Dropbox Says the Real AI Advantage Is Not Faster Tasks but Better Business Outcomes

Artificial intelligence is rapidly increasing the amount of work companies can produce, but Dropbox CTO Ali Dasdan argues that output alone is the wrong measure of whether AI is creating value. The more important test is whether greater speed translates into better products, stronger decisions, higher customer satisfaction and measurable business results.

Speaking at Ai4 2026 in Las Vegas, Dasdan described how Dropbox is applying AI across engineering, collaboration and product development. His central argument was that companies should distinguish between inputs, outputs and outcomes. AI may generate more code, documents, summaries or decisions, but those outputs only matter when they eventually improve something important to the customer or the business.

That distinction is becoming increasingly relevant as companies report large increases in AI-assisted work. Dropbox says adoption among its software engineers rose rapidly from around 40% in March 2025 to effectively universal use by the end of that year. Dasdan said AI use has since become widespread across the wider company, while many engineers now work with several AI tools rather than relying on a single system.

Dropbox has also developed Nova, its internal platform for running coding agents. The system allows engineers to assign work to AI agents operating within Dropbox’s engineering environment, while retaining the context, validation processes and human oversight required to move changes into production. The experience illustrates one of the emerging problems with AI productivity. Faster coding does not automatically result in faster product delivery. As developers generate more code, pressure moves further down the development chain to code review, continuous integration, testing, validation, security and deployment.

Dropbox has acknowledged this effect in its own engineering research. Its engineers have found that increasing AI-assisted coding throughput can expose capacity constraints elsewhere in the software development lifecycle. The implication is that companies cannot simply introduce coding tools and expect overall productivity to rise at the same rate.

Measurement therefore becomes critical. Dropbox is increasingly evaluating productivity through a combination of speed, effectiveness, quality and business impact rather than relying on a single metric such as lines of code or the number of pull requests completed. Quality remains particularly important. AI-assisted development has to operate within security, privacy, reliability, performance and data-quality requirements. Measuring a large increase in code production without examining what happens to testing, vulnerabilities or customer experience could create the appearance of productivity while shifting additional costs elsewhere.

Dasdan also warned against using productivity dashboards as employee surveillance systems. Individual metrics can easily create undesirable incentives and can often be manipulated. A more useful approach is to combine different indicators and use them to identify where processes are improving or where new bottlenecks are developing.

One potentially more meaningful indicator is what employees do with the capacity that AI releases. Dasdan said Dropbox has seen engineers use additional time for work that sits beyond formal product-roadmap commitments, including security improvements, technical-debt reduction and infrastructure migrations. One example presented involved the migration of approximately 1,900 Python packages, which Dasdan said one employee was able to automate using AI over a period of roughly two weeks. Such examples are internal Dropbox results rather than independent productivity benchmarks, but they demonstrate the type of work companies may increasingly automate as AI becomes integrated into engineering systems.

This raises a broader organisational question. Companies originally expected AI productivity primarily to mean completing the existing workload with fewer hours. A potentially more important effect may be that employees use the released capacity to address problems that organisations previously lacked the resources to prioritise.

Collaboration presents a different challenge. AI systems become substantially more valuable when they understand the context surrounding the work rather than responding only to individual prompts. For businesses, that context may exist across files, emails, meetings, messaging systems, project-management platforms and customer databases. Connecting those sources allows an AI system to answer questions based on an organisation’s actual knowledge rather than relying primarily on the general information contained in its underlying model.

Dropbox is pursuing this idea through Dropbox Dash, its AI-powered search and knowledge platform. Dash can connect information from different workplace applications, allowing authorised employees to search across multiple sources from a common environment. Dasdan described situations where an executive entering a board meeting could receive an unexpected question and retrieve relevant internal information through AI without having to locate a colleague who already knows the answer.

The important element is not simply search. It is the combination of search with organisational context and access controls. This creates a major security issue for enterprise AI. Information from different systems cannot simply be indexed and exposed to an AI layer without maintaining the permission structures surrounding it. AI systems are capable of combining information from multiple sources, making access boundaries considerably more complicated than conventional document search.

Dropbox says Dash respects the permissions established within connected applications so users should only receive information they are authorised to access. Dasdan argued that this type of control must be incorporated into the architecture from the beginning rather than added after an AI system has already been created.

Organisational adoption also depends on more than providing employees with software. Dropbox has experimented with AI champions, internal training, hack events and collaboration between technical and non-technical teams. This is becoming increasingly important because generative AI is allowing employees without conventional programming experience to build applications and automate workflows themselves. That democratises software development, but it introduces another type of risk.

A non-technical employee may be able to create a functioning application without understanding monitoring, security, scalability or the downstream consequences of operating it. Dropbox’s approach therefore includes pairing employees with people who understand the technical environment and encouraging AI champions within different business functions.

An AI specialist working inside human resources, for example, may be more effective at demonstrating relevant applications to colleagues than an engineer explaining the same technology from outside the department. The language, problems and workflow are already familiar. The result can become an organisational network effect. Once employees see colleagues performing work that previously required specialist technical knowledge, experimentation spreads more quickly across the business.

The third major area of change is product development itself. Generative AI has dramatically reduced the time required to create software, but the bottleneck is increasingly shifting from writing code to deciding what should be built. If companies can produce new features far more quickly, understanding customer problems becomes more valuable rather than less.

Traditional product development usually involves product managers interviewing customers, conducting research and translating what they learn into requirements for engineers. Generative AI creates the possibility of widening that feedback loop substantially. Customer-support tickets, calls, product feedback, transcripts and other unstructured information can increasingly be analysed continuously. Instead of forming a product hypothesis after speaking with a relatively small sample of customers, companies can potentially identify recurring problems across a much larger proportion of their customer base.

Those insights can then be supplied directly to developers and AI agents. This begins to blur the traditional boundaries between product management, engineering and design. If an AI development agent can access the original customer problem, supporting evidence and criteria defining a successful solution, there may be less need to translate information repeatedly through several organisational layers before development begins.

However, faster product development introduces another constraint that AI cannot easily remove: the speed at which customers themselves can absorb change. Technically, software companies may increasingly be able to release major changes daily or even several times per day. Customers may not want interfaces and workflows to change at anything approaching the same rate.

Companies therefore face an emerging mismatch between machine development speed and human adoption speed. The ability to produce more features will not necessarily justify releasing them all immediately. This reinforces Dasdan’s original argument that productivity cannot be judged by output alone. Generating more software is not useful if customers cannot understand it, do not need it or experience constant disruption as products change.

Economics will also increasingly influence how companies design AI systems. The most powerful model will not necessarily be the appropriate choice for every problem. Businesses are likely to combine different models, using relatively inexpensive systems for routine work and more capable models for complex reasoning. They must also decide which parts of the AI stack should be developed internally and which should be purchased from external providers.

Data and organisational context may ultimately become one of the most defensible advantages. As access to powerful AI models becomes increasingly widespread, competitors can often obtain similar underlying technology. What differs is the information surrounding the model: internal documents, customer knowledge, operational history, workflows, permissions and proprietary data. That context can determine whether AI produces a generic response or a genuinely useful business answer.

Dropbox’s experience therefore points towards a broader transition in corporate AI strategy. The first stage was experimentation. The second was adoption. The next phase is likely to involve rebuilding workflows, development systems and information architecture around a workplace in which AI-generated output is abundant.

The limiting factor may no longer be how quickly employees can produce work. It may instead be whether organisations can identify the right problems, provide AI with the right context, maintain quality and security, redesign processes to absorb much greater output and ultimately convert that additional capacity into something customers actually value. That is the difference between using AI to perform tasks faster and using it to create business leverage.

Source: CIJ.World Research & Analysis Team

Dekpol Adds 106 Homes to Growing Osiedle Pastelowe Development in Gdańsk

Dekpol Deweloper has started presales for the sixth phase of Osiedle Pastelowe in southern Gdańsk, adding 106 apartments to a residential development that is gradually evolving into a neighbourhood of around 1,300 homes.

The latest phase offers two-, three- and four-room apartments ranging from approximately 35 to 82 sqm. Prices for the smallest two-room properties start at around PLN 481,000, according to the developer. The new homes form part of a considerably larger development programme. Osiedle Pastelowe currently comprises 11 buildings containing 754 apartments, while Dekpol ultimately plans to expand the scheme to around 20 buildings and approximately 1,300 homes.

Rather than developing the residential buildings in isolation, Dekpol has been adding commercial and recreational infrastructure as the population of the project increases. The development includes pedestrian areas, playgrounds, outdoor exercise facilities and fitness areas, while a multifunctional sports court is planned as subsequent phases are completed.

“The launch of presales for the sixth phase is a natural continuation of the development of Osiedle Pastelowe. As new buildings are added, the number of residents grows, so at the same time we are expanding services, recreational areas and places that encourage everyday interaction. People choosing an apartment in the new phase are moving into an established environment and joining a community that has been developing here for several years. We have prepared a variety of apartment sizes and layouts so that the offer meets the needs of both people looking for their first home and families requiring more space,” said Rafał Skonieczny, Member of the Management Board and Sales and Marketing Director at Dekpol Deweloper.

A separate commercial building forms part of the wider development, providing space for everyday services. The planned tenant mix includes a grocery store, bakery and confectionery outlet, drugstore, fitness facility and nursery, while some commercial units remain available for lease. The addition of this infrastructure reflects the increasing scale of the development. As the residential population grows through successive phases, the project is creating its own customer base for local retail and services.

Homes in the sixth phase will have private outdoor areas, including balconies and roof terraces. The buildings are planned with separation of around 30 metres, intended to provide additional daylight and privacy between apartments. The technical specification includes photovoltaic panels contributing electricity to common areas, fibre-optic infrastructure, video intercoms and external monitoring. Storage rooms and bicycle facilities are also incorporated into the development.

Osiedle Pastelowe is located in the southern part of Gdańsk, an area that has absorbed a substantial share of the city’s residential expansion. Road connections and public transport provide access to central Gdańsk and other parts of the metropolitan area, as well as the Tri-City Ring Road.

The sixth phase represents another step towards completing the broader Osiedle Pastelowe masterplan. With approximately 1,300 apartments ultimately planned alongside retail, services and recreational facilities, the project demonstrates how larger residential developments in Gdańsk’s expanding districts are increasingly being planned as integrated neighbourhoods rather than collections of individual apartment buildings.

