“Everyone’s all in on AI. The trouble is that no one — or, at least, almost no one — is doing it right.”
Lance Knight, a technology industry veteran, former CEO of ConnectAll and current chief value stream architect at Broadcom®, knows a thing or two about doing technology right. His career in enterprise technology spans more than two decades, and now, he watches the rise of AI from a position of unique insight. His leadership role at Broadcom gives him a window into the inner workings of technology companies and functions across industries, from growing mid-market firms to Fortune 100s and almost everything in between.
Knight has seen companies of almost every size and stripe plunge sizable investments into AI for coding, customer service, cybersecurity, and content creation, to name a few. He sat down with the team at Eliassen (a Broadcom partner) to discuss the challenges these companies encounter — and why overcoming them starts by understanding two fundamental truths about what AI means for business transformation.
Focus, Not FOMO: Why Use Cases Should Drive AI Adoption
To understand the first of those fundamental truths about AI, Knight compared the current enthusiasm for AI to a technology craze from the last decade: 3D printing.
“Think back to 2015 or 2016, when tech media was touting all of these potential use cases for 3D printing, from houses to hospitals to rockets and so on,” he said. “Very few of those use cases have materialized in a meaningful way — and the technology was only part of the problem.”
A much larger factor in 3D printing’s initial failure to launch, Knight said, came from a mismatch between people’s excitement for what the tech could conceivably do and what it could actually do to transform a given business.
“Many organizations wanted to incorporate 3D printing into their manufacturing processes because they thought a competitor was doing it, or just because it was the new cool thing,” he said. “These organizations that gave into FOMO (fear of missing out) tended to give up on 3D printing pretty quickly. But the ones that found real-world use cases for it and made it a core component of their processes are the ones that still use it today.”
Aviation leader Airbus, for example, discovered that they could achieve significant cost savings and achieve finer control over their supply chain by using additive manufacturing to create small, intricate parts for their AW350 aircraft. 10 years later, the same aircraft are still manufactured with over 1,000 3D-printed parts and, in 2024, Airbus crafted a first-of-its-kind 3D printer for use in space.
Knight sees a similar present and future for AI, albeit on a much larger scale.
“Like Airbus and their pursuit of 3D printing, the companies that take AI seriously and invest in it for real-world use cases will succeed where others stumble,” he said. “They’re going to become leaders in their industries, and they’ll probably become leaders in AI.”
Of course, Knight acknowledged that the 3D printing analogy isn’t a perfect one.
“Mastering 3D printing gave some businesses a competitive edge. Mastering AI, on the other hand, may enable some businesses to eliminate the competition altogether.”
Transformation, Not Optimization: Rethinking AI’s Value to the Organization
The second reality Knight urges tech leaders to understand about AI is even simpler than the first.
“Almost every company today says they’re implementing AI,” he said. “But almost none of them are implementing it in a way that’s going to make a real, lasting difference.”
“What these organizations often mean is that some team is using AI to generate code or even help write some emails,” he said. “Is there business value in that? Sure. But implementing an LLM or a RAG isn’t going to transform your business.”
RAGs, or retrieval-automated generation, can query databases and other knowledge repositories and return answers to queries in natural language. What RAGs, LLMs, and systems like them can’t do is apply logic and make autonomous decisions — and that, Knight said, is where the disconnect is happening.
“These systems can only tell you what your organization already knows,” he said. “They can do it quickly and in a way that’s easily accessible, but in most cases, they’re shaving minutes off a process here and there. They’re not truly transformative, and as a result, they rarely deliver transformative value.”
That transformative value, Knight stressed, only comes when organizations reach the “next level” in their adoption of AI. That involves leveraging AI for intelligent automation and agentic AI to manage core business systems and processes. As an example, he pointed to Toyota’s recent successes using agentic AI not just to optimize existing processes, but to reimagine them entirely:
If a team member wants to know how many vehicles are delayed in the West and what’s causing the issues, they can use an agentic AI–powered automation prompt to get a status report on vehicles in that specific region … Say, a vehicle is in the yard ready to be loaded on a truck but, for some reason, is just sitting there. The agent can draft an email to Toyota’s logistics provider, asking them to place that vehicle in a particular bin on a specific truck and expedite the process to maintain the ETA. The agent can also communicate directly with the dealership to explain what’s being done to get the ETA back on track. “And [the agent] can do all these things before the team member even comes in in the morning,” Ballard says. Once fully implemented, these capabilities are expected to allow human employees to focus on higher-order work, like preventing such problems from recurring.
In other words, AI success stories aren’t about writing code — but rewriting entire business processes.
Learn More from Lance Knight and Eliassen Group
Knight summarized his two truths succinctly:
“You can’t implement AI because you think everyone else is doing it, and you can’t do it just to save time or streamline a process here and there. Your organization has to commit to AI, and it has to leverage it in ways that fundamentally transform the business.”
“Organizations that understand these realities will likely succeed,” he said. “The ones that don’t, on the other hand, may not have time to catch up.”
To get additional insights from Lance Knight on the factors that drive successful AI implementations, the future of the technology function, and more, check out this recent webinar Eliassen Group hosted in partnership with Broadcom.
