Benedict Evans

enterprise software sprawl3 sept. 2026

Large companies often lack a clear software inventory

Typical large U.S. companies have hundreds, perhaps thousands, of software tools, including systems such as SAP and Workday, vertical SaaS apps, workflows, scripts, automations, databases and spreadsheets. They often do not know how much software they have, what is used or what they are paying for.

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Benedict Evans·3 sept. 2026

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The typical big American company today has hundreds, and perhaps thousands, of different pieces of software. It has giant ‘big iron’ horizontal systems of record like SAP and Workday, it has hundreds of vertical SaaS applications, and then there are hundreds more workflows, scripts, automations and databases, right down to the 10 meg spreadsheet running a department. Very often, the company doesn’t even know quite how much it has, what’s actually being used, and what it’s paying for.

Contexte

And yet, with all this software, the company is full of boring, repetitive tasks.

AI-enabled tool creation

AI-enabled tool creation3 sept. 2026

AI lowers barriers to building task-specific tools

Evans discusses how AI lets people create tools without being engineers or writing code, contrasting this with software built in advance and considering the possibility of software becoming dynamic and generative.

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Benedict Evans·3 sept. 2026

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But with AI, now you can make that tool in five minutes, and you don't need to be an engineer, and you don’t need to write code. You can just ask the model to make the tool for you, or, more fundamentally, just do the task for you itself . Instead of having to create those tools one at a time, software might be dynamic, generative, free-form, and spontaneous. Massively more tasks can be automated, with massively less software.

Contexte

It can be very tempting to think that AI will sweep most of this away. There’s an old joke that an engineer is someone who’ll spend an hour building a tool to automate a task that would take 10 minutes.

problem identification gap

problem identification gap3 sept. 2026

Recognizing the need for a tool remains the hard part

Evans argues that making code easier to write does not solve the problem of knowing when a tool is needed and what it should do.

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Benedict Evans·3 sept. 2026

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None of this is solved by making easier to write code - by making it easier to make tools. The hard part is knowing that you need a tool for this in the first place, and then knowing what the tool should do.

strategic AI adoption framework

strategic AI adoption framework3 sept. 2026

Three questions for a new transformative technology

Evans outlines three questions companies face with a new transformative technology: how to buy, build and deploy it; how far it changes their operations, with answers differing by industry; and whether it creates challenges to their business economics, new competitive pressures or, perhaps, an existential threat.

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Benedict Evans·3 sept. 2026

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Stepping back, it seems to me that with each new transformative technology, every company has to ask three kinds of questions. First, how do we buy, build and deploy this? Do we do pilots? Should we take the product that's bundled from Microsoft/Google/Oracle, build something ourselves, pay someone to build something, or buy this new thing from a startup? Second, they have to ask how far this changes their operations. What does it mean?

Contexte

What does email mean for us? What does spreadsheets mean for us? The answer to that might be radically different if you were an insurance company or a law firm. And third, you have to ask whether this creates new challenges to your business’s economics, new competitive pressures, or, perhaps, some kind of existential threat.