AI-Powered Cars Could Reshape Cities, Travel and the Economics of Transport

Artificial intelligence is pushing the automotive industry towards a future in which vehicles do considerably more than drive themselves. Cars are increasingly being developed as intelligent platforms capable of understanding their surroundings, communicating with passengers, anticipating journeys, interacting with infrastructure and potentially operating independently when their owners are elsewhere. That wider transformation was explored during the AI4 2026 panel “The AI-Powered Vehicle: Smarter, Safer, and Fully Connected,” where representatives from May Mobility, Tensor and the Toyota Research Institute discussed how artificial intelligence is changing vehicle development and what autonomous transport could eventually mean for cities, dealerships, insurance, logistics and everyday travel.

The three organisations represent different approaches to the same transformation. May Mobility is developing autonomous transport technology for deployment through cities, fleets and ride-hailing partners. Tensor is pursuing a privately owned Level 4 autonomous vehicle designed from the beginning around AI, while Toyota is exploring autonomous driving alongside a broader philosophy in which AI assists and improves human driving rather than necessarily removing the driver completely.

May Mobility has been developing an autonomy architecture that combines learned AI models with predictive modelling of the surrounding environment and real-time reasoning. Jacob Crossman, Senior Vice President of Autonomy at May Mobility, described how the company had been able to learn not only from the behaviour of its own vehicles but also from other road users observed by its sensors. Combining those observations with its existing decision-making technology allowed the system to handle increasingly complicated situations. The broader objective is to create autonomous systems capable of responding to unfamiliar circumstances rather than depending entirely on enormous amounts of location-specific driving data.

Tensor is approaching the problem differently. Rather than adding autonomous technology to an existing conventional vehicle, the company has designed its Robocar around autonomous operation from the outset. The vehicle incorporates more than 100 sensors, including cameras, lidars, radars, microphones and other monitoring equipment intended to provide extensive awareness of both its external environment and what is happening inside the vehicle.

The company’s ambition extends beyond replacing the driver. Tensor envisages the vehicle becoming a personal AI assistant that can understand natural-language instructions and eventually connect journeys with other aspects of its owner’s daily life. Instead of simply entering a destination, a passenger could ask the vehicle to stop at a particular entrance or alter a journey conversationally. Connected with a person’s calendar and other applications, a vehicle could potentially recognise an upcoming meeting, calculate additional journey time caused by congestion and recommend leaving earlier.

Toyota’s research highlights another possible direction for automotive AI. Rather than assuming autonomous technology must ultimately eliminate human driving, the company is investigating whether increasingly capable AI could make people better drivers. One example discussed at AI4 was an AI driving instructor capable of helping inexperienced motorists improve their skills. Such technology could potentially identify dangerous habits, anticipate risks and assist drivers before mistakes become accidents.

The distinction is important because fully autonomous vehicles are unlikely to arrive uniformly around the world. Road quality, regulation, infrastructure, driving behaviour and consumer acceptance vary considerably between countries. Technology capable of operating without a driver in a carefully mapped American or Gulf city may face very different conditions in markets where road markings, infrastructure or driving behaviour are less predictable.

The panellists were consequently relatively cautious about how quickly complete autonomy will dominate global vehicle sales. When asked to estimate the share of new vehicles that could be fully autonomous 15 years from now, their estimates generally remained below or around one quarter of worldwide sales. The discussion suggested that technological capability may eventually advance faster than the regulatory systems determining where and how autonomous vehicles can operate.

For the property industry, one of the most significant consequences could be the changing relationship between cars and urban land. Private vehicles spend much of their lives parked, meaning offices, shopping centres, residential developments, airports and city centres have historically been designed around substantial amounts of parking infrastructure. If autonomous vehicles can deliver passengers and then leave independently, parking no longer necessarily needs to be located immediately beside a building. Shared autonomous fleets could reduce that requirement further by keeping vehicles moving between users.

Over time, this could release valuable urban land currently occupied by surface parking areas and multi-storey garages. For developers and city authorities, sites previously required for parking could potentially accommodate housing, commercial development, public spaces or other uses. Building design could also change, with greater emphasis on passenger drop-off areas, autonomous vehicle waiting zones and charging infrastructure while conventional parking requirements decline.

Hotels and the wider travel industry could experience another change. Once passengers no longer have to concentrate on driving, travelling time becomes usable time. People could work, communicate, rest or consume entertainment while moving between destinations. An autonomous vehicle capable of travelling for several hours while passengers work or sleep could alter the perceived distance between cities and potentially affect competition between road, rail and short-distance air travel. Hotels could eventually interact directly with vehicles, coordinating arrival times, parking, luggage handling and other services before guests reach their destination.

Autonomous vehicles could also influence how cities manage traffic. Vehicle-to-vehicle and vehicle-to-infrastructure communication has been under development for years, but AI potentially allows cars, traffic signals, mobile devices and transport infrastructure to exchange information and respond more dynamically. Vehicles could receive warnings about dangerous road conditions detected by cars ahead, while traffic systems could potentially adjust signals and vehicle speeds to improve flows through congested areas. The challenge is that many of these benefits become substantially greater only when a meaningful proportion of the overall vehicle fleet and surrounding infrastructure is connected.

Commercial transport may move faster towards autonomy in some areas because the financial case can be easier to demonstrate. Trucks operate for long periods, while drivers represent a significant component of transport costs. Long-haul freight routes, distribution centres, ports and logistics corridors could therefore become important environments for autonomous transport. That development would also affect logistics real estate as warehouses and distribution centres adapt to autonomous vehicle movements, automated loading, charging infrastructure and more integrated fleet-management systems.

Car dealerships could face an equally significant transformation. The traditional model assumes that customers travel to a physical location to inspect, purchase and service vehicles. Autonomous cars could reverse part of that relationship. A demonstration vehicle could theoretically travel to a prospective customer’s home, provide a test journey and return to the dealership independently. That could reduce the importance of some traditional showroom functions while increasing the role of technology, servicing and customer-experience facilities.

Vehicle maintenance could change in a similar way. A car capable of identifying a technical problem could potentially schedule its own service appointment, travel to the workshop when its owner does not require it and return after the work has been completed. This could allow dealership workshops to operate more intensively outside conventional customer hours and potentially alter the design and location requirements of automotive service properties.

AI could simultaneously create new dealership revenue opportunities. Vehicles capable of extensive personalisation may support software services, digital upgrades, customisation and aftermarket products throughout their operating lives. The economic relationship between manufacturer, dealer and customer could therefore become less dependent on the original vehicle transaction.

Insurance presents another potentially significant disruption. When a person is driving, responsibility for an accident traditionally rests largely with the driver and their insurer. When a Level 4 autonomous system controls the vehicle and the passenger cannot intervene, responsibility increasingly moves towards the manufacturer or technology provider. Tensor told the AI4 audience that it intends to assume liability when its vehicle is operating autonomously at Level 4, while responsibility would return to the human driver during manual operation.

That distinction could eventually produce more dynamic insurance models. Vehicles could record how much distance was travelled autonomously and how much was driven manually, potentially allowing insurance exposure and pricing to reflect who or what was controlling the vehicle. The relationship between automotive manufacturers and insurers could consequently become considerably closer as responsibility gradually shifts from individual driving behaviour towards the performance of autonomous systems.

Privacy represents another major challenge. Intelligent vehicles generate enormous amounts of information through cameras, microphones and other sensors. That information could potentially reveal where people travel, who accompanies them and what happens inside their vehicles. Autonomous vehicles will therefore have to establish trust not only through driving performance but also through their handling of personal information, cybersecurity and accountability when systems fail.

One of the most difficult technical problems remains uncertainty. Driving environments are inherently unpredictable: pedestrians change direction, vehicles behave unexpectedly, weather affects visibility and unusual circumstances arise that cannot all be individually programmed. May Mobility’s approach involves allowing its system to evaluate multiple potential outcomes before selecting an appropriate response, particularly when uncertainty increases.

The industry’s progress will therefore depend on considerably more than developing cars capable of remaining within lanes without human intervention. The larger transformation involves creating machines capable of understanding people, roads and increasingly the wider infrastructure around them. That brings autonomous vehicles directly into questions concerning urban planning, commercial property, logistics, insurance, energy, dealerships and tourism.

The most important consequence of the AI-powered vehicle may therefore not be that people eventually stop driving. It may be that cities, buildings and businesses gradually stop being designed around the assumption that every vehicle requires a human driver.

Source: CIJ.World Research & Analysis Team

Germany’s Rearmament Drive Is Reshaping the Industrial Property Map

Germany’s rapid expansion of defence spending is beginning to create consequences far beyond military procurement. As manufacturers increase production of vehicles, ammunition, electronics, aerospace systems and other equipment, a parallel requirement is emerging for factories, engineering facilities, secure warehouses, testing locations and supplier capacity. For Germany’s commercial property market, defence could become an increasingly important new source of industrial demand.

The scale of government spending provides the foundation for that shift. Germany has substantially increased its defence budget for 2026 and plans further increases over the coming years as it rebuilds military capabilities and expands domestic and European production capacity. The programme represents a structural change rather than a short-term procurement cycle, potentially giving manufacturers sufficient visibility to invest in additional factories and equipment. That matters for property because military production cannot be expanded indefinitely within existing facilities. Companies can initially increase shifts, reorganise production lines and install additional machinery, but larger and longer-term order books eventually require additional physical capacity.

The German industrial market is beginning to see evidence of this process. During the first half of 2026, industrial and logistics take-up reached around 3 million sq m, approximately 11% higher than a year earlier. Investment in the sector also increased, reaching roughly €3.3 billion. Within that market, defence companies are emerging as an additional source of competition for suitable industrial land.

This demand differs substantially from conventional logistics. A distribution warehouse primarily requires road access, loading capacity and an efficient building. Defence manufacturing can involve far more specialised requirements, including substantial electricity supply, controlled access, perimeter security, reinforced structures, engineering facilities and secure digital infrastructure. Certain production activities require even more specialised locations. Ammunition manufacturing, explosives storage and weapons testing can require extensive safety zones, environmental approvals and separation from residential areas. These requirements significantly reduce the number of sites capable of accommodating some forms of defence production.

Germany’s existing manufacturing geography therefore provides an important advantage. The country already contains major concentrations of aerospace, automotive, mechanical engineering, electronics and precision manufacturing. These regions offer not only industrial buildings but also something considerably harder to create from scratch: skilled workers, engineering expertise, established suppliers and transport infrastructure.

Bavaria is particularly well positioned because of its combination of aerospace, electronics, automotive and defence industries. Baden-Württemberg provides another deep engineering and supplier base. Northern Germany combines aerospace, shipbuilding and military manufacturing, while Lower Saxony contains important industrial and defence production locations. North Rhine-Westphalia also has a large industrial workforce and established defence businesses, while the Kassel area has long been associated with military vehicle manufacturing. As major contractors increase production, these established clusters could attract further suppliers, logistics companies and engineering businesses.

