The clear advantage of an 80/20 AI operating model

AI is everywhere and nowhere at the same time. Every week brings another headline about breakthroughs, pilots, or fears of being left behind. For CIOs, the harder question isn’t what AI can do but where to start.

Praveen Jonnala, global CIO at global network infrastructure provider CommScope, has a deceptively simple answer: apply the 80/20 rule.

In his view, 80% of success with AI has little to do with the technology itself. It comes from preparing the organization by clarifying purpose, aligning skills, and putting the right guardrails in place. “Success comes from organizational readiness,” he says. “The other 20% is the tech.”

And even within that 20%, Jonnala insists on continued focus. Just as many industrial markets derive 80% of revenue from 20% of customers, AI value follows a similar curve. The companies that win aren’t the ones experimenting everywhere but the ones doubling down on the 20% of use cases that can transform revenue, profitability, customer experience, or time to market.

CommScope, based in North Carolina with about 20,000 employees, reflects that philosophy by not selling AI models but gaining a competitive edge by how effectively the company applies AI to factories, procurement, engineering, and customer service. That pragmatism is the blueprint of the 80/20 operating model to be focused, measurable, and built for repeatability.

Culture before code

When AI enthusiasm surged a few years ago, CommScope encouraged exploration but soon saw the downside of too many pilots and tools. “Everyone tinkered with chatbots, and then the noise started,” Jonnala says. “We had to bring it back to value.”

In a manufacturing business, he adds, adoption depends on making employees see AI as a tool, not a threat. “People are rejecting AI and worry it’ll replace them,” he says. “You have to put them in the position of validators so the workflow changes but the work still belongs to them.”

CommScope let employees try different tools in 2024 while quietly monitoring usage. The next step was standardization, launching Microsoft 365 Copilot and consolidating code assistants. Leadership buy-in proved decisive. “If a manager says, don’t worry, this is IT’s problem, adoption dies,” he says. “If leaders become the messengers, adoption grows.”

For Jonnala, education is ongoing. “With 20,000 employees, the challenge is keeping awareness alive without pulling people out of their day jobs,” he says. “We’re trying short videos, sharing wins, and making the ‘why’ explicit. Culture compounds over time.”

Focus, not frenzy

To keep ambition manageable, CommScope deliberately limits scope. “We allow curiosity, but we don’t chase 50 things hoping one works,” Jonnala says. “We’re in the business of consuming AI for impact, not selling it.”

The filter is simple: Will the use case transform the customer experience, drive revenue or margin, improve factory performance, or accelerate the roadmap? If the answer is fuzzy, the idea stays experimental. If it’s concrete, the company funds it and holds business owners accountable.

Procurement is one example. During supply chain disruptions, an AI assistant helped triage suppliers and accelerate approvals, and the results of shorter cycle times and higher adoption among buyers were measurable, which earned credibility. “Improving procurement during volatility is tangible,” he says. “That kind of result earns adoption.” Of course, the plumbing makes or breaks scale. CommScope treats data as an asset and builds in oversight, lineage, and controls. Jonnala stresses risks that are easy to overlook, like feeding supplier code into a generation tool without consent. “Our job is to keep people safe while letting them move,” he says.

The company tightened boundaries by standardizing on a small set of tools, embedding security and IP reviews in the intake process, and adding governance as pilots scaled. Boards, he notes, start with risk. “We make it explicit,” he says. “We’re protecting the company, experimenting in a governed way, and investing where the scoreboard shows value.”

Measuring results that matter

Boards don’t need to understand model parameters. They need consistent signals of progress reported by business owners with technology at the table. CommScope tracks adoption, cycle time, quality, throughput, customer impact, financial contribution, and risk posture. The point is not perfection but consistency quarter after quarter.

That ties into an annual rhythm. In Q1, the company selects high-impact use cases and readies data. In Q2, it delivers a POC and shares lessons learned. In Q3, it scales what works by strengthening pipelines and controls, and in Q4, it abstracts and reuses components for the next wave. “A focused workshop with a partner can compress months of learning into a day,” Jonnala says. “We’re not reinventing the wheel. Our problems look like everyone else’s in manufacturing. We can learn from those ahead of us.”

Operating model, not model hype

What endures, Jonnala says, isn’t the toolset but the operating model, since tools evolve every week but culture compounds over time. So that means embedding technology leaders in the business, being transparent about risks, and insisting on measurable outcomes.

He reframes the developer debate as an example: “We didn’t set a target for fewer people,” he says. “We set a target for earlier releases and safer code. That’s a better conversation.”

Boards, for their part, focus on risk, product roadmap, and customer experience. Revenue follows when those are managed well.

The question every CIO should answer

AI tempts organizations to do a little bit of everything, but the enterprises pulling ahead are the ones doing a lot with very few things, and with discipline. They put culture before code and focus on two use cases at a time. They also invest in data and guardrails so pilots can scale, and report the same scoreboard quarter after quarter. And they reuse what works while retiring what doesn’t.

Jonnala’s challenge to peers is direct. “Be honest about your focus,” he says. “Are you spending most of your energy on the 80 — readiness, leadership, governance, adoption — and when you do invest in the 20, are those the few use cases that can move your P&L?”

For CIOs forging AI as consumers not creators, that’s the operating model that compounds.