For the better part of two years, I’ve watched business leaders divide into two camps when the subject of artificial intelligence comes up. The first camp dismisses it as overhyped — another technology cycle that promises to change everything and ends up changing less than advertised. The second camp swings in the opposite direction, declaring that every competitor is already ahead and that missing this wave will be existential.
Neither camp has it right. And after 25 years of advising businesses across North America, Europe, and Asia through every major technology shift of the modern era — the internet, mobile, cloud, big data — I’ve learned that the leaders who navigate these transitions well are rarely in either extreme. They’re the ones who ask better questions.
The Question Nobody Is Asking
Most conversations I have with executives about AI start in the wrong place. They ask: “Should we be using AI?” That’s not the right question. By now, the answer is almost certainly yes in some capacity, even if it’s just basic process automation or research tooling.
The right question is: “Where does AI change the actual work — and where does it just add noise?”
That distinction matters enormously. In my advisory practice, I work with founders and leadership teams across industries, and the pattern I see most often is organizations deploying AI tools on top of broken processes. They automate chaos and call it progress. The result is faster, more expensive chaos.
Before any business integrates AI into its operations, it needs to understand its own workflows well enough to know where intelligence — artificial or otherwise — will create leverage. That requires something no AI can do for you: honest diagnostic thinking about where your business actually operates versus where it thinks it operates.
What AI Can and Cannot Replace
Let me be direct about something that gets glossed over in most coverage of this topic. AI is remarkable at pattern recognition, synthesis, and execution of well-defined tasks. It can compress
hours of research into minutes. It can surface insights from data that would take a human analyst days to extract. It can draft, format, translate, and iterate at a pace no human team can match.
What it cannot do is exercise judgment in ambiguous situations. It cannot read a room. It cannot build trust with a client who has been burned before. It cannot sense that a deal is structurally sound but culturally wrong, or that the right move in a negotiation is to say nothing for thirty seconds.
These are precisely the capabilities that define high-quality advisory work — and they are precisely what clients are paying for when they engage a firm like mine. AI makes me more effective at the preparatory and analytical work that supports that judgment. It does not replace the judgment itself.
This is a crucial distinction for any business thinking about where AI creates value and where it creates risk.
The Real Risk Is Not the Technology
When I talk to business leaders about AI adoption, the risks they focus on are typically technical: data security, accuracy, hallucinations, regulatory compliance. These are real concerns and deserve attention.
But the deeper risk I see in organizations is cultural. Companies that rush to deploy AI without building internal understanding of how it works — and how it fails — are creating a new category of organizational vulnerability. They are outsourcing judgment to systems their teams don’t understand and can’t effectively audit.
I have seen this movie before. In the early 2000s, I watched companies across Asia implement sophisticated ERP systems on aggressive timelines, driven by pressure to modernize. Many of those implementations failed — not because the technology was bad, but because the organizations hadn’t done the slower, harder work of aligning their people, processes, and culture around what the technology required.
AI adoption carries the same risk at greater speed. The solution is not to slow down, but to invest proportionally in organizational capability alongside technology deployment.
A Framework I Share With Clients
When I advise businesses on how to think about AI integration, I start with three questions:
First, where are your people currently spending time on work that is repeatable, rule-based, or research-intensive? That’s where AI creates the most immediate leverage with the least risk.
Second, what decisions in your organization are currently made with incomplete information — not because the information doesn’t exist, but because no one has time to find it? AI can close that gap and meaningfully improve decision quality.
Third, what is the tolerance for error in each of these areas? The higher the cost of a wrong answer, the more critical human review becomes — and the more carefully you need to design the human-AI workflow, not just the AI component of it.
This isn’t a technology framework. It’s a business strategy framework that happens to apply to a technology question.
The Leaders Who Will Get This Right
In my experience, the executives who navigate technology transitions well share a few consistent traits. They are genuinely curious rather than performatively enthusiastic. They invest time in understanding the tools themselves, not just the briefing decks about them. And they hold both the opportunity and the risk in mind simultaneously rather than defaulting to excitement or skepticism.
AI is not going to transform every business overnight. It is also not going to leave businesses that ignore it unaffected. The leaders who approach it with that kind of grounded, honest clarity — who ask better questions and make thoughtful bets rather than sweeping decisions — are the ones I would back in any technology cycle.
That’s been true for 25 years. It’s still true now.
Scott Gelbard is the Founder of SGI Global Partners Inc., a boutique family office and strategic advisory firm, and Managing Partner of Peak Ventures, an international business consulting firm. With three decades of experience advising businesses across North America, Europe, and Asia, he specializes in strategic growth, capital markets, cross-border transactions, and long-term value creation for private and family-owned businesses.