SAP Sees AI Moving Finance Towards Continuous, Agent-Led Operations

Artificial intelligence could automate or support the majority of routine finance processes within the next several years, changing how CFO teams handle forecasting, financial close, tax, treasury, compliance and reporting, according to a presentation at AI4 2026 examining SAP’s vision for the finance organisation of the future.

Speaking during the session “Finance Reimagined: How AI is Automating Every Finance Function With SAP,” the SAP presentation argued that finance departments are moving beyond isolated machine-learning applications and generative AI tools towards a model in which specialised AI agents work across interconnected financial processes.

SAP calls this direction “autonomous finance”. The concept does not envisage finance departments operating without people. Instead, AI assistants and agents would perform more of the repetitive analysis and processing while employees remain responsible for oversight, judgement, approvals and higher-level decisions.

The shift comes as finance departments face growing external pressures. Regulatory requirements are becoming more complicated, including the continuing expansion of electronic invoicing regimes, while companies also have to react more quickly to tariffs, geopolitical disruption, supply shortages and other unexpected economic developments.

Talent represents another challenge. Experienced finance professionals will continue to leave the workforce through retirement, while younger employees are entering organisations with different expectations about the type of work they want to perform. Routine processing, repetitive reconciliations and lengthy manual reporting may become increasingly difficult to justify when technology is capable of carrying out a growing share of those activities.

At the same time, removing repetitive work creates its own problem. Junior accountants traditionally gained experience through precisely the tasks that AI may increasingly perform. If young employees no longer spend years manually processing transactions and preparing financial information, businesses will need alternative methods of developing the judgement required by future controllers, finance directors and CFOs.

The presentation suggested that mentorship could therefore become more important rather than less. Young professionals may need to work more closely with experienced employees so that judgement, context and business understanding can be transferred deliberately instead of being accumulated gradually through repetitive work.

This becomes especially important as companies introduce agentic AI into processes where a human remains responsible for the final decision. The more work delegated to machines, the greater the importance of ensuring that employees reviewing their recommendations understand what a correct result should look like.

SAP’s broader proposition is that a large proportion of finance processes can eventually be fully or partly automated with AI. The presentation put the potential figure at around 80%, although this should be understood as SAP’s view of the opportunity rather than an independently established measure of current automation across businesses.

The commercial consequence could be substantial. Finance teams traditionally spend considerable time collecting data, preparing reports, reconciling accounts and explaining historical results. If AI performs more of that work, employees can devote greater attention to planning, financial analysis, scenario testing and advising senior management.

This could accelerate the transition from periodic financial management towards continuous financial management. Planning and budgeting are one example. Annual budgets and quarterly forecasts exist partly because producing and updating them requires substantial effort. If AI agents can continuously process operational and financial information, companies could increasingly move towards rolling forecasts that adjust as business conditions change.

That would make finance less dependent on reporting cycles and potentially allow companies to respond more quickly to changes in sales, costs, working capital, currencies or external market conditions.

The presentation also demonstrated how this could change the financial close. Instead of employees manually reviewing every clearing item or reconciliation, specialised agents can analyse transactions and prepare proposed actions for human approval. An accounts receivable agent, for example, could identify transactions that appear suitable for clearing, while an intercompany reconciliation agent could compare receivables and payables between entities and highlight exceptions requiring attention.

Journal entries could similarly be prepared for review rather than constructed completely manually. The important distinction is that the accountant remains involved. In the demonstration, employees reviewed the agent’s reasoning and approved proposed actions before they were posted.

Once routine closing activities had been completed, the same AI environment could move into financial analysis, assessing measures such as gross margin, liquidity, returns and expense ratios.

This combination of execution and analysis illustrates a larger change occurring in enterprise software. Rather than employees moving between multiple applications and extracting information themselves, conversational interfaces are increasingly becoming a control layer through which users instruct software to retrieve information or perform tasks.

SAP’s Joule platform is designed around this concept. Instead of functioning simply as a chatbot, it is intended to connect users with financial information, applications and specialised agents through natural-language instructions.

For finance departments, the difference is important. A general-purpose chatbot may answer questions or generate text, whereas an enterprise agent connected to financial systems could potentially analyse transactions, apply business rules and initiate workflows.

That capability also raises governance questions. Financial systems require considerably stronger controls than many everyday AI applications because errors can affect company accounts, tax positions, payments and regulatory reporting.

SAP is therefore placing significant emphasis on auditability. The presentation said activity performed by AI agents can be recorded alongside information identifying the agent, the assistant involved and the employee who reviewed or approved the action.

Taxation is another area where greater automation could have an important financial effect. Tax departments frequently depend on information produced elsewhere in an organisation. Delays in receiving that information can reduce the time available to assess liabilities, structure transactions or plan efficiently.

Giving tax teams faster access to relevant information could therefore provide benefits beyond reducing administrative work. The larger advantage could come from allowing tax specialists to make decisions earlier.

Treasury presents similar opportunities. AI systems could monitor liquidity, cash transfers, currency exposures and working capital against predefined policies, escalating exceptions to employees rather than requiring people to examine every transaction manually.

Compliance monitoring could also become more continuous. The presentation cited an example involving Pfizer, where AI was described as supporting internal-control monitoring across the organisation rather than relying exclusively on periodic reviews. Such examples illustrate how finance controls could gradually move from retrospective sampling towards more continuous monitoring.

Reporting itself may also change. Companies often continue producing reports because they have historically been requested rather than because management still uses them. AI-powered self-service tools could allow authorised employees to retrieve or generate information when required, reducing the need for finance departments to maintain large catalogues of recurring reports.

One organisation referenced during the presentation was said to have removed approximately three quarters of its previous reports after introducing greater self-service capabilities. The figure should be regarded as an individual case study rather than evidence of a typical outcome across companies.

The implications for CFOs extend beyond technology expenditure. Boards are increasingly likely to ask finance leaders what return companies are receiving from their investment in AI. Some benefits will be measurable through lower processing costs, shorter close cycles or reduced manual effort.

Other returns will be less straightforward. Giving skilled finance professionals more meaningful work could improve employee satisfaction and retention. Faster information could improve management decisions without producing an easily identifiable cost saving. Better control monitoring could reduce risk even if it does not immediately increase reported profit.

The challenge for CFOs will therefore be developing measures that capture both direct financial returns and improvements in organisational capability.

SAP’s position is also that AI transformation should not automatically become a multi-year corporate programme. The presentation recommended beginning with a specific problem. Finance leaders could ask experienced employees which activities consume significant amounts of time while providing relatively little professional value.

Companies could then select one suitable process, introduce automation, evaluate the result and progressively move into other areas. This reflects a broader message emerging from AI4 discussions on finance: businesses should begin with an operational outcome rather than deciding they need AI and then searching for somewhere to use it.

The longer-term change, however, could be much larger than individual use cases. If financial close, forecasting, treasury, tax, compliance and reporting increasingly become supported by connected AI agents, the traditional finance organisation may gradually shift from producing information towards supervising a continuously operating financial system.

That would not necessarily eliminate the CFO organisation. It would change what people inside it are expected to do.

The finance professionals who remain most valuable would increasingly be those capable of interpreting results, challenging AI recommendations, understanding controls, communicating with operating teams and translating continuously updated financial information into business decisions.

The ultimate destination is therefore not finance without people. It is finance in which considerably less human effort is spent assembling information and considerably more is spent deciding what the organisation should do with it.

Source: CIJ.World Research & Analysis Team

Japan’s Creative Industries Are Opening a New Real Estate Frontier

Japan’s global influence in animation, gaming and film is increasingly developing a physical property dimension. As the country seeks to expand its creative industries internationally, companies need more sophisticated places to produce, develop and commercialise content. Studios, digital workplaces, post-production facilities and dedicated creative campuses are consequently becoming a small but increasingly relevant part of Japan’s commercial real estate landscape.

The opportunity is being strengthened by government ambitions to turn Japanese entertainment and intellectual property into a considerably larger export industry. Animation, games, film and other forms of content are increasingly viewed as areas where Japan can use its established creative reputation to generate economic growth overseas. Expanding these industries, however, requires investment not only in people and technology but also in the buildings and technical infrastructure where production takes place.

Film and television demonstrate this relationship particularly clearly. Japan has introduced substantial financial incentives to encourage qualifying international productions to carry out filming and post-production work in the country. Greater production activity can translate directly into demand for sound stages, production offices, editing facilities, equipment storage and other specialist space.

Large film campuses illustrate how different these properties are from conventional offices. TOHO Studios in Tokyo occupies approximately 78,000 sqm and includes ten sound stages alongside production, post-production and supporting facilities. Such campuses require substantial amounts of land, specialist construction and technical infrastructure, making them a distinct form of commercial property.

If Japan succeeds in attracting more domestic and international screen production, utilisation of existing studios should increase while additional investment could become necessary in both production and post-production capacity. This could also benefit supporting businesses located around established production centres, from equipment companies and digital specialists to logistics and technical services.

Gaming creates a different property requirement. Japan remains one of the most influential countries in the global games industry, but contemporary game development increasingly resembles a combination of software engineering, entertainment production and digital design.

Large development teams can bring together programmers, artists, sound specialists, designers and other technical employees. Their requirements are consequently closer to those of technology companies than traditional media businesses, creating demand for well-connected offices with strong digital infrastructure and flexible environments capable of accommodating collaborative production.

Competition for talent makes location particularly important. Tokyo’s high-quality office market has become increasingly tight, meaning gaming and other creative businesses compete with technology companies and conventional corporate occupiers for suitable premises. Convenient rail access and proximity to established employment and entertainment districts can therefore become important elements of a company’s ability to recruit and retain specialist employees.

Animation provides perhaps the clearest indication that Japan’s creative property requirements are beginning to change.

The industry has traditionally operated through a fragmented network of studios and subcontractors, many occupying comparatively modest premises. As Japanese animation generates greater international commercial value, larger media companies have stronger incentives to improve working environments, consolidate production functions and create facilities capable of supporting larger teams.

KADOKAWA’s Studio One Base in Tokyo demonstrates this transition. Scheduled to begin operating in autumn 2026, the approximately 4,628 sqm facility in Ikebukuro is expected to bring several animation businesses and related functions together under one roof, accommodating around 400 employees.

The significance for real estate extends beyond the size of the project. Consolidating production teams within a purpose-designed environment can allow companies to share facilities, improve communication between departments and create better working conditions for employees.