The relationship between Germany’s automotive restructuring and defence expansion may become particularly important. Parts of the German automotive industry are reducing capacity and reconsidering manufacturing footprints at the same time that defence companies need additional production space. This creates the possibility that factories originally built for cars and automotive components could find new uses within the defence supply chain.

The Volkswagen plant in Osnabrück became one of the clearest examples during the second quarter of 2026. Discussions involving Israeli defence company Rafael demonstrated that an established automotive manufacturing site could be considered for defence-related production. Regardless of the final outcome at that particular facility, the case illustrates a much broader property opportunity.

Automotive factories already possess characteristics that would take years and substantial capital to reproduce on greenfield sites. They typically have significant power connections, large production halls, loading infrastructure, road and rail connections, extensive land and access to skilled industrial labour. For defence manufacturers facing pressure to increase production rapidly, acquiring or adapting an existing manufacturing facility can therefore offer advantages over developing an entirely new factory. This could give some ageing or surplus German industrial properties an unexpected second life.

The opportunity is particularly interesting because defence manufacturers do not necessarily follow the same location priorities as logistics operators. A distribution company generally values motorway access and proximity to large consumer markets. A defence manufacturer may place considerably greater importance on engineering labour, existing suppliers, secure land and proximity to testing or military infrastructure.

As a result, locations considered secondary by conventional logistics investors could become strategically valuable to defence companies. This could change industrial property values in selected regional markets. A former manufacturing site outside Germany’s largest logistics corridors might have limited appeal as a conventional warehouse development but become highly attractive if it sits within an established engineering cluster and can accommodate secure production.

Large defence investments can also create secondary property demand. When a major manufacturer expands a factory, suppliers frequently have an incentive to locate nearby. Precision engineering businesses, electronics companies, component manufacturers, software specialists, maintenance providers and logistics operators can all require additional premises around an anchor facility. The resulting clusters could create demand for smaller factories, industrial parks, research buildings and warehouses extending well beyond the property occupied by the main defence contractor.

This multiplier effect could prove more accessible to commercial real estate investors than the largest weapons-production facilities themselves. Many strategically important defence factories are likely to remain owner-occupied. Manufacturers may prefer direct control over properties containing sensitive production processes, specialist machinery and security infrastructure. The wider supply chain presents a different opportunity.

Secure warehouses, engineering buildings, component factories, maintenance facilities and conventional logistics properties supporting defence manufacturers may be capable of institutional ownership. If the underlying buildings retain alternative industrial uses, they could potentially offer investors long leases without assuming the full redevelopment risk associated with highly specialised military facilities.

Sale-and-leaseback transactions could also emerge as manufacturers expand. Companies receiving substantial new orders may prefer to direct capital toward machinery, technology and production capacity rather than owning all of their property. Selling a facility to a property investor and leasing it back could release capital while allowing the manufacturer to continue operating from the same location. For investors, long leases to financially strong industrial occupiers could create an attractive income profile, provided the building remains suitable for alternative uses.

Germany’s defence expansion is also arriving at a time when competition for industrial land is already intense. Logistics companies, data centre developers and advanced manufacturers are competing for sites with sufficient electricity, transport connections and planning certainty. Defence businesses now add another source of demand to that equation.

This competition could become particularly visible around major metropolitan and industrial regions where suitable development land is already scarce. Industrial land with strong power capacity and appropriate planning status may consequently become increasingly valuable. However, the emerging defence-property opportunity should not be treated as a conventional logistics growth story.

Defence procurement remains dependent on government budgets, political decisions and individual programmes. Contracts can be delayed or redesigned, while planning requirements for certain facilities can be lengthy and complex. Highly specialised buildings also carry greater residual-value risk. A conventional warehouse can normally be occupied by numerous logistics businesses. A factory specifically designed for ammunition or military systems may have a much smaller group of potential future users.

This means investors will need to distinguish carefully between property benefiting from defence-sector growth and property that becomes permanently dependent on a single defence programme. The strongest opportunities may therefore lie in buildings that combine defence demand with broader industrial flexibility.

An engineering facility capable of supporting aerospace, automotive or defence businesses offers considerably more alternative-use potential than a highly specialised weapons plant. The same applies to warehouses, component factories and research facilities that can serve multiple advanced-manufacturing industries.

There is another reason why the property impact could become significant. Germany is attempting to increase defence production while much of its traditional manufacturing economy remains under pressure. Defence investment could therefore absorb some industrial capacity, labour and property released by sectors undergoing restructuring.

Rather than constructing every new facility from the ground up, manufacturers may increasingly look at existing industrial estates, former automotive plants and underused production locations. This could create a new redevelopment strategy for industrial investors. Properties previously considered obsolete because they were too manufacturing-specific for modern logistics could regain relevance if they provide the heavy infrastructure required by defence and advanced manufacturing.

The geographic impact is unlikely to be evenly distributed across Germany. Regions already containing major defence contractors, aerospace companies, automotive engineering, military installations or specialist suppliers should have an advantage. Once expansion begins in those locations, clustering effects can reinforce their position. Suppliers follow customers, engineering talent concentrates around employers and local authorities gain experience dealing with specialised industrial requirements.

Over time, this could produce identifiable defence-oriented manufacturing corridors and industrial clusters. For the property industry, the most important development is that defence is beginning to move from being primarily a government spending story to becoming a physical-capacity story.

Germany can approve larger military budgets relatively quickly. Creating the factories, testing facilities, warehouses and supplier networks required to turn those budgets into equipment takes considerably longer. That gap creates opportunities for developers, landowners, municipalities and investors capable of providing suitable industrial capacity.

The eventual property market may not develop into a clearly defined defence real estate sector. Much of the demand will overlap with existing industrial, logistics, research and advanced-manufacturing property. Nevertheless, the underlying occupier base is changing.

Germany’s industrial property market has traditionally been shaped by automotive manufacturing, engineering, logistics and increasingly data centres and technology. Defence companies are now joining the competition for land, infrastructure and skilled manufacturing locations. The most valuable opportunities may consequently appear where these industries intersect.

Former automotive factories could become defence production sites. Aerospace clusters could attract military technology companies. Existing industrial parks could accommodate suppliers. Secure warehouses could serve expanding manufacturers, while engineering campuses could support research into drones, electronics, communications and other defence technologies.

For investors, the key question is therefore not simply how much Germany intends to spend on defence. The more important property question is where the physical infrastructure required to deliver that expansion will be located.

Germany’s military investment programme will ultimately require considerably more than equipment orders. It will require buildings, land, electricity, transport connections, skilled workers and secure industrial environments. As those requirements become clearer, defence could emerge as one of the most important new sources of specialised industrial property demand in Germany, and potentially provide a new purpose for manufacturing locations that only a few years ago appeared to be facing structural decline.

Source: CIJ.World Research & Analysis Team

Swedish Residential Investment Surges as New Housing Supply Remains Constrained

Sweden’s residential investment market accelerated sharply during the second quarter of 2026, with transaction activity rising as institutional investors returned to the sector despite a considerably weaker housing development pipeline. Residential property transactions reached SEK 22.5 billion during the quarter, an increase of 57% compared with the same period of 2025. Housing accounted for approximately 28% of investment across all Swedish property sectors, making it the country’s largest investment segment by transaction volume during the period. The number of residential transactions also increased by 41% year-on-year to 62 deals.

The improvement comes as the broader Swedish investment market experiences a significant recovery. Total property transaction volume reached approximately SEK 80 billion in Q2, representing an increase of 146% from a year earlier. Residential assets therefore captured more than a quarter of the capital deployed during the quarter.

Large portfolio transactions played an important role in the resurgence. The combination of Sveafastigheter and KlaraBo created a residential company with properties valued at approximately SEK 47 billion. Connected with the transaction, KlaraBo acquired around 4,100 apartments from SBB for approximately SEK 6.8 billion. KPA Pension, part of the Folksam Group, also acquired 26 residential properties from SEB’s Domestica funds, comprising approximately 2,500 apartments and 150,000 sqm across the Stockholm-Mälardalen region, Malmö and Lund.

Development-stage rental housing also attracted investment. JM agreed to sell three projects containing a combined 304 apartments in Upplands Väsby, Uppsala and Solna to Hemvist for approximately SEK 1.1 billion. The properties are scheduled for completion between 2027 and 2029.

Pricing indicators suggest that renewed demand is translating into firmer investment values. The prime yield for newly developed residential property in Greater Stockholm stood at 3.85% during Q2, approximately 30 basis points lower than a year earlier. International investors represented only 7% of residential investment during the quarter, indicating that domestic capital continues to account for the majority of activity.

The investment recovery contrasts with conditions in the construction market. Sweden completed 38,851 multifamily apartments in 2024, but that figure dropped to 25,792 in 2025. Boverket has subsequently lowered its expectations for new construction and now anticipates approximately 22,000 multifamily housing starts in 2026 and 27,100 in 2027, compared with previous forecasts of 26,100 and 28,200 respectively.

The slowdown is also exposing increasingly significant differences between individual Swedish housing markets. The number of municipalities reporting housing shortages declined from 127 to 102, suggesting that supply and demand are becoming better balanced nationally. However, 42 of the municipalities still reporting shortages are located within the Stockholm, Gothenburg or Malmö metropolitan regions. Sweden’s national residential vacancy rate remained low at 1.3% in 2024.

Rental indicators remain comparatively stable in Stockholm. Prime annual rents for newly developed residential properties in Greater Stockholm were estimated at SEK 3,000 per sqm in Q2, unchanged from a year earlier. Average Greater Stockholm rents stood at SEK 1,693 per sqm annually in 2025, an increase of 6.5% compared with 2024.

Conditions have also improved in Sweden’s owner-occupied housing market. Condominium prices were 4.4% higher year-on-year in May, while house prices increased 2.2%. Approximately 49,100 homes changed hands during the three months to May, around 9% more than during the equivalent period a year earlier.

Changes to mortgage regulations may be contributing to stronger purchasing activity. From April 2026, Sweden increased the maximum loan-to-value ratio from 85% to 90%, reducing the minimum deposit from 15% to 10%, while an additional amortisation requirement introduced in 2018 was removed. Early analysis cited in the report indicates that the changes have so far had a clearer effect on transaction activity than on prices, particularly in the condominium market.

Financing conditions have also improved substantially from the previous interest-rate cycle. Following its June meeting, the Riksbank maintained its policy rate at 1.75%, leaving cumulative reductions since May 2024 at 2.25 percentage points. Lower borrowing costs have gradually strengthened household purchasing capacity, although inflationary pressure could affect the future direction of interest rates.

Sweden’s residential market is therefore entering a different stage of its recovery. Investment volumes and transaction numbers are rising rapidly while prime yields have compressed, yet construction remains subdued and housing shortages are becoming increasingly concentrated in the largest metropolitan areas. For investors, this divergence could become one of the defining characteristics of the Swedish living market, with capital returning faster than new housing is being produced and increasingly different supply-demand conditions emerging between the major metropolitan regions and smaller municipalities.

