NatWest Group has expanded the use of artificial intelligence in several areas of its operations, citing customer service, document management in its wealth management division, and software development. According to a blog post by its chief information officer, Scott Marcar, 2025 was the first year in which these systems were deployed at scale. The aim is to improve productivity and customer engagement.

Generative AI in customer service

In customer service, generative AI has been added to Cora, the bank’s digital assistant, and the number of possible customer journeys that can be supported by generative AI increased from four to 21. The bank reports this has let led to quicker resolution times and a reduced need for human intervention.

Early this year, 25,000 customers will get access to a new agentic financial assistant in Cora, which is built on OpenAI models. Cora will let customers ask questions in natural language about recent transactions and their spending patterns from the bank’s app.

The next phase involves adding voice-to-voice abilities that incorporate tone and conversational nuance. Customers will be able to report suspected fraud and manage related cases through the interface.

The impact of AI on internal customer service operations has been largely in the creation time savings. In the bank’s retail division, for example, automated call summaries and complaint drafting tools have saved more than 70,000 hours of staff time. These generated summaries of customer calls help with written responses to complaints.

Staff access to Copilot

Marcar says all of its c. 60,000 employees have access to AI tools that include Microsoft Copilot Chat and the bank’s own LLM. More than half of staff have taken extra training beyond the basic training offered.

Summarising wealth

In the NatWest’s private banking and wealth management operations, AI is used to improve document management and client records. Relationship managers use notes, meeting summaries, and correspondence to understand clients’ circumstances. The systems generate summaries of meetings and documents, reducing the time required to review and record information, releasing 30% more time for direct client face time: Advisers allocate more hours to the giving of advice rather than administration.

AWS Cloud

The above changes depends alterations NatWest has made to its data infrastructure. It’s restructured its data estate to create unified customer views, and moved workloads to Amazon Web Services while simplifying some legacy systems. Access to data and scalable computing capacity supports the summarisation tools and the conversational systems used in customer service.

Software development

Software development is the third area in which AI is deployed. The bank’s 12,000 engineers use AI coding tools, and Marcar says AI now produces over a third of the company’s code, drafting, reviewing and testing software. In 2025, NatWest hired nearly 1,000 graduate software engineers in India and the UK.

Trials of agentic engineering in its financial crime units led to a tenfold increase in productivity, and NatWest plans to extend agentic engineering practices more widely. Its stated objective is to build and iterate systems more quickly.

Fraud prevention

The bank has also invested in AI-powered analytics fraud detection and risk monitoring, designed to identify unusual activity and advise customers when risk is detected.

Alongside operational deployment, NatWest has established an AI research office that focuses on technologies like audiovisual conversational systems and proprietary small language models. It’s also formalised governance structures through an AI and Data Ethics Code of Conduct and the organisation is part of the Financial Conduct Authority’s Live AI Testing programme.

Conclusions

Across customer service, wealth management document processing, and software development, AI is embedded in workflows at NatWest, producing time savings and productivity increases. The scale of deployment, covering tens of thousands of employees and a growing proportion of customer interactions, indicates that AI now forms part of NatWest’s operating model not an experimental adjunct.

(Image source: Pixabay)

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Retail decisions often depend on weekly performance reports, but compiling those reports can take hours of manual work. Urban Outfitters Inc. (URBN) is testing a new approach by using agentic AI systems to generate those reports automatically, changing routine analysis from staff to software.

The retailer runs brands like Urban Outfitters, Anthropologie, and Free People, and has deployed AI systems that analyse store-level data and produce weekly summaries for merchandising teams. Instead of reviewing multiple spreadsheets or dashboards, staff receive a report that highlights patterns and areas that need attention.

Industry coverage indicates the automation saves merchants from reviewing more than 20 separate reports each Sunday by synthesising the information into one overview. The goal is to reduce the time spent collecting and organising data before decisions are made. The rollout offers a practical example of how “agentic AI” is beginning to enter everyday enterprise operations.

How agentic AI is taking over routine retail reporting

Weekly reporting sits close to the core of retail management. Merchandising teams use these updates to monitor sales trends, check inventory movement, and decide where to adjust pricing, stock levels, or promotions. Because the process repeats in many stores and regions, it can consume a large share of operational time.

URBN’s AI agents take over the structured parts of that workflow. The systems gather store data, organise results, and present a digestible summary for teams to review. Employees remain responsible for interpreting the findings and taking action, but the groundwork is handled automatically.

This mirrors a change in enterprise AI adoption. Early deployments frequently aimed at helping individuals complete tasks faster, like drafting text or searching internal information. Instead, agentic systems run processes in the background and present completed outputs, allowing staff to focus on judgement not preparation.

Retail analysts have pointed to growing interest in this model in the sector. Discussions at recent National Retail Federation events have highlighted how retailers are exploring autonomous AI workflows to support merchandising and operational monitoring at scale. URBN’s reporting automation shows how those ideas are moving into production environments not staying in pilot stages.

Why reporting is an early target for automation

Reporting is one of the first operational areas that many companies try to automate because it is based on organised data and predictable formats. Weekly summaries follow a repeatable pattern, making them easier to test using automation while keeping oversight in place.

Starting with reporting allows URBN to evaluate how reliable the AI outputs are and how well teams adapt to receiving automated insights. If the system consistently produces accurate summaries, it can reduce delays between identifying trends and responding to them.