Its location also demonstrates the importance of creative clustering. Ikebukuro already has strong links with anime, manga, entertainment and character-based retail. Bringing production companies into districts where related businesses, consumers and creative talent are already concentrated can reinforce local economic ecosystems.

Similar clustering has long been visible in technology and life sciences, where businesses benefit from proximity to specialist employees, suppliers and research institutions. Creative industries can generate comparable effects, although the buildings they occupy may range from conventional offices to highly specialised production facilities.

This diversity is important for property investors. Creative real estate should not be regarded as one uniform investment sector.

Film studios can require large sites, high ceilings, acoustic treatment and expensive technical infrastructure. Animation businesses may occupy adapted offices or purpose-built creative campuses, while gaming companies are more likely to compete within the wider market for high-quality digital workplaces.

The investment opportunity therefore lies in understanding individual occupier requirements rather than simply attaching a creative-industry label to conventional property.

Tokyo’s rising office costs could also influence where the next generation of creative businesses chooses to locate. Companies requiring substantial floor space may find it increasingly difficult to justify the most expensive central districts, particularly when their employees do not need to be located alongside traditional corporate headquarters.

Well-connected districts outside Tokyo’s conventional business core could consequently benefit. Locations offering strong public transport, comparatively affordable premises and existing cultural or technology clusters may become increasingly attractive to growing creative companies.

Government ambitions add a longer-term dimension to this trend. Japan wants overseas revenues generated by its content industries to increase substantially over the coming decade. Achieving that objective will require greater production capacity alongside improvements in international distribution and commercialisation.

That creates a straightforward property implication. More content production requires places in which that content can be created.

The result is unlikely to be a wave of investment comparable with Japan’s logistics or residential sectors. Creative property is more specialised, individual occupiers have very different requirements and many facilities may continue to be developed or controlled directly by media companies.

Nevertheless, the sector represents an interesting extension of Japan’s commercial property market. Purpose-built studios, creative campuses and specialised offices can become increasingly valuable infrastructure as entertainment companies expand their international businesses.

Japan has spent decades exporting some of the world’s most recognisable games, characters, films and animation. The next stage of that growth will depend partly on something considerably less visible to global audiences: the physical infrastructure behind the content.

As Japan turns its creative industries into a larger component of its economic strategy, the studios, offices and production hubs where those ideas are transformed into commercial products could become an increasingly important niche within the country’s real estate market.

Source: © CIJ.World Japan Research & Analysis Team

England’s Investment Map Is Changing as Regional Cities Challenge London for Capital

For decades, institutional property investment in England followed a relatively simple hierarchy. London occupied the top position, while Birmingham, Manchester and Leeds offered higher yields to investors willing to accept smaller markets and lower liquidity. That hierarchy still exists in 2026, but the relationship between the capital and the major regional cities is becoming considerably more complicated. The change is not because London has suddenly lost its appeal. It remains England’s deepest property market, attracts the largest volume of international capital and offers a scale of occupational demand that no regional city can reproduce. Instead, investors are becoming more selective about how much they are willing to pay for those advantages.

Across offices, rental housing, logistics, hotels and mixed-use development, the strongest risk-adjusted opportunity is no longer automatically found in London. Manchester, Birmingham and Leeds are increasingly capable of offering combinations of income, rental growth and development potential that can compete with the capital, particularly when London’s substantially higher entry prices are taken into account.

Investment volumes still demonstrate the scale of London’s advantage. During the first five months of 2026, approximately £5.3 billion was invested in London commercial property. Manchester attracted around £780 million and Birmingham approximately £600 million during the same period. The difference remains enormous. But investment volume measures where capital has been deployed rather than whether it achieved the best return. London’s scale allows investors to buy larger assets and portfolios and provides considerably more liquidity when they eventually want to sell. That security is valuable, particularly for global institutions deploying hundreds of millions of pounds. The question is how much investors should pay for it.

The office sector illustrates the changing relationship particularly clearly. Central London leasing remained healthy during the second quarter of 2026, with approximately 2.5 million to 2.7 million sq ft of take-up depending on the market measure used. Around three-quarters of that activity involved high-quality accommodation, reinforcing the continuing preference for modern offices. The strongest London buildings are benefiting from scarcity. In the West End, exceptional offices in Mayfair and St James’s can command rents dramatically above the rest of the country. Prime City accommodation is also experiencing rental growth as businesses compete for modern, energy-efficient buildings in strong locations.

Investors pay heavily for that security. Prime West End office yields remained below 4% around the end of the second quarter. This means buyers accept relatively modest initial income because they expect strong rental growth, low long-term vacancy and continued demand from international investors when the building is eventually sold.

Manchester offers a very different equation. Office take-up during the first half of 2026 was close to half a million square feet, broadly consistent with recent averages. The more important statistic is the shortage of new accommodation. Available newly built Grade A offices have fallen to exceptionally low levels, while prime rents have moved towards £50 per sq ft.

That scarcity creates potential pricing power for landlords. Manchester occupiers pay far less than their London counterparts, but businesses seeking the best buildings have increasingly limited choice. With little speculative development expected to provide immediate relief, prime rents have room to increase. For investors, this creates an attractive combination. They can acquire property at a higher initial yield than in prime London while potentially benefiting from rental growth caused by limited supply.

The trade-off is liquidity. A major London office can attract capital from investors around the world. Manchester has a smaller pool of buyers, particularly for very large transactions. Investors therefore receive additional income partly because they are accepting greater exit risk.

Leeds takes this scarcity argument even further. The city’s office market strengthened during the second quarter, while availability of newly built Grade A accommodation fell to extremely low levels. The construction pipeline is also limited. This creates an opportunity for investors prepared to deliver new offices or reposition existing buildings.

A good secondary office in Leeds that can be refurbished to modern standards may have considerable upside if occupiers have few newly constructed alternatives. The investment strategy is therefore less about acquiring passive income and more about creating the quality of accommodation tenants increasingly demand. Leeds remains a smaller and less liquid market than Manchester, but that can also produce more attractive acquisition prices.

Birmingham presents a different opportunity. Its office market was considerably softer during the first half of 2026, with leasing below recent historical averages. Nevertheless, high-quality accommodation continued to dominate the deals that did occur. The weakness creates greater risk but potentially more interesting entry pricing.

Investors buying Birmingham offices today are less likely to be purchasing a strong momentum story. They are making a longer-term judgement about the city’s population, universities, infrastructure, regeneration and eventual economic growth. That makes Birmingham potentially the most contrarian of England’s major regional office markets. An investor prepared to buy high-quality property while occupational conditions remain relatively subdued could benefit if leasing strengthens later in the cycle.

The hierarchy therefore changes depending on strategy. London offers the greatest liquidity and highest rents. Manchester combines strong institutional depth with constrained modern supply. Leeds offers a particularly interesting scarcity story. Birmingham provides greater potential for repricing but requires more patience.

Rental housing produces another answer entirely. Institutional investment in Britain’s living sectors accelerated during the first half of 2026, with billions of pounds committed to multifamily housing, student accommodation and other managed residential formats. Manchester, Birmingham and Leeds are increasingly important destinations for this capital because their populations contain large numbers of graduates and younger professionals who rent for extended periods.

The attraction is not simply tenant demand. Regional cities can offer development economics that are increasingly difficult to achieve in London. London rents are much higher, but so are land values, construction costs and planning obligations. A developer may generate significantly more rent from a London apartment while spending disproportionately more to create it. Regional Build to Rent projects can sometimes achieve a better relationship between construction cost and rental income.

Manchester is particularly advanced in this respect. Large institutional rental developments have transformed parts of the city centre, creating an established market with professional operators, operating data and comparable transactions. That matters to institutional capital. Once a market contains numerous operating schemes, investors can evaluate occupancy, rent growth and operating costs using real evidence rather than relying primarily on forecasts. Manchester has therefore moved beyond being an experimental BTR market. It is increasingly an established institutional residential location.

Birmingham offers a different advantage: development scale. Large regeneration sites around the city provide opportunities to deliver hundreds or thousands of homes within mixed-use districts. Institutional rental housing can form a major component of those schemes. Leeds is smaller but is moving in the same direction as its city-centre residential population expands and more institutional capital enters the market.

London nevertheless retains enormous advantages in living investment. Housing supply remains severely constrained, barriers to home ownership are high and rental demand is exceptionally deep. The difficulty is converting that demand into acceptable development returns. Land values weakened further during the second quarter of 2026 as high construction costs and financing expenses continued to restrict what developers could afford to pay.

This produces one of the clearest examples of the changing investment geography. London can have stronger housing demand than Manchester, Birmingham or Leeds while simultaneously offering weaker development economics. For investors prepared to sacrifice some liquidity and absolute rental level, regional BTR can therefore produce more attractive risk-adjusted opportunities.

Logistics reverses the traditional property hierarchy even more dramatically. A warehouse does not become strategically important because it is located in a prestigious city centre. Its value depends on how efficiently goods can reach customers. Motorway access, population coverage, labour availability, power and land supply matter far more.

This gives the Midlands a structural advantage. Distribution centres around Birmingham and the wider motorway network can serve large parts of Britain within a relatively short journey. The M1, M6, M42 and associated routes make the region central to national logistics networks. Modern warehouse demand remained resilient during the second quarter of 2026, while occupiers continued concentrating on high-quality accommodation.

For national distribution, a Midlands warehouse can therefore be strategically more important than a considerably more expensive building in the South East. London retains a powerful advantage in last-mile logistics. Millions of consumers live within a relatively small area, while industrial land is extremely scarce. Warehouses capable of serving central London quickly can consequently command very high rents and land values.

But investors pay a substantial premium for that scarcity. For national distribution centres, the calculation frequently favours the Midlands. Manchester and Yorkshire also benefit from their ability to serve large northern populations, providing alternatives to the traditional concentration of logistics capital in southern England. This means there is effectively no single English logistics investment hierarchy. The appropriate location depends on whether the building serves national distribution, regional distribution or last-mile delivery.

Hotels produce another contrasting picture. London dominated hotel investment during the first half of 2026, attracting roughly £2 billion of transactions under one major market measure. International capital remains comfortable buying London hotels because the city has one of the world’s deepest combinations of leisure, corporate and international visitor demand. That liquidity is difficult for regional cities to match.