Source: CBRE Sweden

Sustainable Warehouses Are Becoming the New Standard for India’s Logistics Market

fletcher@cijeurope.com
5 September 2026
CIJ - Spotlight, Environmental, Logistics

India’s logistics property market is moving into a new phase in which the environmental performance of a warehouse is becoming increasingly important to occupiers, developers and institutional investors. The rapid expansion of e-commerce, organised retail, manufacturing, third-party logistics and rapid-delivery networks has already transformed warehousing from a largely fragmented property sector into one of India’s important institutional real-estate markets. Sustainability is now becoming part of that transformation.

Modern occupiers increasingly expect warehouses to consume less electricity and water, provide access to renewable energy and operate more efficiently. For developers, this is changing the specifications required to compete for major corporate tenants.

India’s Grade-A warehouse stock across its principal markets increased from approximately 88 million sq. ft. in 2019 to around 238 million sq. ft. by the end of 2024. Institutional-quality property expanded particularly quickly, increasing from approximately 28 million sq. ft. to around 90 million sq. ft. during the same period.

Environmental standards are increasingly concentrated within this higher-quality segment. Approximately 65 million sq. ft. of institutional warehouse stock was already certified or progressing towards certification by 2024, and sustainable logistics space could approach 270 million sq. ft. by 2030 if current development trends continue.

The growth raises an important commercial question for property owners: will tenants actually pay higher rents for greener warehouses? Current evidence suggests that there is no simple national premium. Large occupiers increasingly favour environmentally efficient buildings, but there is insufficient evidence to conclude that a warehouse automatically commands a specific increase in rent simply because it has obtained environmental certification.

The financial argument is instead becoming centred on the overall cost of occupying the property. A more efficient warehouse can consume less electricity and water while potentially generating part of its own energy through rooftop solar. For a major occupier leasing hundreds of thousands of square feet, those savings can become significant over the duration of the lease.

This means a tenant may be prepared to accept a somewhat higher headline rent where the building delivers sufficiently lower operating expenditure, but the decision is based on economics rather than environmental branding alone. Modern green warehouse design can potentially reduce energy consumption by approximately 20–30% and potable water requirements by around 30–40%, although actual savings depend on the building, equipment and nature of its operations.

Warehouses are particularly well suited to solar power because their large roofs can accommodate extensive photovoltaic installations without requiring additional land. For energy-intensive logistics and manufacturing occupiers, electricity generated at the property can become a meaningful consideration when selecting between competing facilities.

This is particularly relevant in India, where corporate tenants are becoming more interested in securing renewable electricity for their operations. Recent occupier research indicates that more than half of surveyed logistics companies regard access to renewable energy at the property as an important sustainability consideration.

The shift is being reinforced by corporate environmental commitments. Large international retailers, manufacturers, technology companies and logistics operators increasingly measure emissions throughout their operations and supply chains. Warehouses form part of that footprint.

Companies attempting to reduce their environmental impact cannot concentrate exclusively on offices while overlooking distribution centres and industrial facilities that may consume considerably more electricity. This is gradually changing the relationship between sustainability and building quality.

Several years ago, environmental certification could be promoted as an additional feature differentiating one warehouse from another. For many large occupiers, efficient energy use, renewable power and modern environmental specifications are increasingly becoming expected components of institutional Grade-A property.

That change could eventually make the idea of a separate green premium less relevant. Instead of environmentally efficient warehouses commanding significantly higher rents, older and less efficient properties could increasingly suffer a competitive disadvantage.

The distinction is important for investors. A modern sustainable warehouse may not necessarily generate substantially more rent today, but it could be easier to lease, retain tenants for longer and remain competitive as environmental standards become more demanding. An older building may require substantial capital expenditure to achieve the same performance.

The financial consequences of sustainability therefore extend well beyond the initial rental agreement. Lower operating expenditure, reduced vacancy risk, stronger tenant demand, longer economic life and lower future refurbishment requirements can all contribute to investment returns.

Institutional investors are particularly sensitive to these factors because warehouses are acquired as long-term income-producing assets. A logistics property developed today could remain operational for several decades. During that period, electricity prices, building standards, environmental regulation and corporate procurement requirements are likely to change considerably.

Buildings developed only to minimum present-day specifications could consequently become less attractive before reaching the end of their physical life. This creates the possibility of environmental obsolescence becoming an increasingly important investment risk.

India has not yet developed sufficient transaction evidence to calculate a consistent discount for inefficient logistics property, but the direction of travel is becoming clearer. The country’s institutional logistics developers are increasingly incorporating sustainability into new projects from the design stage.

This approach can be more economical than retrofitting existing warehouses later. Building orientation, insulation, natural lighting, efficient electrical systems, rainwater collection, wastewater treatment and rooftop solar can all be incorporated during development. Green features can therefore improve operating performance without necessarily transforming the property into an expensive specialist building.

The continuing expansion of India’s logistics market provides developers with an opportunity to introduce these standards across a large volume of new supply. Industrial and warehousing leasing remained strong through 2025 and the first half of 2026, supported by demand from third-party logistics companies, manufacturers, automotive businesses, engineering companies, retailers and e-commerce operators.

Modern Grade-A stock across India’s leading markets has reached approximately 300 million sq. ft. under current industry measurements and is expected to continue expanding substantially towards 2030. As this new stock is delivered, competition between developers is increasingly likely to be determined by building quality rather than simply available floor area.

Location will nevertheless remain fundamental. A highly sustainable warehouse situated far from major highways, consumers, manufacturing clusters or labour pools will not necessarily outperform a less environmentally advanced building in a strategically superior location. Logistics occupiers ultimately make property decisions according to the efficiency of their supply chains.

Sustainability therefore needs to complement traditional property fundamentals rather than replace them. The strongest buildings will combine transport connectivity, modern technical specifications, appropriate labour availability and lower operating costs.

Different occupier groups are also likely to place different values on environmental performance. Multinational companies, major Indian corporations, export-oriented manufacturers and institutional logistics operators generally face greater reporting and environmental requirements than smaller local businesses. These companies are therefore likely to drive demand for sustainable warehouses first.

Smaller occupiers may remain more sensitive to headline rent, particularly where utility consumption represents a relatively small proportion of their total costs. India could consequently develop a two-tier market in which environmental performance becomes essential at the institutional end while remaining less influential among basic or locally owned warehouse stock.

This division could become increasingly visible as international investment continues entering the sector. Institutional capital has already helped transform India’s logistics market by creating large portfolios of modern facilities with consistent technical standards. Sustainability is becoming another element of that institutional specification.

Investors want properties capable of meeting international environmental requirements, while multinational tenants increasingly need buildings that support their own corporate targets. The interests of landlords and occupiers are therefore beginning to align.

The development of quick commerce adds another dimension. Rapid-delivery networks require facilities closer to consumers, creating demand for urban distribution and fulfilment space where land and electricity costs can be relatively high. Energy efficiency could become particularly valuable in such locations, although the technical requirements of smaller urban facilities differ considerably from large regional distribution centres.

Manufacturing expansion is also increasing demand for modern logistics property around India’s industrial corridors. Automotive, electronics, engineering, pharmaceutical and other manufacturers frequently have extensive environmental reporting obligations throughout their supply chains. Warehouses serving these industries may consequently face increasingly detailed sustainability requirements.

The result is that environmentally efficient logistics property is becoming relevant across a wider range of Indian occupiers. For landlords, however, the investment case should not be reduced to whether they can charge several percentage points more in rent.

The more important advantage may be protecting the long-term competitiveness of the asset. A warehouse capable of reducing an occupier’s energy costs, supplying renewable electricity and meeting corporate environmental requirements has more reasons to remain attractive as the market develops. A building unable to provide those benefits could gradually find itself competing primarily on price.

India’s green warehousing market is therefore entering a stage where sustainability is moving from differentiation towards expectation. There is not yet sufficient evidence to claim that environmentally certified warehouses consistently achieve a separate nationwide rental premium.

What is becoming clearer is that large occupiers increasingly expect modern logistics properties to deliver environmental as well as operational performance. For India’s institutional warehouse market, the long-term value of sustainability may therefore come less from charging tenants more and more from avoiding the discount that could eventually be attached to buildings that fail to keep up.

Source: © CIJ.World India Research & Analysis Team

France’s Data-Centre Expansion Is Creating a New Market for Powered Land

France’s accelerating data-centre development is beginning to introduce a new factor into the country’s industrial property market. Location, motorway access, fibre connectivity and planning remain essential, but another consideration is becoming increasingly influential: whether a development site can secure enough electricity to support the enormous computing facilities now being proposed.

The challenge is not that France lacks electricity at national level. The country benefits from substantial generating capacity and a predominantly low-carbon electricity system. The more immediate difficulty is delivering very large amounts of power to individual locations through the transmission and distribution networks. For the largest data-centre projects, that can mean securing hundreds of megawatts at a single site. The availability and timing of a grid connection can therefore determine whether otherwise suitable industrial land can actually accommodate the development being considered.

The scale of demand has increased rapidly. France had around 300 data centres in 2026, while by May the national transmission operator RTE had reserved close to 18 GW of potential connection capacity for approximately 80 proposed data-centre projects. At the end of 2024, the comparable pipeline had been around 5 GW across approximately 40 projects.

Those figures demonstrate the extraordinary increase in electricity being requested by the sector, but they should not be interpreted as a forecast that all 18 GW will be constructed. Some proposed developments will be delayed, changed or abandoned. Others may use considerably less electricity than the maximum capacity requested, particularly during their initial years of operation. Data centres also tend to increase their electricity consumption progressively as servers and computing equipment are installed.

The pipeline nevertheless shows why electricity infrastructure is becoming increasingly important to property development. Île-de-France remains the country’s largest data-centre market and accounted for approximately 7 GW of reserved connection capacity by May 2026. Hauts-de-France followed with around 6 GW, indicating that the next development cycle could become considerably more geographically dispersed.

Paris continues to dominate existing capacity. New facilities were delivered during the first half of 2026 and available capacity remained relatively tight. Demand from cloud computing, artificial intelligence and digital services continues to support expansion. But building the next generation of facilities close to established Paris clusters is becoming increasingly complicated.

Suitable large sites are limited, planning can be difficult and the electricity required for major campuses cannot necessarily be delivered everywhere. This is encouraging developers to examine a broader range of industrial and brownfield locations around the metropolitan region.

The scale of individual projects makes this particularly important. Many large facilities now seek electricity connections of between 100 MW and 200 MW, while some exceptional proposals require more than 400 MW. At these levels, electrical infrastructure becomes a major part of the development process. New substations, high-voltage equipment and substantial transmission connections may be necessary before construction can result in an operational data centre.

This creates the potential for a new distinction within the industrial land market. Two development sites may have similar road access, planning conditions and plot sizes, yet have very different prospects for data-centre use if one can obtain a large grid connection within a commercially acceptable period and the other cannot.

Electricity availability does not automatically make land valuable. Developers still require fibre connections, suitable planning, adequate physical space and a location capable of accommodating the environmental and operational requirements of a major computing facility. But access to high-capacity power is increasingly becoming part of the assessment of whether a site has additional development potential.

