It is seven o’clock on a cold, windless evening: demand is still high, solar output has disappeared, an interconnector is constrained and every available source of flexibility is suddenly valuable.

That hour may last four hours. It may last forty, or become a multi-day weather event.

We often discuss energy storage as though it were a single answer to all of these problems. It is not. A battery that smooths short fluctuations, a system that shifts solar electricity into the evening, and an asset designed to cover several windless days provide fundamentally different services.

Yet electricity markets, procurement programmes and investment models do not always recognise those differences. They can count megawatts while overlooking time.

That is the central finding of my new executive briefing, The missing market for time, based on a structured review of 50 interviews from my Climate Confident and associated podcast archives.

The conclusion is not that technology no longer matters. It plainly does. The more interesting conclusion is that long-duration energy storage is not waiting for one decisive invention. In many cases, it is waiting for power systems to define the service they need, value it properly, contract it over an investable period and create a credible route from procurement to operation.

Battery storage is booming. Long duration is not

Storage is already one of the fastest-moving parts of the energy economy. According to the International Energy Agency’s Global Energy Review 2026, 108 GW of battery storage was deployed worldwide in 2025, 40% more than in 2024. Installed capacity is now eleven times higher than in 2021.

That is amazing progress. It can also obscure the problem.

The IEA says most projects still cluster around two hours, although durations are beginning to lengthen. Those systems can shift solar output, provide balancing services and reduce peaks. But success in short-duration batteries does not mean the market for assets covering ten hours, several days or longer is advancing at the same rate.

The scale required across the broader storage category is substantial. The IEA estimates that global capacity must reach 1,500 GW by 2030 to support the tripling of renewable generation while maintaining electricity security. Batteries provide most of that growth in its scenario. This is not, however, a 1,500 GW target for long-duration storage.

Even the term “long-duration” is unsettled. A US National Renewable Energy Laboratory review found definitions ranging from more than two hours to seasonal storage, with ten hours a common threshold. Buyers can discuss Long Duration Energy Storage (LDES) without agreeing on the problem it must solve.

Start with the uncovered period. Model what remains after transmission, interconnection, demand response, renewable diversity, short-duration storage and firm generation have all been considered. Then specify the necessary duration, location, response speed, cycling pattern and reliability.

The correct unit of analysis is not “a storage project”. It is the system condition that must be covered.

Electricity markets are good at pricing energy. Time is harder

In my recent Climate Confident conversation with Julia Souder, CEO of the Long Duration Energy Storage Council, her central argument was that markets still struggle to price time.

Wholesale markets can put a visible price on electricity delivered now and reward short-term balancing or reserve. Fewer provide stable, duration-sensitive revenues for dependable availability across a prolonged period of system stress.

An LDES asset can potentially provide energy arbitrage, capacity, congestion relief and ancillary services. On a presentation slide, these can be assembled into an attractive “revenue stack”. In the real market, however, the asset may not have access to every service, may not receive payment from every beneficiary, or may lack sufficient certainty about future revenues to support financing.

Value exists. A bankable cash flow may not.

The US Department of Energy’s 2025 commercial-liftoff analysis reaches a similar conclusion. It considers capacity markets, long-term bilateral contracts, cap-and-floor mechanisms, targeted tenders and more transparent system modelling among the potential routes to scale.

The striking point is not that one design has won. It is that technology improvement and market design must proceed together.

Targets do not finance projects. Contracts do

Governments and utilities increasingly recognise the need for storage. Recognition is welcome, but a target is not a transaction.

A developer cannot finance a project with a press release announcing a future gigawatt goal. Investors need to understand construction risk, performance, counterparties and lifetime revenues.

Procurement design therefore matters enormously.

If a tender is written around a familiar short-duration product, longer-duration technologies may be excluded before their system value is compared. If selection is dominated by initial capital cost, an asset with a longer useful life, more cycles or lower replacement costs can appear uncompetitive even when it offers a better lifecycle result.

Procurement should be technology-neutral and performance-specific. Buyers should define the service, require evidence, allocate risk explicitly and allow qualified resources to compete.

