SOFIAH NICHOLE SALIVIO

News Editor

Dayshape has published research suggesting poor data quality is the biggest barrier to effective AI adoption for many UK professional services firms, with 34% of senior leaders identifying it as the main obstacle.

The survey of 200 UK senior leaders in professional services found that investment in new technology has become a major management priority. Some 61% of organisations said it is now a top business priority, while 50% of respondents said it is their main personal focus for the year ahead.

The findings point to a gap between the sector’s appetite for AI and the operational conditions needed to support it. Integration with existing systems was cited by 32% of leaders as a barrier to effective AI use, while 28% pointed to cost and investment requirements and 22% to a lack of internal capability.

That puts data issues ahead of problems that often dominate discussion around AI adoption, including budgets and skills. The figures suggest firms are finding the quality and consistency of their underlying information a more immediate challenge than buying software or assigning staff to manage it.

Current use

More than half of respondents, 54%, said their organisations already use AI for data analytics. Another 47% said it is used for data entry, while 41% use it for innovation.

AI is also spreading into operational planning. The survey found that 39% of firms use it for workforce optimisation, 34% for project and resource planning, and 29% for capacity modelling.

These figures indicate that AI is moving beyond isolated experiments into areas that affect staffing, utilisation and client work. In professional services businesses, where margins and delivery schedules often depend on accurate planning, weak data quality can directly affect how useful AI tools prove to be.

Expansion plans

Leaders also expect AI use to increase in business areas tied closely to forecasting and service delivery. About 32% said they plan to expand its use in client delivery tools, 32% in capacity modelling, 31% in project and resource planning, and 30% in workforce optimisation.

The emphasis on these functions suggests firms are not treating AI simply as a back-office tool. Instead, they are looking to deploy it more widely in decision-making and operational management, where inaccurate or fragmented data can limit the value of automated systems.

The study was conducted by OnePoll on behalf of Dayshape. According to the company’s background notes, it surveyed senior leaders at professional services firms in the UK and US with more than 750 employees. The UK findings released by Dayshape focused on responses from 200 senior leaders.

The data comes as professional services groups face pressure to improve productivity while managing hiring costs and shifting client demands. Many firms have invested in digital tools in recent years, but the survey suggests that connecting systems and improving data standards remain unresolved challenges.

Andrew Bone, Vice President of Product at Dayshape, said the value of AI depends heavily on the quality of the business information beneath it. He said firms would fall short if they treated AI deployment as a standalone initiative without addressing broader operational weaknesses.

“Many people in professional services firms already struggle with poor data quality, disjointed systems and internal silos, placing a huge drag on operational effectiveness. Similarly, AI initiatives will not meet expectations if they are layered on top of those foundations. The organisations seeing the most value are the ones focusing not just on adopting AI, but on strengthening their data and building the internal capability to use these tools effectively. The findings highlight a shift in how organisations need to approach AI adoption, with greater emphasis on readiness as well as investment,” said Bone.

Curve has used Google Cloud’s BigQuery Graph in its fraud prevention work, helping it uncover linked fraud networks.

The UK fintech, which offers a wallet app and card that combine customers’ debit and credit cards, adopted graph analytics to address organised fraud that standard transaction monitoring can miss.

Fraudsters often share attributes across accounts, including devices, funding cards and contact details. In a conventional relational database, those links can be difficult to trace because analysts must run repeated self-joins to follow chains of connections between accounts.

At the scale of millions of users and tens of millions of connections, that process became expensive and difficult to maintain, Curve said. Some of its most detailed fraud signals also involve billions of possible links, creating performance constraints in standard relational systems.

Graph analysis

To address this, Curve modelled its payments environment as a graph, with users represented as nodes and shared identifiers as edges. This lets analysts search for suspicious patterns across the dataset using graph query language while keeping the data in its existing BigQuery warehouse.

The approach avoided a move to a separate graph database and reduced the time and cost of migration. It also allowed teams to combine graph traversals with SQL analysis and machine learning workflows in the same environment.

According to Curve, the system can analyse links at user, device and card level. Its existing SQL pipelines still build the underlying nodes and edges tables, with graph queries then used to traverse relationships and SQL used again for aggregation.

Financial effect

Curve said automated blocks triggered by graph-based analysis saved about USD $12 million in transaction losses during 2025. It also said its graph-based queries reached roughly 72% accuracy in identifying fraudulent users.

That level of precision has helped fraud mitigation staff focus manual reviews on cases more likely to involve fraud, while graph query language has made it easier to refresh fraud rules more often.

Previously, hourly rules were limited to one-hop queries because more complex scripts were too slow to run efficiently, Curve said. The newer approach has allowed the company to optimise those processes as it looks for broader account and device networks linked to organised crime.

Model training

Curve also said faster graph traversal has implications for the machine learning models it uses in fraud monitoring. Daily graph rebuilding and traversal are sufficient for training models, but too slow for live transaction decisions that may need to be made in less than a second.

