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Problem Radar · the Index

Problems the world wants solved.

Big problems. Small first steps. Problem Radar collects real-world problems worth solving, spotted by Ainna's Radar or found in public reporting, and Ainna's innovation team chooses and edits every one before it is published. Each one is framed to the same standard: who lives with it, why now, why it matters and what it would take. Each shows where it came from and is scoped to what a small team or a startup could start on. Free to read: pick one and solve it with Ainna. Nothing here comes from a user.

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31 on the Index · 31 framed · latest edition: Edition 1

Collections: Hackathon problem statements 2026When the machine decidesThe measurement gap

Framed 5 NovOn the Radar today

Enterprise AIProblem 0037

An AI research agent gathers market information. Then a second agent analyses it; a third recommends an investment, and another takes action. But what happens to the original evidence as information moves through the chain? In multi-agent AI systems, agents frequently exchange summaries, conclusions and recommendations rather than complete evidence. Source identities, publication dates, assumptions and uncertainty can disappear during these handoffs. A tentative finding can become a confident conclusion, eventually informing a business decision without anyone knowing how trustworthy the original information was. This creates a fundamental weakness in multi-agent automation: a chain of decisions may depend on evidence that can no longer be verified. Even when individual agents perform well, the overall system may amplify errors or lose the context needed to recognise them.

Who lives with itOperators of multi-agent AI systems · enterprise automation teams · AI platform providers · organisations using autonomous decision workflows

Why nowAgentic AI is evolving towards networks of specialised agents that collaborate across research, analysis, decision-making and execution. As these workflows become more complex, preserving the provenance and reliability of information across agent handoffs becomes increasingly important.

Why it mattersA decision is only as reliable as the evidence behind it. Without a traceable connection between claims, original sources and subsequent actions, organisations struggle to verify results, investigate errors or establish accountability for autonomous systems.

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Framed 5 NovNew

Enterprise AIProblem 0035

AI agents are taking over tasks that once required human judgement, from customer support and financial transactions to business research and operational decisions. But they often rely on information that is outdated, incomplete, contradictory or unverified. The problem goes beyond traditional data quality. Agents consume database records, internal documents, web search results, external feeds and outputs from other AI agents. Much of this information changes continuously, and some comes from sources the organisation cannot control. An agent can execute its task correctly while acting on the wrong facts, potentially triggering incorrect transactions, misleading customers or passing errors into other systems. Organisations lack consistent ways to establish whether everything an AI agent reads is trustworthy enough for the decision it is about to make. As autonomy increases, poor data quality becomes a source of real-world business risk.

Who lives with itOrganisations deploying AI agents · AI engineering teams · business process owners · customers affected by automated decisions

Why nowAI agents are moving beyond generating answers to executing business processes. Traditional data quality checks are not sufficient for the constantly changing information these systems consume.

Why it mattersBad data no longer produces only bad reports. It can produce bad actions, repeatedly and at machine speed. The challenge is to make autonomous AI dependable when the quality of its information cannot be assumed.

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Framed 5 NovNew

Enterprise AIProblem 0036

A company updates its refund policy, but the old version remains in its document library. An AI assistant retrieves it, answers a customer with confidence and makes a promise the business can no longer honour. Across organisations, thousands of documents, policies, reports and internal messages coexist without clear indicators of which are approved, current or authoritative. Enterprise AI assistants and retrieval-augmented generation (RAG) systems can retrieve outdated policies, treat drafts as approved decisions or miss critical information altogether. The information often exists somewhere in the organisation. The problem is that AI systems cannot reliably distinguish what is true, current and applicable at the moment someone needs an answer.

Who lives with itEmployees using enterprise AI assistants · customer service teams · legal and compliance departments · organisations with large knowledge repositories

Why nowCompanies are connecting internal knowledge bases to AI assistants and increasingly allowing those systems to support business decisions. As the content changes, the models can continue functioning normally while the answers quietly become outdated or inconsistent.

Why it mattersOne company can end up providing contradictory answers through different AI channels. Customers lose trust, employees return to manual processes and automation creates more work than it saves. Reliable organisational knowledge is becoming a prerequisite for useful enterprise AI.

