The enterprise artificial intelligence (AI) game has a new player.
Up until recently, businesses looking to capture the operational efficiencies of having an AI-powered workplace chatbot on hand to search for information, generate content, write code, or assist with customer support would have to shell out around $30 per month per user to start.
That’s how much Microsoft’s enterprise AI tool Copilot costs, and the $30 base-level price tag is the same price Google charges for its similar Duet AI service. Generative AI chatbot pioneer OpenAI refers enterprise prospects to their sales team.
But now web giant Amazon has joined the corporate chatbot race, and the Seattle-based company is looking to undercut its big tech rivals with a starting price of $20 per month (per user) subscription.
This, as Amazon Web Services (AWS) on Tuesday (Nov. 28) announced its own business assistant, Amazon Q, at AWS re:Invent in Las Vegas, describing the product as new type of generative AI powered assistant that is “specifically for work and can be tailored to a customer’s business.”
Accenture, Amazon, BMW Group, Gilead, Mission Cloud, Orbit Irrigation and Wunderkind are among the businesses already signed up for the AI service, which is currently only available to users of Amazon Connect, AWS’s service for contact centers.
Per the company’s press release, the Amazon Q chatbot is available on preview for Amazon Supply Chain — a supply chain management service — and Amazon QuickSight, the company’s platform for business intelligence.
Outside of its competitive subscription cost, AWS is also touting Amazon Q’s data security and privacy controls as an advantage relative to existing enterprise AI offerings — and as one of the world’s biggest providers of cloud computing, Amazon already has a baked-in audience of tech-savvy business customers to pitch its new corporate chatbot to.
But Amazon will need to assuage ongoing enterprise worries around the tendency of AI chatbots to hallucinate and generate synthetic, wrong information, a concern that has held back broader enterprise adoption of the innovation around security-critical areas.
For example, PYMNTS reported that 72% of lawyers doubt their industry is ready for generative AI, and just 1 in 5 believe that the advantages of using AI surpass the disadvantages.
Underneath all the buzz around generative AI, the technology powering the foundational large language models (LLMs) at its heart is merely a prediction tool that generates probabilistic outcomes. Only, the scale and speed at which those predictions compute, as well as the data they draw from, is like nothing the world has ever seen.
“Generative AI has the potential to spur a technological shift that will reshape how people do everything, from searching for information and exploring new ideas to writing and building applications,” said Swami Sivasubramanian, vice president of data and artificial intelligence at AWS, in a statement announcing the rollout of Amazon Q.
That’s why, as PYMNTS Intelligence reveals, that 84% of business leaders say that AI will have a positive impact on their operations. And increasingly, business success and competitive advantage in the 21st century boil down to the ability to access and leverage best-in-class data.
Amazon claims that its new chatbot will “never use” its business customers’ content to train its AI models and highlights that Amazon Q can personalize its interactions to each individual user based on an organization’s existing identities, roles and permissions — a feature presumably designed to help woo companies in highly regulated industries like finance and healthcare.
Data fragmentation is an ongoing problem — and side effect — of the business world’s digital transformation. Organizations are sitting on vast amounts of information spread across multiple documents, systems and applications — which can be a challenge not just for employees looking for insights, but also for models looking to corral and surface this information.
Corporate-focused AI chatbots aim to help solve these challenges, but many general-purpose AI solutions are not connected to internal resources and do not understand a company’s existing identities, roles and permissions to determine which resources an employee should have access to for their work.
For organizations to succeed with the integration of innovative enterprise technologies, like AI, the tools need to be pointed toward a definitive business goal with a clearly auditable process, as well as be highly interoperable across existing operational workflows.
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