As a result, Curve is moving towards micro-batch or streaming graph traversals so fresher network data can be fed into fraud models during monitoring. It is also working to incorporate higher-volume signals, including billions of IP address connections, into real-time detection processes.

Another area under review is graph visualisation for analysts, which could give security and data science teams a way to inspect fraud networks visually as they emerge. Overall, the shift is helping Curve treat fraud detection as network analysis rather than isolated transaction review.

The change has also given teams a way to connect relationships directly inside the existing data environment while reducing the operational burden of more complex relational queries.

Research published by ReLondon, in partnership with the University of Oxford, shows that London households buy almost twice as much electrical and electronic equipment as they discard.

The average London household buys about 57.8kg of new electrical and electronic goods each year and gets rid of 30.2kg, the findings show. Between 2023 and 2024, the volume of these items held in homes across the capital rose by 4.4%, leaving an estimated 700kg in the average home.

The report tracks the movement of electrical and electronic equipment across London from purchase and use to disposal. It covers household goods as well as products used by businesses and institutions, from smartphones and laptops to washing machines and solar panels.

Consumption has risen sharply. In 2024, households, businesses and institutions in London bought 255,600 tonnes of new electrical and electronic equipment and discarded 134,500 tonnes, according to the analysis.

Households accounted for most of what was thrown away, with 80% of electrical and electronic items discarded in London during 2024 coming from homes rather than businesses or public bodies.

The research also points to a large reserve of products that could stay in use for longer. It estimates that around a quarter of the 134,500 tonnes discarded across the capital could have been reused or repaired rather than thrown away.

Had those items been redistributed instead of disposed of, they could have met close to 10% of London’s total demand for new electrical and electronic goods. The analysis also found that households currently repair or pass on only 21% of products at the end of their first life.

Waste routes

The study also highlights disposal methods. Of the electrical and electronic items discarded in 2024, 47.5% went through formal routes such as commercial collections, retail take-back schemes, council collections, household waste recycling centres, and doorstep or bring-bank services.

Another 52.3%, equal to 70,700 tonnes, went through non-official routes. Of that amount, 62.3% was placed in residual waste.

Households were responsible for 63% of the total volume of improperly disposed electrical and electronic items. Businesses and institutions, however, discarded more per unit: the average London business sent the equivalent weight of 24 laptops to non-official routes each year, compared with six laptops for the average household.

The research also examined emissions linked to the production and disposal of these products, excluding emissions created during use. It found that London households’ electrical and electronic goods generated 5.9 million tonnes of CO2e in 2024, rising to 9.1 million tonnes when business and institutional equipment was included.

According to the report, that footprint is larger than the carbon impact of the capital’s packaging and clothing sectors, though smaller than that of its food system. Five categories made up 41.9% of the household carbon footprint: small IT equipment, games consoles, flat-panel televisions, photovoltaic panels and washing machines.

Repair potential

The report suggests that diverting more discarded items into repair and reuse would materially change the picture. It estimates that household repair rates could rise from 1.7kg to 4.8kg per household if reusable and repairable goods now being thrown away were recovered, while reuse could increase from 8.1kg to 12.4kg.

Lamia Sbiti, Director of Business and Sector Support at ReLondon, said: “While this research highlights a gap between our current habits and our true potential, the exciting reality is that the foundations for a circular future are already here. From the everyday Londoners engaging in repair and redistribution to London’s growing ecosystem of innovators and repairers, the ingredients for change are in place. By tapping into these forces, we have a unique opportunity to shape a system that unlocks social value for residents, drives economic growth for the city and tackles climate change head-on.”

Mete Coban, Chair of ReLondon, said the study clarifies the scale of the problem in the capital. “Londoners are using more electrical items than ever before, from laptops and phones to kettles and air fryers. As we use more of these products, ReLondon’s new report helps us understand the impact they have on waste and pollution and shows where we can make a difference. Too many electrical items are still being thrown away when they could be repaired, reused or recycled. By helping people keep products for longer and recycle them properly, we can cut waste, reduce pollution, create green jobs, support our transition to a low-carbon, zero-waste city and continue building a greener, fairer London for everyone.”

Academic researchers involved in the work said the study provides a first broad picture of how products move through the capital’s economy. Lucia Corsini, Head of the Circular Economy and Sustainability Lab at the University of Oxford, said: “We are delighted to have undertaken this complex analysis of London’s electrical and electronic equipment system in partnership with ReLondon. For the first time, the study quantifies first-life and second-life pathways across a diverse range of household and non-household products – everything from a kettle to a washing machine to a solar panel – while also shedding light on informal and improper waste handling and treatment. We hope these findings provide an evidence base to support more informed industry and policy decision-making, helping to accelerate the transition to a low-carbon, circular economy.”

Hannah Jameson, Corporate Director for Delivery, Innovation and Climate at London Councils, said: “This report provides a valuable evidence base for understanding how London can reduce waste, recover more value from electricals, and build a more resilient circular economy. It highlights the importance of working together across boroughs, businesses and communities to increase repair, reuse and proper collection of electrical and electronic equipment.”