Amazon Q, one of the company’s most important new AI products, became publicly available in late April. Four months into its launch, Q is facing a host of teething problems.
Some of these concerns were shared in August in an internal Slack channel, where several Amazon Web Services employees voiced opinions about how Q had been doing in its early rollout phase, according to the messages and an internal memo seen by Business Insider.
They said Q lacked certain features common in competing products. It’s also sometimes expensive for users and has trouble integrating with other software. Worst, Q may be losing customers to Microsoft’s Copilot.
“My view is that Q is fit for demos and very tightly controlled simple use cases right now,” one of the AWS employees wrote in the Slack channel.
Q’s early struggles illustrate the challenges facing business-software makers in the age of AI. Buzz, hype, and hope have fueled intense competition that forces tech companies to rush new product launches, often with incomplete features, flawed internal tracking systems, and unintended IT effects. Amazon competitors such as Microsoft and Google have also experienced problems with early AI launches.
For Amazon, this is yet another test of its AI prowess. The company expects several billion dollars in revenue from AI services this year. But AWS’s AI chips that compete with Nvidia’s GPUs are seeing slow adoption rates, while efforts to upgrade the Alexa voice assistant with ChatGPT-like technology are facing headwinds.
‘Not on par’ with Copilot
Internally, Amazon employees are worried that Q’s shortcomings are causing customers to switch to Microsoft’s Copilot.
One common customer complaint is Q’s inability to process images embedded in PDF files, according to the Slack messages and a separate internal memo BI viewed. As a result, one of the AWS employees wrote in Slack, Q’s answers are “not on par” with Copilot’s, and customers have to wait until AWS’s Re:Invent conference in December, when upgrades are expected.
Another issue is image creation. The same AWS employee said Q didn’t support “multi-modal capabilities” or the ability to handle both text and image files, making it less appealing. One customer switched to Copilot after finding out that Q couldn’t create a marketing campaign that includes both an image and text, this staffer said.
Some customers are complaining about Q’s high data-integration cost, according to the internal memo and Slack messages. For example, one customer found that Amazon Q didn’t have the tools to choose which email content to ingest when integrating with Microsoft’s Exchange for enhanced email search. The initial cost estimate for this customer was about $400 per user per inbox. Microsoft’s Copilot, meanwhile, has “out of the box functionality” to support this use case, the employee said.
“Is this product even ready for adoption at scale?” this person wrote in Slack.
A healthy culture of self-criticism
Some Amazon employees raised red flags before Q’s launch, saying the release felt “rushed,” with little testing and heavy reliance on human reviewers.
This is part of a broader culture at Amazon that encourages employees to speak up about issues so they can be fixed sooner. Other major technology rollouts by the company have faced early headwinds only to steadily improve and succeed later. Amazon Q could be one of these products.
In an email to BI, an AWS spokesperson, Patrick Neighorn, said the open discussions were part of Amazon’s culture of encouraging employees to be “vocally self-critical” about their work. He added that Amazon Q was seeing strong growth across the board.
“Being vocally self-critical is core to how we earn the trust of customers, and we seek and value builder feedback,” Neighorn wrote. “We already see large customers like Bayer, Smartsheet, and National Australia Bank using Amazon Q today, and we continue to use Amazon Q across our businesses, including the AWS sales organization and Prime Video.”
‘Fire drill’
Some AWS employees in the Slack channel pointed out the company’s ambitious marketing language for Q and asked whether the company needed to “tone it down” so customers wouldn’t lose trust.
“It’s a fire drill waiting to happen,” one of these AWS employees wrote in Slack. “All we can do is plan on picking up the pieces once marketing is done slamming our customers against the wall of what Q can’t do.”
Neighorn told BI that Amazon had clear pricing for Amazon Q and that customers could use the AWS Pricing Calculator to calculate Amazon Q charges. He added that Amazon Q gives customers control over which documents are indexed and how they’re ingested.
“Amazon Q only launched in April, and we continue to add new features based on customer feedback,” Neighorn said. “Amazon Q has a rapidly growing customer base that is using Q for a variety of use cases.”
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Disappointing early sales data
Internal data also hints at disappointing sales for Q.
One part of AWS’s sales team with more than 3,000 people missed a sales target for Q that it was supposed to hit by July, according to internal data obtained by BI. The data, which was tracked by Amazon’s most senior leadership team, showed that Q’s sales fell short of its goals across all regions, most notably the North American market.
Amazon has also struggled to keep track of how this new AI product is selling.
Neighorn said the internal sales data BI obtained came from “inaccurate preliminary figures based on an incomplete methodology that we fixed weeks ago.” BI isn’t publishing the specific sales numbers and targets from the internal data because of Amazon’s correcting its methodology.
“Customers are excited about Amazon Q, which is seeing rapid customer adoption since its launch only four months ago and is already close to meeting our ambitious sales goals,” Neighorn said.
$260 million in efficiency gains
AWS’s CEO, Matt Garman, instructed a part of his sales team with roughly 6,500 employees to undergo a mandatory half-day training for Amazon Q earlier this year, according to an internal message seen by BI.
The training, designed to build the “confidence and skills” needed to sell Q, covered everything from Amazon Q’s branding and security features to supply chain and coding overview, the message said.
Neighorn told BI that sales trainings were “standard practice” for enterprise technology companies and that any suggestion that it’s unusual is “false.”
Garman appears to have high hopes for AI assistants like Amazon Q. At an internal talk in June, he said software engineers might soon stop coding because of the advancements in these types of AI tools. Amazon is also working on a separate AI chatbot, internally code-named Metis.
For now, Amazon CEO Andy Jassy may be the biggest salesperson for Amazon Q. Last month, he wrote on LinkedIn that Amazon’s internal use of Q had helped the company gain “significant efficiencies,” adding that it saved the company “4,500 developer-years of work” and an estimated $260 million in “annualized efficiency gains.”
“It’s been a game changer for us, and not only do our Amazon teams plan to use this transformation capability more, but our Q team plans to add more transformations for developers to leverage,” Jassy wrote.
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