Authors: Craig LeGrande | Venky Lakshminarayanan | Authors of AI-Driven Value Management, Wiley Publications
In the 1800s, the big winners weren’t always the gold miners. Often it was the companies selling the shovels and pickaxes (and blue jeans). There’s an obvious parallel with the AI Boom. Today’s headlines are screaming about massive ROI from AI and autonomous systems, smart cities, etc. This of course makes intuitive sense to anyone and requires no justification. However, if you consider the Gold Rush metaphor, there is more significant, consistent, high-leverage value potential in the foundational tech enabling them. Everyone needs them, no matter which AI “application” they’re building or running. Here are some of those AI foundational tech examples: high-performance networking, zero-trust security, real-time observability & telemetry, edge-to-cloud scalability… (and, yes, utility companies and data centers). These technologies don’t generate the buzz that makes for good hype headlines but they quietly generate excess EBITDA from supporting AI deployments.
In the world of Intelligent Transportation Systems (ITS) and intelligent infrastructure, imagine an AI-driven traffic optimization system without robust network uptime. Your LiDAR, 3D Perception, and edge computing capabilities need a solid foundation to deliver the promise of smart cities and intelligent, autonomous transportation. Without such a foundation, sooner or later, the latency, security, and scalability will be an issue. You can’t risk to develop an AI solution without a strong foundation, or risk it turning into a liability!
According to Gartner, 81% of IT leaders say infrastructure scalability is a top concern in AI adoption. And IDC predicts Global AI spending to hit $300B+ by 2030. But, surprise, surprise… 70% of AI project costs are not in AI model development, but in data pipelines, infrastructure, and integration.
Foundational tech is easy to overlook in investment justifications, but only until there’s an outage, breach, or scaling problems. Whether it’s the buyer or the seller of such foundational technology solutions, a proactive value management strategy is required to ensure these capabilities are quantified and communicated – and prioritized. We are talking about important metrics such as reduced downtime, lower incident response time, faster time-to-scale for AI workloads, improved energy efficiency and sustainability metrics, opportunity cost avoided, etc.
Without proactive value management, there is a real risk of your AI initiative being underfunded; ultimately this will end up choking innovation.
Strategic AI and intelligent infrastructure leaders are looking beyond the headline AI use case. They are working on quantifying and communicating the ROI of the security, observability, and scalability layers underneath. It is a best practice to build value management frameworks into every AI infrastructure project from day one.
Our recently published book, AI-Driven Value Management is not just about how you can use AI to accelerate and expand the impact of your value management programs. The book is an “operating manual” for business value. It covers all the basics of business cases, including how to discover hidden value with breadth and depth. We share real world examples from companies like Salesforce, ServiceNow, etc. on how to put the building blocks together to create a scalable value management program, that doubles your pipeline and revenue, without having to spend the hundreds of millions of dollars that companies spend on such programs.
We, as authors, believe every customer-facing employee should practice value management, the art and science of communicating business value to your customer.
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