A few weeks ago, I contributed to a Forbes expert panel on how companies are preparing their go to market plans for competitive shocks. My answer, in the condensed form the format allows, was this: we should stress test two combined realities, constrained spending in a slower economy and margin pressure as AI lowers the cost of capability, and respond with a focus on cost reduction, faster decisions, revenue activation with fewer but better qualified conversations, and a clear path to monetization from day one.
I want to expand that answer here, because there is something underneath it that I think matters especially for institutions across Lebanon and the Arab region right now.
In an AI economy, the institution that wins is not the one with the longest pipeline. It is the one with the most precisely qualified one.
How is AI changing the cost of competitive capability?
The phrase AI lowers the cost of capability sounds abstract until you sit in enough strategy meetings across the Arab region to see what it actually means in practice.
For most of the last two decades, the competitive advantage of a well resourced institution rested partly on the cost of doing things that smaller or less sophisticated competitors simply could not afford to do. Building a sophisticated risk model required expensive data science teams. Personalizing customer communications at scale required significant technology investment. Analyzing competitive intelligence across a large market required human research capacity that took time and money to build.
AI is compressing that cost structure dramatically and rapidly. A mid sized Lebanese bank or a regional advisory firm in Jordan, Egypt, or the Gulf can now access AI tools that, five years ago, would have been the exclusive province of institutions with significantly larger technology budgets. The capability gap that used to function as a competitive moat is narrowing on one side, and doing so faster than most strategy cycles can anticipate.
This creates the competitive shock that the Forbes panel question was addressing. When capability costs fall across the market simultaneously, the institutions that were relying on capability as a differentiator find themselves needing a different basis for competitive advantage, and they need to find it quickly.
What does a qualified conversation actually mean in this context?
My answer to the panel emphasized fewer, better qualified conversations rather than a higher volume of outreach. I want to be precise about what that means, because it runs counter to an instinct that many sales and business development leaders have when they feel competitive pressure, which is to increase volume.
A qualified conversation is one where the person on the other side of the table has a problem that your institution is specifically positioned to solve, where the value exchange is clear before the conversation begins, and where both sides understand what a successful outcome looks like. These conversations have a shorter path to revenue, a higher conversion rate, and a lower cost of sale than volume based approaches.
AI makes this kind of qualification more achievable than it used to be, because AI can help institutions analyze behavioral signals, prior engagement patterns, and contextual indicators that tell you, with reasonable precision, which conversations are worth having before you have them. This is not theoretical. It is operational, and institutions across the Arab region that are building these capabilities now are finding that their pipeline quality improves even as the volume of initial outreach decreases.
How should Arab institutions think about monetization from day one?
The third element of my panel answer was a clear path to monetization from day one. This is perhaps the most counterintuitive element for institutions that have grown accustomed to extended sales cycles and relationship based revenue development, which is a real characteristic of how business gets done across Lebanon, the Gulf, and the Levant.
What I mean is not that every interaction needs to generate immediate revenue. What I mean is that the revenue pathway should be defined, specific, and agreed internally before a go to market initiative launches, not determined retrospectively after a pilot period during which nobody was measuring the right things.
In a constrained spending environment, this discipline is not optional. Institutions that cannot articulate their monetization path clearly at the beginning of a cycle will find it increasingly difficult to justify continued investment when budgets are under pressure, because they will not have the evidence they need to make the case.
This applies to AI investments as much as to any other strategic initiative. An AI deployment without a defined monetization path is a cost center waiting to be cut. An AI deployment with a clear revenue thesis and measurable milestones is a strategic asset that can survive a difficult budget cycle.
If you are leading business development, strategy, or AI adoption at an institution in Lebanon or the Arab region and want to talk through how this applies to your specific context, you can reach me through hildamaaloufmelki.com. I also write about related questions in my book AI Simplified, at hildamaaloufmelki.com/signature-book.
About the Author
Hilda Maalouf Melki is an Oxford-Certified AI Expert, an Official Member of the Forbes Business Development Council, Chair of the AI & Innovation Committee at Lions Clubs International – District 351 (Lebanon, Jordan & Palestine), and the author of AI Simplified (الذكاء الاصطناعي ببساطة).
With more than 25 years of experience spanning banking, digital transformation, brand strategy, and artificial intelligence, she advises executives and institutions on AI strategy, AI governance, responsible AI adoption, and institutional transformation. Through her writing, speaking, and advisory work, she helps leaders move beyond AI hype to practical, strategic implementation that creates measurable business value.
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Website: hildamaaloufmelki.com