The Build vs. Buy Dilemma in Enterprise AI
We’re at a turning point where many enterprises are faced with a major challenge, which is to understand how to implement AI from the outer to the inner layer of their organizations.
Indeed, since 2022, we’ve seen a major explosion of executive roles tied to technological implementations.
A key reminder here, is the executive roles will be critical to help redefine the overall company’s business model strategy as it goes through “the Incumbent Paradox:”
The incumbent paradox gives you good news for the short-term but a very bad one for the long run.
Indeed, in the first phase, where the Incumbent in a sector will experience a distribution advantage, it will find solace in just implementing AI without thinking too much about it and doing it only on the less strategic stuff.
That is tied to the distribution advantage of the incumbent.
In short, take any company, add an agentuc AI customer support layer, independently on whether that’s strategic or not, and you get a massive saving, now what?
Yet, and that’s the key take, this distribution advantage won’t last too long, and within a decade, the company will need to understand how to redefine its business model completely.
From that perspective, most enterprise businesses will have a plethora of innovation projects in their pipelines, with the major quandary: to build or to buy.
In this specific piece, I’m tackling a very hard topic that those who have been working in the enterprise space for long enough know to be one of these issues that sound easy in theory but extremely hard in practice.
As usual, faced with a complex real-world issue, we got to use a mental model, a heuristic, to help us drive change.
The Enterprise AI Adoption Matrix
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