The Tool Desk
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Are councils accelerating and leaving citizens behind?
There is clear evidence of growing interest in AI among the English councils that answered the LGA’s survey. There is also evidence that councils are at very different stages, face practical barriers, and are still working out how to govern AI responsibly. What is not established is whether AI-enabled council services are excluding residents or producing worse outcomes for particular groups.
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The distinction matters: a council exploring a tool, running a pilot, using AI for limited tasks, and operating a sustained resident-facing service are not equivalent cases. The LGA’s headline figure combines councils using AI with those exploring it. It should be read as momentum, not as a measure of scaled deployment.
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What does the council adoption data actually show?
The LGA conducted its latest council AI survey from December 2024 to February 2025 and published the update on 2 June 2025. Its respondents represented about one-third of councils. Because participation was not a census and the LGA says the results are not representative of all councils, the figures describe responding councils rather than the entire sector.
| Measure among LGA survey respondents | Reported figure | How to interpret it |
|---|---|---|
| Using or exploring AI | 95% | Up 10 percentage points from the 2024 survey; includes exploration as well as use. |
| At the beginning of their AI journey | About half | Indicates that a high headline adoption figure coexists with early-stage activity. |
| Developing capacity and capabilities | 22% | A maturity stage, not a measure of resident-facing deployment. |
| Making some use of AI | 14% | Some use does not by itself establish scale, duration, or service impact. |
| Described as innovative leaders | 7% | A small share of respondents were placed in this category. |
The categories illustrate why “95% of councils have adopted AI” would overstate what the survey says. A council may be investigating options without using them in a live service, while another may use tools for a narrow task. The survey does not turn those different stages into a single deployment rate.
What are councils using AI for?
Generative AI was the most commonly used or explored type in the LGA survey, followed by perceptive and predictive AI. These percentages refer to survey respondents and include exploration; they do not mean that each technology is deployed across council services.
| Type of AI | Respondents using or exploring it |
|---|---|
| Generative AI | 83% |
| Perceptive AI | 28% |
| Predictive AI | 20% |
The LGA reported common corporate applications in human resources, meeting administration, procurement, finance, and cyber security. Respondents also reported AI use or exploration in adult social care (44%) and children’s social care (31%). Those social-care percentages are not evidence that AI makes decisions about every person receiving support, nor do they specify how many uses were pilots, internal tools, or live services.
Just over two-thirds of respondents using or exploring AI said they were paying external suppliers or procuring AI tools or technologies. This points to a procurement and governance challenge for councils: they need to understand what a product does, how AI is embedded in it, and how to assess whether it is suitable. It is not a reason for residents to buy anything.
Rank #2
What benefits and obstacles do councils report?
Respondents reported benefits including staff productivity, service efficiencies, and cost savings. These are reported outcomes, not independently verified causal effects. The available figures do not establish net savings, show what would have happened without AI, or compare outcomes across councils.
| Reported benefit | Share of respondents | Reported barrier | Share of respondents |
|---|---|---|---|
| Staff productivity | 36% | Lack of funding | 62% |
| Service efficiencies | 33% | Staff capabilities | 56% |
| Cost savings | 21% | Staff capacity | 52% |
Among respondents using or procuring external tools, project scoping, understanding how AI is built into a product, and evaluating solutions were also commonly reported challenges. Taken together, the findings suggest that uptake is not simply a matter of deciding to buy a tool: councils need funding, staff time and expertise, and a way to judge whether a proposed use works safely in its actual setting.
What risks and safeguards are councils reporting?
The LGA survey asked respondents about significant AI risks and the arrangements they used to manage them. Cyber security, deepfake disinformation, and organisational reputation and resident trust were the risks most frequently identified. Respondents also described a mix of existing policies, AI-specific policies, and named senior responsibility.
| Survey measure | Share of respondents |
|---|---|
| Identified cyber security as a significant risk | 83% |
| Identified deepfake disinformation as a significant risk | 69% |
| Identified organisational reputation and resident trust as significant risks | 68% |
| Used existing policies to manage AI risk | 49% |
| Had a specific AI policy | 41% |
| Used a Senior Responsible Owner | 38% |
Having a policy or named owner is not, on its own, proof that controls are adequate. A useful test is whether a council can explain what data a tool uses, how privacy and bias are addressed, who checks outputs, how errors are corrected, and how residents can challenge a consequential decision.
