The First Move Most AI Transformations Skip
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Listen First: Effective AI transformation begins with honest cross-functional conversations before leaders choose tools, vendors, or pilots.
Build Trust: Leaders gain adoption by reflecting employees’ concerns in strategy and connecting change to existing priorities.
Start Small: Low-risk experiments create evidence, confidence, and momentum without forcing teams into premature organization-wide AI adoption.
Shared Ownership: Successful AI transformation depends on data, analytics, product, technology, and marketing solving problems together.
Sequence Growth: Small wins reduce resistance, strengthen future proposals, and build organizational capacity for increasingly ambitious transformation efforts.
Most leaders walk into an AI transformation knowing what they want to build. That confidence is often the first thing that gets in the way.
Before Lisa Checchio drafted a single slide, mapped a single vendor, or greenlit a single pilot at EBG Solutions, she did something that runs against almost every instinct a senior leader is rewarded for.
She asked her peers what marketing was getting wrong. And not as a formality or a box to check before the strategy was presented. She asked because she genuinely wanted to know, and because she understood that no transformation survives the organization if the organization doesn't feel heard.
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That kind of discipline, sitting with the discomfort of not yet having the answer, is rarer than it should be.
McKinsey's State of Organizations 2026, drawn from a survey of more than 10,000 senior executives, found that 86% of leaders believe their organizations were not prepared to integrate AI into day-to-day operations.
The same report makes the investment gap explicit. For every dollar organizations spend on AI technology, they should be putting five dollars into people. Most are doing the opposite, and then wondering why the tools don't take.
The problem was never the technology. The problem is what leaders skip on the way to selecting it.
Lock The Brief Before You Build Anything
When Checchio came back to those same counterparts with the transformation strategy, she could hold up their own words and show them where they appeared in the plan.
"It helped to point back to say, I heard you say this, this is what we're doing," she explains. "This is how we will integrate into things you're already building in product or in data or in technology."
That reframe, from pitch to reflection, changes the room. Leaders who feel heard don't need to be convinced. They're already oriented toward yes before the proposal is finished.
Most AI transformation efforts never get there because the strategy is written in a silo and presented as a fait accompli. The marketing leader arrives with a roadmap and a vendor shortlist, and then spends the next six months fighting for alignment they could have built in six weeks of conversations before the roadmap existed.
Maryssa Miller saw both sides of this dynamic. As EBG's Senior Vice President of Digital Commerce and Customer Engagement, she owns the member lifecycle, the email program, and the marketing execution layer that depends entirely on the organizational infrastructure Checchio helped build. Her read on what happens when the pre-work gets skipped is pointed.
"It's not just about letting the senior leaders understand why we're moving in this direction," Miller says. "It's the teams executing too. If they don't believe in it, you're going to have resistance from their side."
It's not just the senior leaders understand why we're moving in this direction. It's the teams executing too. If they don't believe in it, you're going to have resistance from their side.
This is what the AI transformation case study almost never includes. Not the tool selection, not the pilot results, not the before-and-after productivity numbers.
The part where a leader had to sit with a peer from finance or operations and ask genuinely uncomfortable questions about what marketing was getting wrong. That conversation doesn't have a slide deck. It doesn't produce a deliverable. And it's doing more work than most of the tools that come after it.
Parallel-pathing The Strategy And The Proof Points
What makes EBG's approach worth paying attention to isn't that they had a perfect plan. It's that they ran the strategy and the small wins at the same time.
While Checchio built organizational alignment at the leadership level, Miller's team ran low-stakes experiments: a copy generation tool, creative tests on email, early segmentation work combining first and third-party data they hadn't previously used together.
These weren't pilots in service of a business case. They were trust-building in disguise.
"We gave people permission to try," Miller says. "We hadn't made it a mandate. And where I think other companies have fallen short is saying you must use AI to be more productive. It comes so top-down, and the teams get very frustrated."
We hadn't made it a mandate. And where I think other companies have fallen short is saying you must use AI to be more productive. It comes so top-down, and the teams get very frustrated.
She's describing something a lot of CMOs have lived through from the other direction, being handed a directive from above to make the organization AI-ready, with a timeline and a metric attached and no clear path for how to get people there.
The mandate approach mistakes adoption for belief. You can get people to use a tool. You cannot mandate them into trusting it.
The small wins changed that at EBG. When the team saw that an AI copy tool trained on their internal style guide could produce on-brand output that copywriters approved rather than rewrote, the next proposal carried less risk in the room. The QA automation that followed was easier.
The proprietary merchandising engine that came after that was easier still. Each win reduced the friction on the next ask.
Miller shares how, "when we found those small wins, we continued to build off of them. And they just led to larger and larger wins."
This sequencing matters more than most transformation frameworks are willing to say out loud. You are not just building tools. You are building your organization's capacity to change, and that only happens at the pace your people can absorb.
What Cross-functional Really Looks Like
EBG's AI-powered merchandising engine, a proprietary tool built on their own data that recommends and ranks offers across an email program operating at billions of sends per year, did not come from the marketing team. It required data, analytics, product, and technology working toward the same outcome, which only happened because the groundwork existed for those teams to work together.
"It was a large team effort," Miller says. "Everyone from the data team to analytics to merchandising to marketing. It's really across the board."
Checchio puts it plainly: "Collaborative teams cross-functionally cannot be overlooked. The trust of your internal constituents and other leaders – it has to happen at every level in order for this type of transformation to occur."
Collaborative teams cross-functionally cannot be overlooked. The trust of your internal constituents and other leaders – it has to happen at every level in order for this type of transformation to occur.
Trust, in this context, is not a soft concept. It's the mechanism. Internal trust determines whether a transformation strategy survives contact with the rest of the organization, whether the data team shares access, whether product prioritizes the integration, whether finance approves the next phase. You cannot engineer it after the fact. You build it before you need it.
If your AI transformation is stalling, the most useful question is not which tool to add or which process to automate. Ask who in the organization doesn't yet have a reason to want this to succeed, and go have that conversation. Find out what they think marketing is getting wrong.
Do that before you build anything else.
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