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Framed 10 Oct

EducationProblem 0034

Students choosing what to study need a fair picture of the work their degree leads to. The evidence they are given describes an earlier market. Official long-run earnings figures, and the rankings that use them, follow graduates who started work before generative AI. Government job forecasts are built on historical patterns, and their authors warn against reading them as predictions. School advisers are stretched, and the people building AI contradict one another in public on what to study. Meanwhile employers are redesigning entry-level roles in AI-exposed occupations, and some students are already switching subjects because of AI, before the evidence settles. The choice comes early, costs years and often debt, and is hard to undo. It stays unsolved because the current evidence is unsettled, the trusted evidence is old, and the two reach students separately.

Who lives with itStudents choosing what to study · parents helping to pay for a degree · school counsellors and university careers services · governments, schools and universities that fund guidance

Why nowIn September 2026 a Federal Reserve governor said many recent graduates find first jobs harder to get, “possibly partly related” to AI taking on entry-level tasks. Stanford’s Digital Economy Lab finds that employment of 22-to-25-year-olds in AI-exposed occupations has fallen 19% behind less-exposed peers, a descriptive finding, while Yale’s Budget Lab finds no significant effect so far. In a Lumina–Gallup panel, 16% of US students say they have already changed major because of AI.

Why it mattersOfficial graduate earnings statistics provide valuable evidence about past outcomes, but they cannot by themselves establish which degrees will offer the strongest prospects in a changing labour market. The US Bureau of Labor Statistics explains that its projections assume technological progress broadly in line with historical experience, while acknowledging that unusually rapid technological change could make historical relationships unreliable. Students and families must therefore make consequential choices by interpreting evidence that varies in age, certainty and relevance to their circumstances.

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Framed 5 Oct

WorkProblem 0032

AI is changing work at several levels at once. Inside organisations, tasks are being automated, roles redesigned and entry routes narrowed; across markets, demand is moving between kinds of work and between sectors; across countries, exposure is uneven and highest in the richest economies. Some jobs will go, but far more will change. For the people doing the work, the transition opens three gaps. Awareness: many cannot clearly see how their role, employer or industry will change, or which of their skills will stay valuable. Readiness: the capabilities their work needs are shifting, and for many the training to match is unlikely to arrive. Defined alternatives: routes from changing roles into viable new work, much of it still taking shape, are not yet mapped at scale. Left unmanaged, the transition risks economic and social disruption.

Who lives with itWorkers in roles AI is changing, from clerical and customer service to professional work · young people entering the workforce · employers restructuring around AI · governments, educators and employment services

Why nowThe ILO finds 25% of global employment in occupations potentially exposed to generative AI. The World Economic Forum expects disruption equal to 22% of jobs by 2030, driven by technology alongside demographic, geoeconomic and economic forces, and says that if the global workforce were 100 people, 59 would need reskilling and 11 of them are unlikely to get it. In the first half of 2026, US employers cited AI in 101,743 announced job cuts, Challenger, Gray & Christmas reports.

Why it mattersThe cost of an unmanaged transition is in the forecasts: the World Economic Forum counts over 120 million workers at medium-term risk of redundancy, and the IMF expects AI to worsen overall inequality in most scenarios, warning of social tensions. Employers are already acting: 77% plan to upskill staff and almost half expect to move people out of roles exposed to AI, the WEF found. Governments, employers and educators would pay for ways to do this well and at scale.

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Framed 5 Oct

CitiesProblem 0033

Dublin has many homes approved for development, but too many never make it from planning permission to construction. Projects can get stuck because of funding, costs, infrastructure, planning or construction capacity, while people continue to struggle to find homes they can afford. The result is a gap between homes that exist on paper and homes that people can actually live in.

Who lives with itPeople looking to rent or buy in Dublin · young people and families trying to find a home · builders and developers with approved projects · employers trying to attract people who can afford to live in Dublin · public bodies responsible for housing and infrastructure

Why nowDublin has a substantial number of homes already approved, but too many are still stuck before construction. The Department of Housing reported in August 2026 that only 41.9% of the 80,285 homes with planning permission across Dublin's four councils had started by the end of June. Unstarted homes rose 10% in a year to 46,662, more than 41,000 of them apartments, and an estimated 6,664 have less than a year left on their permission. In July, the CSO reported that new-home completions in Dublin fell 16.4% year on year in Q2 2026.

Why it mattersThe city does not just need more planning permissions. It needs to turn approved projects into homes people can actually live in. Dublin's median residential property price was €500,000 in the 12 months to July 2026, and BPFI reports that the share of newly completed homes bought by households in Dublin fell from 55% in 2019 to 25% in Q2 2026. The opportunity is to find where projects get stuck, understand what is preventing them from moving forward, and help the right people act before another approved development sits unused. Developers and lenders would pay to know which stalled schemes can still work; councils and state agencies, to aim funding and infrastructure where they unlock the most homes.

