Broadcom Extends Value Stream Management Portfolio to AI

Broadcom has added a platform that enables organizations to convert operational metrics collected via artificial intelligence (AI) platforms into return on investment (ROI) insights.

The ValueOps AI Tokenomics tools and applications from Broadcom enable organizations to correlate every AI investment to a specific business initiative, says Sreenivasan “Raj” Rajagopal, head of ValueOps for AI and Infrastructure for Broadcom R&D Software.

This offering is an extension of an existing ValueOps portfolio that is based on Clarity, a project management application, Rally, a value stream management (VSM) application, ConnectALL, a data integration tool, and Insights, an analytics application. That portfolio of tools and applications for analyzing the business value of any IT initiative has now been extended to track and analyze spending on AI, said Rajagopal.

Additionally, organizations can leverage ValueOps AI Tokenomics to create audit-ready documentation. The overall goal is to make it simpler to identify which AI initiatives are delivering a meaningful return on investment (ROI), notes Rajagopal.

Once those projects are identified, organizations can then better prioritize allocation of tokens that have become a scarce resource, adds Rajagopal. “You can map consumption to business value,” he says.

That’s critical for organizations where line of business units (LOBs) don’t always fully appreciate the total cost of a service being provided by an IT organization, adds Rajagopal.

Each organization will need to decide for itself to what degree they might prefer to extend existing FinOps and VSM tools and best practices to manage AI costs versus acquiring a new tool for that purpose. As spending on AI increases, however, costs are becoming a more pressing concern. Not every token spent is of equal business value, so organizations will need to carefully evaluate which tasks are being automated. Allocating funds to use AI to create marketing materials, for example, might be coming at the expense of a research and development effort that desperately needs additional tokens.

It’s not clear just how aggressively organizations are now moving to rein in AI spending. After initially finding it challenging to encourage end users to adopt AI tools, many organizations quickly discovered that costs have, as of late, been rising sharply. As a consequence, many of them need to revisit their AI strategies either by relying on less costly models or reducing token consumption.

Every organization eventually is going to need to find some way to measure the ROI of AI. However, in many cases they may discover that AI is rapidly becoming a new set of table stake investments required just to remain competitive. As such, the actual ROI derived from AI might actually be limited if every competitor is able to develop the same AI capability.

Hopefully, the total cost of AI will continue to decline as AI models become more efficient at consuming IT infrastructure. In the meantime, however, organizations will need to become more prudent about how AI tokens are being consumed, especially if paying for AI tokens is coming at the expense of other business priorities that might provide more value to the business overall.