IBM Consulting’s Mohamad Ali Warns CEOs: AI Isn’t Failing—Your Organisation Is
When Mohamad Ali talks about artificial intelligence, he doesn’t sound like a man selling technology. He sounds like someone issuing a deadline.
As Senior Vice President at IBM Consulting, Ali has spent years advising global enterprises racing to turn AI ambition into growth. What he increasingly encounters is not resistance to AI—but misplaced confidence. Budgets are approved, pilots are launched, productivity ticks up. And then, quietly, momentum fades.
The technology works. The organisation often doesn’t.
Ali’s warning is blunt: companies are not struggling with AI because models are immature. They are struggling because their operating models were never designed for machine-scale decision-making. The longer leaders delay confronting that reality, the harder the eventual reckoning becomes.
Who Mohamad Ali Is—and Why His Warning Matters Now
Ali’s perspective carries weight because it sits at the fault line between strategy and execution. At IBM Consulting, he works with enterprises across industries that have already moved beyond experimentation. These are not AI skeptics. They are firms that have invested heavily—and now want results.
What Ali sees is a growing divide. Some organisations treat AI as an enhancement layer, bolting it onto legacy workflows to extract incremental gains. Others are quietly redesigning how work gets done, how decisions are made, and how value is created.
By 2030, Ali believes, that divide will no longer be theoretical. It will determine which companies still compete—and which quietly fall behind.
The Breakpoint Insight: AI Exposes Broken Work
At the core of Ali’s argument is an uncomfortable truth: AI magnifies organisational flaws.
Many companies automate processes that were inefficient to begin with. The result is speed without transformation. Leaders celebrate faster execution, only to discover that the underlying logic—approvals, handoffs, reporting layers—remains unchanged.
Ali argues that AI-first organisations start with a different question: should this work exist at all?
If a process exists solely to manage human delay, information silos, or manual reconciliation, AI doesn’t just improve it—it renders it unnecessary.
This is where most transformations stall. Optimising work feels safe. Eliminating work requires courage. Yet Ali notes that a significant share of enterprise activity exists only because older constraints demanded it. AI removes those constraints—and exposes how much effort no longer earns its keep.
The Deeper Forces Behind the Failure
Ali’s assessment aligns with broader forces reshaping leadership and economics.
From a psychological standpoint, organisations are deeply prone to status quo bias—the instinct to preserve familiar structures even after they stop delivering value. AI disrupts that comfort by forcing leaders to confront how much work is performative rather than productive.
Economically, AI compresses decision cycles. Advantage increasingly accrues to firms that can sense, decide, and act faster than competitors. That capability does not come from better models alone, but from architectures that allow intelligence to flow across the enterprise without friction.
From a governance perspective, AI introduces new accountability challenges. When machines inform decisions at scale, boards must rethink oversight, transparency, and risk ownership—areas many governance frameworks were never built to handle.
Why Most Leaders Still Get AI Wrong
Ali observes a recurring executive blind spot: confusing adoption with progress.
AI pilots multiply. Dashboards improve. Productivity gains appear. But incentives remain unchanged, approval chains stay sequential, and data ownership remains fragmented. AI is asked to operate inside structures that limit its impact.
In these environments, efficiency gains are often treated as cost savings rather than strategic fuel. Margins improve temporarily, but growth stalls. Meanwhile, competitors reinvest those gains into new products, services, and markets—building momentum that compounds over time.
What CEOs Should Take From Ali’s Warning
For chief executives, Ali’s message translates into hard leadership decisions—not technology choices:
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AI exposes organisational weaknesses faster than it creates advantage
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Productivity gains without reinvestment cap long-term growth
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Operating model design now determines competitive position later
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Eliminating work is harder—and more valuable—than automating it
These are questions of culture, capital allocation, and governance. They cannot be delegated to IT teams or innovation labs.
The Bigger Trend: From Optimisation to Reinvention
Ali’s warning reflects a broader shift underway in global business. AI is accelerating the move from incremental optimisation to structural reinvention.
Enterprises that treat AI as an add-on will become more efficient. Those that treat it as a design principle will redefine their business models. Over time, the gap between the two becomes exponential.
By the end of the decade, Ali suggests, the difference will not be measured in productivity percentages. It will be visible in entirely different organisations—operating in markets competitors didn’t anticipate, with capabilities they cannot easily replicate.
A Final Reflection for Leaders
The real risk, in Ali’s view, is not moving too quickly on AI. It is clinging to organisational designs built for a human-only world.
AI does not just change how work gets done. It forces leaders to decide what work is worth doing at all.
For CEOs willing to confront that question honestly, the next decade offers extraordinary opportunity. For those who delay, the redesign will still happen—just not on their terms.
AI and Leadership: FAQs for Executives
Is AI failure usually a technology problem or a leadership problem?
More often, it’s a leadership and operating model issue. AI exposes broken workflows, misaligned incentives, and slow decision structures that technology alone cannot fix.
Why don’t AI productivity gains translate into growth?
Because many organisations bank efficiency gains as cost savings instead of reinvesting them into new products, services, and markets.
What should boards focus on when overseeing AI strategy?
Boards should scrutinise operating model redesign, accountability for AI-driven decisions, and how productivity gains are redeployed—not just technology adoption.
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