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Framed 2 Oct

MediaProblem 0031

False and misleading information can now be created, replicated, manipulated and distributed across interconnected websites, social platforms and other channels at remarkable speed and scale. A single fabricated claim can evolve into multiple articles, images, videos and social posts, often stripped of their original context and increasingly difficult to trace back to a common source or narrative. Meanwhile, verification remains fragmented, reactive and largely focused on individual pieces of content.

Who lives with itSocial-media users and news consumers · journalists and fact-checkers · publishers and platforms · organisations responsible for public information and trust

Why nowGenerative AI is enabling misinformation operations to produce, rewrite and distribute deceptive content at scale. Anthropic documented one commercial operation managing more than 1,000 fake X accounts, and another that used AI to publish at least 8,913 articles across about 70 fabricated news websites in around 20 languages.

Why it mattersWhen misinformation can be produced and amplified faster than it can be traced and verified, reliable information becomes harder to distinguish from manipulation. The consequences extend beyond individual false stories: persistent exposure to false and misleading information can undermine trust and make informed decision-making harder. In 2026, the Reuters Institute found that 62% of respondents across 48 markets were concerned about misinformation online, while trust in news fell to 37%, its lowest level in the report's series.

Inspired by reporting from The Guardian

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Framed 1 Oct

FinanceProblem 0030

For generations a voice on the phone or a face on a video call was good enough proof of who was there. Banks built call-centre checks on it, companies approve payments on it, and families trust it when a relative asks for help. AI now copies voices and faces cheaply and well enough to pass, so the signal most people rely on has stopped being proof. Scammers pose as banks, officials and relatives, and victims move money believing the person is real. Checks that ask a person to judge whether a call is genuine keep failing, because that judgement is exactly what the fakes defeat. What is missing is a way to prove a real person is present that does not depend on how they look or sound.

Who lives with itPeople targeted by impersonation scams · families asked for money by someone posing as a relative · banks and payment firms checking customers by phone or video · companies that approve payments on calls

Why nowUS Federal Trade Commission data released on 15 June 2026 show that people reported losing $3.5 billion to imposter scams in 2025, nearly three times the losses reported in 2020, making them the most reported fraud. UK Finance, the banking industry body, has warned that voice cloning is eroding call-centre authentication and that deepfake video is undermining facial biometric checks. Y Combinator's Fall 2026 Requests for Startups asks for ways to prove a person is human.

Why it mattersIdentity checks rest on judging whether a voice, a face or an answer is genuine, by a person or by software, and AI fakes are eroding that judgement. Newer checks, from one-time codes to biometrics, still rely on a customer who can be talked into responding. Banks and payment firms facing fraud losses and regulatory pressure, and companies that approve payments on calls, would pay to prove a real person is present without relying on how they look or sound.

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Framed 1 Oct

Enterprise AIProblem 0029

Organisations that run websites and online services need to know who is using them, so they can serve people, share data safely and hold someone to account when something goes wrong. AI agents, software that browses and acts on its own for a person or a company, now visit those sites. Most arrive looking like any other visitor, with no sign of which agent it is, who runs it or what it may do. When one misbehaves, the site can find out months later, and only if the AI company tells it. Tools that block bots were built to stop scrapers, not to tell a trusted agent from a rogue one, and there is no widely used way for an agent to show whose it is.

Who lives with itOrganisations running websites and online services · public bodies that publish data online · people whose data sits behind those sites

Why nowThe BBC reported on 25 September 2026 that OpenAI had alerted dozens of institutions, including the US Securities and Exchange Commission and the Census Bureau, that its AI agents may have meddled with their websites, in some cases bypassing security controls. Two days earlier, the BBC reported that an OpenAI agent had reached non-public files on an Australian government health statistics portal in June; OpenAI emailed a general government inbox about it on 10 September.

Why it mattersBot defences block scrapers but cannot tell a rogue agent from a trusted one, so site owners depend on the AI company's goodwill to learn of misuse. Organisations with websites now need to know which agents act on their pages and for whom. Those holding valuable or sensitive data, and the security and web-infrastructure firms that serve them, would pay for that knowledge.

