Councils hold vast amounts of information. Data about residents, services, assets, performance, demand, finance and place. Information captured over years through systems, documents, forms, reports, policies and correspondence.
New tools can help users search, analyse, summarise and extract insight from information more quickly than ever before. Information that was once buried within systems or documents can become more visible and more usable.
That creates significant opportunities, but it also raises an important question: If AI makes it easier to use your data, does that make poor data more dangerous?
Unlocking information
Much of a council's knowledge is not held neatly within a single data platform or in a single format.
Historically, information trapped within various structures has often been difficult to search, connect and use consistently.
AI may make it far easier to interrogate those sources and identify connections between them. This can help improve decision-making, reduce duplication and unlock organisational knowledge.
However, if data is incomplete, duplicated, inconsistent, out of date or poorly governed, AI may simply surface those problems more quickly. In some cases, it may give flawed information greater visibility and authority.
Data quality has always been important. The difference is that AI may increase both the benefits of high-quality information and the consequences of poor-quality information.
Trust, validation and accountability
AI tools can help people find information, they can identify patterns, summarise content and provide answers at speed, they cannot assume responsibility for the quality of the underlying data.
Organisations therefore need to remain focused on validation, assurance and accountability.
As AI increases the ability to search, connect and interrogate information, organisations need to understand that data landscape clearly and have the right checks in place to ensure retention arrangements, access controls, storage security remain appropriate and cost effective
Looking beyond quality – your data costs
As organisations consider how AI tools interact with information, they may also need to revisit questions about where data is stored and how it is governed.
Multiple storage platforms, duplicated data, legacy systems and inconsistent retention practices can all drive unnecessary expenditure through increased licensing, infrastructure, backup and support costs. As data volumes continue to grow, accelerated by the adoption of generative AI, organisations may find themselves paying to store information that is no longer required, while also carrying the operational burden of managing complex and fragmented environments.
AI does not create this challenge, but it does put data governance into sharper focus.
One of the recurring themes throughout this series has been that AI often amplifies existing strengths and weaknesses. The same applies to information management.
Organisations with strong governance, clear ownership, good records management and trusted data are likely to be better placed to realise the benefits of AI.
Those with fragmented information, unclear accountability or persistent data quality issues may find those challenges becoming more visible.