Yet operational growth has not necessarily been strongest in London. Several major regional hotel markets recorded faster growth in room revenues and profitability during the first half of the year. Manchester has a particularly diversified demand base. Business travel is complemented by football, concerts, conferences, nightlife and leisure tourism. Major events can generate substantial compression in room availability and pricing.

Birmingham benefits from conferences, exhibitions and the National Exhibition Centre as well as corporate and leisure demand. Leeds has a smaller hotel market but benefits from business activity, retail, entertainment and regional tourism.

For investors, the choice again becomes one between liquidity and potential return. A London hotel may be easier to sell and finance. A regional hotel purchased at a higher yield may provide stronger income relative to its acquisition price, particularly if operating performance continues improving.

Mixed-use regeneration may represent the area where regional cities possess their greatest structural advantage. Large development sites within London are extraordinarily expensive and frequently complicated by planning obligations, fragmented ownership and infrastructure requirements. Manchester, Birmingham and Leeds still contain substantial central sites capable of accommodating entire new districts.

Manchester has demonstrated the model through major developments around Mayfield, St John’s, Circle Square and Victoria North. Birmingham has long-term regeneration opportunities around Smithfield, Paradise, Digbeth and areas influenced by future HS2 infrastructure. Leeds continues expanding through South Bank and other large developments.

These projects allow investors to combine residential, offices, hotels, retail, leisure and public spaces within a single strategy. The ability to acquire land at sufficient scale can create value that would be extremely difficult to reproduce in central London.

But regional regeneration carries greater market-creation risk. A London developer can generally assume an enormous underlying population and employment market around a project. In a regional regeneration district, the developer may need to establish the neighbourhood itself before targeted rents and values can be achieved. Public realm, restaurants, retail, transport connections and amenities become part of the investment strategy rather than simply features surrounding the property. That requires patient capital.

Among the regional cities, Manchester currently appears closest to functioning as a fully developed institutional property market. It offers meaningful investment opportunities across offices, rental housing, student accommodation, hotels, industrial property and regeneration. Capital can therefore be deployed repeatedly across several sectors rather than relying on a single asset class.

This depth matters. Large institutional investors rarely want to undertake one transaction and leave. They prefer markets where they can build portfolios, develop local operating knowledge and eventually sell to other institutional buyers. Manchester increasingly provides that environment.

Leeds offers a smaller but potentially compelling proposition. Its limited supply of modern offices creates opportunities for rental growth and refurbishment, while institutional residential investment is becoming more established. The lower level of liquidity remains the trade-off.

Birmingham could offer the greatest value for investors willing to take a longer view. Its weaker current office performance means pricing may not reflect the same optimism seen in stronger markets, while the city retains substantial structural advantages including population scale, universities, regeneration land and major transport investment. That makes Birmingham particularly interesting for investors seeking to buy before the market fully strengthens rather than after it has already repriced.

None of this means London has lost its premium. For the capital’s best assets, that premium remains justified. London provides unparalleled transaction depth, international capital, occupational diversity and financing liquidity. A prime office in Mayfair, a major central London hotel or a strategically located urban logistics facility can attract a pool of buyers that regional property cannot easily reproduce. That reduces exit risk. For investors managing very large amounts of capital, liquidity itself has value.

The problem arises when the London label is applied to secondary property. A mediocre office in London does not automatically represent a safer investment than an exceptional office in Manchester or Leeds. A London residential development with difficult land economics may produce a weaker return than an institutional rental scheme in Birmingham. A South East distribution warehouse acquired at an aggressive price may be less strategically important than a Midlands facility capable of serving most of the country.

The comparison investors need to make is therefore changing. It is no longer simply London against the regions. It is prime London against prime Manchester, secondary London against prime Leeds, London residential development against Birmingham Build to Rent, and South East logistics against Midlands distribution. Once England is examined this way, the traditional hierarchy becomes much less rigid.

The country’s institutional property market is becoming increasingly specialised. London remains the centre of global capital and the deepest market for premium property. Manchester has developed into the strongest all-round regional institutional market. Leeds offers scarcity-driven opportunities, particularly where modern supply is limited. Birmingham provides greater potential for repricing and regeneration but requires investors to accept more occupational and development risk.

The best location therefore increasingly depends on what the investor is trying to achieve. Capital seeking maximum liquidity may still prefer London. Investors seeking income and rental growth can increasingly find stronger opportunities in regional cities. Development capital may prefer locations where land remains affordable enough for projects to proceed. Logistics investors will follow transport networks rather than traditional city hierarchies. Living investors will concentrate on the relationship between rents, population growth and development cost.

England is consequently becoming less dependent on a single dominant property market. London is not being replaced. Its advantages remain too substantial. What is changing is the assumption that paying London’s premium automatically reduces investment risk.

For exceptional London assets, it often does. For everything else, investors increasingly have alternatives. As Manchester, Birmingham and Leeds develop deeper institutional markets of their own, the question facing global capital may no longer be how much exposure it wants to London.

It may be how much of England it can afford to ignore.

Source: CIJ.World UK Research & Analysis Team

From Pharma to Property: India’s Life Sciences Sector Opens a New Real Estate Frontier

India’s pharmaceutical and biotechnology industries are creating a new opportunity for the country’s commercial property market as scientific research, advanced manufacturing and global corporate operations require increasingly specialised buildings.

For decades, India’s life-sciences story was largely associated with pharmaceutical manufacturing, generic medicines, active pharmaceutical ingredients and vaccines. That industrial base remains important, but the sector is gradually moving towards higher-value activities including biotechnology research, biologics, clinical development, medical technology and advanced pharmaceutical services.

This evolution is changing the type of real estate companies require. Conventional offices and industrial buildings cannot always accommodate modern scientific operations. Research laboratories may need sophisticated ventilation, greater electrical capacity, controlled environments, specialist waste systems and temperature management. Pharmaceutical production can require highly regulated manufacturing areas, while biological products and clinical materials create additional demand for temperature-controlled logistics.

As a result, life sciences is beginning to occupy a distinctive position between office, industrial, healthcare and infrastructure real estate.

India has the economic base to support the sector’s expansion. The country’s bioeconomy exceeded USD 165 billion in 2024 and the government is targeting approximately USD 300 billion by 2030. Continued expansion would create opportunities not only for pharmaceutical manufacturers but also for developers providing the physical infrastructure required by research and technology businesses.

Government policy is reinforcing this transition. The 2026–27 Union Budget introduced the Biopharma SHAKTI programme, committing ₹10,000 crore over five years to strengthen India’s capabilities in areas including biologics and biosimilars.

The programme also envisages new and upgraded pharmaceutical research institutions and a much larger network of accredited clinical-trial locations. Although primarily an industrial and healthcare initiative, its implementation could have significant property consequences because additional research, testing and manufacturing activity requires specialised facilities.

The long-term potential of the sector has already attracted attention from the property industry. Earlier market research estimated that India could require around 96 million sq. ft. of life-sciences research and development property between 2021 and 2030, potentially creating an investment opportunity measured in billions of dollars.

Those projections pre-date the current market and should not be interpreted as new 2026 forecasts. Nevertheless, they illustrate the potential scale of the property requirement if India’s scientific industries continue expanding.

The challenge for investors is that life-sciences real estate remains considerably less mature than India’s office, logistics and data-centre markets.

Laboratories and regulated manufacturing facilities can be expensive to develop and difficult to convert for other users. Their investment value therefore depends heavily on the quality of the occupier, length of the lease and strength of the surrounding scientific ecosystem.

These characteristics can also become an advantage.

Companies that invest heavily in laboratory infrastructure and regulatory approvals are less able to relocate casually than conventional office tenants. Established clusters containing research institutions, skilled employees, suppliers and other pharmaceutical companies can therefore create particularly resilient locations.

Hyderabad provides India’s strongest example.

The city’s life-sciences ecosystem has developed over several decades, with Genome Valley becoming one of the country’s most important concentrations of pharmaceutical and biotechnology businesses. More than 200 companies operate within the cluster, supported by research organisations, manufacturers and specialist scientific infrastructure.

Recent leasing activity reinforces Hyderabad’s position. Between 2023 and 2025, the city accounted for approximately 45% of India’s life-sciences global capability centre activity, substantially ahead of other major markets.

Bengaluru followed with around 26%, demonstrating the growing relationship between India’s technology economy and pharmaceutical research. Chennai accounted for approximately 13%, giving southern India a particularly strong position in the emerging market.

Hyderabad’s advantage could strengthen further as Telangana expands its life-sciences strategy.

The state is targeting USD 25 billion of additional investment and approximately 500,000 jobs under its 2026–30 plans. Telangana says it already accommodates more than 2,000 life-sciences companies and over 250 manufacturing facilities approved by the US Food and Drug Administration.

New property development is beginning to reflect those ambitions. A specialist campus announced for Genome Valley in 2026 is expected to provide more than one million sq. ft. of laboratory facilities across approximately 12 acres.

Projects of this scale are important because they indicate how the sector could evolve from companies predominantly developing their own facilities towards a market in which specialist property businesses create scientific infrastructure for multiple occupiers.

That transition would make the sector considerably more accessible to institutional real-estate capital.

Bengaluru offers a somewhat different proposition. Its strength lies in the convergence of biotechnology with software, data science and artificial intelligence. As pharmaceutical companies increasingly use advanced computing for drug discovery, clinical analysis and research, the city’s enormous technology workforce could become increasingly relevant to life-sciences investment.

This relationship is also visible in the expansion of pharmaceutical global capability centres.

International healthcare and pharmaceutical companies increasingly use Indian operations for sophisticated functions including analytics, clinical support, digital technology, research services and product development rather than simply administrative processing.

That creates demand for premium office space as well as laboratories.

The result is a property sector that does not fit neatly into traditional classifications. Some assets will resemble offices with scientific facilities incorporated into them. Others will function as advanced industrial buildings. Cold-chain operations will require specialised logistics facilities, while research campuses may combine laboratories, offices and supporting amenities within a single development.

For institutional investors, this variety creates a more complicated market but also opens several potential routes to participation.

Purpose-built laboratories leased to established pharmaceutical companies could provide long-term income. Research campuses could accommodate multiple occupiers, while specialist developers could create portfolios across India’s major scientific clusters.

Sale-and-leaseback transactions could eventually provide another source of investment opportunities if pharmaceutical businesses decide to release capital tied up in their property while continuing to occupy strategically important facilities.

The development of specialist operating platforms may prove particularly important.