Inspired by reporting from BBC News

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Framed 27 Sep

FinanceProblem 0028

Europe already has a way to move money between bank accounts in seconds, at any hour: instant payments. Merchants can rarely take them, and almost never from a customer in another country. Each country's card scheme works at home and stops at the border, and the software that would let a till or an online checkout accept an instant payment has been slow to arrive. So money can move, but a shop cannot be paid that way. Meanwhile the international card networks keep their hold because every shopper and every merchant already uses them, which makes any alternative hard to adopt even when the plumbing beneath it is ready.

Who lives with itEuropean merchants · shoppers · payment service providers

Why nowWriting for CEPR on 25 September 2026, Agnès Bénassy-Quéré of the Banque de France reports that 13 of the euro area's 21 countries still rely entirely on non-EU networks for card payments, and that even the domestic schemes stop at national borders, although the euro area has had its own instant-payment system since 2018. She argues the private sector has been too slow to build the software at tills and online checkouts that would let people use it.

Why it mattersA way to move money is not yet a way to be paid. The gap stays open because the card networks already connect most shoppers and merchants, while the newer schemes stop at national borders, so a merchant gains little by adding one. A small team could start narrow: let merchants who trade across one border accept instant payments at checkout, instead of trying to replace Europe's payment networks.

Inspired by reporting from Centre for Economic Policy Research

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Framed 26 Sep

HealthProblem 0027

Before a hospital or clinic can use an AI tool, the tool is tested and approved. That happens once. Afterwards, the rules mostly wait for someone to report that something went wrong. But an AI tool does not stay the way it was tested. It gets updated. It behaves differently with real patients than it did in the test. And it can work well in one place and less well in another. So a single test, done once before the tool went into use, cannot tell you whether it is still safe where you are treated today. What is missing is a way to keep testing it while it is in use, to update it carefully, and to help hospitals and clinics see for themselves how it is doing.

Who lives with itHealthcare provider governance teams · Healthcare professionals using AI · Patients receiving AI-supported care

Why nowThe Medicines and Healthcare products Regulatory Agency’s report, published on 10 September 2026, warns that reliance on one-off pre-market assessment is insufficient and calls for more continuous monitoring after deployment.

Why it mattersExisting assessment approaches suit relatively static products, while continuing safety depends on both the product and its clinical setting. A small team could begin with measuring changes in performance for a single clinical use across deployments, working with a provider to connect those measurements to review and escalation responsibilities.

Inspired by reporting from Medicines and Healthcare products Regulatory Agency

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Framed 21 Sep

HealthProblem 0019

Caring for an ageing parent means medications, appointments, home visits, bills and forms, shared between siblings in different cities and often different countries. It runs on a group chat, a shared folder and whoever remembers. Things fall through, one person quietly carries most of it, and the professionals involved each see a fragment of a picture nobody holds whole.

Who lives with itAdult children caring for parents · home-care workers · family doctors seeing only their slice

Why nowPopulations are ageing across high-income countries and many middle-income ones while the paid care workforce is not growing with them, so more of this coordination falls to families who were never set up to do it.

Why it mattersThis is coordination, exactly what software is good at, and the tools built for it have yet to become the way families actually run it. The work is already being done; it is simply being done badly, by people with no tools and no training, at one of the hardest times of their lives.

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Framed 21 Sep

MediaProblem 0012

Use of AI assistants for news is rising while stated trust in them stays very low: people take answers from a source they openly doubt, because it is faster than the alternative. The gap is not ignorance; it is the absence of anything that lets a reader check an answer at the moment they receive it, without abandoning it and starting a separate search.

Who lives with itAnyone getting news through an assistant · publishers whose work is summarised without attribution · educators teaching source literacy

Why nowThe Reuters Institute's 2026 report, across 48 markets, records overall trust in news at 37%, its lowest since measurement began in 2015, trust in news from AI chatbots at 20%, and weekly use of chatbots for news rising from 7% to 10% in a year.

Why it mattersDoubt without a remedy is where trust products get built. Verification that travels with the answer, rather than requiring the reader to go and find it, is a defensible position and nobody serving that moment owns it yet.

Inspired by reporting from Reuters Institute for the Study of Journalism

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Framed 21 Sep

FinanceProblem 0007

Small firms are assessed on documents they were never required to produce. Audited accounts, filed returns and collateral registries are what credit models expect; a profitable shop with a phone, a supplier relationship and ten years of trading has none of them. The lender cannot see the business, so the business does not exist to the lender, and the money goes to whoever already had the paperwork.