Where merchant markets cannot yet support financing, competitive long-term contracts may be needed to bridge the gap. These should retain demanding availability and performance obligations. Public support should help discover prices, build operating evidence and reduce risk, not guarantee every project a return.

The goal is a repeatable asset class, not an endless parade of demonstrations.

A signed contract is not an operating asset

Grid connection queues are long. Permitting can take years. New designs may depend on immature supply chains. Utilities demand exacting reliability and service arrangements, often before manufacturers have accumulated enough production and operating evidence.

Technologies bring different constraints. Compressed-air storage has site requirements. Flow batteries trade energy density for long cycle life. Hydrogen incurs substantial losses when electricity is converted to hydrogen and back. Thermal storage can be compelling for industrial heat, but depends on the duty and integration pathway.

That variety is a strength. It also makes the idea of declaring “LDES is ready” or “LDES is not ready” largely meaningless.

Ready for what, where, for how long and against which alternatives?

The archive evidence supports readiness in the plural. Some technologies are commercially credible for defined applications now. Others still need cost reductions, operational proof or supply-chain development. None should be selected through a technology beauty contest detached from the system requirement.

Five decisions that would move the market

For senior energy leaders and policymakers, the practical agenda is clearer than the technology debate suggests.

First, model the residual flexibility gap. Identify the hours or days that remain difficult after other resources have been included. Do not begin with a preferred storage chemistry.

Second, specify the service in time. Duration, location, availability, recovery time and cycling requirements should appear in operational language that procurement teams and investors can use.

Third, create a route to investability. Targets must become competitive tenders and contracts with sufficient revenue certainty, measurable performance and a credible allocation of risk.

Fourth, make deliverability an investment gate. Grid access, planning permission, manufacturing capacity, testing, warranties, skilled labour and long-term service should be assessed before an award, not left as implementation details.

Fifth, govern a portfolio rather than a favourite technology. Storage must be compared with transmission, flexible demand, interconnection, firm clean generation and renewable overbuild. The objective is not to maximise storage. It is to deliver the required reliability at the lowest credible system cost.

The market is moving, but not evenly

The 108 GW of battery capacity added in 2025 shows that storage can scale rapidly once technologies, revenues and customer needs align. Around 80% of those additions were utility-scale, according to the IEA. Durations are gradually increasing too, with more projects reaching four hours or beyond.

But four hours is not forty. The commercial momentum behind lithium-ion systems should not be mistaken for proof that every longer-duration pathway has crossed the same threshold. Interviews in my archive point to larger projects and accumulating performance data, but much of that evidence comes from technology providers and has not been independently audited.

Industrial applications are broadening the opportunity too. My conversation with Rondo Energy CEO John O’Donnell explored electric thermal storage as a way to convert renewable electricity into dependable industrial heat. That is not interchangeable with every grid-storage application, but it illustrates a crucial point: storage becomes valuable when it is designed around a specific service and connected to a real operating need.

Moving energy through time, and through space

Return to that difficult hour after sunset.

Storage can move plentiful electricity from an earlier period into it. But storage cannot solve every constraint. Sometimes the required electricity exists at the same moment in another region. The obstacle is the network between them.

This is the complementary infrastructure challenge at the heart of the energy transition:

Storage moves energy through time. Grids move energy through space.

The IEA estimates that meeting the global renewable goals agreed at COP28 requires not only 1,500 GW of storage by 2030, but also 25 million kilometres of new or modernised electricity grids. If grids and storage lag behind generation, its modelling shows more curtailment, higher prices and almost 40% more coal generation than under full implementation.

I will return to that second half of the equation in another executive briefing in the coming weeks, examining what prevents electricity networks from expanding and adapting at the pace the transition requires.

For now, the storage lesson is direct. We do not merely need more capacity. We need power markets and institutions capable of identifying which hours matter, paying for dependable service and turning technical capability into assets that can be financed, connected and operated.

That is the focus of my new executive briefing, The missing market for time. Drawing on evidence from 50 interviews, it sets out a practical decision framework for leaders responsible for energy planning, procurement, investment and delivery.

Read The missing market for time.