The approach also highlights that automation does not remove accountability. Staff still review the reports and make final decisions, but they spend less time assembling information manually.

A signal of changing enterprise priorities

URBN’s rollout suggests that the next phase of enterprise AI adoption may be embedding automation into everyday workflows. Companies are asking increasingly whether AI can handle recurring operational tasks reliably enough to become part of normal business processes.

When those tasks are automated successfully, the benefits extend beyond time savings. Consistent reporting can help ensure that teams in regions work from the same information, which may improve coordination and speed up responses to emerging issues. In large retail networks, even small improvements in how quickly insights reach decision-makers can influence stock management and sales performance.

If reporting automation proves dependable, similar systems could expand into adjacent areas like demand forecasting, promotion analysis, or supply monitoring. Each step would follow the same pattern: automate the repeatable groundwork, keep people responsible for oversight and decisions.

From AI assistance to agentic AI execution

URBN’s use of agentic AI illustrates a gradual change in how enterprises are integrating artificial intelligence. AI is starting to run defined operational processes automatically while humans supervise results.

The change moves AI from supporting individual productivity to shaping how work is organised. By starting with a recurring task like weekly reporting and keeping review firmly in human hands, URBN is testing how far automation can be trusted in real retail operations.

For other enterprises watching the evolution of agentic systems, the lesson is practical, namely about deciding which everyday processes can be handed to software – and how to manage that transition.

(Photo by Clark Street Mercantile)

See also: Agentic AI drives finance ROI in accounts payable automation

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AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here.

Murder Mystery 2, commonly known as MM2, is often categorised as a simple social deduction game in the Roblox ecosystem. At first glance, its structure appears straightforward. One player becomes the murderer, another the sheriff, and the remaining participants attempt to survive. However, beneath the surface lies a dynamic behavioural laboratory that offers valuable insight into how artificial intelligence research approaches emergent decision-making and adaptive systems.

MM2 functions as a microcosm of distributed human behaviour in a controlled digital environment. Each round resets roles and variables, creating fresh conditions for adaptation. Players must interpret incomplete information, predict opponents’ intentions and react in real time. The characteristics closely resemble the types of uncertainty modelling that AI systems attempt to replicate.

Role randomisation and behavioural prediction

One of the most compelling design elements in MM2 is randomised role assignment. Because no player knows the murderer at the start of a round, behaviour becomes the primary signal for inference. Sudden movement changes, unusual positioning or hesitations can trigger suspicion.

From an AI research perspective, this environment mirrors anomaly detection challenges. Systems trained to identify irregular patterns must distinguish between natural variance and malicious intent. In MM2, human players perform a similar function instinctively.

The sheriff’s decision making reflects predictive modelling. Acting too early risks eliminating an innocent player. Waiting too long increases vulnerability. The balance between premature action and delayed response parallels risk optimisation algorithms.

Social signalling and pattern recognition

MM2 also demonstrates how signalling influences collective decision making. Players often attempt to appear non-threatening or cooperative. The social cues affect survival probabilities.

In AI research, multi agent systems rely on signalling mechanisms to coordinate or compete. MM2 offers a simplified but compelling demonstration of how deception and information asymmetry influence outcomes.

Repeated exposure allows players to refine their pattern recognition abilities. They learn to identify behavioural markers associated with certain roles. The iterative learning process resembles reinforcement learning cycles in artificial intelligence.

Digital asset layers and player motivation

Beyond core gameplay, MM2 includes collectable weapons and cosmetic items that influence player engagement. The items do not change fundamental mechanics but alter perceived status in the community.

Digital marketplaces have formed around this ecosystem. Some players explore external environments when evaluating cosmetic inventories or specific rare items through services connected to an MM2 shop. Platforms like Eldorado exist in this broader virtual asset landscape. As with any digital transaction environment, adherence to platform rules and account security awareness remains essential.

From a systems design standpoint, the presence of collectable layers introduces extrinsic motivation without disrupting the underlying deduction mechanics.

Emergent complexity from simple rules

The most insight MM2 provides is how simple rule sets generate complex interaction patterns. There are no elaborate skill trees or expansive maps. Yet each round unfolds differently due to human unpredictability.

AI research increasingly examines how minimal constraints can produce adaptive outcomes. MM2 demonstrates that complexity does not require excessive features. It requires variable agents interacting under structured uncertainty.

The environment becomes a testing ground for studying cooperation, suspicion, deception and reaction speed in a repeatable digital framework.

Lessons for artificial intelligence modelling

Games like MM2 illustrate how controlled digital spaces can simulate aspects of real world unpredictability. Behavioural variability, limited information and rapid adaptation form the backbone of many AI training challenges.

By observing how players react to ambiguous conditions, researchers can better understand decision latency, risk tolerance and probabilistic reasoning. While MM2 was designed for entertainment, its structure aligns with important questions in artificial intelligence research.

Conclusion

Murder Mystery 2 highlights how lightweight multiplayer games can reveal deeper insights into behavioural modelling and emergent complexity. Through role randomisation, social signalling and adaptive play, it offers a compact yet powerful example of distributed decision making in action.

As AI systems continue to evolve, environments like MM2 demonstrate the value of studying human interaction in structured uncertainty. Even the simplest digital games can illuminate the mechanics of intelligence itself.

Image source: Unsplash