India’s logistics and data-centre markets both became more attractive to international capital as experienced developers created portfolios capable of being expanded and eventually consolidated. Life-sciences property could follow a similar path, although the technical requirements of the buildings mean that development is likely to be more specialised.

Location will consequently matter enormously.

States cannot create successful life-sciences clusters simply by providing inexpensive land. Companies require access to scientists, universities, hospitals, research organisations, pharmaceutical suppliers, transport infrastructure and appropriate utilities.

Hyderabad demonstrates the value of concentrating these elements within an established ecosystem.

Other cities may develop different specialisations rather than attempt to reproduce the same model. Bengaluru can exploit its technology and biotechnology capabilities, Chennai can combine manufacturing and research, while Pune and other established pharmaceutical centres could develop additional specialist property markets around their existing industrial bases.

The opportunity extends beyond laboratories themselves.

Growing pharmaceutical manufacturing generates demand for temperature-controlled warehouses and distribution infrastructure. Expanding research campuses create requirements for offices, housing and hospitality. Large employment clusters can also support retail and other commercial development.

Life-sciences investment can therefore influence considerably more real estate than the specialist buildings occupied directly by pharmaceutical companies.

The sector remains at an early stage from an institutional property perspective. India does not yet have the depth of transactions or large stabilised portfolios found in established life-sciences real-estate markets internationally.

That distinction is important. India’s pharmaceutical industry is already global in scale, but its institutional life-sciences property market is still being created.

The combination could ultimately be what makes the opportunity significant.

India already possesses pharmaceutical manufacturing expertise, a substantial scientific workforce, major technology centres and a rapidly expanding biotechnology economy. What is now emerging is the specialist real estate required to support the industry’s next stage of development.

If more pharmaceutical and biotechnology companies choose to lease professionally developed laboratories, research campuses and specialised manufacturing facilities rather than owning all their property directly, institutional investors could gain access to an entirely new segment of Indian commercial real estate.

India’s next major property growth story may therefore emerge not from another generation of conventional offices or warehouses, but from the laboratories, research campuses and specialised facilities supporting its transformation into a higher-value global life-sciences centre.

Source: © CIJ.World India Research & Analysis Team

Paris Is Expanding Its Property Frontier as New Metro Connections Take Shape

fletcher@cijeurope.com
4 September 2026
Business, CIJ - Spotlight, Indicators, Transportation

The transformation of Greater Paris is entering a new stage as one of Europe’s largest transport projects moves progressively from construction into operation. After years in which investors could only anticipate the impact of the Grand Paris Express, the expanding network is beginning to provide a clearer indication of which suburban districts could develop into stronger property markets.

The scale of the programme is substantial. Around 200 kilometres of automated metro infrastructure and dozens of stations are being developed across the metropolitan region, alongside the extension of Line 14. Rather than simply improving journeys into central Paris, much of the new system is designed to connect suburban employment centres, residential districts, airports, universities, hospitals and research clusters directly with one another.

For real estate, that distinction is fundamental. The traditional Greater Paris investment map has been heavily influenced by distance and travel time to the centre. The new network has the potential to make accessibility between suburban economic centres increasingly important, allowing some districts to compete for residents, businesses and investment capital on different terms.

The effect will not occur everywhere at the same time. Grand Paris Express lines are being delivered progressively, with individual sections reaching operation at different points through the remainder of the decade and into the early 2030s. Timetables have also changed during construction, making the actual stage of each station and line more important to investors than earlier projected completion dates.

Saint-Denis Pleyel is already demonstrating what happens when anticipated infrastructure becomes operational. The district has been served by the extended Line 14 since 2024 and is intended ultimately to become one of the largest interchange points on the new network, connecting several metro lines.

Transport investment has arrived alongside extensive redevelopment in and around Saint-Denis, including projects associated with the transformation of former industrial land and the legacy of the 2024 Olympic and Paralympic Games. Housing, offices, public facilities, hotels and mixed-use developments are gradually changing the character of an area that historically sat outside the core institutional property markets of Paris.

For investors, however, the important question is no longer whether Saint-Denis Pleyel will become better connected. That process has already begun. The question is whether improved transport can generate sufficient long-term occupier demand and investment liquidity to support the volume of development taking place around it.

That distinction applies throughout Greater Paris. A metro station can reduce journey times dramatically, but it cannot by itself create a successful property market.

Villejuif provides a good illustration of where transport investment is being combined with an existing economic specialisation. Villejuif–Gustave Roussy is already connected to Line 14 and is intended to become an interchange with Line 15 South. The surrounding district benefits from the presence of the Gustave Roussy cancer treatment and research centre, giving it a substantial healthcare and scientific employment base.

Better accessibility could strengthen demand for more than conventional offices. Laboratories, healthcare-related facilities, residential accommodation, hotels and other property serving employees, patients, researchers and visitors could all benefit as connections across Greater Paris improve.

This makes Villejuif different from locations where developers are relying primarily on infrastructure to create demand. Transport is strengthening access to an economic cluster that already exists.

The Paris-Saclay corridor presents an even larger version of that opportunity. Universities, engineering schools, laboratories, research organisations and technology businesses have been concentrated across the area, creating one of France’s most important centres for science and innovation. Accessibility has nevertheless remained one of its weaknesses.

Line 18 is designed to change that by connecting the Saclay area with Massy and subsequently Orly Airport, with later development extending the route further west. During 2026, the first section has been moving through the testing and commissioning process ahead of passenger services.

The real estate consequences could extend across several sectors. Research facilities and specialist offices may benefit from easier access to the scientific cluster, while student accommodation and residential development could respond to better connections for the large academic population. Hotels and supporting commercial services could also gain from increased movement through the area.

Yet Saclay demonstrates why investors need to separate infrastructure potential from guaranteed property performance. Considerable development has already taken place, and future demand remains dependent on continued expansion of the area’s education, technology and research economy.

A new metro line makes Saclay easier to reach. Whether every development around it becomes more valuable will depend on the quality, location and use of the individual property.

Orly represents another type of transport-led opportunity. The airport has been connected directly with Paris by Line 14 since 2024, substantially changing public transport access to one of the metropolitan area’s largest employment centres. Line 18 will eventually create an additional connection between Orly, Massy and the Saclay corridor.

The implications extend beyond airport passengers. Hotels are an obvious component of the market, but the wider Orly and Rungis area also contains substantial logistics, industrial and commercial activity. For these sectors, metro access is unlikely to replace the importance of motorway connections or proximity to consumers. It can, however, improve access to labour, which has become an increasingly important consideration for logistics and industrial occupiers.

East of Paris, Noisy–Champs represents a different stage of the investment cycle. It is intended to become an interchange between Line 15 South and Line 16, strengthening connections with other parts of the metropolitan region.

The surrounding market contains residential neighbourhoods, university activity and substantial development potential. Its future property proposition may therefore depend more on housing, education and mixed-use regeneration than on becoming another large conventional office district.

Improved suburb-to-suburb connectivity could be particularly significant for residential markets such as this. Workers who previously needed to travel through central Paris to reach employment elsewhere in the metropolitan region could eventually gain much faster direct connections. That could expand the number of residential locations considered practical for people employed in suburban business, healthcare, education and technology centres.

This does not mean housing values near every station will automatically rise. Construction costs, mortgage conditions, planning policy, affordability and the amount of new supply will continue to influence individual markets. Transport is one component of the investment case rather than a substitute for these fundamentals.

Line 15 South could nevertheless become one of the most important changes to the southern and eastern property geography of Greater Paris. The route is intended to connect Pont de Sèvres with Noisy–Champs through a series of established municipalities without requiring passengers to travel through central Paris.

The current programme places its opening in 2027, although Grand Paris Express schedules have changed during the project’s development. Investors assessing sites around future stations therefore need to work from the latest official commissioning programme rather than older development documents.

The eventual effect could be the emergence of a stronger investment corridor running around Paris rather than towards its centre.

There is, however, an important reason to remain cautious about offices. Île-de-France entered the second half of 2026 with approximately 6.5 million square metres of immediately available office space. Leasing activity during the first half remained subdued, while investment capital continued to show a strong preference for the most established Parisian locations and highest-quality buildings.

Against that background, accessibility alone is unlikely to rescue every secondary office market. An older building with significant vacancy, weak environmental performance or expensive refurbishment requirements does not automatically become attractive because a new station opens nearby. Investors still need evidence that occupiers want to locate there and that rents can justify the capital required.

This could make the Grand Paris Express more important for some alternative property sectors than for traditional offices. Healthcare and research property around Villejuif, technology and education-related development around Saclay, hotels and commercial assets around Orly, residential and mixed-use projects around Noisy–Champs and regeneration around Saint-Denis each represent different ways in which transport investment can influence real estate.

Student accommodation may also become increasingly relevant as university campuses become easier to reach. Residential developers could benefit where journey times to major employment clusters are materially reduced, while hotels may gain around airports and large interchanges.

The immediate areas around stations deserve particular attention. Urban planning analysis of Grand Paris Express neighbourhoods frequently considers roughly an 800-metre radius around each station, broadly corresponding to a 10-to-15-minute walk.

For property investors, this provides a useful framework for evaluating where transport accessibility can interact most directly with land use and development. But even within that radius, opportunities can vary considerably. A redevelopment site beside a major interchange has different economics from an ageing office building. Residential land close to a university has a different demand profile from a hotel near an airport. Research space next to an established scientific institution may have considerably stronger fundamentals than speculative offices in a location without a substantial business base.

The Grand Paris Express therefore should not be viewed as a single property investment story. It is creating dozens of individual markets at different stages of development, with different economic drivers and different levels of transport benefit already reflected in property prices.

Some districts have spent years anticipating their new connections. Others are only now reaching the point where trains begin operating and theoretical accessibility improvements become part of everyday commuting. Locations attached to later phases of the network still carry greater delivery and timing risk.

That uneven development may create the most interesting opportunities. Investors who entered the best-known locations years ago were effectively buying the expectation of infrastructure. The next phase will be different. Increasingly, investors will be able to examine actual passenger movements, occupational demand, development activity and transaction evidence to determine whether individual station districts are genuinely becoming stronger property markets.

The eventual winners are unlikely to be determined simply by proximity to a metro entrance. They will be places where transport connects with employment, housing demand, universities, healthcare, development capacity and attractive urban environments.