The next phase of the energy transition will be defined not simply by how much clean electricity we produce, but by whether we can deliver it at the right place and the right time.

The model produces a better forecast. The dashboard looks polished. The project team demonstrates recommendations that planners could not have generated as quickly on their own.

Approval follows. So does talk of rolling it out across the network.

Six months later, the model is still running, but the planners are back in spreadsheets. Recommendations arrive too late to affect the decision. One site trusts the output; another ignores it. When the data deteriorate, nobody owns the repair. When a recommendation is wrong, nobody is certain who owns the consequence.

The technology worked.

The operating model did not.

That distinction is the subject of my new executive brief, Why supply-chain AI pilots fail to deliver lasting value.

The report draws on a structured analysis of 43 interviews from my Resilient Supply Chain podcast. Those conversations bring together people designing, selling, implementing and working with supply-chain technology. Rather than relying on one survey or a handful of headline case studies, I compared their accounts to identify recurring operating problems, successful deployments, contradictions and areas where the evidence remains weak.

The central conclusion is simple:

The unit of scale is not the model. It is the operating capability surrounding it.

The pilot passed the wrong test

A proof of concept can establish whether a model is capable of producing a useful forecast, recommendation, classification or alert.

That is valuable. It is also incomplete.

Pilots operate under unusually favourable conditions. The scope is controlled. Data are selected and prepared. A small team pays close attention. Exceptions can be handled manually. Project sponsors are available to settle disagreements. If an output looks wrong, somebody investigates.

Production removes that protective layer.

Data continue to arrive from ERP and planning systems, spreadsheets, equipment, suppliers and logistics partners. Users have competing priorities. Local conditions differ. Decisions are time-sensitive. Experienced people change roles. Integrations fail. Models encounter events poorly represented, or entirely absent, in their historical data.

At that point, model performance is only one part of the investment case.

The harder question is whether the organisation can turn the output into a better operational decision, consistently enough to justify the full cost of doing so.

That full cost includes much more than the model or software licence. It includes integration, sensing, data maintenance, governance, support, process redesign, training, exception handling and provider continuity.

A technically successful pilot can therefore conceal a commercially weak production proposition.

Value is lost at the hand-offs

Across the interviews, four mechanisms received strong support: production data, connection to execution, decision authority and work design.

They should not be treated as four independent items on an AI-readiness checklist. They form a chain.

Break any critical link and value can disappear.

From prepared data to production data

A pilot can be built around a curated dataset. Production depends on information that continues to arrive at the required quality, frequency and speed.

That changes the nature of the data problem.

It is no longer a one-off cleansing exercise. It is an operating responsibility with named owners, maintenance rules, latency requirements, external dependencies and recovery procedures.

In one interview, Andy Kohm of SCIP explains how contradictory and poorly maintained source data can cause AI to accelerate the wrong decisions.

The danger is not simply that the model stops working. It may continue to produce confident, plausible outputs while the quality of the underlying signal declines.

That is harder to detect, and potentially more expensive.

From insight to action

Even an accurate recommendation has no economic value until it changes a real decision.

This sounds obvious. In practice, it is where many analytical systems become detached from operations.

Spencer Penn of Lightsource.ai describes sourcing decisions that may be completed through email and spreadsheets before the ERP record is updated. Intelligence connected only to the formal system of record can arrive after the consequential choice has already been made.

The model has an insight.

The workflow has moved on.

Similar failures occur when an alert reaches somebody who cannot act, when a recommendation is not integrated with the system of execution or when a standard design collides with the realities of a particular warehouse, factory or transport operation.

The production test must therefore include the entire action path: where the output appears, who receives it, which system executes it, how quickly action must follow and how the result returns as feedback.

From recommendation to authority

The next hand-off is organisational.

What may the AI observe? What may it recommend? What may it execute? When must a person intervene? Who may override the system? Who owns the result?

These are operating-model decisions, not technical settings.

Simon Bezrukov of Bristlecone distinguishes between automating the administration around a decision and owning its consequences. A system might gather missing information, open a ticket, propose a revised plan or select from an approved set of responses. That does not mean it should receive unrestricted authority over every decision.