Southern Île-de-France provides an example of how infrastructure investment could influence this market. RTE is preparing approximately 1.2 GW of additional connection capacity in the area, using infrastructure capable of supporting several large electricity consumers. For nearby landowners and developers, the significance extends beyond the data-centre industry. Additional grid infrastructure can change the range and scale of projects that are technically possible in an area.

The French government and RTE have also identified five priority locations intended to accommodate particularly large computing projects. Two are in Île-de-France and three in Hauts-de-France. Their advantages include large development sites and proximity to France’s high-voltage transmission infrastructure.

The initiative illustrates an important change in site selection. Traditionally, a property developer might identify land first and then arrange the utility connections required for the project. For a very large data centre, developers increasingly need to understand where sufficient electricity can realistically be supplied before committing to the land. In effect, infrastructure can help determine the geography of future development.

Hauts-de-France could become one of the principal beneficiaries. The region’s approximately 6 GW of reserved data-centre connection capacity places it surprisingly close to Île-de-France despite the much greater concentration of existing facilities around Paris.

Large industrial sites, established energy infrastructure and access to northern European markets strengthen the region’s proposition. Major technology and infrastructure investors have already announced substantial projects, although the eventual scale of operational development will depend on how many of these schemes progress from proposed connections to completed facilities.

Marseille represents another important market, but for different reasons. The city has become a significant international digital gateway because of the submarine telecommunications cables connecting Europe with Africa, the Middle East and Asia. This communications infrastructure has encouraged data-centre investment and gives Marseille an advantage that cannot simply be replicated by providing electricity elsewhere.

Further growth will nevertheless require additional power. Around Plan-de-Campagne, between Aix-en-Provence and Marseille, approximately 500 MW of additional grid connection capacity is being prepared. Combined with the region’s telecommunications infrastructure, that could support further digital development.

The emergence of large electricity users also creates a wider industrial question. France is simultaneously attempting to electrify manufacturing and reduce emissions from heavy industry. Battery factories, steel production, hydrogen projects and other industrial developments increasingly require very large electricity connections.

Industrial areas including Dunkerque, Fos-sur-Mer and Le Havre are therefore attracting infrastructure investment for reasons extending far beyond data centres. This means digital infrastructure will not be the only sector seeking access to new grid capacity.

For property investors, the resulting competition could become significant. Industrial sites capable of accommodating energy-intensive uses may attract interest from several types of occupier, each with different requirements and development economics.

The overlap with logistics is particularly worth watching. Data centres and large distribution facilities can both require substantial plots, infrastructure access and planning environments capable of accommodating major commercial buildings. Former industrial sites can potentially appeal to both sectors.

Their electricity requirements are very different, but logistics is becoming more power-intensive as well. Automated warehouses, electric vehicle charging and increasingly sophisticated building systems are increasing the amount of electricity required by modern distribution facilities. This creates the potential for competition over certain well-located industrial sites, particularly where land also benefits from unusually strong electrical infrastructure.

It would be premature to conclude that data centres are already displacing logistics development across France on a significant scale. Warehousing remains a much larger user of industrial land, and the site requirements of the two sectors are not identical. However, where a plot is genuinely capable of supporting a large computing campus, the economics of the alternative uses can change.

A data centre represents an extremely capital-intensive investment, meaning the land cost forms only one part of a much larger development budget. This can potentially allow digital infrastructure developers to compete strongly for scarce sites offering the right combination of electricity, fibre and planning.

Brownfield industrial property could become particularly interesting as a result. Former manufacturing sites can sometimes provide large plots, existing utility infrastructure and locations where intensive commercial uses are already established. They may also sit further from dense residential neighbourhoods, reducing some of the planning conflicts associated with major data-centre developments.

But investors need to distinguish carefully between land located near electrical infrastructure and land that genuinely has access to usable capacity. A transmission line passing close to a site does not guarantee that hundreds of megawatts can be supplied. Network capacity may already be committed, reinforcement may be required or connection works may take years.

This creates an increasingly important due-diligence issue. The value of a potential data-centre site depends not simply on theoretical electricity availability but on how much power can actually be delivered, when it can be supplied and what infrastructure investment is necessary to achieve the connection.

The same caution applies to the national development pipeline. The almost 18 GW of reserved capacity demonstrates extraordinary developer interest, but it represents potential rather than completed demand. The eventual operational market will be smaller if projects fail to secure financing, planning or customers. This makes the distinction between announced projects and deliverable projects increasingly important for investors assessing industrial land.

Planning could also become more significant as the sector grows. Very large data centres consume substantial electricity while employing fewer people than some alternative industrial uses. Local authorities may therefore examine their economic contribution, environmental impact, noise, visual footprint and infrastructure requirements more closely as development expands.

France nevertheless has significant advantages in the European competition for digital infrastructure. Its electricity system provides access to large quantities of relatively low-carbon power, Paris remains a major European business and technology centre, and Marseille provides exceptional international telecommunications connectivity.

The constraint is increasingly about getting those advantages to the right physical location. As artificial intelligence increases the scale of computing infrastructure, developers may have less freedom to choose sites according to traditional property criteria alone. The electricity network will increasingly influence where projects can realistically be built.

That could gradually change how some French industrial land is valued. Roads, labour, planning, population and proximity to customers will remain central to industrial property. But for the most electricity-intensive developments, another layer is being added to the location equation.

The emerging premium will not simply belong to land near electricity infrastructure. It will belong to sites where substantial electrical capacity can genuinely be secured within the timeframe required by investors. As France’s data-centre pipeline expands, the difference between land that is merely available and land that can actually be powered could become one of the defining property questions of the next development cycle.

Source: CIJ.World UK Research & Analysis Team

China’s Warehouse Correction Is Turning Low Rents Into a Competitive Weapon

China’s logistics property market is moving through one of the most important adjustments since institutional investment began transforming the country’s warehouse sector. The investment case that prevailed several years ago—rapid e-commerce growth, insufficient modern stock and expectations of steadily increasing rents—has been replaced by a market characterised by abundant supply, aggressive competition for occupiers and declining rental levels. Yet the latest leasing data suggest the correction is beginning to produce a response. Tenants are taking more space precisely because warehouses have become cheaper.

Across China, logistics absorption reached approximately 2.73 million square metres during the second quarter of 2026, more than double the level recorded during the previous three months. Third-party logistics companies generated more than half of new leasing activity, while South China experienced particularly strong take-up. National vacancy consequently declined to around 18.5%. This improvement occurred despite another substantial volume of new development entering the market.

The contradiction is visible in rental performance. Average logistics rents declined by approximately 2.5% quarter-on-quarter nationally even as absorption accelerated. Rather than landlords recovering pricing power because demand has returned, many are using lower rents and more competitive leasing conditions to attract occupiers and protect occupancy. The result is a recovery in physical demand without a corresponding recovery in income.

Shanghai provides a clear example. Logistics leasing remained active during Q2 and vacancy declined, yet average rents fell approximately 2.9% during the quarter. Depending on the properties and geographic areas included in different market surveys, quarterly absorption was measured at between approximately 124,000 and 242,000 square metres. Although the figures differ, both datasets show the same underlying pattern: tenants are taking space while landlords continue cutting prices.

This is beginning to change how companies use the Shanghai logistics market. Businesses with less time-sensitive distribution requirements can increasingly consider warehouses farther from the most expensive consumption and transport locations. Where delivery frequency allows it, the savings achieved through cheaper rents can compensate for additional distance. Logistics operators and retailers have also been using the softer market to improve the quality of their facilities or reorganise warehouse networks.

The implications for investors are significant. Falling rents do not automatically indicate declining demand. In some locations, they are creating demand that did not exist at previous pricing levels. A warehouse that struggled to secure occupiers at one rental level can become competitive once its cost falls sufficiently below alternative locations.

South China demonstrates this mechanism particularly clearly. The region accounted for approximately 1.09 million square metres of national logistics absorption during Q2, supported by third-party logistics providers, manufacturers, retailers and businesses involved in newer industrial sectors. But performance varies substantially between individual markets.

Shenzhen continues to face considerable pressure. Logistics rents declined during the first half of 2026 while vacancy remained elevated following new deliveries and softer requirements for some bonded facilities. Nevertheless, demand has emerged from semiconductor, robotics, new-energy and advanced-manufacturing companies requiring modern non-bonded warehouses. This creates a market where overall conditions can remain weak while particular buildings benefit from occupiers linked to expanding industries.

Dongguan offers another example of the relationship between pricing and demand. A substantial amount of new warehouse space was completed during the first half of the year, increasing competition between landlords. Rents fell as owners attempted to secure tenants, but the cheaper space subsequently attracted logistics operators, cross-border e-commerce businesses, retailers and companies connected with robotics and advanced manufacturing. Around 320,000 square metres of net absorption was recorded during the first six months.

The adjustment has been even more pronounced in Huizhou. Vacancy rose above one quarter of available logistics stock following development completions and tenant departures, while rents experienced a double-digit decline during the first half. Those reductions eventually helped stimulate a substantial rebound in leasing during Q2, particularly from logistics companies serving manufacturing businesses.

These markets demonstrate why China’s warehouse downturn should no longer be analysed exclusively through falling rents. Price reductions are increasingly becoming part of the mechanism through which excess capacity is absorbed.

Beijing provides another variation. Leasing activity during the second quarter was heavily concentrated in Pinggu, where competitive rents attracted occupiers. Other districts simultaneously experienced tenant consolidation and cost reduction. Aggressive pricing in one logistics cluster can therefore affect neighbouring markets by forcing competing landlords to adjust their own rental expectations.

This competitive process is transforming the economics of warehouse ownership.

China’s modern logistics stock expanded rapidly during the previous investment cycle. Developers and investors responded to extraordinary growth in e-commerce, expectations of increasing domestic consumption and the expansion of sophisticated distribution networks. High-standard warehouse stock approximately doubled between 2021 and 2025, leaving several markets with significantly more space than occupiers immediately required.

The Q2 absorption rebound therefore needs to be considered against that expansion. The almost 120% quarterly increase is substantial, but it followed a relatively weak first quarter and was supported partly by seasonal activity, relocations and tenants taking advantage of favourable leasing conditions. It does not mean the national supply imbalance has disappeared.

What has changed is the composition of demand.

Third-party logistics companies remain the largest source of leasing, but manufacturing is becoming increasingly important in several regions. Semiconductor producers, robotics companies, new-energy businesses and other advanced manufacturers require storage and distribution facilities close to production clusters. Cross-border e-commerce is also generating requirements, while retailers continue reorganising distribution networks.

This produces a more diversified logistics market than the earlier period when e-commerce expansion dominated much of the investment narrative.

For property investors, location consequently needs to be assessed in relation to specific industries rather than simply population size or historical warehouse demand. Facilities connected to manufacturing clusters, ports, established distribution corridors and major consumer markets can still capture structural demand even when the national market is oversupplied.

Building quality is becoming equally important. Modern loading facilities, sufficient clear heights, efficient vehicle circulation, adequate power capacity and the ability to divide large warehouses between different occupiers can make properties considerably more competitive. Older or inflexible facilities may find that reducing rent is insufficient to compensate for their disadvantages.