For decades, central Paris has dominated the investment geography of the metropolitan region. The Grand Paris Express is unlikely to overturn that position. What it can do is make a larger number of suburban districts viable as independent investment markets.

As more of the network opens, the central question for property investors is therefore changing. It is no longer simply where the next station will be built, but which of the places surrounding those stations can turn improved accessibility into lasting real estate demand.

Old Oak’s £10 Billion Transformation Could Create London’s Next Major Investment District

For years, the property story surrounding Old Oak Common was dominated by a railway station that had not yet opened. In 2026, that story is beginning to change. Land consolidation, a search for a major private development partner and the expansion of university-backed research and innovation activity are starting to turn one of west London’s largest brownfield areas into a genuine real-estate investment proposition.

The scale is exceptional. Plans for the core regeneration area envisage approximately 8,000 homes, thousands of jobs and up to 200,000 sq m of commercial and community accommodation. Around 70 acres of publicly controlled land are being brought together to support development with an estimated value of approximately £10 billion. This is significant because fragmented land ownership has historically been one of the greatest obstacles facing large London regeneration schemes. Acquiring dozens of individual sites can take years, introduce compulsory-purchase risk and make infrastructure planning difficult. At Old Oak, most of the land required for the core project is already controlled by public bodies.

That gives government and the Old Oak and Park Royal Development Corporation an unusual opportunity. Instead of selling individual plots independently, they can create a much larger development platform and bring private capital into the regeneration at district scale. During the second quarter of 2026, that process reached an important stage as the search began for a private development and investment partner capable of helping deliver the core area. This arguably represents a more important property milestone than another announcement concerning HS2 construction. For the first time, investors are being offered a realistic route into the creation of the district.

The railway remains central to the long-term investment case, but expectations around timing need to change. Old Oak Common will eventually become one of Britain’s most connected transport locations, bringing together HS2, the Elizabeth line, Great Western services and Heathrow Express connections. However, the high-speed railway will arrive considerably later than once anticipated. Current government projections suggest passenger services between Old Oak Common and Birmingham may not begin until sometime between 2036 and 2039, while the complete route into Euston could extend into the early 2040s.

That fundamentally changes the way property investors should underwrite the opportunity. Old Oak is not a short-term station regeneration play where developers can build immediately ahead of a transport opening and capture a sudden increase in land values. Investors entering the market today may need to hold development positions for many years before the full HS2 connectivity advantage is realised. Yet the delay does not necessarily undermine the regeneration case.

Old Oak already sits within west London’s existing transport network. The Elizabeth line has transformed east-west travel, while Great Western services provide connections towards Reading and the Thames Valley. Heathrow is accessible to the west and central London to the east. The regeneration can therefore begin functioning before HS2 arrives. In some respects, this may produce a healthier property market. Instead of the entire investment case depending on one railway opening, Old Oak can develop gradually as housing, employment, public spaces and local services are completed.

Residential development is likely to provide the first major test. Delivering around 8,000 homes would create a sizeable new London neighbourhood in its own right. The opportunity could encompass conventional apartments, affordable housing and potentially substantial institutional rental accommodation. London’s chronic housing shortage provides a powerful underlying demand argument, while the site’s transport accessibility should make higher residential density commercially attractive.

But building thousands of apartments does not automatically create a successful neighbourhood. The early phases will carry a disproportionate burden. Residents need shops, schools, healthcare, public spaces, restaurants and other amenities. Streets and pedestrian routes need to work before the area can establish a recognisable identity. This creates a development challenge that is very different from constructing an individual apartment block within an established neighbourhood. The first investors are effectively helping create the market in which their own properties will eventually operate.

That is why the choice of development partner will matter so much. Old Oak needs capital capable of looking beyond individual buildings and coordinating housing, infrastructure, public realm and commercial uses over an extended period. Short-term development strategies may struggle with the scale and sequencing involved.

Institutional capital could therefore enter the district in stages. The first wave will probably include developers, infrastructure investors and long-term capital prepared to accept construction, planning and placemaking risk. A second group may arrive once residential buildings are completed and rental evidence becomes available. Pension funds, insurers and other income-focused investors could then acquire operational rental housing or participate in later development phases. Eventually, completed buildings could trade between investors in the same way as institutional assets elsewhere in London.

That transition is what will determine when Old Oak becomes a genuine property submarket rather than simply a regeneration zone. Commercial development faces a more complicated path. The area has capacity for a substantial amount of employment space, but it would be risky to assume that Old Oak will immediately become another conventional office district.

London’s office market is increasingly selective. Businesses are concentrating demand on high-quality buildings in established locations, while speculative development has become more difficult to finance without strong pre-leasing. Old Oak therefore needs a reason for companies to locate there beyond cheaper rent.

One of the most important developments during 2026 may provide exactly that. Imperial College London and the development corporation have begun working together on a broader innovation and research strategy for west London. Imperial already controls a substantial landholding in the Old Oak area and has the capacity to support millions of square feet of future development.

The potential uses extend beyond conventional offices. Laboratories, technology businesses, advanced manufacturing, research facilities, student accommodation, housing and workspace for growing companies could all form part of the district. This creates a much more distinctive proposition.

Rather than trying to compete directly with the West End, City or Paddington for mainstream corporate offices, Old Oak could become part of a west London innovation corridor linked to Imperial’s established presence at White City. Universities can play an unusually powerful role in property regeneration because they create several different forms of demand simultaneously.

Researchers need laboratories. Technology businesses need specialist workspace. Students need accommodation. University staff need housing. Spin-out companies need space to grow. Investors need credible occupiers. An established university can therefore provide the economic anchor around which an entire development ecosystem emerges.

Imperial’s involvement potentially gives Old Oak something many regeneration areas spend years trying to attract: an institution with the ability to generate businesses and occupiers rather than simply lease space. That could ultimately prove as important to the property market as HS2.

The relationship with White City is particularly significant. The area has already developed into a growing science, technology and research cluster. Extending that activity towards Old Oak could create a larger west London innovation district with excellent connections to Heathrow, central London and eventually Birmingham.

For property investors, this opens opportunities beyond traditional office development. Specialist laboratories, research buildings, flexible workspace, advanced manufacturing facilities and university-linked accommodation can attract different forms of institutional capital. These buildings can also create stronger reasons for occupiers to remain in a location because specialist facilities are harder to replicate than conventional offices.

Industrial property adds another dimension to the regeneration. Old Oak sits alongside Park Royal, London’s largest strategic industrial area, where thousands of businesses operate across logistics, food production, manufacturing, automotive services, film production and numerous other activities.

This creates a major planning tension. Land surrounding a future high-speed railway station naturally attracts pressure for higher-density housing and commercial development. But London cannot simply remove the industrial activities that keep the city functioning. Urban logistics facilities are particularly difficult to relocate.

Delivery businesses need to be close to customers. Food manufacturers need access to the London market. Service companies need industrial premises within the city. Moving these activities far beyond London can increase transport costs, delivery distances and congestion.

The regeneration strategy therefore needs to distinguish between Old Oak’s development core and the wider Park Royal industrial district. Higher-density residential and commercial development can be concentrated around the station while much of Park Royal remains protected for employment and industrial activity.

This could create an unexpected investment consequence. As residential development increases around Old Oak, the industrial land that remains in Park Royal could become more valuable rather than less. London already has a severe shortage of well-located industrial sites. If redevelopment removes some older industrial buildings while planning protection restricts the creation of replacement sites elsewhere, the scarcity of remaining logistics property increases.

The future Old Oak property market could therefore contain two apparently contradictory trends at the same time: intensive residential development around the station and increasingly valuable industrial property nearby.

For investors, that creates several different strategies within a relatively small geography. Development capital can participate in the creation of the new residential district. Living-sector investors can target rental housing. Specialist investors can develop science and technology property. Logistics investors can seek industrial assets around Park Royal. Infrastructure capital can participate in utilities and transport-related development.

This diversity may ultimately distinguish Old Oak from some previous London regeneration schemes. King’s Cross demonstrated how railway land could become a mixed commercial, residential and educational district. Stratford showed how major infrastructure and public investment could transform east London following the Olympics. Old Oak has elements of both models but on a different timetable.

Its transport infrastructure is still being constructed, while the surrounding district remains heavily industrial. The full regeneration could therefore take decades rather than years. That makes the investment horizon particularly important.

A developer acquiring land or entering a partnership today needs to think beyond the next property cycle. Interest rates, construction costs, housing policy and office demand could all change several times before the district is fully built. This uncertainty partly explains why public-sector land consolidation matters so much.

Private investors are more likely to commit long-term capital when ownership is clear, infrastructure responsibilities are understood and development can be coordinated through a single framework. The approximately 70-acre land agreement provides that foundation. It does not remove development risk, but it makes the risk easier to understand and price.

The £10 billion projected development value should also be interpreted carefully. It describes the potential scale of the completed regeneration rather than capital that will immediately enter the market. Development expenditure will be deployed progressively over many years as individual phases become viable.

Institutional investors will therefore judge Old Oak one phase at a time. Residential performance will be watched closely. So will construction costs, achievable rents, sales values and the amount of affordable housing that can be delivered without undermining development viability.

Commercial investors will look for evidence that technology and research occupiers are genuinely establishing themselves rather than simply appearing in planning documents. Logistics investors will monitor whether regeneration restricts industrial supply sufficiently to drive rents and land values higher around Park Royal.

The most important indicator may eventually be transaction liquidity. Established London submarkets have rental evidence, investment transactions and comparable assets that allow investors and lenders to price buildings with confidence. Old Oak does not yet have that depth.

Its early development phases will therefore carry an additional uncertainty premium. Investors must estimate what the future district will be worth before there is enough completed property to demonstrate it. As more buildings are occupied, that uncertainty should gradually decline.

Once several thousand residents live in the area, shops and amenities are operating, major employers have arrived and institutional properties begin changing hands, Old Oak will start generating its own pricing evidence. At that point it can become a recognisable investment submarket rather than an extension of surrounding locations.

The arrival of institutional capital will consequently not happen on a single date. Development capital is already beginning to engage with Old Oak. Long-term investors will follow as individual buildings become operational. Income-focused capital will become more comfortable once rents and occupancy can be demonstrated.

Eventually, investors may begin buying Old Oak assets because they want exposure specifically to Old Oak rather than simply because the properties happen to be located near Paddington, White City or Park Royal. That will be the moment the regeneration has created a property market of its own.

