No data, no artificial intelligence (AI) party. Without data, AI – whether traditional, generative, or agentic – cannot exist. No surprise, then, that the vogue for AI, which has been gathering incredible momentum since the advent of generative AI towards the end of 2022, has had a significant impact on the dusty realm of information management.
This selection of 10 information management stories from Computer Weekly in 2025 inevitably bears the imprint of AI. (We are sticking with the word “information” since it includes unstructured content and sits on a higher plane conceptually than “data”; it also nods towards “knowledge”, which is more high-flown still than mere “data”. But these are Jesuitical distinctions without much of a difference.) That said, AI does not exhaust data management, or data governance, or master data management – those traditional disciplines continue to add value for organisations, whether touched by artificial intelligence or not. The case studies below – Manchester Airport and the RAF – and the interviews with distinguished chief data officers (CDOs) Paul Neville from the Pensions Regulator and Ian Ruffle from the RAC, all speak to an information management vocation with a broader perspective than just the vogue for AI.
On the supplier side of the IT market, SAP, Oracle and Amazon Web Services (AWS) figure in this list of stories. But the information – or data – management field comprises much more than these three. As the AI hype cycle develops into 2026, we can expect the fundamentals of data management – how data is ingested, cleansed, organised and distributed to feed business applications that deliver real-world value – to reassert themselves.
Here, for now, are Computer Weekly’s top 10 information management articles of 2025.
Small language models (SLMs) could be more cost-effective to deploy than large language models (LLMs), offering greater privacy and performing specific tasks better. But is it too early, asked Stephen Pritchard, for SLMs?
LLMs use vast amounts of data and computing power to create answers to queries that look and sometimes even feel “human”. LLMs can also generate music, images or video, write code, and scan for security breaches, among a host of other tasks.
This capability has led to the rapid adoption of generative artificial intelligence (GenAI) and a new generation of digital assistants and “chatbots”. GenAI has grown faster than any other technology.
LLMs, however, are not the only way to run GenAI. Small language models, usually defined as using no more than 10 to 15 billion parameters, are attracting interest, both from commercial enterprises and in the public sector.
SLMs avoid some of the disadvantages of LLMs. These include the vast resources they demand, either on-premise or in the cloud, and their associated environmental impact, the mounting costs of a “pay-as-you-go” service, and the risks associated with moving sensitive information to third-party cloud infrastructure.
Customer data platforms are being touted by suppliers as the means to deliver the single customer view after which organisations have long lusted. Will, asked Marc Ambasna Jones, the promise finally be delivered?
Businesses have long chased the promise of a single customer view. As far back as 1999, a group of suppliers, including Oracle and Siebel, backed the Customer Profile Exchange (CPEX) standard, aiming to offer businesses a “holistic view” of online consumers. But the initiative was quickly mired in complexity and privacy concerns, highlighting a challenge that persists today.
While the idea of unified customer insight has long been used to sell software, few businesses have ever truly realised a single view that proves consistently useful across departments. So why is the customer data platform, or CDP, any different?
Suppliers at the forefront of the market include Adobe, SAP, Salesforce, Oracle, Twilio Segment, Tealium and Microsoft, each bringing varying strengths in data integration, real-time orchestration, and artificial intelligence (AI) enablement.
Generative AI’s use for knowledge management (KM) is growing in customer experience programmes, suggesting a new vocation for KM. Madeline Bennett found out how.
Companies are sitting on a wealth of valuable data that could be used to share relevant information with employees to improve the customer experience (CX).
However, this data can often be buried in multiple systems, requiring staff to wade through reams of irrelevant information to find the precise detail they need, or work out the exact search term needed to access the crucial data.
Recent advances in GenAI technology offer an opportunity for firms to offer a better customer experience by applying AI to KM.
“AI is revolutionising knowledge creation and maintenance by automating and enhancing knowledge practices. Knowledge creation, improvement and sharing can be incorporated into core CX processes,” said Kate Leggett, Forrester Research vice-president and principal analyst for customer relationship management and customer services.
“GenAI-powered KM also makes it easier to access comprehensive information from multiple sources in real time. And knowledge can be co-created with GenAI, which speeds up its creation and helps generate new customer insights.”
Manchester Airports Group is using AWS technology to improve passenger experience through a revamp of its data strategy, as set out by the organisation’s chief digital officer, Ryan Cant.
More than 50 million people passed through one of Manchester Airports Group’s (MAG) three sites in 2023, travelling to one of 250-plus destinations around the globe.
As well as the millions of passengers flying in and out of Manchester, London Stansted and East Midlands airports, MAG handled 395,000 tonnes of cargo out of the latter, making it an important international trade hub for the UK.
MAG has ambitions to increase the number of passengers from 53 million to 60 million by the end of 2024, and to carry on that growth over the next five years.
To enable this level of expansion, the organisation is working with AWS to better integrate its systems, banish data silos and deliver efficiencies.
One of the main elements of the project was investing in the AWS data lake infrastructure and establishing a visualisation strategy for how to plug various data sources into that.
Computer Weekly visited RAF Lossiemouth to see how its fleet of Boeing P-8A surveillance aircraft, supported by NetApp storage, keeps watch over the North Atlantic gap.