This is where the risk of stranded logistics stock begins to emerge.

A warehouse can become cheap without becoming attractive. Properties in locations with excessive competing supply, weak infrastructure or limited access to growing industries can continue losing tenants even after substantial rental reductions. Owners may then face declining income, longer void periods and increasing incentives while the capital required to reposition the building becomes harder to justify.

The distinction between a temporarily repriced warehouse and a structurally impaired one is therefore becoming central to investment decisions.

A repriced property still has an underlying occupier market. Its problem was that rents or capital values had moved beyond what tenants and investors were prepared to pay. Once prices adjust, demand returns, vacancy begins falling and reduced development activity gradually restores equilibrium.

A structurally impaired property faces a different problem. Demand may remain insufficient even after rents decline because too much competing stock exists, the location has become less relevant or the building no longer meets modern occupier requirements.

Both assets may initially appear inexpensive. Their long-term investment outcomes can be entirely different.

This distinction becomes particularly important as warehouse capital values adjust. In a growing number of Chinese cities, modern logistics assets can now be purchased at valuations below the estimated economic cost of constructing equivalent facilities. For long-term investors, that creates an intriguing opportunity.

Buying below replacement cost can provide protection against future competition because developers have less incentive to construct new warehouses when completed properties can be acquired more cheaply. If new development slows while existing vacancy is absorbed, investors purchasing strong assets during the correction could eventually benefit from tightening supply.

But replacement-cost discounts are not themselves proof of value. A building worth less than its construction cost can become cheaper still if rental income continues deteriorating or occupancy cannot be restored. Investors therefore need evidence that lower pricing is actually producing tenant demand.

Shanghai illustrates both possibilities. Leasing activity is improving, but some districts continue to face substantial pipelines. Songjiang, for example, has experienced significant expansion of its warehouse stock and additional projects are expected to add further capacity. Even healthy tenant demand can struggle to absorb repeated waves of speculative development without continued pressure on rents.

The next phase of China’s logistics market is therefore likely to be increasingly local rather than national. Two warehouses in the same metropolitan region can experience completely different outcomes depending on transport connections, surrounding industrial activity, competing development and building specifications.

The supply cycle may eventually provide further support. Development economics have become less attractive as rents and property values decline, which should gradually reduce speculative construction. If leasing continues improving while fewer projects begin development, the imbalance created during the previous expansion could progressively narrow.

That process is unlikely to produce an immediate nationwide rental rebound. Instead, individual markets should stabilise at different times, with the strongest locations recovering first and heavily oversupplied districts taking considerably longer.

For investors, this changes the strategy.

China’s previous logistics investment cycle rewarded exposure to scarcity and rapid expansion. The emerging cycle could reward the ability to distinguish between buildings that have simply become cheaper and those that have permanently lost their competitive position.

The second quarter of 2026 provides early evidence that the price adjustment is beginning to work. Absorption accelerated sharply, vacancy declined nationally and heavily discounted markets attracted tenants back. At the same time, rents continued falling and several important logistics markets remain burdened by substantial availability.

China’s warehouse correction is therefore neither a straightforward recovery nor simply a continuing downturn. It is becoming a process of redistribution in which occupiers gain access to better or cheaper facilities, successful landlords rebuild occupancy through competitive pricing and weaker assets face increasing pressure.

The opportunity for investors lies where lower rents are converting vacant warehouses into sustainable occupancy while future development is becoming more difficult to justify. The danger lies where discounting merely hides a deeper mismatch between buildings and demand.

In the next Chinese logistics cycle, cheap warehouses may indeed become an investment strategy. But the winners are unlikely to be determined by who buys at the lowest price. They will be determined by who correctly identifies the properties that tenants will still want once the price war is over.

Source: CIJ.World Research & Analysis Team

Power Before Location: England’s Data-Centre Boom Creates a New Class of Investment Land

England’s data-centre market is undergoing a geographical change that could have significant consequences for industrial and development land. For years, the sector has been concentrated overwhelmingly around London, particularly Slough and the western edge of the capital. Artificial intelligence, rapidly increasing computing requirements and severe electricity constraints are now beginning to challenge that model.

London remains by far the country’s dominant data-centre market and there is little evidence that this position is about to disappear. The capital accounts for more than four fifths of existing UK data-centre capacity according to industry estimates, supported by extensive fibre infrastructure, established cloud networks and a large concentration of corporate customers. What is changing is the ability to add very large amounts of capacity in the locations where operators traditionally wanted it.

Modern data centres can require extraordinary amounts of electricity. AI facilities in particular are being planned at a scale that would have appeared exceptional only a few years ago. Projects requiring 100MW or more are becoming increasingly common, while some proposed campuses are considering several hundred megawatts or even gigawatt-scale requirements. Finding the land is rarely the greatest difficulty. Finding the electricity can be.

That distinction is beginning to transform property investment decisions. A conventional industrial development starts with questions about location, road connections, labour, occupier demand and land price. For a large data centre, one of the first questions is increasingly whether enough electricity can realistically reach the site within an acceptable timeframe. This is creating what amounts to a new category of real estate: land with credible access to large quantities of power.

The difference between such a site and an ordinary development plot can be substantial. Market research indicates that locations capable of securing 50MW or more within a relatively short period can, under the right circumstances, command multiples of conventional industrial land values. Across Europe’s major data-centre markets, the cost associated with powered development land has also increased sharply during the past five years. The premium does not simply reflect electricity consumption. It reflects scarcity.

Grid connections can take many years to secure, particularly around established data-centre clusters. Landowners that can demonstrate a realistic route to substantial power therefore possess something competitors may be unable to replicate regardless of how much land they own. This is especially visible around Slough. The area remains one of Europe’s most important data-centre concentrations and continues to attract development despite pressure on electricity infrastructure. Sites that do have a credible path to power can become more valuable precisely because so few alternatives exist.

The investment logic is consequently changing. Electricity constraints are not necessarily destroying the value of established data-centre locations. In some cases they are increasing the value of the limited sites that can still accommodate additional capacity. At the same time, the shortage is widening the industry’s search radius.

Oxfordshire is emerging as one of the clearest examples. Culham has been selected as part of the UK’s strategy to create large-scale artificial intelligence infrastructure, while the wider Thames Valley provides an established technology ecosystem with connections back towards London. The government’s criteria for new AI development locations demonstrate how dramatically site-selection priorities have changed. Potential locations are expected to show how very substantial electricity capacity could be delivered, with hundreds of megawatts potentially required over the coming years.

Reading provides another illustration. Proposals around Thames Valley Park have explored alternative on-site electricity generation while waiting for greater grid capacity. The significance for property investors extends beyond the individual development. If developers are prepared to incorporate substantial energy infrastructure simply to make a location viable, access to electricity has clearly become part of the underlying real-estate value.

The shift becomes even more interesting further north. Large AI computing facilities do not necessarily need to be located beside London’s established financial and corporate districts. Some computing tasks require extremely fast connections to users, but the training of large artificial intelligence models can tolerate greater geographical distance. That gives locations hundreds of kilometres from London an opportunity that would have been considerably harder to justify during earlier generations of data-centre development.

Leeds provides one of the strongest examples. At Skelton Grange, Microsoft is progressing plans for a major hyperscale campus on former industrial land. The company previously acquired approximately 27 acres for more than £50 million, while the wider redevelopment includes remediation, infrastructure and additional commercial development. The transaction demonstrates the potential impact on land economics. Former industrial property in a regional market can achieve a very different valuation when it becomes suitable for one of the world’s largest technology companies and has the infrastructure needed to support a major data-centre operation.

Northumberland provides an even larger example. At Cambois, near Blyth, plans are progressing for a multibillion-pound data-centre campus on land associated with former heavy industrial and energy uses. The scale of the proposed development places it among the most significant digital-infrastructure projects being pursued in Britain.

The location illustrates why England’s industrial past may become an advantage in the AI economy. Former power stations, steelworks, chemical complexes and other energy-intensive industrial locations frequently possess characteristics that are increasingly difficult to create from scratch. They can offer large areas of contiguous land, connections to high-voltage electricity networks, existing substations and infrastructure designed for industries that once consumed enormous amounts of energy. As traditional heavy industry disappeared from some of these locations, much of the underlying infrastructure remained. AI may now give that infrastructure a second economic life.

Teesside demonstrates similar potential. Large-scale data-centre proposals around established industrial areas have considered capacity measured in hundreds of megawatts, with some ambitions reaching approximately 1GW. The attraction again comes from the combination of substantial sites and an energy network originally developed to support power-intensive industry.

This creates an important new dimension for brownfield investment. A former industrial site can no longer be assessed solely according to its potential for warehouses, manufacturing or redevelopment. Investors increasingly need to understand what electrical infrastructure exists nearby, how much capacity could realistically be obtained and when that electricity could be delivered. In some cases, the value hidden beneath the site may be more important than the buildings standing on it.

The Midlands could eventually benefit from the same trend. Its central location, manufacturing heritage, extensive logistics infrastructure and comparatively lower land costs provide many of the characteristics data-centre developers require. However, evidence of a broad Midlands data-centre cluster remains less developed than the projects emerging around Oxfordshire, Leeds, Teesside and Northumberland. The regional opportunity should therefore not be interpreted as a uniform migration away from London. England is more likely to develop several different data-centre markets serving different requirements.

London and its surrounding areas can remain dominant for cloud computing, financial services and applications where connectivity and proximity to established digital infrastructure are critical. Large AI campuses, by contrast, can increasingly follow electricity. This distinction could fundamentally alter the geography of technology property investment. Locations that were once considered too distant from London may become viable if they can offer substantial power earlier and more reliably than sites in the South East.

However, the growing value attached to electricity also creates significant investment risk. Britain has accumulated a large pipeline of proposed data centres, but not every project with a grid application will ultimately be built. A requested connection is not the same as available power, just as a development concept is not equivalent to planning permission.

Investors therefore need to understand precisely what sits behind claims that a site is powered. There is a major difference between submitting a connection request, receiving an offer, funding the necessary network reinforcement and obtaining electricity on a commercially usable date. Those stages can represent very different levels of development certainty and therefore very different land values.

This is likely to become increasingly important as grid authorities attempt to distinguish credible projects from speculative applications. Large amounts of proposed electricity demand can otherwise occupy positions within connection queues even when the underlying developments have little prospect of proceeding.

For property investors, due diligence consequently needs to extend beyond conventional title, planning, contamination and construction analysis. Grid capacity, connection agreements, reinforcement requirements, delivery dates and the financial obligations attached to electricity infrastructure are becoming fundamental components of development value.

Government policy is reinforcing the change. Britain’s programme for large AI development zones is attempting to bring together land, planning, electricity and technology investment in locations capable of supporting substantial computing capacity. Yet designation alone will not create successful data-centre markets. The locations that ultimately attract investment will be those capable of turning political ambition and theoretical electricity capacity into infrastructure that operators can actually use.

