Seeing losses in the fiscal Q3, GameStop also saw its shares fall by 4%, CNBC reported Wednesday (Dec. 8).
The company’s net loss was $105.4 million — an increase from the $18.8 million loss from 2020 at the same time — while its total revenue was at $1.30 billion.
GameStop said its sales grew as it fostered new relationships with brands like Samsung, LG, Razer and Vizio. Meanwhile, inventories grew during the quarter as the company tried to get ahead of supply chain challenges, particularly as the holidays approached.
GameStop’s current goal is to get away from brick-and-mortar retail and transition to eCommerce. To help achieve this, it has acquired several new leaders, including Chewy co-founder Ryan Cohen to lead its turnaround as chairman of the board.
Cohen was behind hiring ex-Amazon execs Matthew Furlong and Mike Recupero as CEO and CFO, respectively.
However, the new leaders have been vague on strategy and haven’t answered questions as to their outlook. In a call Wednesday (Dec. 8), Furlong said that the company has hired over 200 senior employees from top tech companies and has expanded merchandise, adding more personal computing gaming items.
Furlong also said that GameStop was looking into its options with things like blockchain, non-fungible tokens (NFTs) and Web 3.0 gaming.
GameStop faced more attention earlier this year when it got flooded with attention from Reddit posts and the “meme stock” frenzy, with day traders looking to fight back against what they saw as overly powerful hedge funds. Other companies that fell under that trend included AMC and Bed, Bath & Beyond.
Last month, PYMNTS reported on GameStop’s intentions to add jobs in NFTs and Web3 gaming, writing that there were eight new openings as of October which focused on product marketing for NFTs, software engineers and Web3 gaming heads.
See also: GameStop Opens NFT, Web3 Opportunities
The listing said creators would not only build games, but also “the components, characters and equipment.”
The company was also looking at hiring a senior software engineer for supply chain technology, to help get products to customers “fast and efficiently.”
Agentic artificial intelligence (AI) promises to improve operational efficiencies and the customer experience offered by enterprises.
The advanced technology is finding applications in loan underwriting and fraud detection, and now it’s moving across borders.
TerraPay Co-Founder and Chief Operating Officer Ram Sundaram told PYMNTS as part of the “What’s Next in Payments” series focused on exploring AI’s use in banking and by FinTechs that automated decision making and streamlined processes will continue to transform global money movement, especially as faster payments gain ground in cross-border transactions. That’s the inexorable trend, but as Sundaram put it, there’s still room, and a necessity, to have some human interaction in the mix.
In terms of global fund flows, TerraPay’s single connection ties more than 3.7 billion mobile wallets together across 200 sending and 144 receiving countries, touching 7.5 billion bank accounts. As one might imagine, coordinating and enabling the transactions is complex.
“Obviously, in the best-case scenario, everything goes smoothly, but when things are not going smoothly, that’s when the customer queries come in,” Sundaram said.
It’s no easy task to find out straight away where a transaction is, as analysts and representatives at the company have to look at logs and query partner systems.
“A lot of that work is done manually,” said Sundaram, who added that the agents “know the corridors and the markets that they are working in, but it still takes some time.”
TerraPay is using AI models with machine learning to bolster customer support and automate tasks as financial institutions (TerraPay’s client base) send payments in real time, and those payments are processed into local markets’ beneficiary banks.
“We still don’t trust [AI models] to let them respond to the customer straight away, but we can do the analysis, and then that gets reviewed by an agent who decides if [information] is accurate or not and then sends it off,” Sundaram said.
The same principles are guiding AI models and company practices to improve technical and security operations, analyzing and categorizing anomalous transactions and automating integrations with partner firms.
“Compliance is an issue where there is a lot of review needed of the alerts, and we are using [AI models] to speed up those processes,” Sundaram said.
Asked by PYMNTS about how agentic AI can be harnessed, he said: “In financial services, you can’t take chances on technology like this, which has the freedom to go wrong. You have to be careful about making sure that it’s 100% reliable before we can let things run entirely by automation.”
Agentic AI also remains pricey. For example, OpenAI is charging $20,000 a month for its specialized agents. However, Sundaram said the industry will become commoditized quickly, which will lower prices, and some open-source offerings are capable.
“There’s a fire hose of news about breakthroughs and new ideas and new ways of doing things that are coming out on a daily basis,” he said.
Data underpins it all, and Sundaram told PYMNTS that no matter what the application, the information fed into the models must be clean. Most organizations have a range of data sitting in different intra-company silos, and those silos need to come down.
In addition, the data must be structured so that it is accessible and can be synthesized by the models. Many firms may have more than 1,000 software-as-a-service (SaaS) resources to which they are subscribed but are not accurately tracked or monitored.
“Every database is separated, each one sitting somewhere else,” he said.
The days of stitching together those separate SaaS offerings to run an enterprise are ending, he said, and we’re headed to a future when data is collected in one place.
AI models and agentic AI “are extensions of what we’ve always valued at TerraPay, which means building the most efficient infrastructure possible in order to make sure that transactions are processed safely, quickly and affordably,” Sundaram told PYMNTS. “We see AI and [AI models] as powerful tools that help us scale all this very quickly while making sure we build more and more efficiency into the system.”