Who lives with itOwner-managed and informal firms · community and development lenders · the suppliers already extending them credit informally

Why nowThe IFC estimates an unmet financing need of about $5.2 trillion for formal micro, small and medium enterprises and a further $2.9 trillion for informal ones, with roughly 70% of MSMEs in emerging markets lacking adequate finance: firms that together account for around 70% of employment worldwide.

Why it mattersThe signal exists and is being thrown away: supply ledgers, payments, logistics records and phone data describe a business more honestly than a filed account. Turning transaction exhaust into a defensible credit view is the product, and the financing gap it would help close is measured in trillions.

Inspired by reporting from International Finance Corporation

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Framed 21 Sep

EnergyProblem 0002

The constraint on the energy transition moved from manufacturing to administration, and no tooling followed it. Projects sit in interconnection queues longer than they took to design, studies are redone by hand each time the queue ahead reshuffles, and developers cannot see where they stand or what would move them. Most of the capacity that enters the queue is eventually withdrawn, often after years of waiting.

Who lives with itIndependent power producers · utility interconnection teams · the financiers whose models assume a connection date

Why nowLawrence Berkeley National Laboratory's latest queue analysis counts roughly 8,200 active projects representing over two terawatts of generation and storage, a median wait from request to operation of more than five years, and about three quarters of the capacity that requested connection between 2000 and 2020 later withdrawn.

Why it mattersA queueing and document problem wearing an engineering costume, exactly the kind that goes unsolved because no single party owns both ends of it. Faster paperwork is the cheapest capacity available anywhere on the grid.

Inspired by reporting from Lawrence Berkeley National Laboratory

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Framed 21 Sep

Enterprise AIProblem 0009

AI has made cyber attacks cheap to run and easy to scale. Large firms can answer in kind: they have security teams, budgets and tools, and they can grow each as the threat grows. Small businesses cannot. Their systems are often set up and looked after by outside suppliers, their defences are seldom tested, and a break-in is often found by someone else. So the same change raises the risk for big and small firms alike but the defence only for the large, and the gap between big and small firms widens. What is missing is defence that scales the way attacks now do: priced for a small firm, and able to run without a security team.

Who lives with itOwners of small businesses without a security team · the IT and web suppliers that set up and look after their systems · customers whose data is taken in a break-in · the banks and insurers that carry the losses

Why nowAn ENISA survey of small and medium enterprises, carried out during the pandemic, found that around nine in ten expected serious harm to their business within a week of a cyber incident and that over half believed it would likely end the business, against a backdrop of low security budgets and a lack of in-house cyber skills. BleepingComputer reported on 23 September 2026 that the security firm Gambit traced one operator who used open-source AI agents to breach at least 27 companies in five days and take more than 600,000 card records, at an average of $25.46 per completed scan. The UK government's Cyber Security Breaches Survey, published in April 2026, shows the defending side split by size: 76% of large businesses had a formal plan for handling an attack, against 21% of micro businesses

Why it mattersAn existential risk to the firms that make up the overwhelming majority of businesses, where the binding constraint is operator time rather than money or intent. Security that needs nobody to watch it is a different product from the one being sold. Security products assume someone to run them: alerts to sort, tests to commission, fixes to make. Small firms buy what their IT or web supplier offers, and many already pay one for security. A small team could start with one case where the stakes are clear, such as small online shops whose checkouts draw card thieves: constant, automated checks of the site, priced for a small firm and sold through the suppliers that already look after it.

Inspired by reporting from ENISA (European Union Agency for Cybersecurity)

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Framed 21 Sep

WorkProblem 0010

We can now say with some precision which occupations generative AI reaches first, how that varies by country, and that the exposure falls hardest on clerical and administrative work done mostly by women. What does not exist is the other half: a route from an exposed role into an adjacent one that uses the same accumulated judgment. Retraining is too often offered as courses unrelated to what the person already knows how to do.

Who lives with itClerical, administrative and business support workers · employers holding the institutional knowledge in those roles · public employment services

Why nowThe ILO finds 29% of female-dominated occupations carry exposure to generative AI against 16% of male-dominated ones, with exposure reaching around 41% of jobs in high-income countries versus 11% in low-income ones, and women more exposed than men in 88% of the countries analysed.

Why it mattersThe measurement is done and the response is missing, which is an unusually clear place to build. Mapping what an exposed role actually contains onto roles needing those same capabilities is a matching problem, and matching problems are tractable.

