2025 VKTR Contributor of the Year: Myles Suer

“AI… will be critical to the success of almost every business in the future… and companies that apply it with vigor and intelligence will likely dominate their industries over the next several decades.”

Myles Suer has never been one to tiptoe around the realities shaping enterprise technology. And that line is one that sets the tone for his reporting throughout the year — pragmatic and focused on what business leaders actually need to understand as AI reshapes strategy, operations and competition.

Below, we break down Suer's top takeaways from across 2025 and the patterns every enterprise should be paying attention to as AI enters its next chapter.

1. AI Isn't Slowing Down — So Act Now

AI is no longer optional, and the clock is ticking.

In the third installment of Suer's "AI Revolution" series, he looks to advice from authors Tom Davenport and Nitin Mittal, who said: “AI… will be critical to the success of almost every business in the future,” and companies that embrace it “with vigor and intelligence” will define their industries for decades.

That urgency is echoed by Marco Iansiti and Karim Lakhani, who warn that “our entire economy is now effectively subject to Moore’s Law,” a shift that demands faster adaptation than most enterprises are prepared for.

The leaders Suer highlights also make clear that hesitation comes with a steep cost. Vijay Govindarajan and Venkat Venkatraman caution that companies fail because they “overinvest in what they are good at today and underinvest in what they need to be good at tomorrow,” while David De Cremer calls AI adoption “a no-brainer for businesses to grow today.”

Taken together, these perspectives paint a vivid picture: AI is reshaping every industry at once, the leaders who act decisively will seize the advantage and those who wait risk being left behind by a technological wave that isn’t slowing down.

2. AI Literacy Is the New Imperative

You don’t need to code — you need to understand. Citing David De Cremer in the fourth installment of his "AI Revolution" series, Suer argues that leadership in the AI era hinges not on Python skills but on grasping the fundamentals behind machine learning, generative AI and the new frontier of agentic systems.

As De Cremer puts it, the real requirement for leaders is “a foundational understanding of artificial intelligence,” because success depends on knowing how AI works, how it breaks and how it should be deployed responsibly. Leaders who understand how models learn and behave are the ones who can “ask the right questions, interpret insights accurately” and ensure their organizations adopt AI with both ambition and guardrails.

Suer also draws a sharp distinction between AI types: machine learning as the engine of prediction, deep learning as pattern recognition at scale, generative AI as the creator and agentic AI as the actor capable of taking real-world actions. His takeaway is blunt: “The AI revolution is not about mastering code — it’s about mastering understanding.”

3. Leaders Must Act Together

AI has created a new category of enterprise risk, and the old data-security divide is now dangerously obsolete.

As Deloitte’s Sharon Chand puts it, “Agentic AI elevates the risk level beyond that which GenAI introduced,” because autonomous systems can propagate malicious code without human intervention. Suer adds that GenAI exposes organizations to data leaks and prompt injection attacks, but agentic AI introduces far higher-stakes threats — from unsupervised system changes to automated exploitation of vulnerabilities.

Suer also points to a fast-spreading threat many leaders still underestimate: shadow AI. Chand warns that employees often turn to unvetted tools “to boost productivity,” while enterprises unknowingly adopt AI features embedded in SaaS platforms they never evaluated.

Meanwhile, governance gaps persist at the top. Executives often expect collaboration to happen naturally among CISOs, CDOs and CIOs, even though overlapping responsibilities frequently create conflict. Suer's message? Siloed leadership is now a liability.

4. Don't Ignore the Data

In one research-packed article, Suer offers leaders a big reality check: AI cannot scale without data engineering.

More than 90% of organizations now say data engineering is essential — yet most organizations still have tiny, overstretched teams. With up to 70% of AI development time spent wrangling data, Suer argues that companies aren’t held back by models or compute, but by the inability to deliver clean, reliable, well-structured data at scale.

The findings show a widening gap between ambition and readiness. Data engineers are juggling everything from data pipelines and governance to cloud warehouses, lakehouses and a growing universe of sources, all while typical teams contain zero to four people.

The companies leading in AI are the ones investing heavily in data engineering as a strategic discipline, not an afterthought. In Suer’s words, data engineering is a “critical discipline,” and those who neglect it risk stalling before they ever get AI off the ground.

Related Article: Is Your Data Good Enough to Power AI Agents?

5. It's Time to Rethink the Cloud

Within the next two years, 90% of organizations plan to make substantial changes to their cloud approaches. AI, Suer notes — especially GenAI — is the disruptor forcing this rethink, pushing organizations toward more flexible hybrid and multi-cloud architectures that can adapt to rapidly shifting compute, data and cost demands.

There's a mounting pressure driving this shift. Security and compliance top the list of concerns, followed closely by integration challenges and rising cloud costs. Remarkably, Suer notes, 67% of organizations are considering repatriating workloads back to private or on-prem infrastructure to regain control and optimize spend.

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