This is why England’s emerging data-centre geography should be viewed as a property investment map as much as a technology map. Slough demonstrates the premium attached to scarce power inside an established cluster. Oxfordshire and the Thames Valley illustrate the expansion of the London technology ecosystem into locations with greater development potential. Leeds shows how former industrial land can attract hyperscale investment, while Teesside and Northumberland demonstrate how Britain’s legacy energy infrastructure could support a new generation of enormous AI campuses.

The common factor is not cheap land. It is electricity. As computing requirements continue to increase, the most valuable development sites may increasingly be those where investors can answer three questions with certainty: how much power is available, when it can be delivered and whether that capacity is genuinely secured.

That could have profound consequences for industrial land values across England. Sites previously valued according to warehouses, factories or conventional redevelopment potential may command entirely different prices when their electrical infrastructure is recognised. The next generation of England’s data-centre market may therefore be determined less by where technology companies would ideally like to locate and more by where the electricity system allows them to build.

For property investors searching for the next data-centre location, following the power network may increasingly prove more important than following the motorway map.

Source: © CIJ.World UK Research & Analysis Team

Africa’s Green Office Divide Is Becoming an Investment Risk

Africa’s commercial property market is entering a period in which the environmental performance of buildings is becoming increasingly connected to their financial performance. What was once primarily treated as a sustainability commitment is beginning to influence leasing decisions, operating costs, access to capital and the ability of older properties to compete with newer office developments. The change is not occurring evenly across the continent, but evidence from South Africa and developments in Nairobi, Lagos, Cairo and Casablanca suggest that energy efficiency and building resilience are moving closer to the centre of commercial real estate investment decisions.

The timing is important because Africa still has an enormous amount of urban development ahead of it. Buildings and construction already account for roughly 37% of global emissions, while African cities are expected to accommodate hundreds of millions of additional residents over the coming decades. Much of the property that will serve this population has yet to be developed. Decisions being made today about energy consumption, cooling, water, materials and infrastructure resilience could therefore determine the operating costs and competitiveness of African buildings for decades.

For property investors, however, the immediate argument for greener buildings is becoming increasingly financial. South Africa provides the clearest evidence that higher-performing offices can produce better investment outcomes. Long-term market data covering hundreds of prime and A-grade properties show that certified offices have outperformed comparable conventional buildings over the past decade. During 2025, vacancy among certified properties stood at approximately 10.3%, compared with 13.1% for comparable buildings without certification. Certified properties also produced substantially stronger net operating income per square metre and carried considerably higher average valuations.

Over the ten years to the end of 2025, certified South African offices generated annualised total returns of almost 7%, compared with just over 5% for conventional properties. These figures do not mean that environmental certification alone created the difference. Many certified offices are newer, situated in stronger locations, operated by institutional landlords and equipped to higher overall specifications. Nevertheless, the results indicate that sustainable characteristics are increasingly associated with the segment of the market performing most successfully.

This is particularly visible in Johannesburg, where corporate occupiers increasingly concentrate on better-quality properties in established business districts such as Sandton and Rosebank. The definition of a high-quality office has also changed. Modern interiors and prestigious addresses remain important, but electricity security, water resilience, efficient cooling and increasingly on-site renewable energy have become part of the building proposition.

This introduces an important African dimension to the green-building investment case. In European markets, reducing energy consumption is frequently discussed primarily in terms of emissions and operating costs. In several African cities, it can also be a question of business continuity. A property capable of producing some of its own electricity, storing water and reducing dependence on unreliable infrastructure can offer occupiers protection against disruption as well as lower consumption.

Cape Town is developing along similar lines, although its property economics differ from Johannesburg. Demand for high-quality offices remains strong, but land and development costs can be considerably higher. New construction therefore has to justify a larger capital commitment, increasing the importance of securing strong tenants and protecting long-term property income. Efficient buildings with lower operating requirements and modern infrastructure can become more attractive in that environment, particularly to multinational and larger domestic companies.

The implications for existing office stock could be significant. Older buildings were frequently designed when electricity and water security were less prominent leasing considerations and when corporate environmental requirements were considerably weaker. Properties with inefficient cooling, poor insulation and limited backup infrastructure are increasingly competing against newer buildings offering tenants lower consumption and greater operational reliability. The commercial consequence may not necessarily appear as a simple green rental premium. Instead, the more important effect could be a growing discount applied to inefficient buildings through higher vacancy, weaker tenant demand and increased capital expenditure requirements.

This is why applying a single green rent premium across Africa would be misleading. There is insufficient evidence to support the idea that every certified African office automatically commands rents 5% or 10% above comparable conventional buildings. Rental performance depends heavily on location, specification, age, tenant demand and market supply. Sustainability is increasingly one component of Grade A quality rather than an isolated characteristic capable of determining rent by itself.

Nairobi illustrates this relationship particularly well. Kenya has developed one of Africa’s more established green-building markets outside South Africa, and international certification has become increasingly common among newer high-quality office developments. Multinational companies, international organisations and larger professional occupiers increasingly expect efficient buildings with reliable utilities as part of their accommodation requirements.

Nairobi also remains a competitive office market. Occupancy varies considerably between districts, meaning landlords need to differentiate properties from competing Grade A developments. Environmental performance can contribute to that differentiation, but it cannot compensate for a poor location or excessive supply. A certified building in a weak office district can still struggle to attract tenants, while a well-positioned conventional building can continue to perform strongly.

The more accurate conclusion is that sustainability increasingly strengthens the competitiveness of an already attractive building. It should not be treated as a guarantee of occupancy. This distinction is important because claims that certified Nairobi offices automatically achieve occupancy rates 10 or 15 percentage points above conventional properties cannot be supported reliably by current market evidence.

Lagos presents a different sustainability argument. Nigeria’s commercial property market operates against a background of high energy costs and infrastructure constraints, giving efficient building systems an immediate economic value. Offices that can reduce dependence on externally supplied electricity through solar generation, efficient cooling and improved building management can lower exposure to volatile operating expenses.

For Lagos landlords, the investment case can therefore be less about achieving a theoretical environmental premium and more about controlling the cost of operating the property. Tenants occupying large offices are similarly exposed to electricity and backup-generation expenses, making buildings capable of reducing those requirements potentially more competitive.

International development capital is beginning to reinforce this direction. New projects receiving institutional and development financing increasingly incorporate environmental standards and resilience requirements into their designs. Developments associated with Lagos Free Zone, for example, include plans for certified commercial property alongside broader infrastructure improvements. However, Lagos remains at an earlier stage than Johannesburg when it comes to demonstrating a measurable market-wide relationship between certification and rents or valuations.

Cairo represents another potentially important market. The Egyptian capital’s office stock approached three million square metres during 2026, with new Grade A development increasing competition between landlords. New Cairo and other expanding commercial districts are adding modern office buildings, requiring developers to differentiate projects through specification, amenities, technology and increasingly environmental performance.

Egypt’s green-building market has also expanded significantly. Around 1.8 million square metres of development has obtained EDGE certification, while financial institutions are increasing the availability of capital intended for energy-efficient buildings and related projects. This could gradually influence both new development and refurbishment economics.

The financing issue requires careful interpretation. A certified building does not automatically receive cheaper debt. Lending costs continue to depend on the borrower, currency, project risk and wider financial environment. The emerging advantage is that qualifying projects can access dedicated sources of capital that may not be available to conventional developments. Green loans, sustainability-focused investment programmes and development finance can therefore broaden the financing options available to property owners.

Morocco is developing along a similar path. International financial institutions and domestic lenders have established programmes intended to support energy efficiency and environmentally improved buildings. Casablanca, as the country’s dominant commercial centre, could become an important testing ground for whether this capital produces a larger pipeline of sustainable office development and refurbishment.

This financing could become particularly important for existing properties. Africa’s future green-building market cannot depend exclusively on constructing new certified offices. Large quantities of existing commercial stock will remain in operation for decades, meaning landlords will increasingly have to decide whether refurbishment can extend the competitive life of those assets.

The investment requirements can be substantial. Improving glazing and insulation, replacing cooling systems, installing solar generation, introducing battery storage, reducing water consumption and upgrading building-management systems all require capital. The financial return arrives gradually through lower operating expenditure, stronger tenant retention and potentially improved property values.

Access to longer-term sustainability-linked capital can help make those refurbishment programmes more feasible. This could prove particularly important in markets where conventional financing remains expensive and landlords might otherwise postpone major upgrades.

For investors, the calculation is increasingly becoming one of future income protection. A property may still be well located and structurally sound while gradually losing competitiveness because its operating costs and infrastructure no longer meet occupier expectations. Environmental obsolescence can therefore develop before physical obsolescence.

This creates a potentially significant challenge for owners of ageing office portfolios. Delaying investment may preserve cash in the short term but increase the amount of capital eventually required to reposition the building. In extreme cases, refurbishment costs could become difficult to justify relative to the property’s value, leaving landlords with increasingly stranded secondary assets.

The most sophisticated investors are therefore likely to examine environmental performance as part of normal asset management rather than treating it as a separate sustainability exercise. Energy consumption, water security, cooling efficiency, renewable generation and resilience can increasingly be evaluated alongside rent, vacancy, lease expiry and capital expenditure.

Corporate occupiers are reinforcing this change. International companies attempting to reduce emissions across their operations increasingly need information about the environmental performance of the properties they occupy. As more high-quality certified alternatives become available, leasing inefficient space may become harder for those companies to justify internally.

That creates an advantage for landlords capable of providing credible performance data. Certification can support this process by offering independent verification, but the underlying performance of the property remains more important than the label itself. A building that consumes less electricity, manages water efficiently and maintains operations during infrastructure interruptions provides tangible benefits regardless of the certification displayed at its entrance.

This could gradually create a more pronounced two-tier office market across Africa. At the upper end will be modern and successfully refurbished properties combining strong locations with efficient building systems, dependable infrastructure and credible environmental performance. These buildings will be best positioned to compete for multinational companies, financial institutions and larger domestic occupiers.

The second tier will contain older buildings requiring increasing investment to maintain their position. Some will be successfully refurbished. Others may have to compete primarily through lower rents, while the weakest properties could eventually require conversion or redevelopment.

South Africa is already providing measurable evidence of this divide. Nairobi is demonstrating how sustainability can strengthen a building’s position in a competitive leasing market. Lagos shows how energy resilience can become a direct operating advantage, while Cairo and Casablanca illustrate how green financing can begin influencing the development and refurbishment pipeline.

The markets remain at very different stages, making continent-wide assumptions about rental premiums or occupancy advantages inappropriate. The direction of travel, however, is increasingly visible.

Africa’s green-building transition is becoming less about whether a property can carry an environmental label and more about whether the building can remain financially competitive as tenant requirements, operating costs and financing standards change.

For commercial property investors, that changes the central question. The issue is no longer simply whether a green office can command a higher rent. It is whether an inefficient building will increasingly have to accept lower rents, spend more capital and face greater vacancy simply to compete.

If that trend continues, sustainability will become more than a competitive advantage for Africa’s best buildings. For the continent’s ageing commercial property stock, it could increasingly determine which assets protect their income and which begin to lose value.