The significance of 2026 is that the route towards that point has become much clearer. The combination of public land consolidation, the search for a major private development partner and Imperial College London’s expanding presence means Old Oak is no longer principally an infrastructure project surrounded by speculative development plans. It is beginning to become an investible district.

The delayed arrival of HS2 means investors will need patience. But it may also force the property market to develop on stronger foundations, driven by housing demand, existing transport connections, innovation, industrial scarcity and neighbourhood creation rather than relying solely on the promise of high-speed rail.

Old Oak therefore represents an unusual London investment proposition. The first institutional investors will not simply be buying buildings. They will be investing in the creation of the market in which those buildings will eventually be valued.

If that process succeeds, the £10 billion regeneration could transform a fragmented area of railway land and industry into one of London’s largest new residential and employment districts. By the time the first HS2 passengers eventually arrive, much of the property market the railway was expected to create may already have been built around it.

Source: CIJ.World UK Research & Analysis Team

Africa’s Rental Housing Paradox: Huge Demand, but Where Can Institutional Capital Make It Work?

Africa’s accelerating urbanisation is creating an enormous requirement for rental housing, but the scale of that need should not be confused with the size of the investible market. Across the continent, millions of additional urban households will require accommodation over the coming decades, yet relatively few cities currently offer the combination of achievable rents, development costs, financing and professional management required to support institutional rental housing at scale. That distinction is becoming increasingly important for investors looking at Africa’s emerging living sectors. Population growth provides an exceptional long-term demand story, but institutional residential investment depends on something more specific: households capable of paying rents that support development costs and provide sustainable returns.

Current demographic projections reinforce the scale of the challenge. Africa’s urban population is expected to approach 1.4 billion by 2050, while estimates of the existing housing deficit already run into tens of millions of homes. The continent therefore needs an extraordinary amount of new residential development. But a housing shortage does not automatically create a Build-to-Rent investment opportunity. Many African households rent because purchasing a home is financially inaccessible rather than because professionally managed rental accommodation is their preferred lifestyle choice. Limited mortgage availability, high borrowing costs, expensive housing and informal employment all contribute to keeping households within rental markets. This produces one of the central contradictions facing residential investors: a city can simultaneously suffer from an acute housing shortage and still struggle to support the rents necessary to make new institutional apartment development financially viable.

South Africa currently provides the strongest evidence that the model can work at scale. Johannesburg and Pretoria have a considerably more developed professional rental market than most other African cities. Large residential portfolios are already owned and managed by institutional property companies, creating an established investment market rather than simply a future development opportunity. SA Corporate’s residential platform illustrates the scale that has already been achieved. Its portfolio contains approximately 15,600 apartments and represents a substantial part of the listed property group’s asset base. Residential vacancy remained below 4% at the end of 2025, demonstrating the depth of tenant demand across the portfolio.

Institutional capital is also actively being deployed and recycled. SA Corporate’s acquisition of The Parks Lifestyle Apartments at Riversands for R1.64 billion demonstrates that completed rental communities can trade as substantial investment assets. Older properties are simultaneously being sold as portfolio owners adjust exposure and concentrate capital in stronger locations. This is important because it represents the characteristics of a genuine institutional residential market: large professionally managed portfolios, measurable occupancy, recurring rental income, acquisitions, disposals and established property-management platforms.

Johannesburg can also offer comparatively high headline rental returns, although gross yields should not be confused with the income ultimately received by institutional owners. Maintenance, property management, vacancy, security, utilities and continuing capital expenditure can reduce the effective return considerably. Cape Town presents a different investment equation. Residential demand remains strong, but land and construction costs are higher than in Johannesburg. Average construction costs are estimated at more than US$1,300 per sqm, making it harder to deliver rental apartments at prices affordable to middle-income households. The result is that South Africa itself contains several different rental markets. Gauteng can support large middle-market residential portfolios, while Cape Town’s higher development costs can push institutional projects towards more affluent tenants.

Nairobi appears to be one of the most interesting markets outside South Africa, but its economics demonstrate why urban growth alone cannot underpin institutional investment. Apartment rental yields across much of the Nairobi metropolitan market are generally around the mid-single digits, with stronger apartment schemes reaching approximately 7%. At the same time, borrowing costs remain substantially higher, with commercial lending rates still above 14% during 2026 despite monetary easing.

This creates a difficult development equation. If completed apartments generate gross rental returns of around 6–7% while debt costs remain in double digits, conventional leveraged Build-to-Rent development becomes difficult to justify. Institutional investors could overcome this through lower-cost equity, inexpensive land, greater development density or expectations of strong long-term rental growth. Nairobi’s expanding population, technology sector and professional workforce provide reasons for continued interest, but the market has not yet reached the institutional scale visible in Johannesburg.

Lagos represents an even more dramatic example of the difference between rental demand and investible demand. Nigeria’s largest city has an enormous housing requirement, limited mortgage penetration and rapidly increasing rents. Many households have little realistic alternative to renting because formal home finance remains inaccessible. That would appear to provide ideal conditions for institutional rental development, but the difficulty emerges when development costs are compared with what households can actually afford.

Lagos is one of Africa’s more expensive construction markets, with average building costs approaching US$2,000 per sqm. Prime land can add substantially to the development budget, particularly in locations such as Ikoyi, Victoria Island and parts of Lekki. Financing represents an even greater obstacle. Nigeria’s exceptionally high interest-rate environment makes conventional development borrowing prohibitively expensive for many residential projects. Prime apartments can generate relatively attractive headline rental yields, but those returns do not necessarily compensate institutional investors for construction costs, financing, currency movements, management expenses and development risk.

Lagos consequently presents perhaps Africa’s clearest rental-housing paradox. There is enormous demand for accommodation, rapidly rising rents and an acute housing shortage, yet producing professionally managed apartments at rents affordable to the mass market remains exceptionally difficult. The opportunity may therefore lie less in conventional premium Build-to-Rent and more in finding development models capable of reducing the cost per apartment. Higher density, modular construction, cheaper peripheral land and infrastructure partnerships could all become important if institutional rental housing is to reach beyond relatively affluent households.

Accra presents another early-stage market. Ghana has substantial rental demand partly because mortgage finance remains extremely limited. Mortgage lending represents only a very small proportion of the economy, although new government-supported programmes are attempting to improve access to home ownership. Residential yields in stronger Accra locations can reach the mid-to-high single digits, but professionally owned rental portfolios remain relatively limited compared with South Africa.

Institutional participation in housing is nevertheless beginning to develop through different structures. Pension and capital-market funding is being considered alongside rent-to-own programmes and other mechanisms intended to bridge the gap between renting and eventual ownership. This means Ghana’s institutional residential sector may not develop according to the conventional European or North American Build-to-Rent model. Instead, investment structures could combine rental housing, affordable ownership and long-term financing products designed around local household economics.

Cairo provides another distinct proposition. Headline residential yields can reach high single digits and, in some districts, move into double-digit territory. Rental growth has also been strong, supported by population pressure and rising housing costs. The challenge for international investors is that nominal rental growth needs to be considered alongside inflation and currency movements. A property generating an attractive return in Egyptian pounds may deliver a very different result when measured in euros or dollars. For domestic institutional investors with local-currency liabilities, those economics can be considerably more attractive. This illustrates why African residential markets cannot be assessed using a single international yield benchmark.

Casablanca offers yet another model. Residential yields can broadly reach the 7–9% range, while mortgage borrowing costs are substantially lower than in many Sub-Saharan African markets. That produces a more conventional relationship between property income and financing costs. However, Morocco does not yet possess institutional multifamily ownership on the scale visible in South Africa. Casablanca may therefore offer potential for future residential consolidation rather than an already established Build-to-Rent sector.

These differences demonstrate why applying a continent-wide yield assumption to African rental housing is misleading. Residential returns vary enormously according to city, district, apartment type and management model, while gross rental yields provide only part of the investment picture. The cost of capital is equally important. A 7% rental yield in a market where institutional debt costs 5% presents a fundamentally different investment proposition from the same yield where development borrowing costs 15% or 20%.

Currency exposure creates another layer of risk. International investors may receive strong local rental growth but still experience weaker hard-currency returns if the local currency depreciates substantially. Domestic pension funds and insurers can approach the same asset differently because their liabilities are denominated locally. Affordability may ultimately become the most important constraint. Institutional investors need sufficient rental income to recover land, construction, financing and management costs. Tenants, meanwhile, can only pay what household incomes permit. Where the rent required to make a project financially viable exceeds what the target population can afford, the housing shortage itself does not solve the problem.

This is why Africa’s institutional rental opportunity should not be measured simply by the number of homes that need to be built. The investible market is the smaller intersection between housing demand, household purchasing power and financially viable development. Professional management could nevertheless create an important competitive advantage. Much of Africa’s rental stock remains fragmented among individual landlords. Larger portfolios can centralise maintenance, leasing, security and tenant services while providing investors with more predictable operating information.

Scale also creates the possibility of portfolio transactions and eventually deeper capital markets. South Africa demonstrates how residential properties can evolve from individual apartment investments into large income-producing portfolios capable of attracting listed property companies and institutional capital. Similar platforms could eventually develop elsewhere, particularly as pension funds and insurers search for assets capable of producing long-term recurring income.

The cities most likely to attract that capital will not necessarily be those with the largest housing shortages. They will be the markets where land prices, construction costs, achievable rents and financing conditions can be brought into balance. Johannesburg already demonstrates that institutional rental housing can operate at scale. Cape Town has strong demand but higher development costs. Nairobi has attractive demographics but difficult financing mathematics. Lagos combines extraordinary housing demand with some of the continent’s toughest development economics. Accra remains early but is experimenting with institutional housing structures, while Cairo and Casablanca offer potentially attractive rental returns under very different macroeconomic conditions.

Africa’s urban expansion therefore represents a major long-term residential investment opportunity, but institutional capital will have to be far more selective than the continent’s demographic statistics initially suggest. The defining question is not how many Africans will need rental accommodation. That demand is already clear. The investment question is which cities can deliver apartments at a cost that allows tenants to afford the rent while still providing owners with predictable income and acceptable returns.

The African cities that solve that equation could turn today’s fragmented rental markets into one of the continent’s next significant institutional property sectors.

