Why Speed Beats Perfection in the AI Era

In this week’s episode of Voices of Search, we spoke with Silvia Oviedo López, founder of Blomma and former leader of Canva’s AI transformation. Before that, Silvia scaled a 1,500-person organization at Pinterest and spent more than two decades in tech dating back to the early UGC era at Yahoo.

Our conversation covered why most AI rollouts fail for reasons that have nothing to do with the technology itself, why leaders keep blaming AI for workforce changes that have repeated every business cycle for decades, and why the real risk right now isn’t moving too fast. It’s moving too slowly.

Key Takeaways From This Episode:

• Execution is the layer of leadership changing fastest right now, and it’s where most AI transformation efforts quietly fall apart
• Blaming AI for workforce restructuring isn’t new. The same pattern repeats roughly every seven to ten years with a different technology attached to it
• Learning velocity, not any specific skill, is becoming the trait companies actually hire and reward for
• The fundamentals of search haven’t changed even as the broker shifts from Google to ChatGPT and Claude, because the underlying human need hasn’t changed
• AI rollouts should be measured by behavioral change and daily value extracted, not time spent or engagement volume

The Layer That’s Breaking Is Execution

Silvia opened with a framework she’s carried across two decades of leadership: a high-performing team needs a clear vision, a reasonable plan to reach it, and strong execution. The first two haven’t gotten harder. The third has.

“Ideas are great, plans are fantastic, but if you don’t have the execution layer, that’s where things really start decomposing,” she said. 

Her read on the current moment of uncertainty isn’t that leaders suddenly lack vision. It’s that the mechanics of execution are shifting faster than most teams can absorb, and everyone is being asked to adapt to that pace in real time.

Why That Layer Feels So Unstable Right Now

That instability traces back to something more basic than any specific tool: people don’t like change, and they especially don’t like change they didn’t choose. Silvia described it as a near-universal reflex, not a leadership failure. 

“Any disruption and change is something that the system tries to reject,” she said.

The discomfort gets sharper inside organizations because execution is one of the few things employees feel they can control. When a new technology changes how that work gets done, it doesn’t just change a process. It removes a sense of agency, and Silvia was direct about where that leads: 

“When that agency and that autonomy are taken away, it’s frustrating. It leads to burnout.” Her advice to leaders isn’t to force faster adaptation. It’s to make room for people to say, plainly, that they don’t like it yet.

The AI Restructuring Story Isn’t New, It’s Recycled

That same discomfort is exactly what Silvia thinks leaders are exploiting when they announce a restructuring and attach it to AI. She’s watched this play out under a different banner every cycle: SaaS, big data, social media, and now AI, roughly every seven to ten years, at nearly every major company she’s worked for.

“There’s a moment where it’s like, oh, we’re restructuring because of AI, we’re restructuring because of SaaS,” she said. “It happens every seven to ten years, and we’ve lived through it. We will go through it again.” 

Her point isn’t that AI has no real impact on jobs. It’s that using it as cover for a restructuring that would have happened anyway obscures the actual work leaders need to be doing: figuring out which skills are genuinely shifting in value, and hiring, pricing, and supporting people accordingly.

The Skill That Actually Matters Is Learning Velocity

Once you strip away the restructuring narrative, Silvia sees a clearer signal in how the strongest companies are hiring right now, particularly at smaller startups. They’re optimizing for potential over credentials, and specifically for how quickly someone can absorb something new.

“The landscape is going to change every three to six months, and the real skill set is adaptability,” she said, “the ability to work with the technology, the knowledge that you’re given at a given point in time.” 

That’s a durable trait in a way that any specific tool fluency isn’t, since the tool itself will likely look different by the next hiring cycle.

What Adaptability Looks Like in Search

Silvia grounded that idea in an example close to this show’s audience. SEO has gone through more visible transformation in the last three to four years than in the fifteen before it, and it would be easy to read that as the fundamentals breaking down. 

She doesn’t see it that way.