Inspired by reporting from International Labour Organization

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Framed 21 Sep

Public sectorProblem 0013

Benefits, banking, health records and school places are moving to interfaces that begin by asking you to prove who you are online. Hundreds of millions of people cannot prove who they are at all, and billions more have no digital means of doing it, so each service added to the digital channel quietly removes itself from the people who most need it, while the statistics record improved efficiency.

Who lives with itPeople without foundational or digital ID · agencies moving services online · the frontline staff handling every exception by hand

Why nowWorld Bank ID4D data puts around 800 million people without official proof of identity and about 2.8 billion without an online digital ID, improving from 3.3 billion in 2021, with the steepest barriers still falling on women, rural populations and poorer households.

Why it mattersDigitising a service without a route for people who cannot authenticate does not reduce the work, it moves it to an exception queue nobody resourced. Tools that let agencies serve people who cannot authenticate online (assisted verification, trusted intermediaries, alternative credentials) are unglamorous, needed at scale, and a place a focused team can start.

Inspired by reporting from World Bank ID4D

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Framed 21 Sep

HealthProblem 0005

When a patient has an infection, the doctor, nurse or pharmacist has to choose an antibiotic that will work, and which ones still work depends on the place: resistance differs between countries and regions, and it is growing. The surest way to know is a laboratory test, but much prescribing happens in clinics without a laboratory nearby, and a test takes days. So the decision is based on guidance and national advice that may not match local patterns. What happens next is rarely recorded, so a failed treatment does not inform the next prescriber, and the maps of resistance that guide treatment are drawn from the minority of cases that reach a laboratory, often missing the places where resistance is worst.

Who lives with itPatients with common infections · doctors, nurses and pharmacists prescribing without a laboratory · hospitals and health ministries setting treatment guidelines

Why nowThe World Health Organization reported on 13 October 2025 that one in six laboratory-confirmed common bacterial infections worldwide were resistant to antibiotics in 2023, rising to one in three in South-East Asia and the Eastern Mediterranean, and that resistance rose in over 40% of the combinations it monitors between 2018 and 2023. Yet 48% of countries reported no data that year, and about half of those that did lacked systems to produce reliable data.

Why it mattersLaboratories are costly and slow to build, and rapid tests answer for one patient at a time without building a shared picture. The knowledge that matters, which drug failed for which infection and where, is lost at the point of care. Health ministries and funders tracking resistance, hospital groups writing local guidelines, and makers of tests and prescribing software would pay for a way to see what still works, place by place.

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Framed 21 Sep

EducationProblem 0011

Most institutions are writing or planning a policy about AI; few have changed what they actually measure. The essay, the problem set and the report were proxies for thinking that worked only while producing them was expensive. That assumption is gone. Detection tools are unreliable enough to risk accusing the wrong students, and much of what is graded no longer shows reliably what a student understands.

Who lives with itTeachers and lecturers redesigning assessment alone · students wrongly suspected · institutions whose credentials depend on the answer

Why nowA UNESCO survey found 19% of higher education institutions had formal AI guidance with a further 42% developing it, while more than half reported uncertainty about how to apply AI pedagogically and one in four had already encountered ethical problems including authorship disputes.

Why it mattersThe question has moved from catching misuse to evidencing learning, and nothing is built for the new question. Making a student's reasoning visible, cheaply and without treating them as a suspect, solves what detection never could.

Inspired by reporting from UNESCO

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Framed 21 Sep

FinanceProblem 0018

Insurers are withdrawing from places where people still live and still owe money. The mortgage requires cover; the cover depends on a risk model that has stopped holding. Homeowners find out at renewal, with weeks to replace a policy that may not exist at any price, and often no accepted way to show an insurer the work they have done to make the building safer, because that evidence is rarely collected in a form an underwriter will accept.

Who lives with itHomeowners in high-risk areas · mortgage lenders and brokers · local authorities watching values move

Why nowIn January 2025 the US Treasury's Federal Insurance Office, drawing on over 330 insurers and 246 million policies, found non-renewal rates in the highest climate-risk ZIP codes running about 80% above the lowest, with premiums averaging 82% higher.

Why it mattersNobody owns the gap between the loan and the policy, and the one thing that would narrow it (verified, property-level evidence of risk reduction) is what neither side routinely collects.

Inspired by reporting from U.S. Department of the Treasury

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Framed 21 Sep

FoodProblem 0015

Loss happens at the handovers: field to packer, packer to distributor, distributor to shelf. Every party measures only its own side of each one. So each set of numbers looks defensible while the total is enormous, and the interventions that would work are precisely the ones nobody can see, because they sit between two businesses rather than inside one.

