SOFIAH NICHOLE SALIVIO

News Editor

Floward has deployed Infobip’s AgentOS to manage spikes in customer service demand, with the system handling peak WhatsApp volumes during major seasonal surges.

The online flower and gifting company said the setup enabled it to cope with conversation volumes up to 13 times higher on peak days, including Valentine’s Day, when it handled more than 54,000 daily conversations. It introduced a group of specialised AI agents to replace rule-based chatbots and route more complex cases to human staff when needed.

The move addresses a persistent operational challenge. As a same-day delivery company serving customers in the UK and the Middle East, Floward faces sharp increases in demand around occasions such as Mother’s Day, Valentine’s Day and Ramadan, when queries about orders, delivery addresses and changes can rise quickly.

Under the new arrangement, customer conversations are routed to different AI agents depending on the task, including address collection, frequently asked questions and order changes, while live agents remain available for escalation. The service runs through WhatsApp, where recipients can provide address details and ask related questions in the same chat.

Floward said the deployment was designed, tested and launched in less than two months. It reported a 15% reduction in customer service costs and a 12 percentage point increase in customer satisfaction after introducing the AI-based system.

It also said it maintained one-minute response times and achieved 95% service level agreement performance during its busiest periods. AI containment rates also improved from the previous year, indicating that a larger share of customer interactions were resolved without being passed to human agents.

Seasonal pressure

Retailers and delivery businesses have been experimenting with generative AI and automated customer service tools to absorb temporary peaks in demand without increasing staffing at the same rate. The challenge has been particularly acute for businesses tied to seasonal events, where traffic can surge for short periods while customer expectations for speed and order accuracy remain high.

Floward said the project was intended to show that customer service scale no longer had to depend on proportional increases in headcount. It worked with Infobip to redesign customer journeys so more steps in the support process could be handled within a single messaging conversation.

“One of the biggest achievements of this transformation was proving that scaling customer service no longer means scaling headcount at the same rate,” said Lujain Mallosh, Customer Care Senior Manager at Floward.

“By combining AI agents, WhatsApp journeys and customer data on a single platform, we significantly improved efficiency and automation while maintaining high service standards during our busiest periods. As a result, we reduced customer service costs by 15%, handled 54,000 conversations on Valentine’s Day alone, improved AI containment rates year over year, and consistently achieved one-minute response times with 95% SLA performance. This approach also helped us increase CSAT by 12 percentage points, even during extreme peak demand,” Mallosh said.

Broader use

Beyond customer support, Floward is extending the same system to other customer interactions tied to sales. One example is an e-invitations feature that uses the platform for approval workflows, recipient notifications and gift prompts.

This points to a wider shift in how online retailers use messaging systems. What began as a customer support channel is increasingly being tied to order management, fulfilment and follow-on commercial activity within a single conversation thread.

Infobip, which provides cloud communications software, said Floward’s use case reflected the type of workload for which multi-agent AI systems are most useful: high-volume, repetitive requests with clear handover points to human staff. It argued that the main benefit came from reducing fragmentation in customer journeys rather than simply automating single tasks.

“Floward’s peak-season challenges are a textbook example of where agentic AI delivers the most value,” said Emir Kalem, Head of Customer Success EMEA at Infobip.

“By orchestrating AI agents, WhatsApp Flows and live agent escalation on a single platform, we helped Floward turn fragmented customer journeys into a seamless experience that handles extreme volume spikes while improving end-user service quality and delivery performance,” Kalem said.

The pellets are being used to care for the school’s White Pekin ducks.

For several months, the ducks have been benefiting from the pellets thanks to a donation from Land Energy, a nationwide biomass manufacturer and distributor.

St Peters school pupils feeding the ducks (Image: St Peter’s CE Primary School)

Emily Lemaire, school administrator, said: “We rallied the community and quickly raised more than £1,000 to buy and build a new duck run for our Forest School area.

“Nearly half of the school is now involved in helping to care for our chickens and ducks, with volunteer ‘duck leaders’ taking part in a day-to-day care rota.

“It can be quite costly to look after the ducks, which is why we were so grateful to Land Energy for donating some of their Sorbeo horse bedding pellets for us to try. The pellets have been fantastic, helping to keep the ducks’ sleeping areas clean, dry and comfortable.”

The ducks in the forest school area (Image: St Peter’s CE Primary School)

The project has engaged a large part of the school community, with many pupils helping care for the animals.

David Bone, communications officer at Land Energy, said: “We are always keen to support schools and community groups that help strengthen our relationship with the natural world, while contributing to our goal of creating real environmental gain and encouraging wider community engagement.

