Much has written about the California Gold Rush in the mid-1800s. Many miners had a miserable time and made little money, whereas those who sold them jeans, shovels, meals, banking and other services did a lot better
Stephen Klein, CEO of Curiouser.ai draws a similar parallel in a LinkedIn post.
“While startups bleed cash and foundational model companies lose billions on every product, five consulting firms: Accenture, BCG, Deloitte, McKinsey, and PwC, have quietly built the fastest-growing revenue engine in the GenAI ecosystem.
In fact they are the ONLY ones making money.”
And he goes on to describe the consulting firm version of picks and shovels:
- Strategy frameworks
- Integration blueprints
- Responsible AI protocols
- Change management programs
Let’s be fair. These firms are not taking advantage of desperate miners who are a long way from home. They sell to corporations who have plenty of sharp analysts, seasoned IT and procurement folks. These firms are the tip of the spear and often bring early knowledge of what works and doesn’t in a fast emerging sector.
However, it is still hugely important to do your due diligence on how these firms themselves are applying LLMs, copilots, Agentic AI and other automation to their own operations. The reality is one of the biggest costs of migrating to the cloud (the platform for many AI solutions) is that of systems integrators like Deloitte. These firms have done literally thousands of such migrations and have countless artifacts related to data conversion, code conversion, end user training, unit testing and other phases of such conversions. Are they using LLMs to harvest that intelligence from previous projects and only need to focus on deltas unique to your project? Are they using conversion and other factories and specific tools to automate various phases? Do they have digital agents who can turbocharge their own consultant productivity and can they show metrics that they are showing on today's projects?
Do the cobbler’s children have the best fitting and looking shoes?
Most of these firms are also much more familiar with white collar, back office processes and have large ERP and CRM practices in the Western world. Clearly useful but when it comes to AI payback and ROI that only delivers to a tiny portion of modern economies. And they will likely deliver even less to the re-industrialized workforce and robotics, UAVs, sensors and other automation coming to US and other factories, logistics, farms, construction and other sectors as a result of global and vertical re-alignment coming especially with Trump's "tariff turmoil"
Stephen cautions the “success stories” we hear from these firms come from:
- Reports authored by the same consulting firms selling the services
- Surveys funded by GenAI vendors
- Case studies with no external verification
My firm, Deal Architect has helped clients run RFPs, negotiate contracts and do due diligence on service providers in a wide range of emerging markets – when they first started selling offshore services in the early 2000s, when they first started offering cloud data centers, when they first started bragging about their “SaaS” expertise. It has continually evolved market intelligence I did at Gartner helping clients evaluate SIs on their large ERP projects in the run-up to Y2K. Each wave of services called for different skills, pricing and service levels but a common thread is firms tend to “spray paint” on top of older capabilities.
I distinctly remember sitting with the CIO of a Fortune 500 at a bar of a Bangalore hotel. Jet lagged and wide awake way past midnight he looked at me “What the fxxx are we doing here? Can’t you find us a vendor close to home?” We changed course and did. Very differently, an exec of a software vendor looking to diversify his development centers told me (also in India) “It’s such a wide world. I hope you are also planning to take me to meet vendors in E. Europe and S. America.” We did. Infrastructure services vendors would promise cloud capabilities but take us to visit data centers from the 1990s and offer none of the scale and unit pricing that hyperscalers (they weren’t called that back then) were starting to offer. We advised several clients to stay put with their existing, fully depreciated infrastructure for a couple of more years.
The new generation of AI services are very different but require a similar sense of curiosity and cynicism. Hopefully, without the jet lag.