“Human in the loop” does not resolve the issue on its own.

A nominal approval step can add delay without adding judgement. Equally, removing people too quickly can leave nobody capable of recognising a change in conditions, challenging a plausible error or taking responsibility for an exception.

Human involvement should reflect consequence, reversibility, confidence and the maturity of the deployment.

Authority can expand as evidence accumulates.

From adoption to work design

When people reject a new system, the explanation is often “resistance to change”.

Sometimes it is.

But that phrase can also conceal a design failure.

The software buyer may not be the person expected to use the output. A standard process may ignore legitimate site differences. An alert may add work without removing an existing task. Automation may eliminate the routine activities through which less experienced employees acquire judgement.

Training cannot compensate for a poorly designed job.

A production deployment must be developed with the people who will use and supervise it. It must be tested under realistic pressure, not only in a demonstration environment. That means rehearsing exceptions, degraded data, integration failures, provider outages and the moments when a person must take control.

The objective is not to persuade users to accept the technology.

It is to design a better way of working.

The successful cases sharpen the lesson

A failure-only account would be just as misleading as uncritical enthusiasm.

The interviews also contain examples of AI and automation producing operational value. These cases challenge two common assumptions.

The first is that an organisation must perfect its entire data estate before it can begin. It does not.

Focused modelling, simulation and an 80/20 approach can support a bounded decision without cleansing every field in every system.

The second is that every AI-supported action requires permanent human approval. It does not.

Opening a service ticket is not the same as committing inventory. Retrieving missing information is not the same as selecting a supplier. Adjusting a low-risk parameter within an approved range is not the same as controlling a safety-critical process.

The right degree of autonomy depends on the decision.

Some contributors also report material operational outcomes. Penn, for example, describes an unnamed automotive customer whose platform-supported categories completed sourcing 25% faster and experienced 37% less post-award cost creep than categories managed outside the platform.

That is a provider-reported comparison, not a controlled or independently audited study. It should not be treated as a universal benchmark.

It does, however, illustrate the architecture of a scaling case: a defined decision, operational integration and measures tied to the outcome.

The counterexamples do not weaken the four findings. They make them more precise.

The lesson is not to wait for perfect conditions.

It is to choose a consequential but bounded decision, test the complete operating chain and expand only when the evidence supports expansion.

Stop approving isolated technology experiments

Before approving another AI pilot, leaders should be able to answer eight questions:

1. Which consequential operational decision or workflow are we trying to improve?
2. Who owns that decision and its outcome?
3. What is the current performance baseline?
4. Can the required data be maintained under production conditions?
5. How will the output reach the person or system capable of acting?
6. What may the AI recommend or execute?
7. What happens when the data, model, integration or provider fails?
8. Which thresholds will cause us to scale, revise or stop?

Together, those answers form a bounded production hypothesis.

That is a better basis for investment than a loosely defined technology experiment. It forces the team to test whether the organisation can operate the capability, not merely whether the model can generate an impressive output.

It also creates a legitimate way for a pilot to succeed without being scaled unchanged.

A well-designed experiment may show that the expected value is not there. It may reveal an integration cost that changes the economics. It may demonstrate that process redesign would solve more of the problem than machine learning.

That is useful knowledge.

A pilot fails when it neither improves a material outcome nor resolves an uncertainty worth funding.

How the research was developed

I created the report by returning to recent Resilient Supply Chain interviews and retesting an earlier set of conclusions rather than assuming they were correct.

The analysis examined 43 interviews and 154 relevant transcript sections published between 3 November 2025 and 31 August 2026. It deliberately searched for evidence that challenged the emerging argument: successful deployments, alternative explanations, contradictory experiences and differences between what technology providers promise and what organisations report in practice.

This matters because retrieval is not proof. Finding several people making similar claims does not establish how common a problem is across the industry. Nor does a persuasive customer story become an independent benchmark simply because it includes an impressive number.

The strength of the research lies elsewhere.

It brings together detailed accounts from people working across supply-chain technology and operations, compares the mechanisms they describe and tests whether the initial explanation survives contact with exceptions and disagreement.

