Everywhere I go lately, leaders are talking about AI. Some with genuine excitement, others with a quiet sense of fatigue. Underneath all the noise is a simple reality: almost everyone is experimenting with AI, yet only a small minority are actually winning with it.

Boston Consulting Group estimates that only about 5% of companies are truly translating AI investments into measurable business value. For Malaysian and ASEAN business leaders, that number is not just a statistic—it is a warning. If your AI initiatives are not tied to strategy, business model design, and clear outcomes, you are simply adding cost and complexity.

The AI Investment Paradox: High Adoption, Low Impact

On paper, we are living in an AI success story. Most organisations can point to a pilot, a chatbot, a dashboard, or an automation project and proudly say, “Yes, we’re using AI.”

But adoption is not the same as impact. When AI is treated as an add-on rather than a lever to reshape how the business creates and captures value, the result is predictable: low ROI, frustrated teams, and leadership that quietly wonders if AI is overhyped.

The paradox is clear: AI adoption is high, but genuine business transformation is rare. The gap is not in the tools—it is in how we think about, design, and lead AI initiatives.

The Illusion of Progress: Why “Using AI” Is Not Enough

Studies consistently show that a large majority of companies are using AI in at least one function, yet only a fraction manage to scale it across the enterprise. The blockage is rarely technical. It sits in integration, operating models, and readiness.

In many organisations, AI lives on the edges: experimentation in a single department, a proof of concept that never makes it into production, or a productivity tool adopted by a few early adopters. It looks like progress, but it doesn’t change how the business works.

For SMEs in particular, I see another pattern: generative AI is used for “nice-to-have” tasks—social media captions, text drafts, presentation edits—while core revenue processes remain untouched. That hesitation is understandable, but it also means the biggest levers remain locked.

Real transformation begins when AI is no longer a side project and instead becomes part of the way your organisation designs offers, serves customers, manages risk, and plans growth.

What the Winners Do Differently: From Tools to Business Model Shifts

The companies that are truly benefitting from AI start with a different question. They do not ask, “Which AI tools should we try?” They ask, “Which part of our business model needs to evolve?”

They anchor AI to specific value levers: customer lifetime value, margin expansion, service innovation, or platform plays. Everything flows from there—data strategy, workflow design, talent, and governance.

Consider the example of Penske Truck Leasing. Instead of simply automating existing maintenance workflows, they used AI-powered telematics and predictive analytics to re-engineer how they run their fleet. They shifted from reactive repairs to proactive optimisation: predicting failures, scheduling interventions earlier, reducing downtime, and making better decisions on asset utilisation.

That is not just automation; it is business model innovation. They changed the operating rhythm of their business by designing around AI insights, not bolting AI onto the old way of working.

How to Make Your AI Investment Count

For Malaysian and ASEAN leaders, here is how you can turn AI from an experiment into a strategic advantage.

1. Anchor AI to a Single Business-Model Lever

Start with one critical lever—customer lifetime value, a new service line, platform innovation, or a specific cost driver. Go deep before you go wide.

Ask yourself: “If AI worked perfectly here, what business outcome would change?” Let that clarity guide your choices on tools, partners, and priorities.

2. Define Your Scoreboard Before You Start

Decide upfront what winning looks like. Revenue uplift, margin improvement, time saved, error reduction, churn reduction—choose the metrics that truly matter and make them visible to your team.

Track results regularly. AI initiatives often fail not because the idea is wrong, but because no one is clearly measuring impact or accountable for the outcome.

3. Embed, Don’t Bolt On

AI creates value when it is woven into the daily workflow, not when it lives in a separate pilot or innovation lab. That might mean changing how decisions are made, who approves what, or which tasks humans still own.

Redesign the workflow first, then apply AI. For example, move from “humans review every case” to “AI prioritises and humans focus on exceptions.”

4. Invest in Trust, Data, and Skills

No AI strategy scales without trust and readiness. You need clean, usable data; people who understand how to work with AI; and governance that keeps usage ethical, secure, and compliant.

Sometimes the bravest decision is to slow down a flashy pilot and first fix foundational issues—data quality, process clarity, or basic digital skills.

5. Lead the Narrative, Not Just the Budget

AI transformation is as much about leadership as it is about technology. Your team and stakeholders need to see AI as a growth engine, not a threat.

Communicate clearly: why you are investing, what will change, how success will be measured, and how people’s roles will evolve. Leaders who own the narrative create momentum and trust.

A Leadership Call for Malaysian & ASEAN Decision Makers

In our region, AI is often framed as a productivity tool or a way to “catch up” with global players. I believe the opportunity is bigger than that. AI can be the catalyst that helps ASEAN businesses leapfrog traditional models and design offers, platforms, and experiences that are uniquely ours.

The organisations that thrive in the next decade will not be those that simply use AI. They will be the ones that rethink who they are, how they create value, and how humans and machines work together inside their business model.

The question is no longer, “Should we invest in AI?” The real question is, “How will we lead differently because of it?”

If you want support in mapping this out, explore the AI-Powered 10xAI CEO Club or start with a structured 10X Business Health Check to understand where AI can move the needle fastest.

FAQs on AI Investment and Business Value

Why do most AI investments fail to deliver real business value?

Most AI investments fail because they are not rooted in strategy. They are treated as technology projects instead of business model decisions. Without clear value levers, ROI metrics, workflow redesign, and leadership accountability, AI stays in “experiment mode” and never becomes a growth engine.

How should SMEs in Malaysia and ASEAN get started with AI?

Start small but strategic. Choose one concrete business outcome—more repeat customers, faster fulfilment, better lead conversion—and redesign the key workflow with AI in mind. Use off-the-shelf tools where possible, build basic data hygiene, and involve your team early so they feel ownership rather than fear.

What is a good example of AI driving business model innovation?

Penske Truck Leasing’s use of AI for predictive maintenance is a strong example. They moved from reacting to breakdowns to anticipating them, redesigning operations based on data. The result is higher uptime, better asset utilisation, and a fundamentally different way of running the business.

What role should leaders play in AI transformation?

Leaders must set the direction, choose the right levers, protect focus, and communicate constantly. They don’t need to be technical experts, but they do need to be very clear on why AI matters, how it ties to strategy, and how the organisation will measure progress and learn along the way.

Conclusion: From Experiments to Engine of Growth

AI has moved beyond the phase of novelty. The tools are here, the capabilities are real, and your competitors—both local and global—are already experimenting. The differentiator now is not who has access to AI, but who can turn that access into a disciplined, strategic advantage.

If you anchor AI to real business model levers, define success clearly, redesign workflows, and lead your people through the change, AI shifts from being another cost line to becoming the engine of your next stage of growth.

The time to rethink your business model with AI is now—before disruption arrives on your doorstep. Start with one lever, one workflow, one clear outcome. Then build from there.

Related Reads

• Midlife to AI Mastery: Your Guide to Reinvent, Coach & Lead with Confidence
• AI-Powered Business Model Innovation Workshop
• AI Strategy Coaching for SMEs & Business Owners