The Geo Tech Landscape in 2025

Automation is the reason behind AI hype

Automation, that's what it's all about, really. That’s what the industrial revolution and the information age have been driven by: doing things faster or automatically or in greater quantities than when done 'by hand'.

That's why organisations strive for digitalisation, AI, sensors, 'digital twins', data hubs. Those aren't useful outcomes in themselves but they can enable 'the useful stuff' to be done automatically, faster, more efficiently or more safely - and that's especially important when government budgets are being squeezed but with the expectation of the same or higher output.

The prospect of more automation is the reason why there's such hype around AI, not because it gives any insights into the nature of intelligence (hint - it doesn't) but because it can help with automating things that are more ambiguous and have been harder to automate. That ambiguity though is also what makes people wary of it when those decisions and outputs are hard to scrutinise and validate and are sometimes confidently incorrect.

The phrase of the year is 'AI Slop'

I've seen a growing phrase that will be used more and more this year: 'AI Slop'. This is when Generative AI creates comically wrong images, generic social media posts or bland, soulless articles which get ridiculed and have become synonymous with spamming, click farming or naïve adoption.

Large Language Models such as GPT have their place, mostly in helping interpret human instructions: in fact ‘prompt engineer’ is the task of carefully giving the right inputs to get a useful output from an LLM and even then, some organisations are avoiding AI generated content because otherwise their written output can sound too generic.

The successes of 'good old-fashioned' machine learning

What gets less attention are the quieter successes using 'good old-fashioned' machine learning. These are used in our industry mostly for analytics such as predictive maintenance or for automatic structured data capture from unstructured imagery or scans, and I expect successes in those areas to increase and be more widely publicised – though they rely on ensuring the data that is being used to train them being complete, correct and free of bias.

Automation and digitalisation is at different stages depending on the technical maturity of the industry or organisation, but I expect - and hope - to see more interoperability via data hubs and APIs across the public and private sector.

A simple and visible example of the benefits of API integration is car number plates in the UK. There are many services where you can enter your number plate and from that alone, you can check your tax and MOT status, pay a toll, or order the exact spare parts that you need. These services don't each maintain their own list of number plates and matching car colour and type; they just interact with a single government-maintained API to get this information.

This very useful automation is only possible because of digital structured data being maintained, up-to-date, centralised and made available via an API.

Governments' priorities for 2025

Governments are prioritising programmes which will need and encourage digitalisation and integration via APIs.

In the USA, the Next Generation 911 programme for emergency call handling is pushing data validation and API interoperability which is especially important in the highly federated landscape of counties, states, federal government and private telecom operators in the USA. For a user calling 911 in an emergency, you don’t care about this complexity, you just want your call routed to the right local call centre, whichever type of phone you are using.

The UK Government priorities announced in 2024 will also drive more automation this year, through more use and sharing of structured data via APIs, especially via the National Data Library.

Existing projects such as the National Underground Asset Register (NUAR) will go fully operational in 2025. NUAR provides a single, secure portal for people who dig up roads to see harmonised information about underground assets that they need to avoid; saving lives and reducing cost and disruption and 1Spatial provides the automated data aggregation, validation and harmonisation process for NUAR. The next steps with NUAR are use-cases beyond ‘check before you dig’.

The data is a hugely valuable resource especially when combined with other public sector data such as topography or land use information.

New UK programmes such as the acceleration of the planning and house building process or delivering clean and resilient energy grids via the new National Energy System Operator will also drive the need for automation, digital processes, and clean data served via data hubs and APIs.

The need for clean and harmonised data

Data harmonisation to agreed data models will be needed not only for these digitalisation and data hub projects, but also to leverage the latest applications and technology stacks.

This is where the AI story comes back in because we will see more use of AI tools to support people defining the cleaning and transformation needed to get their data into these harmonised models. Automation that increases the ability to automate, for cumulative gains.

The problem with automation is that when it's working seamlessly it often goes unnoticed and unrecognised but looking back you realise how much more convenient life is, so let's see how much new automation we can achieve in 2025.