“What makes someone stand out, what makes a company or a formerly known-as SEO manager successful in this world, is you’re ultimately solving for a user’s need and pain point,” she said. 

The broker connecting that need to an answer used to be Google. Now it’s increasingly ChatGPT or Claude, and more players will likely enter that role over time. What hasn’t moved is the underlying need itself: a person, or increasingly an agent acting on a person’s behalf, trying to find the right information. 

Anchoring to that need, rather than to whichever broker currently sits in front of it, is what she means by first principles.

The Same Tension Is About to Break Content Wide Open

That first-principles lens carries directly into the problem Silvia sees coming for content and search marketing more broadly. Everyone is being asked to produce more, using largely the same set of AI tools, which means output volume has roughly multiplied while genuine differentiation has not kept pace.

The tension she described is structural, not just fatigue: algorithms are currently rewarding polished, high-volume AI output, while the humans on the receiving end are increasingly able to spot that output and disengage from it. 

“I can tell if something is AI, even if someone has done a very good job of not making it look AI,” she said. Her prediction is that this tension resolves the way every content ecosystem eventually does: with a break. “It cracks and it breaks, and we are getting ready for that to happen in the next six to 12 months.” 

What survives that break, in her view, is the content built by people who spent the time to make it feel real.

Building Blomma as a Bet on Human Judgment

Silvia’s own company is a direct answer to the same tension. Blomma is an AI-powered career coach built to extend what she’d seen work at scale in her own leadership roles: high-quality coaching, historically available only to a small slice of an organization because of cost.

The product philosophy she described is deliberately restrained. Blomma doesn’t pretend to be human, and it’s built to work alongside a human coach or manager rather than replace one, with conversation data kept private unless a person chooses to share it. 

“I actually believe in technology enhancing human capabilities, not replacing humans,” she said. 

That same restraint shows up in how the coaching itself works: grounded in more than two dozen frameworks built with actual experts, rather than generic advice, and proactive enough to pick up an existing thread instead of waiting to be prompted every time.

How to Know an AI Rollout Is Actually Working

That product philosophy fed directly into the question Silvia said she hears most often from other leaders right now: how do you actually measure whether an AI rollout is succeeding? Her answer leans on a consumer-product instinct from her time at Pinterest, where the team cared less about time spent browsing and more about whether someone acted on the idea that brought them there in the first place.

“I would measure success of any tool by looking at how many people are coming to use it every day, how they’re using it, and what value they’re extracting out of it,” she said. 

Applied to a broader AI transformation, that means looking past adoption metrics and vanity engagement numbers toward one question: what behavior, specifically, is this supposed to change, and is it actually changing?

Her guidance is to lead with curiosity instead of certainty, because the right answer today is likely to look different in six months regardless of how carefully it was chosen. 

“Being slow is going to cost you more than being wrong,” she said, “because the cost to switch is reducing.” 

The Cost of Waiting Now Outweighs the Cost of Being Wrong

Every thread in this conversation, from execution outpacing planning to the coming break in content, points toward the same closing argument Silvia made about pace itself. Leaders don’t fail because they roll out AI. They fail by rolling it out too fast without a plan, too slow while competitors close the gap, or not at all while hoping the shift passes them by.

Her guidance is to lead with curiosity instead of certainty, because the right answer today is likely to look different in six months regardless of how carefully it was chosen. “Being slow is going to cost you more than being wrong,” she said, “because the cost to switch is reducing.” 

In other words, perfection is a moving target in a landscape that resets every few months, and the organizations that wait for certainty before acting are the ones that end up furthest behind.

Voices of Search is a daily SEO and content marketing podcast hosted by Jordan Koene and Tyson Stockton. The show delivers actionable strategies and data-driven insights to help marketers navigate the ever-evolving world of search engine optimization and content marketing. New episodes air weekly, covering everything from technical SEO to AI discovery, featuring industry leaders and practitioners sharing real-world frameworks and proven tactics.

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