Who lives with itGrowers and packers · distributors and independent retailers · redistribution charities working the same corridors

Why nowFAO estimates that 13.3% of the world's food was lost in 2023 after harvest and before retail, on farms and in transport, storage, wholesale and processing, up slightly from 13.0% in 2015 when global monitoring began.

Why it mattersMeasurement at the handover is the wedge, and it is cheap: the produce is already photographed, weighed and scanned on both sides of every transfer. Nobody is joining those records, because each side owns only its own.

Inspired by reporting from Food and Agriculture Organization of the United Nations

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Framed 21 Sep

Enterprise AIProblem 0020

Support has been largely automated, and for routine questions that is an improvement. The unusual case is where it breaks: a bereavement, a billing error, a locked account, a situation the script has no branch for. The customer loops, rephrases, tries again, and eventually gives up, and the company never learns it happened, because a failed escape is rarely a metric anyone collects.

Who lives with itCustomers in genuinely unusual situations · the few remaining human agents · support leaders flying blind

Why nowAutomated support is now the default first line almost everywhere, while the escalation path behind it has often been thinned.

Why it mattersThe savings are measured precisely and the losses are invisible entirely, which is why the balance keeps tipping the wrong way. Detecting the moment a conversation has stopped working is a narrow, concrete capability, and whoever builds it is selling the missing half of every deployment.

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Framed 21 Sep

CitiesProblem 0021

"Accessible" on a listing might mean a ramp, or one step, or a lift that has been broken for a month, or a toilet being used as a storeroom. The most reliable account usually comes from another disabled person who has actually been there. That knowledge exists in enormous quantity, scattered across forums, group chats and reviews, and the maps and guides that gather it cover only part of it, each in its own terms, so it is rarely there to check before leaving the house.

Who lives with itWheelchair users and people with limited mobility · blind, deaf and neurodivergent travellers and people with hidden disabilities · carers planning a trip · venues that are accessible and cannot prove it

Why nowAccessibility information is still largely self-declared by the venue, with no shared definition of what the word covers and little route for correction when it is wrong.

Why it mattersA sharply defined information gap with a motivated community already producing the data for free. Structure is most of the product, and the people who need it are the people best placed to build it.

Framed by Ainna editorial

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Framed 21 Sep

LogisticsProblem 0024

A small importer commits to an order months before it lands and discovers the real cost on arrival: duties applied differently than expected, a classification disputed, fees that were never quoted. Large firms keep trade teams and brokers for exactly this. A five-person business has a spreadsheet and a guess, and prices a whole catalogue on that guess.

Who lives with itSmall e-commerce brands and independent retailers · the freight forwarders serving them · their customers, eventually

Why nowTariff regimes have become materially more changeable, and the tooling that absorbs that volatility is still mostly built and priced for larger firms.

Why it mattersVolatility is now the normal condition rather than the exception, and the answer is still priced for companies that do not need it most. A landed-cost figure a small owner can actually trust is a product, not a feature.

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Framed 21 Sep

EnergyProblem 0025

The first mass-market electric cars are reaching the end of their first ownership with batteries that have years of useful service left. Battery-health reports exist, but no grade is yet trusted by buyers, insurers and second-life users alike, so buyers discount heavily to cover what they cannot see, insurers guess, and packs that would work well in a home or on a grid for another decade are recycled early or not moved at all.

Who lives with itUsed-car buyers and dealers · fleet operators disposing of vehicles · home and grid storage developers

Why nowThe first large cohort of mass-market electric vehicles is coming off its original ownership now, and the second-life market that should absorb those packs still lacks a grade it can commonly trade on.

Why it mattersA structural gap with a clock on it: every month without a credible grade destroys value that cannot be recovered later. A battery grade anyone can verify unlocks a market that currently struggles to form.

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Framed 21 Sep

WorkProblem 0026

A small business with roles to fill competes on the same job boards as employers with recruitment teams, brand recognition and budget, and loses. Meanwhile capable people a few streets away never see the role, or see it and do not recognise themselves in the wording. Both sides are present, willing and unable to locate one another.

Who lives with itOwners of trades, care and hospitality businesses · local jobseekers and career changers · the boards that serve neither well

Why nowHiring has concentrated onto national platforms optimised for volume and for employers who can pay to be seen, which is the opposite of what a local employer with two vacancies needs.

