In the middle of the current AI wave, it’s tempting to believe the hard part is tooling: Which copilot should we use? Which vendor? How fast can we roll something out?
But if you listen carefully to the people who sit between technology, people, and commercial outcomes, a different story emerges.
That story came into sharp focus during a recent roundtable hosted by BetterEngineer with three very different, deeply complementary perspectives:
What followed was a candid look at how AI is already reshaping work, why demand for strong engineering talent is rising, and what leaders must rethink if they want to build organizations that are truly agentic‑first, not just “AI‑enabled.”
This article distills the key ideas from that discussion.
For Boris Portman, who studied AI at Carnegie Mellon and has spent decades in and around engineering teams, the current moment feels familiar.
The pattern he sees now is the same one he saw then:
This gap between expectation and reality is exactly where many teams are struggling. AI has made “what’s possible” very visible, very fast. It has not magically dissolved the hard operational work beneath it.
Boris used a simple analogy:
“When you add a lane to a highway, it doesn’t actually decrease traffic congestion.”
The same is true with AI. The organizations going all‑in aren’t on the beach. They’re working harder and thinking more broadly about business problems, user experience, and success metrics. More capacity without better design doesn’t fix congestion; it just moves it. AI is no different.
Where Boris focused on the engineering reality, Barry Marshall zeroed in on the organizational one.
Barry has lived through extreme scaling. At JP Morgan, he helped build a global operations center from zero to more than 10,000 employees in five years. That experience wasn’t just about operations—it was about organizational design under pressure:
In today’s AI wave, he sees many leaders repeating a dangerous pattern.
They’re cutting people, layering in AI tools, and assuming the math is simple: fewer people + more tools = more efficiency.
Barry calls the reality something closer to a “Jenga Tower paradox.”
Imagine your organization as a Jenga tower. Each block represents a person. As you remove blocks—cut layers, reduce headcount—you expect AI to fill in the gaps.
On paper, the structure is “leaner.” In reality, what’s left is more fragile:
The leadership implications are the opposite of what many assume:
From P5’s vantage point, this is the crux of the transformation problem. The organizations that will actually capture value from AI are the ones willing to fundamentally rethink how work gets done:
This is the difference between adding AI to operations and building what Barry calls agentic‑first organizations. Arthur summed up the leadership challenge this creates: AI may increase velocity, but leadership still determines trajectory. As AI speeds everything up, “teams need stronger direction, not less management.”
The panel also explored a quieter risk: “work slop”—when teams adopt AI but unintentionally switch off critical thinking.
It shows up when:
Arthur also pointed to a critical risk in how AI is rolling into organizations:
When teams start to treat AI as an answer engine rather than a reasoning partner, there’s a real danger that critical judgment begins to atrophy.
Barry’s response started with something surprisingly simple: an AI philosophy.
Rather than launching tools and hoping people figure it out, he encourages leaders to clearly articulate:
In Barry’s words, this isn’t just a leadership philosophy; it’s something you explicitly say to your team. The expectation is that people apply the same intellectual discipline and rigor when they use AI as they would when producing work entirely on their own. In his view, AI should be positioned clearly as an augmentation of what people do, not as an artificial replacement for their contribution.
One of the myths that surfaced in the conversation: “AI will reduce the need for engineers.”
From BetterEngineer’s vantage point in the market, Boris is seeing the opposite.
“We’re not seeing a drop in demand for engineers. If anything, it’s going up. The skills are changing—data, systems thinking, outcomes over years‑of‑language—but the need for strong technical talent in an AI world is only increasing.”
What’s changing is the shape of that talent:
Barry echoed this from the organizational side. In leaner, more AI‑augmented companies, individual engineers often have larger surface areas:
That demands what he sees as a modern archetype: full‑stack engineers who can think like product owners, not just implementers.
One of the more interesting trends Boris brought back from the TRANSFORM conference was how much of the AI agenda is landing with talent leaders.
What used to sit primarily with the CTO or a head of AI is increasingly in the remit of HR and people leaders:
“Talent leaders are also the ones now responsible for implementing AI strategy where it used to be maybe the CTO or the Chief AI Officer… in many organizations that’s merged into the head of talent, both AI and human.”
Barry argued that this is a side effect:
What’s required is a tighter partnership between engineering, business, and people functions:
Beyond engineers, the conversation also turned to the kinds of people organizations need alongside them.
Barry looks for curious, system‑thinking problem solvers, people who don’t just execute tasks, but question the task itself.
Arthur extended that point to the organization as a whole. In his view, “we’re going to need to upskill the entire organization… to sort of meet more in the middle around both technical and soft skills that are required for this change.” It’s not enough to hire a few AI‑literate specialists; everyone who touches critical workflows will need stronger problem‑solving, comfort with data, and the ability to collaborate across disciplines.
When you bring AI into the mix, that becomes even more important. You don’t want people who only ask, “Which prompt should I use?” You want people who ask:
Boris added that organizations are increasingly evaluating talent on outcomes rather than narrow stacks:
It’s less about ticking every box on a tool list and more about demonstrating the ability to navigate complex, changing systems.
The roundtable wasn’t theoretical. Several concrete steps emerged that any leadership team can start on, whether you’re an early‑stage company or a global enterprise.
Here are a few:
Use individual‑level AI wins (calendar automation, inbox triage, routine task agents) to free up time—and then reinvest that time in higher‑order thinking and experimentation, not just cost‑cutting.
If AI is truly creating capacity, use it to let people explore and build with AI inside the company. Otherwise, it becomes too easy for that experimentation and value creation to happen elsewhere, not for your organization.
There is no static playbook. Whatever you come up with this week will likely need to evolve next month, so talent and org design should intentionally build in flexibility, and prioritize people who can adapt as the landscape shifts.
Arthur captured the spirit of the discussion in his closing:
“This is a paradigm shift. We don’t actually have a playbook… we’re all kind of navigating it together.”
None of these require a new platform. They require new decisions.
If there was a single throughline in the conversation between Barry Marshall, Boris Portman, and Arthur Woods, it was this:
AI is not just a new set of tools. It is a forcing function to confront questions organizations have been able to delay:
Barry and the team at P5 focus on exactly that shift: helping founders, boards, and leadership teams move beyond incremental automation to genuine operational rewiring with an agentic‑first lens.
Boris and BetterEngineer see the same pattern from the talent side: companies don’t just need more engineers; they need the right engineers in the right structures, with clarity on how AI fits into their work.
The organizations that will win this era are the ones that are willing to rethink how work gets done—who does it, how decisions flow, and how humans and AI systems can build something together that neither could build alone.
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This article is adapted from a roundtable conversation hosted by BetterEngineer featuring Barry Marshall (P5), Boris Portman (BetterEngineer), and Arthur Woods on AI, talent, and the future of work.
For leaders who want to hear the full conversation behind these insights, the complete AI Talent Executive Roundtable is live on BetterEngineer’s podcast channel. You can listen to the episode here.