AI Is Moving Asset Management From Faster Research to a New Investment Operating Model

Artificial intelligence is beginning to change asset and wealth management at a deeper level than simply helping analysts read documents faster. Investment firms are now exploring how AI can combine research, market information, portfolio risk, client data and institutional knowledge into continuously operating intelligence systems, potentially changing how investment decisions are researched, communicated and ultimately implemented. That transition was the focus of the AI4 2026 panel “Alpha to Automation: How AI Is Reshaping Asset Management,” moderated by John Divine, Assistant Managing Editor for Investing at U.S. News & World Report, with specialists from Vanguard, State Street and JPMorgan Chase alongside expertise in agentic AI and financial data science.

The central message was that the industry is moving beyond the first generation of AI applications. Summarising an earnings call, searching regulatory filings or preparing research more quickly can already provide useful productivity improvements. The larger opportunity comes when those capabilities are connected with portfolio exposures, market developments, company announcements, risk information and proprietary institutional knowledge. Instead of providing an analyst with a faster research tool, such a system could create an intelligence layer around the entire investment process, allowing an analyst examining a company to receive not only a summary of its latest earnings announcement but also changes in market conditions, relevant portfolio exposures, external events and potential risks.

One area attracting particular attention is earnings analysis. Corporate earnings calls contain large amounts of information that can affect company valuations, but interpreting them involves considerably more than extracting financial figures. Executives choose their language carefully, meaning tone, confidence, changes in wording and what management avoids discussing can sometimes be as significant as the numbers themselves. The panel discussed research into agentic systems capable of examining earnings-call transcripts and producing assessments across different time horizons, potentially ranging from the following day to several weeks.

Such systems could combine the transcript with sentiment, market information and external developments in an attempt to identify signals that would be difficult for an analyst to assemble manually at comparable speed. This does not mean AI has discovered a reliable method of predicting share prices. The panellists repeatedly emphasised the limitations involved. Financial markets are influenced by enormous numbers of interconnected factors, while large language models can hallucinate information and remain unreliable when handling some numerical tasks.

The potential investment advantage may instead come from AI’s ability to examine nonlinear relationships across very large amounts of information. One example discussed was identifying whether markets have overreacted or underreacted to new information. Automated trading systems already respond rapidly to news, but those responses can themselves create opportunities if the resulting price movement becomes disconnected from the broader information available. AI could potentially compare the initial market reaction with company fundamentals, historical behaviour, external events and other signals to identify such anomalies, although whether this consistently generates investment alpha remains an open research question rather than an established outcome.

The discussion also highlighted an important distinction between large language models and the broader field of artificial intelligence. Financial institutions have used statistical models, machine learning, anomaly detection and quantitative systems for years. The emerging opportunity is increasingly about combining these established technologies with generative AI and agentic systems rather than expecting one large language model to perform every function.

That distinction becomes especially important where calculations are involved. A large language model might be useful for extracting information from documents, interpreting language or identifying relevant data, but deterministic software can still perform the underlying financial calculation. A discounted cash-flow model, for example, does not need a language model to calculate its mathematics. This type of architecture could become important to financial institutions seeking greater control over AI, with language models handling uncertain information while conventional software performs calculations and applies fixed rules.

Human judgement is consequently unlikely to disappear from asset management in the near future. The panel generally saw some of the greatest immediate AI opportunities in research and investment ideation, where professionals must process large quantities of information. AI can examine filings, earnings calls and other datasets and potentially identify patterns or questions that an analyst might investigate further. Portfolio construction and client communication require a different level of caution because investment decisions depend not only on available information but also on risk tolerance, objectives, personal circumstances and professional judgement.

This reflects a wider principle emerging across financial AI: the closer an automated system gets to moving money or making fiduciary decisions, the greater the requirement for controls, explainability and human accountability. For established financial institutions, however, one of the biggest obstacles may not be the models themselves. It is the operating structure surrounding them.

Many financial organisations still rely on processes developed decades ago. A client request might move through several departments, with each team adding information, conducting checks or approving part of the transaction. Introducing AI separately into each stage might make every department somewhat faster without addressing whether the original process is still necessary. Brinda Menon of JPMorgan Chase argued that the more transformative question is whether the process should be redesigned entirely. Rather than asking how AI can make stages A, B, C and D more efficient, companies can ask how they can move directly from A to D and whether some of the intermediate stages remain necessary.

That difference separates automation from transformation. Using AI to produce an existing report more quickly improves productivity. Redesigning the organisation so the report is no longer required changes the operating model. The same principle applies to investment research. Giving analysts faster access to information is useful, but connecting research, market developments, portfolio risks and institutional knowledge into a common intelligence environment could have a substantially greater effect.

Institutional knowledge may ultimately become one of the industry’s most valuable AI assets. Investment firms contain decades of expertise distributed between databases, research documents, policies, previous transactions and the experience of employees. Much of the most valuable knowledge remains inside people’s heads. If organisations can capture more of that expertise and make it accessible through AI, they could create an institutional intelligence system that survives individual employees and becomes available across the organisation.

This could be particularly significant as experienced professionals retire. Instead of losing part of the firm’s knowledge whenever a senior analyst, portfolio manager or adviser leaves, AI systems could potentially preserve elements of their processes, reasoning and accumulated expertise. Wealth management presents another major opportunity, particularly where AI could make financial guidance more accessible to people who currently have little or no access to professional advice.

State Street’s Sheema Osmani described work aimed at developing trusted AI-supported financial guidance and expanding access to financial knowledge. The underlying argument is that large numbers of people make important financial decisions without having access to a professional adviser. AI could potentially narrow that gap by providing personalised guidance at a scale impossible through traditional adviser-only models.

Personalisation in wealth management is more complicated than assigning investors to conventional demographic categories. People’s financial priorities change throughout their lives. Marriage, children, business ownership, retirement, inheritance, philanthropy and changes in wealth can substantially alter what an investor needs. AI potentially allows personalisation to become continuous rather than periodic, adapting as circumstances, priorities and behaviour change.

This does not necessarily diminish the importance of financial advisers. The more likely model is a division of responsibilities in which AI handles more information processing while advisers concentrate on the parts of wealth management that depend most heavily on trust, judgement and understanding individual circumstances.

Cost is another increasingly important issue as financial institutions move from experimental AI projects into production. The panel suggested that simply measuring the cost of individual tokens can provide a misleading picture of AI economics. A cheap model that produces unreliable results and requires extensive human checking may ultimately cost more than an expensive model that completes the task correctly. A more meaningful measure could therefore be the cost per successfully completed task.

That becomes particularly important with agentic AI. A single business process may involve several specialised agents communicating with one another, retrieving external information, checking outputs and applying guardrails. Every additional step consumes computing resources. Financial institutions are therefore experimenting with model routing, where relatively simple tasks are assigned to smaller and cheaper models while expensive frontier models are reserved for problems requiring more sophisticated reasoning.

The economics could improve as inference technology becomes more efficient, but the panel stressed that the industry remains too early in its development to know precisely where long-term AI operating costs will settle. The more important economic question may ultimately be the value produced. If AI simply reduces the cost of an existing process by a few percentage points, computing expenditure will remain closely scrutinised. If it allows an investment firm to redesign a process, substantially increase productivity or serve a much larger client base, the economics look very different.

Risk remains the counterweight to that opportunity. Financial institutions cannot simply accept answers from systems they do not understand, particularly where investment recommendations or client assets are involved. Black-box models, inconsistent responses and hallucinations create obvious concerns in a regulated industry.

The panel outlined several approaches to reducing that risk. AI can be required to show the sources used in reaching a conclusion, identify assumptions and provide confidence measures. Systems can also be designed to fail safely when information is insufficient rather than generating an answer regardless. Another approach is deliberately restricting what the AI controls. Language models can retrieve and interpret information while deterministic systems handle calculations and rule-based decisions. Agent activity can also be traced, providing a record of what information was searched, how it was processed and what actions were proposed.

Testing will become equally important. AI systems cannot be evaluated only against situations similar to those on which they were developed. The State Street discussion described using a substantial proportion of out-of-distribution examples when evaluating systems so that unexpected situations form part of testing rather than appearing only after deployment. This is particularly important in investment management because the events that cause the greatest losses are often precisely those that differ from normal market conditions.

For smaller investment firms without the resources of the world’s largest financial institutions, the panel’s recommendation was comparatively straightforward: begin with a narrow problem that is understood well. Document extraction, information retrieval or another repetitive process can provide an initial application where outputs are relatively easy to evaluate. Organisations can then reuse what they learn about models, data, controls and governance as they move towards more sophisticated applications.

The objective should not be to attach AI to every existing process. It should be to identify where intelligence and automation genuinely change the economics or quality of the work. The panel also challenged one of the more exaggerated claims surrounding AI and investment management: that autonomous agents will simply solve financial markets and generate predictable profits.

Financial markets are adaptive systems populated by investors reacting to one another. AI agents can exhibit biases, make incorrect assumptions and respond unpredictably just as human investors can. Asset management combines mathematics with judgement, behaviour and uncertainty, making it fundamentally different from a problem with a single correct solution.

That may ultimately determine how AI reshapes the industry. The near-term winner is unlikely to be an autonomous investment machine that replaces analysts, portfolio managers and advisers. It is more likely to be the investment organisation that learns how to combine machines capable of processing enormous amounts of information with humans capable of questioning what those machines conclude.

The transition from alpha to automation is therefore not simply about automating investment management. It is about rebuilding the investment operating model around a new division of labour between data, algorithms, AI agents and human judgement.

Source: CIJ.World Research & Analysis Team

« Previous 1 … 26 27 28 29 30 … 573 Next »

Latest news

All posts by fletcher@cijeurope.com.

Dropbox Says the Real AI Advantage Is Not Faster Tasks but Better Business Outcomes

Dekpol Adds 106 Homes to Growing Osiedle Pastelowe Development in Gdańsk

SAP Sees AI Moving Finance Towards Continuous, Agent-Led Operations

Japan’s Creative Industries Are Opening a New Real Estate Frontier

England’s Investment Map Is Changing as Regional Cities Challenge London for Capital

From Pharma to Property: India’s Life Sciences Sector Opens a New Real Estate Frontier

Paris Is Expanding Its Property Frontier as New Metro Connections Take Shape

Old Oak’s £10 Billion Transformation Could Create London’s Next Major Investment District

Africa’s Rental Housing Paradox: Huge Demand, but Where Can Institutional Capital Make It Work?

AI Is Moving Asset Management From Faster Research to a New Investment Operating Model