Expert Mode: Beyond the Hype—Putting AI to Work in the Real Economy
This article was based on the interview with Bassem Hamdy , CEO, Co-Founder at Briq by Greg Kihlström, AI and Marketing Technology keynote speaker for the B2B Agility with Greg Kihlström podcast. Listen to the original episode here:
As marketing leaders, we are inundated with conversations about AI. The narrative is often dominated by generative models crafting clever ad copy, AI-powered personalization engines optimizing customer journeys, and predictive analytics forecasting the next market trend. These are, without question, powerful applications that are reshaping our own functions. But this focus on the digital-first, white-collar world risks overlooking a far larger, more foundational part of the global economy where AI’s potential for impact is arguably even greater. I’m talking about what’s often called the “real economy”—the industries that build our roads, generate our power, and manufacture the physical goods that surround us.
This is where the theoretical promise of AI meets the tangible reality of steel, concrete, and complex supply chains. It’s also where technology adoption is frequently mischaracterized as slow or resistant. The truth is more nuanced. These industries don’t suffer from a lack of vision; they suffer from a lack of technology built with a deep understanding of their unique, often gritty, operational realities. Bassem Hamdy, CEO and Co-founder of Briq, has spent years at this precise intersection. His work deploying autonomous AI “digital workers” provides a masterclass in moving beyond the hype to create tangible value. It’s a lesson in understanding the customer, identifying the lowest-friction entry points, and framing ROI in terms that resonate far beyond simple cost-cutting.
The Myth of Resistance: Are Tech Companies the Slow Ones?
One of the most persistent narratives about industries like construction and manufacturing is that they are laggards, stubbornly clinging to old ways of working. It’s an easy trope, but it’s also a lazy one. When you look at the evolution of an assembly line or the sophisticated machinery on a modern construction site, it’s clear these sectors embrace technology that solves real-world problems. The friction, Hamdy argues, often comes from tech companies failing to do their homework.
“I don’t think construction and these industries are slow to pick up technology. I think technology companies are slow to understand these industries and they are so different.”
This is a critical reframing for any leader looking to drive technological change, whether in their own organization or as a vendor to another. It shifts the onus from blaming the user to understanding their world. A generic SaaS platform built for a software company is unlikely to address the specific workflows of a subcontractor managing insurance certificates and purchase orders across a dozen job sites. Hamdy’s point is that adoption isn’t a matter of convincing people to change; it’s a matter of building something so clearly aligned with their needs that the change becomes inevitable. The real challenge isn’t overcoming resistance; it’s achieving relevance. This requires deep domain expertise and a willingness to build for the nuances of a specific vertical, rather than trying to force a horizontal solution into a vertical-shaped hole.
The Practical Entry Point: The Unfilled Seat
The discourse around AI and jobs is often fraught with anxiety about replacement. While workforce displacement is a real consideration, Hamdy’s experience reveals a more immediate and practical application: augmentation and filling gaps that companies are already struggling to fill. The most successful initial deployments of AI aren’t about firing the accounting department; they’re about empowering a lean team that can’t find qualified people to hire in the first place.
“There are 300,000 open positions in white collar swivel seats in these industries. Like people do not wake up Greg… and go, ‘Hey, you know, my dream is to be a payroll clerk at a construction company.’ It’s just not what happens. So these seats are empty. And so that was the lowest friction… we did was bring in, I called it human augmentation. Now we kind of call it, unfortunately, human replacement technology that did the work that a traditional payroll or AP or AR person would do.”
This insight is key to navigating the internal politics and ethical considerations of AI adoption. The lowest barrier to entry isn’t a process that’s merely inefficient; it’s a process that is understaffed and undesirable. By focusing on the “inhumane jobs”—the repetitive, thankless tasks of double data entry, invoice processing, and file management—you’re not taking away a fulfilling career; you’re automating a function that acts as a bottleneck to growth. This approach transforms the conversation from a threat (“The robots are coming for your job”) to a solution (“The robots are here to do the work no one wants to do, so you can focus on more valuable things”). It’s a strategic way to build momentum, prove value, and gain buy-in for more ambitious AI initiatives down the line.
Framing the Real ROI: The Drive for Show, The Putt for Dough
For any C-suite executive, a business case built on reducing headcount is always compelling. It’s the “drive for show”—a big, impressive number that’s easy to understand. But as Hamdy explains, the true, sustainable value of autonomous AI often lies in a less obvious but far more impactful area: risk mitigation and the cost of a single mistake.
“The president pulled me aside and goes, ‘Yes, we saved two headcount. But last year we missed an insurance certificate. There was a loss on a project and that cost us $500,000 and a deductible.’ So yes, we love to talk about, we’re saving a couple of swivel chairs, but the real return is, you know, AI tends to do things… better than a human. You know, AI can read an insurance certificate, just like a human can, but can also look for fraud… Autonomous workers can detect that better than a human.”
This is the “putt for dough.” Saving the salary of two risk managers is a good outcome. Avoiding a half-million-dollar loss because a digital worker caught a fraudulent document at 2:00 AM is a great one. This is the kind of value that fundamentally changes the risk profile of a business. For marketing leaders building a case for new technology, this is a crucial lesson in storytelling. The surface-level benefit (efficiency, cost savings) gets you in the door. The deeper, second-order benefit (risk avoidance, compliance, fraud detection) is what secures the investment and demonstrates profound strategic value. An autonomous system doesn’t get tired, it doesn’t have a bad day, and it can analyze thousands of data points for anomalies in seconds. That consistency and rigor is where the real money is saved and made.
The Prerequisite for Automation: Know Thyself
Perhaps the most potent insight from Hamdy is one that has nothing to do with algorithms or models. The single biggest obstacle to implementing an autonomous workforce is not technological, but organizational. If a company cannot clearly articulate its own processes, no amount of sophisticated AI can help.
“What’s interesting, it’s like coming to Christmas dinner at a divorced couple’s house. This is the first time operations and accounting are talking about a work stream. That’s what it feels like. It’s like, ‘How do you buy something?’ And the accountant will say, ‘I need a purchase order number.’ And the operation guy says, ‘I never use purchase orders. What are you talking about?’ And they’re looking at each other as if they’ve never spoken… They cannot describe their process.”
This brutally honest and painfully relatable analogy underscores a universal truth: you cannot automate chaos. The very act of preparing for an AI implementation forces a level of internal reflection and process mapping that is, in itself, immensely valuable. It exposes misalignments between departments, uncovers shadow workflows, and requires an organization to document how work actually gets done, not just how it’s supposed to get done. For any leader, this is the essential groundwork. Before you can deploy a digital worker, you must first be able to write its job description. This initial phase of process discovery is not a bug; it’s a feature. It’s the organizational therapy session that must happen before true transformation can begin.
The application of AI in the real economy offers a powerful counter-narrative to the often-ethereal discussions happening in the digital marketing world. It’s a story grounded in practicality, focused on solving tangible problems like labor shortages and costly operational risks. The lessons from Bassem Hamdy’s work are universally applicable: success isn’t about having the fanciest technology, but about deeply understanding the environment you seek to change. It’s about finding the path of least resistance by solving the most acute pain points first, and then building from there.
The journey begins not with a flashy demo, but with a clear-eyed assessment of your own internal processes. From there, it’s about framing the value proposition in a way that speaks to both immediate efficiency gains and long-term strategic resilience. As AI continues to evolve from simple automation to possessing a form of digital “instinct,” the companies that will win are not necessarily the ones that adopt it first, but the ones that adopt it most thoughtfully. They will be the ones who use this technology not just to cut costs, but to build more robust, intelligent, and ultimately more human-centric organizations.