AI Is Starting to Rebuild HR From Hiring to Retention

AI is starting to rebuild HR from hiring to retention by connecting recruitment, employee support, workforce analysis and retention through systems that can interpret large volumes of workforce information while still leaving critical employment decisions in human hands. The shift is becoming more significant than simple automation. Traditional HR software was largely designed around standardised processes serving the majority of customers. AI is making it possible to create more adaptable systems that can respond to the structure, policies and needs of individual organisations rather than forcing every company into the same workflow.

That transition was central to a discussion at AI4 2026 involving representatives of HireVue, BambooHR and satellite manufacturer Astranis. Despite approaching AI from different positions, the participants broadly agreed that its greatest value in HR lies not in replacing professionals but in improving access to information, removing repetitive administrative work and allowing human judgement to concentrate on decisions where context matters most. Recruitment is already one of the clearest examples. Generative AI has made it far easier for candidates to produce polished résumés, application responses and cover letters, which has reduced the effort required to apply for jobs and contributed to employers receiving large volumes of increasingly professional-looking applications.

The unintended consequence is that the résumé itself is becoming a weaker indicator of genuine ability. When candidates can use AI to improve how their experience is presented, recruiters need better ways of determining who can actually perform the job. This is pushing recruitment technology away from simply screening applications and towards validating skills. HireVue has been developing AI-supported interviewing around this problem, using technology to expand the number of candidates who can be assessed while maintaining human oversight over final hiring decisions.

The company launched a voice-based AI Interviewer in 2026 that can conduct structured conversations with candidates without requiring a recruiter to personally perform every initial interview. For organisations involved in large-scale recruitment, this can change the economics of the process. Employers may need to handle tens of thousands of applicants within short hiring periods, and conventional recruiter capacity inevitably forces them to reduce those numbers before meaningful conversations can take place.

AI can potentially loosen that constraint. Instead of deciding which applicants deserve an interview primarily from résumés and application forms, employers can conduct structured initial assessments across much broader candidate groups. That could give more people an opportunity to demonstrate their capabilities before a recruiter decides where to invest limited human time. It does not mean everyone reaches a final interview, but it can increase the amount of information available before candidates are eliminated from the process.

The opportunity is accompanied by another problem: candidates now have access to increasingly sophisticated AI themselves. Applicants can use AI to prepare résumés, anticipate interview questions and generate suggested responses. Tools capable of assisting people during interviews are also becoming more accessible. Recruitment is therefore entering an unusual arms race in which employers use AI to evaluate candidates while candidates use AI to improve how they appear to employers.

The result is likely to increase the importance of skills validation. Employers increasingly need evidence that candidates can do what they claim rather than relying primarily on how effectively they describe their experience. AI could also improve matching between applicants and vacancies. Someone might apply for one role while possessing skills better suited to another position within the same organisation. Historically, identifying that opportunity depended heavily on a recruiter recognising it. Systems capable of analysing capabilities across a company’s entire vacancy portfolio can potentially surface alternative matches automatically.

This shifts recruitment from processing applications towards understanding capabilities. The same principle extends beyond the hiring stage. One of the larger opportunities discussed at AI4 involved connecting recruitment information with what happens after someone joins the company. Businesses collect substantial amounts of workforce information throughout the employment lifecycle, including interview feedback, assessment results, performance reviews, engagement surveys, management evaluations and exit interviews. Much of this information has historically remained fragmented across different systems or stored as unstructured text that is difficult to analyse systematically.

Generative AI changes the economics of working with that information. Organisations can potentially examine patterns across thousands of written comments without requiring someone to manually read every document. That makes it possible to ask more complicated questions about employee performance, management quality, retention and organisational behaviour.

Management performance provides one example. Companies have traditionally evaluated managers through financial results, formal appraisals and perhaps employee surveys. AI-supported analysis can potentially combine those measures with turnover patterns, peer comments, direct-report feedback and exit interviews over time. If unusually large numbers of employees repeatedly leave the same team, management can investigate whether the explanation lies with recruitment, workload, leadership or another organisational issue.

The technology does not automatically provide the correct answer. Its value lies in making previously difficult qualitative information easier to examine. HR has never lacked information. The problem has been getting the right information to the right person at the moment it becomes useful. AI can increasingly act as an intelligence layer between workforce databases and managers.

BambooHR is moving in this direction with a connected AI architecture designed around organisational context rather than individual AI features. Its approach combines workforce information with AI systems capable of analysing data, answering employee questions and completing selected actions while operating within existing permissions and governance controls. That reflects a broader transformation occurring throughout enterprise software.

Traditional software-as-a-service platforms were generally built around the requirements shared by the largest possible number of customers. Developers identified what most users needed and constructed relatively standardised products around those workflows. Customers could configure them, but usually only within boundaries established by the software provider. AI makes it possible for the underlying platform to remain standardised while the user experience becomes more individualised.

Different organisations can potentially interact with the same HR platform in different ways according to their structures, policies and workforce characteristics. Individual employees may also receive different information depending on their role, permissions and circumstances. From the customer’s perspective, the number of AI agents operating behind the software matters far less than whether the system improves the experience and produces useful results.

An HR director is unlikely to care whether a provider operates ten agents or several hundred. The important questions concern whether employee requests are answered correctly, managers receive useful information, repetitive administration is reduced and important decisions become better informed. This creates pressure on technology companies to move beyond simply attaching AI features to established software in order to market the product as AI-enabled.

The underlying architecture is becoming more important. HR systems contain some of the most sensitive information in an organisation, including compensation, performance data, employment records, benefits and recruitment information. AI systems operating across these datasets require strict controls governing which information can be accessed and what actions can be taken.

Data architecture is also becoming a competitive issue. Many established enterprise platforms were designed long before generative AI existed. Their databases expanded gradually as companies added customers, integrations and new functions. Information can therefore be fragmented across numerous systems and tables that were never designed to support AI-driven analysis. Placing an intelligent interface above poorly organised information does not solve the underlying problem.

This could trigger a significant investment cycle across enterprise software. Existing platforms may need to redesign data structures, improve interoperability and create more consistent information models if they want AI systems to operate effectively across their products. At the same time, new HR technology companies have an opportunity to design systems around AI from the beginning.

Legacy software providers retain major advantages in customer relationships, historical information and established workflows. New entrants, however, can build platforms specifically around connected data, adaptable workflows and AI without needing to accommodate decades of older architecture. This may create a new divide within HR technology between companies that place AI above existing systems and those that redesign the underlying infrastructure around it.

Corporate customers are simultaneously gaining another option: building applications themselves. This could become one of the more disruptive consequences of generative AI for enterprise software. Historically, an HR department wanting specialised technology generally had two choices. It could purchase a commercial product or ask an internal engineering team to create something. Custom development was normally realistic only for larger organisations with substantial technical resources. That barrier is falling.

At Astranis, members of the people organisation have been building internal AI tools to support recruitment and administrative workflows. According to the AI4 discussion, one system can help recruiters prepare job descriptions, create educational material, map talent pools, develop sourcing strategies, produce outreach campaigns and organise interview plans through a common interface. Tasks that once required substantial manual research can therefore be compressed significantly.

More importantly, the people creating these tools understand the processes themselves. This creates a new form of enterprise development in which domain specialists become builders. HR professionals, recruiters, finance teams and operational managers no longer necessarily need to translate every requirement into a specification for an engineering department. AI-assisted development can allow them to prototype or create applications directly.

The person who understands the problem and the person capable of constructing the solution are therefore moving closer together. That could reshape enterprise software procurement. Companies will still buy sophisticated platforms where reliability, security, compliance and scale justify specialist providers, but they may increasingly create smaller applications around those platforms themselves rather than purchasing another standalone software product for every process.

The future may therefore be neither entirely build nor buy. Businesses could continue purchasing core systems of record while developing customised intelligence and automation layers around them. This places pressure on software providers to make their platforms more open and extensible. Systems that allow customers to connect data, create agents and integrate external AI environments may become more valuable than closed products offering only predetermined workflows.

The implications extend beyond HR technology. Over the past two decades, companies have bought specialised applications for increasingly narrow business requirements. AI could consolidate some of those functions because adaptable agents can potentially perform workflows that previously required separate products. That does not mean established software disappears. Systems responsible for payroll, compliance, financial transactions and authoritative workforce records remain difficult to replace. But the number of additional applications surrounding those core systems could eventually decline.

Value may instead migrate towards the quality of the underlying data and the intelligence capable of using it. HR provides a particularly clear example because context is essential. A general AI model can recognise patterns, generate text and answer broad questions, but it does not automatically understand an organisation’s compensation rules, management structure, recruitment policies, employee history or internal procedures. Connecting AI with that context dramatically increases its usefulness.

An employee asking about leave, for example, does not simply need a generic explanation of company policy. The relevant answer may depend on location, employment status, available allowance, previous leave and the permissions of the person making the request. An HR system capable of understanding those relationships can move from answering generic questions towards performing useful work.

This is also why companies need to improve their data before expecting major returns from AI. Automating a badly designed process does not make it good. Poor information hygiene, inconsistent records and unclear workflows can simply produce faster mistakes. The panel repeatedly returned to this point. Organisations should resist the temptation to automate everything at once and instead identify specific problems where AI can produce measurable value.

High-volume, repetitive processes with relatively limited downside are obvious starting points. Tasks involving irreversible consequences, complex judgement or substantial regulatory exposure require greater caution. Companies can begin with employee questions, reporting, recruitment research, meeting summaries and administrative preparation before progressing towards systems that influence employment decisions or take actions autonomously.

Hiring is particularly sensitive because the consequences directly affect people’s livelihoods and expose employers to regulatory and reputational risk. Human oversight therefore remains important. AI can organise information, conduct preliminary assessments and identify patterns that people might otherwise miss, but final decisions involving hiring, promotion, discipline or dismissal are much more difficult to automate responsibly.

The challenge is finding the appropriate boundary between machine assistance and human authority. There is also an important distinction between productivity claims and actual productivity. AI adoption alone does not guarantee efficiency. Organisations need to determine whether systems genuinely reduce work rather than simply moving it elsewhere. An automated process that requires extensive checking may deliver far less value than the initial time-saving estimate suggests.

This is especially relevant as companies begin deploying increasing numbers of agents. Counting agents is not a useful measure of transformation. Business outcomes are. A company with several carefully designed AI workflows solving important problems may achieve more value than an organisation deploying hundreds of agents without clear objectives.

HR departments therefore need to approach AI as an operational redesign rather than simply a technology purchase. The starting point should be the business problem. Companies can identify a high-friction process, determine why it performs poorly, clean the relevant information and then decide whether AI is an appropriate solution.

Retention could become one of the most important areas. Companies invest substantial resources in recruitment but often possess limited ability to identify early signs that valuable employees are disengaging or likely to leave. Workforce systems can increasingly analyse patterns associated with absence, declining engagement, turnover or other retention risks. Used carefully, these signals could allow managers to intervene earlier.

