Leading Through the AI Disruption: Moving from Vision to Execution

This article draws on insights shared by Ruba Borno in her recent interview with McKinsey & Company. I highly recommend reading the full conversation — it’s one of the clearest, most pragmatic articulations of what leadership in this moment requires. My reflections below distill key themes from her interview and connect them to what we see every day in the field at OpsGuru.

Human innovation has always reshaped how we live and work. But what’s changed over time isn’t just the scale of breakthroughs; it’s the pace at which those breakthroughs are absorbed into society.

It’s easy to compare the rise of AI to the early internet era. The pace feels familiar: rapid innovation, a surge of new companies, and a scramble to build the infrastructure beneath it all. But in Ruba Borno’s recent interview with McKinsey & Company, she draws a more striking and revealing parallel: the industrial revolution of farming.

A century ago, the introduction of tractors, fertilizers, and self-propelled equipment transformed agriculture. But it did so over decades. Society had time to adapt. Workers gradually shifted into new industries. Infrastructure evolved at a measured pace.

Today, the speed of adoption is incomparably different. New technologies scale globally in months. Roles rise and fall within a single business cycle. Just a year ago, “prompt engineering” was the skill everyone was chasing. Now, it already feels like yesterday’s story. As Jensen Huang put it, “the new programming language is human.”

This acceleration is what makes this moment so consequential. The gap between ambition and execution has never been more visible. As Ruba points out, while investment in AI is surging, only about 30% of pilots ever make it into production. That’s not a technology gap. That’s a delivery gap. Leaders need execution models that can match the pace of adoption itself.

1. Data Preparedness Is the First Real Barrier

“Enterprise AI is only as good as the data it learns from, and you need data not to be in silos.” 

Many organizations want to move fast but still operate with fragmented data architectures, inconsistent governance, and weak access controls. Without intentional foundation work, AI can’t scale and stays trapped in proof-of-concept limbo.

At OpsGuru, our approach is to treat the data layer as a product, not an afterthought. We design governed, interoperable architectures with clear ownership and domain alignment. When this is done right, AI systems don’t sit in isolated sandboxes. They work on trusted, real-time data that drives business outcomes.

2. Guardrails Are How You Go Faster

One of the most resonant lines from the interview was:

“Brakes on a car aren’t designed to stop you. They’re designed to help you go faster, safely.”

Security doesn’t slow AI down. Treating it as an afterthought does.

When identity, governance, logging, and explainability are built in from day one, AI becomes safer and more scalable. That’s a cornerstone of our delivery mindset at OpsGuru: secure-first, not security-later. Guardrails enable organizations to move confidently at speed.

3. Culture Is the Invisible Platform

Technology alone won’t transform outcomes. As Ruba notes, 

“Ultimately, this is a people change.”

At Amazon, that means leaders using internal copilots like Cedric, Amazon Q Business, and SIFT to make better decisions daily. That cultural adoption starts at the top. Andy Jassy himself issued a clear mandate: use AI, learn it, build with it.

For our customers, that principle is embedded in delivery. We build with them. We align incentives, operational workflows, and adoption patterns so that AI isn’t a parallel experiment. It becomes part of how the business runs.

4. Partnerships Aren’t Optional

“No one can do it alone. It is a multiparty activity, a multiparty value proposition.”

AI innovation is an ecosystem play. The right partners are as critical as the right models or architectures.

We work closely with hyperscalers, ISVs, and customer teams to design platforms that interoperate and evolve. Whether it’s integrating Amazon Bedrock, open frameworks like LangChain, or agentic orchestration tools, the focus is always on delivery models that can scale responsibly and quickly.

And just as importantly, our customers partner with us not just to build infrastructure, but to co-develop real AI use cases. It’s in these collaborative engagements that strategy turns into measurable business outcomes.

5. From Democratization to Hyperpersonalization

Some of the most compelling parts of Ruba’s interview weren’t high-level strategy; they were practical stories. Canva went from idea to launching Canva Code on Bedrock in 12 weeks. A fragrance lab built agentic AI experiences to design scents, campaigns, and names on demand. Formula 1 uses hyperpersonalized insights to transform how fans, drivers, and engineers interact with data.

These aren’t moonshots. They’re what happens when a solid data foundation meets a modern platform. And they show how personalization at scale is already here.

6. Risk vs. Gamble

“A calculated risk is worth taking. A gamble isn’t.”

This is where many organizations stumble. They invest in AI because it’s fashionable, not because it’s tied to a business objective.

We take the opposite approach: work backward from the strategic outcome, define the measurable value, and architect delivery so the risk is structured, reversible, and transparent. That’s how you close the pilot-to-production gap without gambling on hype.

From Vision to Delivery

This is the defining characteristic of the AI era: the pace of innovation isn’t just faster than before, it’s rewriting how quickly value must be delivered to stay relevant.

At OpsGuru, we help organizations close that execution gap by focusing on:

• Building strong data and security foundations

• Embedding guardrails for speed and trust

• Driving cultural adoption and operational integration

• And ensuring every AI investment is a calculated, measurable risk — not a gamble.

The future won’t wait for slow execution. Neither will your competitors.

What’s holding your organization back from moving pilots into production? How are you building for pace, not just for possibility?