By Ammar Jamal Abdulmalik Nahari, Strategy Consultant / AI Transformation Leader - Raseem Strategy, Saudi Arabia
AI is no longer a secret weapon that employees use to overcome boring hurdles. Big firms understood the value of AI early, formed model alliances, and enabled their staff to fully utilize AI the way it should be. On the surface, it looks like a matter of scalability: whoever succeeds in scaling with AI will win in the long run.
I am Dr. Ammar Nahari, an AI transformation leader at a boutique consulting firm in Jeddah, serving public sector clients. Looking at the ground reality, my take is different: in advisory work, AI may reverse the advantage of scale rather than reinforce it.
This is about structure, incentives, and speed; not about small firms being smarter.
1. The Big Firms’ AI Dilemma: Protect the Machine or Replace It
Global consulting firms; the Big Four and the strategy houses alike; sit on top of a full production organism beneath the brand. This organism includes a deep set of skills and tools: research desks, graphics teams, knowledge management review layers, and massive analyst pyramids. Historically, this end-to-end production toolkit was the core asset big firms brought to the table.
Here lies the dilemma:
“Fully deploying AI means losing the advantage of that whole organism and dismantling the engine that generates profit.”
The alternative move for a big firm is to deploy co-pilots. Co-pilots help oil the gears in the engine without actually replacing them. In a study by McKinsey, only about 6% of companies attribute more than 5% of their EBIT to AI, and those winners did not just buy tools; they invested in redesigning workflows. The bottom line is simple: redesigning a workflow is much easier when no one’s job or billable pyramid is at risk.
Boutiques and small firms have nothing standing in the way of transformation. A decision on Tuesday is a new way of working on Wednesday. There is no transformation office, steering committee, analyst pyramid, or global templates to translate into multiple languages. Partners do the work, so partners change the work. The engine is smaller, and the risk is lower.
At Raseem, we did not write a six-month pilot with a committee approving a transformation plan for a multi-million-dollar decision. What we did was rewrite how a deliverable is made and measured. We took the knowledge our talents have and transferred it into AI workflows. This moves the repetitive research and coordination work to the machine, while keeping our small team focused on client problems and the judgment calls that actually matter.
2. When Everyone Can Produce the Perfect Page
The gap between big firms and small firms is getting smaller. Historically, that gap was the ability to field a massive support team behind every page: tighter decks, cleaner charts, deeper research, and a wider range of case studies because of more hands on deck.
Now, well-instructed AI agents produce a sourced summary, a clean exhibit, great research, and precise analysis in minutes for any firm that sets them up properly.
In the 2023 Harvard Business School study of 758 BCG consultants, those using frontier models delivered faster, produced more, and scored higher on quality, but only on tasks inside the model’s capability frontier. On tasks outside it, AI-assisted consultants did worse than those working without the tool. The exact same tools are available to everyone right now.
When the raw production quality of a slide is no longer the differentiator, what is left?
Judgment, relationships, and knowledge of context. Succeeding here is what drives real success in advisory, tilting the balance toward insight rather than sheer technical horsepower.
Since frontier AIs are trained largely on public text, every consulting firm starts from a broadly similar baseline. You gain an advantage only from the unique context you own. The deeper a firm is rooted in local consulting work, the richer and deeper that context is. This is a real competitive edge that global firms lack and struggle to gain. Keep this sacred to your operation; it is your secret ingredient.
Global firms have larger archives and a far wider knowledge base suited for multinational use. However, being rooted in the local community with genuine relationships brings an entirely different level of client understanding. It moves away from generic databases toward a guided grasp of what the client actually needs and what is closest to their real problem. That is the question context answers.
Look at it from another angle: the first question a government client asks about AI is where the data goes, not just whether it works. In Saudi Arabia, the Personal Data Protection Law (PDPL) and SDAIA’s AI Adoption Framework set a demanding standard that requires knowing every tool and every path the client data takes.
In a ten-person firm, tracking is approachable and partners can vouch for every single line. In contrast, in a firm with ten thousand employees across multiple platforms, entities, and regional policies, tracking every line approaches impossibility. Small firms can be auditably careful. In public-sector work, that trust is the license to operate.
3. Scale Still Wins Implementation, But AI Rewards Better Advice
What small firms cannot guarantee is the deep operational and logistical buffer that big firms offer. They can buy high-level meetings, maintain an army of standby employees ready to scale up overnight, deploy massive capital for proprietary R&D, and absorb the impact of a failed experiment. Resilience of this kind takes decades to build, and big firms are right to take pride in it.
That resilience closes mega-deals and delivers massive, scalable projects; but it does not necessarily produce better advice. If AI compresses the cost of producing advisory work, the advantage shifts to whoever has the best judgment and the closest context. Scale stays an advantage in implementation, but it becomes a liability in pure advice.
Can the boutique out-AI the Big Four?
It will not build bigger platforms, it will not outspend them, and it will not dominate global headlines. But in turning AI into better advice faster for a specific client, the answer is increasingly yes, not because the boutique understands AI better, but because AI simply fits its structure better.
The reasons are structural:
The AI era will not be kind to firms whose value was simply throwing more hands at a deliverable; it will be very kind to firms whose value was always in the judgment behind it. For the first time in the history of the consulting industry, being small might just be the winning strategy.