Rank #3
The LGA’s responsible-adoption guidance calls for strong data foundations, privacy protection, attention to bias, support for digitally excluded residents and staff, transparency, governance, and oversight. Those are practical requirements to examine, not a guarantee that every council’s current arrangements meet them.
Is there evidence that residents are being left behind?
There is a credible inclusion concern, but the available evidence does not demonstrate that council AI has caused exclusion. National figures describe general digital access and public attitudes; they do not track who can use a particular council’s AI-enabled service, who encounters errors, or who gets a different outcome.
The Department for Science, Innovation and Technology’s Public Engagement Survey 2025/2026 found that 95% of UK adults had used the internet in the preceding three months. That fell to 78% among people aged 75–84 and 46% among those aged 85 and over. While 76% said they could complete most digital tasks without difficulty, only 13% of adults aged 85 or over said so.
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These figures show uneven experience of digital services and generative AI across age groups. They do not show that those people were unable to access a council service, or that a council used AI in a way that disadvantaged them.
Digital inclusion is more than internet access
The LGA’s Digital Inclusion Hub describes groups including people characterised as “Not for Me” and “Reliant on Others,” and highlights person-centred support and proxy-user help. Its Digital Communities Survey received responses from 83 local authorities, a 26% response rate. This work reinforces that a service can be difficult to use even when someone has an internet connection: confidence, accessibility, assistance, and whether people can use a non-digital route also matter. It does not establish the effect of AI-enabled council services on those groups.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What has changed in councils’ readiness?
In July 2026, MHCLG Digital described four drop-in sessions held in April 2026 with 60 councils. This qualitative engagement adds detail about practical challenges, but it is not a representative estimate of how common each issue is across local government.
Participants described different adoption stages, fragmented guidance that could be hard to apply, gaps in practical skills and training for procuring, using, and governing AI, and no clear shared minimum expectations for ethics. MHCLG Digital’s AI Ethics Lead, Nimshi Mahamalimage, wrote: “When it comes to ethics, there are no clear minimum expectations that all councils should follow.” Participants sought practical guidance, usable tools, and opportunities to learn from peers.
This points to a readiness gap alongside adoption: councils may be interested in AI but lack common, usable ways to judge risks and manage implementation. The engagement identifies that challenge; it does not quantify how many councils have adequate or inadequate safeguards.
How can residents and councils test whether AI is fair in practice?
To determine whether residents are being left behind, adoption counts and broad digital statistics are not enough. Councils need service-level evidence that connects the use of a particular system to access, errors, appeals, and outcomes. A responsible assessment should ask:
- What is the system doing? Distinguish an internal drafting or administrative aid from a tool that affects eligibility, prioritisation, advice, or another consequential service.
- Who can access the service? Check accessibility, language needs, connectivity, digital confidence, and whether practical human support is available.
- Is there a usable alternative? Establish whether residents can still get help through a non-digital route, and whether people who rely on another person can use appropriate proxy support.
- What happens when the system is wrong? Track error handling, human review, correction procedures, and clear routes to challenge or appeal decisions.
- Are privacy and bias controlled? Explain what data is used, protect it, examine potential bias, and make governance and oversight visible.
- Do outcomes differ between groups? Measure access, errors, appeals, and service outcomes by relevant characteristics, including age, disability, language, income, connectivity, and reliance on assistance.
- Are benefits demonstrated? Evaluate service outcomes and costs against an appropriate baseline rather than treating reported productivity or efficiency as proof of improvement.
The evidence currently available does not join these measures across council AI services. That is why “leaving citizens behind” remains a testable concern rather than a proven consequence of adoption.
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Councils represented in the LGA survey are accelerating their use or exploration of AI, but the headline figure masks uneven maturity and does not measure sector-wide deployment. Councils report potential benefits alongside funding, capability, capacity, procurement, and governance challenges. Separate evidence shows that digital access and AI use vary substantially across the public, making inclusion a necessary part of responsible service design.
But neither the council survey nor the national public-engagement statistics establish that residents are already being excluded by council AI. To answer that, councils need transparent, service-level evidence about accessibility, human alternatives, errors, appeals, and outcomes for affected groups.
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