AI consulting is (generally) a dead end for the little guy
If you can productize your services for a specific niche you may have more luck
Apologies for the light posting as of late. A computer issue, now remedied, kept me away from my laptop.
I keep seeing people trying to spin up AI consultancies. This seems like a hard road to hoe. At the high end you’re competing with McKinsey, Deloitte, etc., for Fortune 500 clients. At the low end, the organizations are too small to squeeze much juice from contracts. In theory this leaves mid-sized clients ripe for the picking, but as I’ll argue later in this piece, there, too, you will run into problem that make AI consulting less than desirable as a business model.
Note that this is a general observation, not a universal rule. You may read this post and think to yourself “but I have an AI consulting business and I’m getting valuable contracts and I’m not McKinsey!” To which I say, great! The point I am arguing here is that AI consulting is, in general, a non-viable business. There will always be an elite few who have access to knowledge and clients that the big consulting firms do not. For example, maybe you’re an expert in construction management or chemicals manufacturing, and you have insight into applying AI to one of these verticals. If that is the case, then by all means disregard everything in this post. But my bet is that for the vast majority of people looking to get into AI consulting, this is decidedly not the case.
Market Segmentation Challenges
High-end market: Competing against established consultancies like McKinsey and Deloitte is daunting. These firms leverage long-standing relationships with Fortune 500 companies, offer comprehensive service portfolios beyond AI, and often have exclsuive technological partnerships. Breaking into this segment requires truly innovative AI solutions or expertise in unexplored niches.
Low-end market: Smaller organizations lack the necessary budget and infrastructure to fully implement and benefit from bespoke AI solutions. Their needs often revolve around more fundamental improvements to data infrastructure, which may not justify the cost of specialized AI services.
Mid-market: While mid-sized companies might seem like an ideal target, they present their own set of obsctables. These companies see AI consultancy services as complex, expensive, and time-consuming. Convincing these companies to invest in AI requires demonstrating clear, immediate ROI, which can be challenging for bespoke solutions.
Scalability issues
As with any kind of consulting, AI consulting, especially at the high-end or bespoke level, faces significant scalability challenges. Each client’s needs are unique, resulting in resource-intensive projects that don’t translate into repeatable or scalable business models. While large firms like McKinsey can manage these inefficiencies, smaller consultancies often struggle to do so.
A potential solution
One solution to the scalability problems inherent in bespoke consulting is productizing one’s services. This involves developing scalable, repeatable AI solutions tailored to specific industry verticals. Offering AI-driven SaaS products or industry-specific tools strikes a balance between high-end bespoke services and affordable AI solutions for smaller companies. This allows entrepreneurs to avoid direct competition with major consulting firms while serving a broad customer base.