AI transformation is not failing because of the models, the data, or the technology. It's failing because of us. Humans.
Before you scoff, stay with me. This isn't an anti-human argument. It's the opposite. It's a call to get specific about what we're actually building toward. In our last article, we introduced OPERA — a decision-making framework to provide cognitive scaffolding for navigating complex organizational change. OPERA stands for Outcomes, Priorities, Exchanges, Risks, and Analytics — five factors that influence our decision-making. When addressed together, they can turn initiatives from projects into organizational movements.
In this series, we're applying it to AI transformation. This article focuses on the first and most foundational element: Outcomes. Specifically, why most AI outcomes fail before the technology is even deployed — and what to do instead.
The Alignment Gap Most AI Outcomes Miss
It's not enough to just say you need clear outcomes. A recent CIO magazine article notes that companies need clear outcomes, then outlines a strategy for successful agentic AI adoption from a highly technical alignment perspective. And for certain types of decisions, that works. In fraud detection, procurement, manufacturing — industries where processes are structured, repeatable, and consistent — clear outcomes come easily. Those decisions are made based on patterns in data. They rarely require organizational and team alignment. But most AI transformations aren't that clean. They involve people, competing priorities, and organizational change — and that's where technical alignment alone breaks down.
When the Outcome Leaves People Out
The O in the OPERA framework addresses the core challenge getting in the way of AI transformation — human alignment. When the outcome is just "deploy the system" or "implement the platform," people feel disconnected and disengaged. The technology becomes something separate from us rather than an extension of our expertise and capability. That separation kills transformation before it starts.
I've seen the same resistance to AI adoption we're experiencing today across multiple technology engagements. I personally felt the pain when government inspectors killed a multimillion-dollar initiative to automate aviation inspections after a two-year big data effort, driven by fear of job loss. They saw technology as a replacement, not an amplification of their expertise.
The First Move: Define the End State Together
We begin reframing the Outcome in OPERA by defining the end state we intend to create together. By doing this up front, we're tacitly beginning the process of aligning a team towards a common goal, a shared objective. And the way we start that conversation matters.
Cleo Abram, the Emmy-nominated journalist behind Huge If True, has built her entire approach to covering technology around a single reframe: instead of asking "what could go wrong?" ask "what could go right?" When we default to pessimism, we shut down the conversation before it starts. But when we flip the question, we open the door to the kind of creative thinking that actually moves people forward. The question isn't "will this technology work?" It's "what does it look like when it works for us?"
What Happens When People See Themselves in the Outcome
When I served as EVP of a cutting-edge emerging AI-powered traffic management software company that automates traffic signals, we changed the message. Instead of selling traffic data, the story became about freedom of movement — an outcome people could see themselves inside of, rather than displaced by. Once we reframed it that way, customers from beyond our initial target market started leaning in. The conversation shifted from "what does this system do?" to "what does this make possible for us?"
I worked on a training program for launching an internal legal platform at a Fortune 10 IT services company. The lawyers were frustrated about yet another platform and didn't care that it was built on the newest tech. We changed the story again to be about collaboration — about amplifying their ability to work together, not replacing their judgment.
When people can visualize the solution working with them, and through them instead of against them, resistance drops. They see the benefit and recognize how their technical expertise becomes more important than ever. They see the technology as part of them, amplifying what they already do well.
From Resistance to Ownership
This shift happens when the outcome stops being about the technology and starts being about what the team builds together. When people see themselves amplified — not replaced — by the outcome, they move from resistance to ownership.
What Comes Next: Developing an Outcome
The O in OPERA is designed to capture that north star — an outcome that's human enough to rally around and clear enough to build toward. But how do you actually build one? How do you move from a vague aspiration to a statement that an entire organization can align behind? Get it right, and everything that follows has a foundation. In the next article, we'll show you how.
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