SOFIAH NICHOLE SALIVIO

News Editor

Hammer has launched Hammer Stack, an on-premises AI infrastructure platform for businesses, positioning it as part of a wider shift towards sovereign AI infrastructure.

The launch comes as many companies struggle to move artificial intelligence projects from pilot stages into day-to-day use. Hammer argues that the main obstacle is not demand for AI tools, but the difficulty of building and running the underlying infrastructure at scale.

Many organisations began AI work in public cloud environments because they offered an easy starting point for experimentation. But as workloads expand, those environments can become harder to manage, with costs rising and large datasets becoming expensive to move.

This issue, often described as data gravity, can leave businesses with data stored in one environment and computing resources tied to another. Hammer says that separation creates architectural constraints that hinder performance and make it harder to generate a return on AI spending.

Sovereign focus

Hammer Stack is designed as a fully integrated platform that keeps AI workloads on premises rather than in hyperscale public cloud systems. It is intended to let organisations decide where models are trained and deployed, with an emphasis on data control, policy requirements and regulatory obligations.

The platform combines AMD EPYC processors, NVIDIA GPUs and networking, and VDURA storage in a rack-level design. The package also includes APC power management and support for liquid cooling.

Rather than selling it as a standalone system, Hammer has presented Hammer Stack as the hardware layer for its existing Hammer AI Works ecosystem. That broader offering includes advisory services, a community model and a sandbox environment called The Labouratory for testing proof-of-concept projects before production deployment.

Production barrier

Hammer argues that many AI initiatives fail not at the software stage, but in the move from test environments into production systems. In its view, piecemeal infrastructure decisions can create what it calls “accidental architectures” that work in isolated trials but break down under full operational demands.

The new platform’s rack-level validation is intended to reduce that risk by integrating compute, storage, networking, power and cooling into a pre-designed module. The aim is to give partners and customers a more predictable route from experimentation to live deployment.

Hammer is also linking the product to a wider market debate over sovereignty in AI systems. For many European organisations, especially those handling sensitive data, questions over where information sits, who controls access to it and how it is processed have become more prominent as AI use expands.

Those concerns have helped create demand for infrastructure that can run advanced AI workloads without relying entirely on large US cloud providers. Hammer is seeking to position itself in that market by offering an option that keeps data and compute resources under customer control on site.

Channel role

Hammer operates as a value-added distributor focused on enterprise infrastructure, cybersecurity and data products across Europe. Hammer Stack will be supported by specialist AI consultancies alongside its channel network, reflecting the technical complexity of designing and managing AI systems in production.

Financing will also form part of the offer, with leasing and subscription-style models available to customers that want to align infrastructure spending with actual use. That approach is aimed at reducing the upfront cost of deploying AI systems on premises at a time when many businesses remain cautious about large capital commitments.

The launch reflects a broader effort by suppliers across the IT market to address frustration over AI returns. While spending on generative AI and large language model projects has surged, many businesses are still trying to prove commercial value from those investments once trial projects end.

Hammer framed that gap between ambition and implementation as one of the defining issues in the current AI market. It argues that organisations need infrastructure that matches their own data location, governance rules and operational priorities, rather than defaulting to the architecture of a hyperscale cloud provider.

“AI should run where your data, policies, and priorities dictate, not where a hyperscaler decides,” Hammer said.

National Car Parks (NCP) entered administration in March after what its insolvency practitioners described as severe financial pressures and insufficient cash to meet its obligations.

As part of the initial restructuring, just over 20 “commercially unviable” car parks across the country were earmarked for closure in late March, with a little over 30 roles made redundant in total at those sites.

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The NCP‑run car park in Marlborough Road in Banbury was included in that first wave of closures and has now shut for good.

It is not known how many jobs have gone in Banbury specifically, but the administrators have confirmed that the redundancies linked to this initial closure programme number slightly above 30 across all affected locations.

The rest of NCP’s estate – more than 300 car parks nationwide – continues to trade while the administrators explore options for the business.

