Technology · United States

Infor combines industry expertise and embedded engineers for process automation

Credited to SiliconANGLE · siliconangle.com · 3h ago

Photograph · SiliconANGLE

AI-powered process automation requires software that understands the industry and workflows it serves. Moving from a promising prototype to practical use also requires implementation expertise.

Infor LLC develops software around the specific processes its customers use in their industries. Its approach combines industry-specific agents, deep vertical expertise and an open architecture, according to Kevin Samuelson (pictured, left), chief executive officer of Infor.

“We had to understand the specific processes that define each of these industries and allow folks to create value,” he said. “Having that depth of industry, understanding processes and how these things work together, and then being open from a technology perspective has given us the ingredients that when AI came along, we could put it on top of all of that to really deliver outcomes that drive value.”

Samuelson and Soma Somasundaram (right), president and chief technology officer of Infor, spoke with theCUBE’s Christophe Bertrand and Alison Kosik at Infor Velocity Week, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed process automation, observability for automated decisions and forward-deployed engineers to accelerate return on investment. (* Disclosure below.)

Industry AI brings precision to process automation

Two in three businesses said off-the-shelf AI doesn’t adequately address their industry’s needs, according to Infor’s new global research. Infor Industry AI is not a single product, but an architecture embedded across the company’s applications, built on its Infor OS foundation and open to the customer’s wider data, Somasundaram explained.

“If you are an automotive supplier [or] parts supplier, material shortage is not an option,” Somasundaram said. “You better deliver the material right at the production line at the right time you promised.”

Trust is the other hurdle as agents take on more decisions. Infor’s approach starts with a human reviewing agent recommendations, then adds observability, auditability and access controls that treat each agent like another citizen of the enterprise, according to Somasundaram.

“If something is being automated, you’re delegating the work to the agent,” Somasundaram said. “When the work gets done, you need a complete trace of how the decisions were made and how the work got done.”

Infor pairs customers with forward-deployed engineers, industry experts who stay on to drive each use case until it delivers value, Samuelson explained. That support is shrinking the time it takes for process automation to go from prototype to production.

“Now it’s taking a customer literally one to two, or at worst, three weeks, even for something that isn’t already a use case,” Samuelson said. “We can prototype so quickly for the customer. That speed will just continue to multiply.”

Here’s the complete video interview, part of SiliconANGLE’s and theCUBE’s coverage of Infor Velocity Week:

(* Disclosure: TheCUBE is a paid media partner for Infor Velocity Week. Neither Infor, the sponsor of theCUBE’s event coverage, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

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Zuckerberg's Biohub leads a $1.8 billion push to build AI models that predict cell behavior

Credited to The Decoder · the-decoder.com1h ago

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AI models are supposed to learn to predict cell behavior, which could speed up drug development. Biohub, the nonprofit backed by Mark Zuckerberg and Priscilla Chan, is coordinating a $1.8 billion effort spanning data, lab equipment, and compute, Reuters reports. The group had already pledged $500 million in April for its five-year "Virtual Biology Initiative." Meta, Google DeepMind, and Isomorphic Labs are contributing a combined $300 million, while the US Department of Energy is investing over $500 million in lab measurements and compute over five years. The National Institutes of Health are coordinating datasets built with more than $500 million in prior federal funding, and Biohub will standardize them for AI training.

Commercial funders get one year of exclusive access to the data they paid for before it goes public, according to Biohub research lead Alex Rives. Government-funded work will be available without those restrictions. A first dataset should be ready in about a year.

Other AI companies are also pursuing biology projects. Anthropic has built its own biology lab for AI-driven drug development, and the OpenAI Foundation is putting more than $125 million toward biological and medical datasets. Ad Ad

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Biohub, the research organization backed by Mark Zuckerberg and Priscilla Chan, is coordinating a $1.8 billion initiative to train AI models that predict cell behavior. Meta, Google DeepMind, Isomorphic Labs, and the US Department of Energy are funding data, lab equipment, and…

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