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.

When Candace Mitchell was young, she discovered a love for computers and haircare. Her interest in technology led her to study coding in high school, leading her to build websites.

Meanwhile, she also considered going to cosmetology school.

She found a middle ground in beauty technology, later becoming co-founder and CEO of Myavana, a Black-owned beauty technology vendor. Myavana uses AI technology to analyze hair strands and make haircare recommendations.

Myavana started with a hair analysis kit; the startup's technology uses machine learning to identify and analyze the different unique combinations in people's hair.

"Our research shows us that there are actually 972 unique combinations of hair profiles," Mitchell said on the latest episode of the Targeting AI podcast. "Using machine learning is how we can automate the process of the analysis and generate those product recommendations."

While Myavana works with consumers, it found that its data on hair is also valuable to enterprises interested in the haircare business.

"When you come to Myavana, you can target consumers based on their hair goals and hair challenges," Mitchell said. "That's the cool thing with AI -- it has uncovered new data that is helpful for businesses and how to target consumers. And again, just making it personalized."

Myavana recently raised $5.9 million in seed round funding.

While the vendor developed proprietary technology, it runs its model on AWS. It also built a conversational AI chatbot with Google.

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.

Open source AI models are closing the gap in the debate between open and closed models.

Since the introduction of Meta Llama generative AI models in February 2023, more enterprises have started to run their AI applications on open source models.

Cloud providers like Google have also noticed this shift and have accommodated enterprises by introducing models from open source vendors such as Mistral AI and Meta. At the same time, proprietary closed source generative AI models from OpenAI, Anthropic and others continue to attract widespread enterprise interest.

But the growing popularity of open source and open models has also made way for AI vendors like Together AI that support enterprises using open source models. Together AI runs its own private cloud and provides model fine-tuning and deployment managed services. It also contributes to open source research models and databases.

"We do believe that the future includes open source AI," said Jamie De Guerre, senior vice president of product at Together AI, on the latest episode of TechTarget's Targeting AI podcast.

"We think that in the future there will be organizations that do that on top of a closed source model," De Guerre added. "However, there's also going to be a significant number of organizations in the future that deploy their applications on top of an open source model."

Enterprises use and fine-tune open source models for concrete reasons, according to De Guerre.

For one, open models offer more privacy controls in their infrastructure, he said. Enterprises also have more flexibility. When organizations customize open source models, the resulting model is something they own.

"If you think of organizations making a significant investment in generative AI, we think that most of them will want to own their destiny," he said. "They'll want to own that future."

Enterprises can also choose where to deploy their fine-tuned models.

However, there are levels involved in what is fully open source and what is just an open model, De Guerre said.

Open models refers to models from vendors that do not include the training data or the training code used to build the model, but only the weights used.

"It still provides a lot of value because organizations can download it in their organization, deeply fine-tune it and own any resulting kind of fine-tuned version," De Guerre said. "But the models that go even further to release the training source code, as well as the training data used, really help the open community grow and help the open research around generative AI continue to innovate."

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.

Nearly two years after the mass consumerization of generative AI with the introduction of ChatGPT, the technology is now moving from experimentation to implementation.

A recent survey by TechTarget's Enterprise Strategy Group found that generative AI adoption is growing. The analyst firm surveyed 832 professionals worldwide and found that adoption has increased in the last year.

"We're in the acceleration phase," said Mark Beccue, an analyst at Enterprise Strategy Group and an author of the survey report, on the Targeting AI podcast.

Organizations are using generative AI in areas such as software development, research, IT operations and customer service, according to the survey.

However, there isn't a particular use case that is a top priority. Organizations are focusing on several applications of generative AI and still face some challenges when trying to adopt generative AI technology.

One is a need for more infrastructure, Beccue said.

"They feel that the changes are needed to support infrastructure before they can proceed with GenAI," he said.

This might include adding platforms for enterprise generative AI projects or more development tools, he added.

"It's really everything that gets you to being able to build an app," Beccue continued.

Organizations also don't have consensus about what kind of AI model is best for their needs: open or closed source.

"It's probably both," Beccue said. "People are thinking about how to use these things and they're understanding that not one model fits everything that they need. So, they're looking through to see what works for them in certain instances."

The enterprises that have found quick success with generative AI are ones that invested in AI years before it was popularized by OpenAI's ChatGPT, Beccue said.

He said these are companies like Adobe, ServiceNow -- which, for example, used machine learning, natural language understanding, process automation and AIOps since at least 2017 -- and Zoom.

"They did it in a way where they said, 'We think there is potential here for this to help us do what we do better,'" he said. "That was their driver."

This was what made them ready when generative AI hit the market.

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, analytics and data management technologies. Together, they host the Targeting AI podcast series.

As one of the top cloud providers, Google Cloud also stands at the forefront of the generative AI market.

Over the past two years, Google has been enmeshed in a push and pull with its chief competitors -- AWS, Microsoft and OpenAI -- in the race to dominate generative AI.

Google has introduced a slate of new generative AI products in the past year, including its main proprietary large language model (LLM), Gemini and the Vertex AI Model Garden. Last week, it also debuted Audio Overview, which turns documents into audio discussions.

The tech giant has also faced criticism that it might be falling behind on generative AI challenges such as the malfunctioning of its initial image generator.

Part of Google's strategy with generative AI is not only providing the technology through its own LLMs and those of many other vendors in the Model Garden, but also constantly advancing generative AI, said Warren Barkley, head of product at Google for Vertex AI, GenAI and machine learning, on the Targeting AI podcast from TechTarget Editorial.

"A lot of what we did in the early days, and we continue to do now is … make it easy for people to go to the next generation and continue to move forward," Barkley said. "The models that we built 18 months ago are a shadow of the things that we have today. And so, making sure that you have ways for people to upgrade and continue to get that innovation is a big part of some of the things that we had to change."

Google is also focused on helping customers choose the right models for their particular applications.

The Model Garden offers more than 100 closed and open models.

"One thing that our most sophisticated customers are struggling with is how to evaluate models," Barkley said.

To help customers choose, Google recently introduced some evaluation tools that allow users to put in a prompt and compare the way models respond.

The vendor is also working on AI reasoning techniques and sees that as moving the generative AI market forward.

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.