technologies. Together, they host the Targeting AI podcast series.