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AI and the Gig Economy

AI and the Gig Economy

Jobs & Future of Work

A look at how AI is reshaping gig and freelance work, covering where competition has intensified and where genuine new demand has appeared.

Freelance and gig work is being reshaped by AI from two directions at once, new competition from AI-assisted generalists, and genuine new demand for AI-specific skills, and the net effect looks different depending on which category of work you’re in.

Routine, well-defined freelance work, basic writing, simple design tasks, entry-level data work, faces real new competition from clients using AI tools directly instead of hiring for the task. Consistent with the broader pattern our look at job exposure found: routine, well-defined tasks are more directly automatable right now.

AI-specific freelance categories, prompt engineering consulting, AI workflow design, AI output review, are genuinely new demand categories that didn’t exist at this scale a couple years ago. Freelancers who’ve built real AI fluency are finding new work specifically in this space.

Using AI tools to work faster on routine parts of a project while offering genuine expertise and judgment on the parts that actually require it is the pattern showing up most among freelancers maintaining or growing income, the same domain-expertise-plus-AI-fluency combination covered in our reskilling guide.

The gig economy isn’t uniformly winning or losing from AI, routine work faces real new competition while AI-fluent specialized work is genuinely growing. See the World Economic Forum’s Future of Jobs Report.

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How to Choose an AI Vendor: A Checklist

How to Choose an AI Vendor: A Checklist

Enterprise Adoption

A practical checklist for choosing an AI vendor, covering data handling, model flexibility, real references, and total implementation cost.

Vendor selection is where a lot of enterprise AI initiatives quietly go wrong, choosing based on the most polished demo rather than the questions that actually predict a good long-term fit.

Ask about data handling before anything else: where does your data go, is it used for training the vendor’s models, what happens if you cancel. Get specific, written answers before any data leaves your systems.

Check for genuine model flexibility. A vendor locked into one model provider inherits that provider’s pricing changes and outages directly. Ask whether the platform can switch models without a full re-implementation, a real hedge against the kind of abrupt product changes that have happened more than once this year.

Request a real reference, not a case study. A direct conversation with a comparable existing customer, ideally one who can speak honestly about what didn’t go smoothly, is worth far more than any polished materials.

Our full budgeting guide covers why integration usually costs more than the license fee. Ask what implementation typically involves and how long it takes for a comparable customer. See McKinsey’s own research on enterprise AI adoption.