Ventana Research Analyst Perspectives

I recently attended the Salesforce Trailblazer DX event to learn more about Salesforce’s artificial intelligence products and strategy. Fueled by generative AI, awareness and investment in AI seems to be exploding. ISG research shows that enterprises plan to nearly triple the portion of budgets allocated to AI over the next two years. This doesn’t come as a big surprise when you look at the outcomes enterprises are achieving: Of those that have invested in AI, more than 8 in 10 (84%) have had...

The first wave of discussions around artificial intelligence (AI) in the contact center was focused on providing software buyers with a general understanding of what the technology could do. Now the conversations are becoming more specific, focused and direct. Buyers are more aware of the spectrum of available use cases and appear to be exploring how to map new tools to the particular business problems they face. Contact center buyers are approaching new technology deployments (or enhancements...

We’ve been saying for years that natural language processing (NLP) and natural language analytics would greatly expand access to analytics. However, prior to the explosion of generative AI (GenAI), software providers had struggled to bring robust natural language capabilities to market. It required considerable manual effort. Many analytics providers had introduced natural language capabilities, but they didn’t really resonate with enterprise requirements. They required significant effort to...

Ventana Research recently announced its 2024 Market Agenda for Artificial Intelligence, continuing the guidance we have offered for two decades to help enterprises derive optimal value from technology and improve business outcomes.

Ventana Research recently announced its 2024 Market Agenda for Analytics and Data, continuing the guidance we have offered for two decades to help enterprises derive optimal value and improve business outcomes.

I previously discussed the trust and accuracy limitations of large language models, suggesting that data and analytics vendors provide guidance about potentially inaccurate results and the risks of creating a misplaced level of trust. In the months that have followed, we are seeing some clarity from these vendors about the approaches organizations can take to increase trust and accuracy when developing applications that incorporate generative AI, including fine-tuning and prompt engineering. It...

It is a mark of the rapid, current pace of development in artificial intelligence (AI) that machine learning (ML) models, until recently considered state of the art, are now routinely being referred to by developers and vendors as “traditional.” Generative AI, and large language models (LLMs) in particular, have taken the AI world by storm in the past year, automating and accelerating the development of content, including text, digital images, audio and video, as well as computer programs and...

As we celebrate the first half of what seems to be the year of generative artificial intelligence, with an apparently unlimited discussion of use cases and bogeymen, my attention is turning to the very mundane question of costs. Specifically, how costs incurred – through investment and operation – will be distributed along the value chain and how this will affect the demand for AI ‒ by whom and for what purpose. It’s a question that needs asking even though, at this stage in the market’s...

A lot has been written about the definition of generative artificial intelligence (AI) and large language models (LLMs), though less has been written about the business considerations for an organization to evaluate adopting and implementing these technologies. And more importantly, does the technology align with the Office of the CIO objectives and the goals of the business? The value of generative AI software must be put into terms that all stakeholders can relate to. And organizations cannot...

The data and analytics sector rightly places great importance on data quality: Almost two-thirds (64%) of participants in Ventana Research’s Analytics and Data Benchmark Research cite reviewing data for quality and consistency issues as the most time-consuming task in analyzing data. Data and analytics vendors would not recommend that customers use tools known to have data quality problems. It is somewhat surprising, therefore, that data and analytics vendors are rushing to encourage customers...