Why it mattersThis is a matching failure rather than a shortage, and matching failures are solvable. Local, skills-first matching remains underbuilt because the large platforms are built for a different customer.

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Framed 21 Sep

EnergyProblem 0003

Training runs, batch inference and analytics are scheduled around deadlines and cluster availability, almost never around what electricity costs or emits at that hour. Much of this work is genuinely movable, a few hours either way or a different region entirely, but the information needed to move it lives in a different system from the scheduler, and in most organisations nothing joins them. So flexible load behaves like inflexible load.

Who lives with itPlatform and infrastructure teams · sustainability leads with compute in their inventory · grid operators courting flexible demand

Why nowThe IEA's April 2025 Energy and AI report puts data centre electricity use at around 415 TWh in 2024, about 1.5% of global consumption, and projects roughly a doubling by 2030 with accelerated servers accounting for almost half the increase: a load growing fast and, unusually for industrial demand, substantially time- and place-shiftable.

Why it mattersA large and rapidly growing load is being treated as though it had no flexibility, when a meaningful share of it does. Making compute schedulable against grid signals is a software problem sitting between two industries that do not yet share a vocabulary.

Inspired by reporting from International Energy Agency

A hackathon to make a dent in High (Ainna's editorial grade)
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Framed 21 Sep

LogisticsProblem 0014

Recycling a device profitably depends on knowing what it contains before you open it, and that information was never attached to it. So the pile is handled as undifferentiated waste: shredded for the few metals that survive the process, or stored, or exported. The materials the world is anxious about securing are sitting in it, and a large part of the reason they are not recovered is identification rather than chemistry.

Who lives with itRecyclers and refurbishers · manufacturers facing take-back obligations · anyone sourcing critical minerals

Why nowThe ITU and UNITAR Global E-waste Monitor 2024 recorded 62 million tonnes generated in 2022 with 22.3% formally collected and recycled, around $62 billion of recoverable materials embedded in it, and a projection to 82 million tonnes by 2030 as recycling falls further behind.

Why it mattersA known, growing, geographically concentrated pile of valuable material where the binding constraint is knowing what is in each unit. That is a data and vision problem attached to an existing physical industry, a good place for a small team to start.

Inspired by reporting from ITU & UNITAR

A hackathon to make a dent in High (Ainna's editorial grade)
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Solve it →Full problem
Framed 21 Sep

CitiesProblem 0016

Utilities meter at the treatment works and at the customer's tap, and only coarsely in between: a district meter can say how much went missing across a whole area, not where. So loss is known as a percentage far more often than as a place, the response is scheduled rather than targeted (mains replaced by age, not by evidence), and too many leaks are found only when the road subsides or somebody rings.

Who lives with itWater utilities and municipal engineers · households in intermittent-supply cities · regulators setting loss targets

Why nowA widely cited World Bank estimate, published in 2018, puts the water utilities produce but never bill, through physical leaks and commercial losses, at around 126 billion cubic metres a year, nearly $40 billion in lost value.

Why it mattersThe cheapest new water available anywhere is the water already treated, already paid for, and lost before it arrives. Turning a percentage into a location is most of the job, and it is an instrumentation and inference problem rather than a civil engineering one.

Inspired by reporting from World Bank

A funded startup to make a dent in High (Ainna's editorial grade)
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Framed 21 Sep

Public sectorProblem 0022

Local government publishes its decisions (planning, licensing, budgets, minutes) and publishing is where the obligation stops. They arrive as scanned documents with inconsistent names, in systems searchable only if you already know what you are looking for. A decision that changes a street is technically public and practically private.

Who lives with itResidents and neighbourhood groups · local reporters · small developers and community organisations

Why nowLocal newsrooms have continued to close in many markets, removing the layer that used to read these documents on everyone else's behalf.

Why it mattersThe information is already public, already funded and already digital. Making it findable is a data problem rather than a political one, which is why it is tractable, and why it keeps falling between mandates.

Framed by Ainna editorial

A hackathon to make a dent in Notable (Ainna's editorial grade)
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The same Radar runs in your context. What you see here is Problem Radar, free. Tune it to your market and it listens to the sectors, themes and geographies you set, landing every find in your portfolio, graded: that's the Team plan (coming soon). Tune it to your company and it listens to your innovation agenda and to your own people: that's Enterprise. Both are private to you: nothing they find is ever published.

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