The danger is allowing probabilistic analysis to become an unquestioned judgement about an individual employee. A model identifying a possible retention risk is very different from knowing why someone may leave. This reinforces the broader principle emerging across HR technology: AI is most useful when it expands human understanding rather than pretending to replace it.

The same applies to recruitment. AI can allow more applicants to be evaluated, but scale creates value only if the assessment remains meaningful. It can analyse years of workforce information, but patterns still require interpretation. It can automate employee support, but sensitive situations continue to require empathy and judgement.

The future of HR may therefore become both more automated and more human. Routine administration can increasingly move into the background while HR professionals concentrate on decisions, relationships and organisational problems requiring context. The skills needed inside HR departments will change as a result.

Process knowledge alone will no longer be enough. Professionals will increasingly need to understand data, automation and the capabilities and limitations of AI systems. Some will effectively become technology builders within their own functions. That transition is already visible in emerging roles combining domain expertise with technical implementation.

Instead of engineers attempting to understand every detail of HR operations, organisations can develop specialists capable of translating directly between workforce problems and AI systems. The broader economic implication is that generative AI may weaken the traditional division between business departments and technology departments.

Marketing professionals can increasingly build marketing applications. Finance professionals can construct analytical workflows. HR teams can create recruitment tools. Engineering does not disappear, but its role shifts towards architecture, security, reliability and helping business specialists move prototypes into production.

That final step remains critical. Building a demonstration has become dramatically easier. Maintaining reliable enterprise software has not. Applications still fail. Models change. Data requires protection. Access permissions need management. Systems require monitoring and support, while regulatory obligations remain.

The democratisation of software development therefore does not eliminate engineering discipline. It makes that discipline relevant to many more people. For HR technology companies, the strategic contest will increasingly concern who can combine flexibility with trust. Customers want systems capable of adapting to their organisations, but they also need confidence that sensitive workforce information remains protected and employment decisions can be explained.

That combination will be difficult to achieve, but it could determine which platforms become the foundations of the next generation of HR technology. AI is already making recruitment faster and administrative work easier, yet the larger transformation is only beginning. Workforce systems are moving from databases that record what happened towards intelligence layers that attempt to understand what is happening and recommend what should happen next.

If that transition continues, the most important HR technology may no longer be the application employees open. It may instead be the intelligence connecting recruitment, performance, workforce data and employee experience behind the scenes. The companies that succeed will not necessarily be those deploying the most AI. They will be those that combine reliable data, organisational context and human judgement to solve specific workforce problems.

That makes the next phase of HR technology less about replacing people than redesigning the systems around them.

Source: CIJ.World Research & Analysis Team

When AI Starts Doing the Shopping, Consumer Brands Will Have to Win Over Algorithms as Well as People

Consumer brands have spent decades learning how to influence people at the moment they decide what to buy. Artificial intelligence is beginning to change that relationship because the next important shopper may not always be a person navigating a supermarket aisle, website or mobile application. Increasingly, an AI assistant could help decide which products consumers see, compare and eventually purchase. That emerging shift was at the centre of a presentation by Lizbeth James of Mars at Ai4 2026 in Las Vegas, examining how artificial intelligence could transform shopper intelligence within the consumer packaged goods industry.

The change is taking place on two sides simultaneously. Retailers are giving manufacturers access to increasingly detailed first-party commerce information, while AI assistants are becoming capable of helping consumers discover, evaluate and purchase products. Together, those developments could fundamentally alter how brands understand demand and compete for sales. Consumer-goods companies have traditionally depended on market research, household panels, retailer reports and periodic sales information, but large retailers can now provide much more detailed data about customer behaviour across physical stores, websites, advertising and digital transactions.

The scale and speed of this information creates a new problem. Brands may receive hundreds of measurements covering sales, inventory, search visibility, advertising performance, product-page engagement, customer switching and other indicators across thousands of products. Giving managers more dashboards does not necessarily solve the problem because humans still need to identify which signals matter, understand how they are connected and decide what action should follow. James argued that the next stage is therefore a move from business intelligence that explains what happened towards systems capable of continuously detecting changes, investigating possible causes and proposing commercial responses.

A decline in one coffee product, for example, might initially appear to be a straightforward sales problem. But the underlying explanation could involve several factors occurring simultaneously, including customers switching brands, changes in price, reduced online visibility, lower availability, stronger competitor advertising, differences between online and in-store purchasing or changes within particular customer groups. Traditionally, different teams might investigate each part separately. An AI system could potentially examine those signals together and identify relationships that are difficult to see through individual reports.

The commercial opportunity lies in connecting that diagnosis directly with action. If a product is losing visibility online, the system might recommend changes to product information or advertising. If consumers are moving towards a competitor because of price, it might model different promotional responses. If certain products are frequently substituted during online fulfilment, the manufacturer and retailer could investigate assortment or inventory. The objective is to shorten the distance between detecting a problem and doing something about it.

This does not mean conventional analytics are disappearing. Traditional statistical methods and machine-learning models remain important alongside generative AI. Forecasting, segmentation, clustering and other established techniques can provide the quantitative foundation, while generative and agentic systems make it easier for employees to interrogate the information, connect different sources and translate results into potential actions. A reliable data foundation therefore becomes more important rather than less important as AI adoption increases, because retailer information, advertising results, inventory data, digital-shelf performance and pricing all need to be harmonised before autonomous systems can make useful decisions.

Privacy creates another important boundary. Consumer-goods companies generally do not need to know the identity of every individual shopper to identify useful patterns. Retailer environments can use privacy-protected identifiers and aggregated groups to understand behaviour without simply handing manufacturers personally identifiable customer information. The resulting intelligence can show how groups of customers purchase, switch brands or respond to promotions while operating within retailer-controlled data environments and applicable privacy rules.

The more disruptive change, however, is occurring on the consumer side. Shopping assistants are moving beyond answering questions towards performing parts of the purchasing process. Consumers can increasingly use AI to research products, compare alternatives, examine prices, assemble shopping carts and automate some routine purchases. As these systems become more capable, people may increasingly delegate parts of product discovery and selection to software.

That development changes the traditional idea of the digital shelf. Until recently, a brand mainly needed to perform well in retailer search results, category pages, advertising placements and conventional search engines. In an agent-mediated environment, the consumer may instead ask for a week’s groceries within a particular budget, a coffee matching certain preferences or a replacement household product offering the best combination of price and quality. The AI assistant then decides which products deserve consideration.

Brands would consequently be competing for algorithmic recommendation as well as human attention. Packaging, imagery and advertising will remain important when consumers make visual choices, but product information will also need to be sufficiently structured, accurate and credible for AI systems to understand. Availability, price, reviews, product attributes, retailer information and external sources could all influence what an assistant chooses to recommend.

This creates an emerging commercial challenge around making products understandable and discoverable by answer engines and shopping agents. The concept remains immature, and brands should be cautious about claims that AI recommendations can simply be manipulated in the same way companies once optimised webpages for search engines. Nevertheless, the direction is significant. If an AI assistant becomes the interface between a consumer and millions of products, being accurately represented by that system becomes commercially important.

Retail media could change alongside it. Advertising has traditionally influenced people while they browse, search or consume media. Agentic commerce raises the question of what advertising means when software is helping make the decision. Retailers and technology platforms will have to determine how sponsored recommendations, organic recommendations, consumer preferences and commercial incentives coexist without undermining trust in the assistant.

For manufacturers, this creates a new competitive dimension. Large consumer-goods groups possess enormous amounts of historical information, established retailer relationships and substantial marketing resources, but they can also carry complicated organisational structures. James argued that this can slow implementation because insights frequently have to travel through multiple departments before action is approved. Smaller brands may have fewer resources but can sometimes respond more quickly, meaning AI does not automatically strengthen the largest companies and could instead reward businesses capable of converting information into action faster.

This helps explain why the organisational side of autonomous intelligence may prove harder than the technology. James described internal experiments in which AI systems improved their ability to answer business questions after receiving repeated feedback from employees. But even an apparently high level of accuracy leaves important questions about the remaining errors, particularly if a system is eventually allowed to take commercial actions without human approval.

Human oversight is therefore likely to remain essential for many decisions. Automatically identifying a sales anomaly is relatively low risk. Changing a product description may carry somewhat more risk. Altering pricing, reallocating substantial advertising budgets, changing assortment or making decisions affecting customers and supply chains can have much larger consequences. The degree of autonomy should therefore depend on the potential impact of the decision rather than on whether the technology is technically capable of executing it.

The transformation may initially be less dramatic than the idea of an autonomous commercial organisation suggests. Instead of removing employees entirely, AI is more likely to compress the time required for analysis. Work that previously required several analysts to collect reports, reconcile data and prepare presentations could increasingly be produced through a conversational interface that alerts different executives to the issues relevant to their responsibilities.

This could eventually reduce corporate dependence on conventional dashboards. A sales executive may not need to open a series of reports every Monday morning if an AI system can identify the most significant changes from the previous week, explain likely causes and suggest which issues deserve investigation. Brand managers, sales teams and senior executives could interact with the same underlying information through different interfaces tailored to their responsibilities.

The economic value comes from speed. Consumer demand can change quickly, competitors can adjust prices or advertising almost immediately and digital availability can fluctuate throughout the day. A company that requires several weeks to understand what happened may be reacting to conditions that have already changed again. Systems capable of reducing that cycle to hours or minutes could provide a meaningful competitive advantage.

But the most consequential development may ultimately take place outside the consumer-goods company itself. If consumers increasingly ask AI assistants to replenish household products, compare alternatives and choose between brands, manufacturers will have less control over the point at which purchasing decisions are made. The traditional battle for shelf space will increasingly be accompanied by a battle for inclusion in the recommendations generated by machines.

That does not mean consumers disappear from the decision. People will continue to have favourite brands, budgets, tastes and values, and they can override recommendations. But AI could increasingly filter the enormous number of choices before a person sees them. In categories involving routine purchases, consumers may eventually delegate much of that filtering altogether.

The implication for Mars, Nestlé, Coca-Cola and thousands of other consumer brands is significant. The next generation of shopper intelligence will not simply be about understanding what people bought yesterday. It will involve understanding how retailer algorithms, advertising systems, consumer preferences and AI shopping assistants interact to determine what gets purchased tomorrow.

Consumer companies have spent generations learning how to win the physical shelf and the past two decades learning how to win the digital shelf. They may now have to master a third environment: the algorithmic shelf. In that market, the brands with the largest advertising budgets will not necessarily have an automatic advantage. Success may increasingly depend on which companies can organise their data, detect changes quickly, make their products understandable to AI systems and respond before competitors recognise that shopper behaviour has changed.

The transition is still at an early stage, and fully autonomous commercial decision-making remains considerably more ambitious than today’s deployments. But agent-assisted shopping is no longer theoretical. As retailers and technology companies give AI systems greater roles in product discovery and purchasing, consumer brands face a strategic question that barely existed a few years ago: when the shopper’s AI starts deciding what deserves to be bought, how does a brand make sure it remains part of the choice?

Source: CIJ.World Research & Analysis Team

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