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.