“We are very proud to support St Peter’s CE Primary School and its now three ducks with our Sorbeo horse bedding pellets in what we believe is an unconventional first.”

The Digital Regulation Cooperation Forum has published findings on how four UK regulators are using generative AI in their work. The review covers the Competition and Markets Authority, the Financial Conduct Authority, the Information Commissioner’s Office and Ofcom.

Generative AI is moving beyond pilot projects into regular use across regulatory analysis, supervision and enforcement, the forum found. Joint work over the past year has focused on governance, prompt design, detecting consumer harm and testing AI tools before wider deployment.

The report offers one of the clearest accounts yet of how UK public authorities are trying to use large language models and related systems in day-to-day oversight of digital markets and services. It also shows the extent to which regulators are building internal tools rather than relying solely on off-the-shelf products.

From pilots

According to the findings, each member regulator has been testing, developing or deploying generative AI tools. Staff have also been given access to AI productivity software as part of a broader effort to build familiarity with the technology and set rules for its use.

That work has been supported by six cross-regulator deep-dive sessions involving leaders working on advanced regulatory technology. The sessions examined how to manage risks such as hallucinations and bias, improve outputs through prompt design, identify harmful online design practices and assess whether AI systems are reliable enough for specific regulatory tasks.

A central conclusion is that governance remains critical. AI tools may offer clear benefits, but in a regulatory setting where errors can affect enforcement decisions and consumer protection, they require proportionate oversight, clear accountability and human review.

The Financial Conduct Authority was cited as one example of a regulator using a structured internal framework, including policies on data management, frontier AI and privacy, alongside staff training on risk management systems.

Consumer harm

Some of the clearest examples relate to monitoring websites and apps for harmful design patterns. These include drip pricing, misleading scarcity claims, reference pricing and so-called sludge practices that make it harder for consumers to cancel subscriptions or navigate terms and conditions.

The Competition and Markets Authority has developed what the forum described as agentic AI that can experience and record the consumer journey at scale. The aim is to detect possible breaches of consumer law by navigating online services in a way that mirrors how users encounter prices, prompts and design choices.

This approach has already fed into enforcement activity. The authority has opened investigations into eight businesses and sent advisory letters to 100 others following the investment, according to the forum.

The Financial Conduct Authority has also tested whether large language models can be used for sludge audits. Instead of relying on staff to click manually through websites and recreate customer journeys, the regulator ran a pilot to see whether models could simulate consumer personas and carry out many of those checks more quickly.

The pilot found that the models could perform a large share of the audit work, but with important caveats. Prompt design was needed to improve consistency, the systems did not always interpret webpages correctly and human review remained necessary to check accuracy.

Ofcom has been using behavioural audits in its own work to examine whether online services meet obligations under the Online Safety Act. These audits look at areas such as sign-up processes, features that influence time spent on a service, negative sentiment tools and reporting mechanisms.

The Information Commissioner’s Office and the Competition and Markets Authority have also worked jointly on harmful design in digital markets, while the Information Commissioner’s Office has monitored compliance on the use of non-essential cookies at scale.

Testing tools

Another strand of the programme focused on how regulators assess the quality of AI systems before wider deployment. Several have created minimum viable internal evaluation frameworks that define the task an AI tool is meant to support, set guardrails for users and test for accuracy, usefulness and obvious failure modes.

These frameworks compare model outputs against reference answers using test questions, source documents and preset criteria such as factual accuracy, substance and citation style. Models are then scored against pass-fail thresholds.

This approach helps technical teams decide whether a tool is suitable for sensitive work and supports a more controlled move from trial use to broader deployment. But the process can be time-consuming because reference materials and benchmark answers must be created in advance.

Prompt engineering emerged as another practical area of work. Regulators reported that better outputs often depend on supplying context, defining a role for the model, giving examples and setting clear constraints. More advanced methods included breaking down complex questions into smaller parts and linking prompts in sequence.

The findings also refer to the use of retrieval-augmented generation, in which models draw on approved internal documents to ground responses in verified material. The forum said this can reduce the risk of hallucinations, but not remove it, so it is being used alongside other checks.

Overall, the picture is of regulators using AI to cut the time and cost of investigations without removing human judgment from the process. In areas such as consumer protection and online safety, the technology is being positioned as a way to monitor large numbers of services and spot patterns that would be difficult to identify manually.

Cross-regulator cooperation has helped speed adoption, reduce duplication and support more consistent approaches across the four bodies, the forum found. Strong governance, evaluation frameworks and human oversight remain essential to the safe and effective use of generative AI in regulation.