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Two NCP car parks in Oxford city centre remain open and are among the sites being operated under the administrators’ control.

NCP, a familiar name in town and city centres for decades, has been hit by changing travel patterns, more home‑working and rising costs.

Its fall into administration has put nearly 700 jobs at risk across the company, although only a small proportion of those have so far been cut through the first round of car park closures.

SOFIAH NICHOLE SALIVIO

News Editor

Anaplan, Incisiv and World Retail Congress have released a global study on retail resilience and AI adoption, highlighting a clear performance gap between faster-moving retailers and the wider sector.

The study surveyed 298 merchandise planning and supply chain executives across North America and EMEA. It found that leading retailers achieve average full-price sell-through of 71%, compared with an industry average of 57%, with faster responses to demand signals setting stronger performers apart.

Many retailers also believe slow decision-making is costing them sales. Two-thirds of respondents said they lose 3% or more of annual sales because they cannot react quickly enough to shifts in demand, while one-third estimated losses above 6%.

For a retailer with annual revenue of USD $1 billion, that would amount to more than USD $60 million in lost sales. The report linked those losses to long planning cycles: 69% of organisations rebalance inventory monthly or less often, and 78% adjust upstream supply quarterly or more slowly, despite having access to real-time demand signals.

“Our research shows that the organisations pulling ahead have restructured accountability and given systems the authority to act. The performance gap between them and the rest of the industry is now measurable in full-price revenue – and it is growing,” said Gaurav Pant, Chief Insights Officer at Incisiv.

AI readiness

The research also highlights a gap between interest in AI and its use in day-to-day retail planning. More than 85% of executives rated AI as critical across retail functions, yet deployment remained much lower.

Only 31% of respondents had deployed AI in demand forecasting, while just 13% had introduced it in exception management, where faster decision-making can have a direct operational impact. The report described this as a 60-percentage-point gap between perceived importance and adoption.

Workforce preparation emerged as another weak point. Executives said more than half of supply chain and merchandise planning roles would require materially different skills by 2030, but only 11% of teams have received any AI training so far.

The study warned that poor preparation could create two problems inside organisations: staff may reject AI outputs because they do not trust them, or accept them without enough judgement to recognise when the system is wrong.

“This research makes clear that the competitive divide in global retail is no longer about who has the best forecast. It is about who can turn insight into action fastest – and that requires a fundamentally different operating model,” said Ian McGarrigle, Chairman of World Retail Congress.

Leaders pull ahead

The report grouped respondents by operational maturity and identified a top 10% tier as leaders. These retailers were more likely to update plans quickly, align teams around common incentives and embed AI more directly in decision-making.

Among that group, 90% refresh demand forecasts weekly or in real time. By contrast, around two-thirds of the wider industry still operate on monthly or quarterly forecasting cycles.

The same pattern appeared in organisational design. According to the survey, 24% of leaders had unified cross-functional incentives, five times the industry average.

AI use in decision-making also differed sharply. The study found that 76% of leaders operate at system-recommended or autonomous AI decision levels, while none rely on fully manual processes.

This suggests the gap is not simply about access to software, but about how companies organise decisions and act on information. The findings indicate that retailers that can shorten planning cycles and connect insight to execution are better placed to protect margins and reduce lost sales.

“The retailers in this study who are winning have moved from just looking at data to acting on it. They are turning analytical noise into precise action by using AI-driven tools that bring purpose-built functionality and deep expertise into their core planning workflows. This connection between insight and execution is what enables them to make informed, confident decisions at the moment they matter, and it’s what truly separates the leaders from the rest,” said EJ Tavella, EVP and GM of Integrated Business Applications at Anaplan.

Respondents represented businesses with annual revenue ranging from USD $250 million to more than USD $5 billion. More than half were based in North America, with the remainder in EMEA. The study assessed supply chain maturity through a 15-question index covering cross-functional integration, responsiveness, technology and AI infrastructure, and organisational adaptability.