Over the inhospitable waters of the North Atlantic, Royal Air Force aircraft play a game of cat and mouse with Russian hunter-killer submarines, and data collection and management are at the heart of its operations.
The primary role of the Poseidon MRA1 maritime patrol fleet is the protection of the UK’s continuous at-sea nuclear deterrent. Detecting and tracking Russian subs, surface vessels and spy ships is their daily task.
But Poseidon’s greatest threat to hostile vessels might not even be a weapon. Instead, it’s the suite of sensors, including a search radar, a powerful camera and sonobuoy acoustic sensors. Its most potent feature is its ability to gather and store vast amounts of data for future analysis. The exact nature of this is classified, but post-mission data is analysed on the ground at Lossiemouth – “terabytes” of it, according to the RAF.
The resulting intelligence “product” is then made available to the RAF, the Royal Navy, and potentially Nato and Five Eyes allies.
SAP TechEd in Berlin put an accent on building agentic and GenAI systems to create real business outcomes, beyond what its executives called excessive hype.
At the event, a troika of technical executives unveiled AI-driven features in the supplier’s SAP Build platform, disclosed more agents in its Joule AI assistance portfolio and pointed to expanded partnerships with data specialist companies, most notably and recently Snowflake.
These relationships betoken, according to SAP, a commitment to opening up its platforms to build a strong foundation for AI among its customers. The main theme of the event was “getting real” about AI.
Revisiting the “flywheel” concept SAP trumpeted at its Sapphire conference in May, Muhammad Alam, executive board member and product and engineering senior vice-president at the company, said: “Innovations across SAP’s unique flywheel of applications, data and AI put developers in the driver’s seat – where they belong.”
Michael Ameling, president of SAP Business Technology Platform, stated that the supplier’s in-memory, columnar database, Hana, is the “database AI has always been looking for”.
SAP chief technology officer Philipp Herzig highlighted predictive use cases, which are the province of traditional machine learning rather than LLMs. He stressed that building AI-based applications “at scale, for large, multinational” companies is of a higher order than building small applications for simpler organisations.
Access to the F1 team’s garage in Abu Dhabi revealed how Monte Carlo simulations, AI and Oracle Cloud Infrastructure power split-second decisions, strategy and championship-winning performance.
Formula 1 has always been a sport defined by extremes, where milliseconds separate victory from defeat and human skill is pushed to its limits. But behind the roar of engines and the spectacle of pit stops lies a quieter, invisible force that increasingly defines success in the sport: technology. At Oracle Red Bull Racing, data, AI and cloud computing have become as important as tyres, aerodynamics and horsepower.
Before the final race of the season in Abu Dhabi, Computer Weekly visited Oracle Red Bull Racing’s garage to observe how human expertise and technological innovation come together to drive one of the most dominant teams on the grid.
Data, automation and AI are driving the regulator to take new approaches to its work and how it supports the pensions industry, leading to improved experiences for everyone in the UK who has a pension.
Paul Neville, director of digital, data and technology at The Pensions Regulator, is building strong IT foundations as part of a five-year strategy to help transform the organisation from a compliance-based to a risk-based regulator. He explained what that change will mean in practice over the next few years.
“As a regulator, we’ll obviously still have specific processes we expect people to follow, but we’ll be much more concerned about the outcome that we’re trying to achieve, and we’ll make decisions based on that demand,” he said.
“To make that shift, we need to understand our data. We need to have the right level of automation to explore information, measure outcomes, and deliver those outcomes with industry and other government bodies interested in pensions. We imagine a future world in which information flows between organisations.”
Ian Ruffle, head of data and insight at the RAC, told Computer Weekly the key to exploiting data assets is twofold – understanding the business problem and having a great team that’s capable of finding the right technological solutions.
“I need people who are empowered, keen, enthusiastic and willing to share knowledge,” he said, outlining the importance of talent to the effective deployment of data-hungry systems and services in the digital age. Rather than finding a suitable challenge for a technology that’s already been procured, Ruffle wants his team to engage with their functional peers.
“As a data leader in business, success is all about people coming to us and saying, ‘We’ve got a problem. Can you find the solution?’” he said.
Once an organisation’s data maturity is sorted, CDOs need to show how strong data management can power tech initiatives.
While the role of chief digital officer is regarded as quite diverse, a study from Deloitte suggested CDOs recognise the need for a strong data strategy, regardless of whether their organisation’s operational model is centralised or decentralised.
Deloitte noted that it’s important for CDOs to outline a clear direction of travel for data in their organisation, to enable a cohesive approach to data and avoid common challenges that arise from siloed operations and duplicated effort.
According to Deloitte, a clear, documented and shared vision is therefore a key tool, enabling CDOs to articulate how data should be used in their organisation to drive performance and achieve strategic objectives.
The company’s Chief data officer survey 2025, based on a poll of 81 CDOs, reported that 70% of respondents are either implementing AI systems or conducting experimental proof-of-concept projects to understand its potential.
Deloitte noted that while few CDOs indicate that AI is transforming their organisations currently, the data showed a positive indication that CDOs are looking to move towards utilising AI, and further development of AI capabilities is therefore required to drive this.