Navigating the shift in AI automation strategies between the UK and US

UK's pragmatic approach to AI automation prioritises pre-built solutions over bespoke development, contrasting with US's costlier custom-centric strategy. Structural market dynamics underpin this shift.

A divergence is emerging in how UK and US technology leaders approach AI agent ecosystems, according to the 2026 Jitterbit AI Automation Benchmark Report.

UK IT decision-makers tend to prioritise efficiency and speed when deploying AI agent automations rather than bespoke development. In the UK, 51% of organisations report using pre-built agents to scale operations, compared with 48% that prefer tailor-made solutions.

In addition, 43% indicate a preference for agents embedded in pre-built SaaS applications, while 28% prefer building their own platforms using low-code or no-code tools.

In the context of discussions around AI-assisted coding and its impact on software development (sometimes referred to as the “SaaSpocalypse”), 20% of respondents prefer in-house custom-coded solutions. Pre-fabricated options are the least preferred at 8%.

In the US, there is a stronger preference for custom-built solutions, with 55% of respondents favouring in-house development and 45% preferring ready-made agents.

These differences are occurring alongside broader market conditions, including increased attention to the cost of using large language models and associated token expenses. As a result, some organisations are reassessing the cost and resource requirements of maintaining custom-built systems, with greater consideration given to pre-built and off-the-shelf agents that may reduce operational complexity.

Even as internal development teams gain expertise, UK organisations continue to use a mix of approaches, with emphasis placed on managing operational costs and deployment efficiency.

The report also highlights a wider shift from isolated AI experimentation towards more integrated orchestration approaches. It notes that 81% of organisations plan to increase investment in AI and automation over the next year, with demand for scalable and quickly deployable solutions remaining a key factor.
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