Four connected mechanisms emerged with strong support: production data, connection to execution, designed authority and redesigned work.

A fifth finding, value discipline, is suggestive rather than equally established. Several contributors argue, persuasively, that AI initiatives should begin with a valuable business problem and measurable baseline. But the interviews do not contain enough retrospective evidence from cancelled pilots to establish weak ROI discipline as a primary cause as strongly as the other four.

The research is qualitative. The interview participants were not randomly selected, and technology providers and advisers are more heavily represented than operators. The findings cannot tell us what proportion of all supply-chain AI pilots fail for a particular reason.

They can do something more useful for an executive deciding whether to fund the next stage: identify recurring failure mechanisms, reveal important exceptions and sharpen the questions that should be answered before more capital and organisational attention are committed.

Ask the production question

Return to that successful pilot presentation.

The forecast may genuinely be better. The recommendation may be useful. The underlying technology may be excellent.

Lasting value will still depend on what happens next: maintaining the inputs, connecting the output to action, allocating authority, redesigning the work, managing exceptions and measuring whether performance improves after the project team steps away.

So the next time a pilot team presents an impressive result, do not ask only:

“Did the model work?”

Ask:

“Can the organisation operate it?”

That is the production question.

And it is the question that separates an interesting demonstration from a capability worth scaling.

Read the research and download the free executive brief.

No registration required.

An inbound shipment will miss its slot. The control tower identifies the delay, highlights the affected order and turns the screen red. By 9.15, the warning has reached procurement, logistics and the warehouse team.

Everyone can see the problem.

But who can change the dock schedule? Is there alternative stock? Can production be resequenced? Who is authorised to accept the additional transport cost? By the time those questions are answered, the useful response window has closed.

The technology worked. The operation still failed.

This is the visibility trap. Businesses have spent years treating end-to-end visibility as a route to resilience, often assuming that more data, more alerts and a more complete digital picture will produce a faster response. Yet information does not move a pallet, switch a supplier or protect a customer commitment. People and systems must convert it into a decision, and the physical operation must be able to carry that decision out.

That gap between seeing and responding is the subject of my new executive brief, Resilience Without Excess: Why Supply-Chain Visibility Still Fails to Create Resilience. It draws on a structured analysis of 43 interviews from the Resilient Supply Chain podcast archive, covering 78 relevant transcript sections and 160 validated episode-level claims.

The research points to a deceptively simple conclusion: resilience is not a dashboard feature. It is an operating capability.

Visibility has expanded. Resilience has not kept pace

The commercial urgency is difficult to miss. Business interruption, including supply-chain disruption, ranks third in the Allianz Risk Barometer 2026, after occupying either first or second place throughout the previous decade. Only 3% of respondents described their supply chains as “very resilient”.

Companies are not blind to the problem. They have invested in supplier mapping, planning platforms, control towers, sensors and predictive analytics. The view is improving, but unevenly. McKinsey’s 2025 survey of 100 global supply-chain leaders found that 95% had visibility into at least tier-one supplier risks. That figure fell to 42% for visibility extending to tier two or beyond.

Technology adoption reveals a similar divide between ambition and operational use. Three-quarters of respondents were planning, designing or piloting supply-chain AI applications; only 19% said they were deploying them at scale.

The problem, then, is not simply that organisations lack information. It is that the route from information to intervention is unreliable.

Across the podcast interviews, that route broke in five recurring places: the signal stopped at the screen; the digital record failed to reflect the physical operation; information stalled at a system or organisational boundary; governance left competing priorities unresolved; or the operation lacked the authority, resources and trust required to respond.

These are not five isolated technology defects. They are parts of one management problem.

A signal has value only while choices remain

Visibility is perishable.

A strategic network review may work with monthly data. A parcel jam, yard backlog or production constraint can deteriorate in minutes. In each case, information has value only if it arrives early enough for someone to choose a better outcome.

That makes “real time” a poor specification on its own. Second-by-second sensing is wasteful when the business cannot act until next week. A daily update is inadequate when the next hour determines whether a shipment makes its cutoff. Leaders need to define the decision window first, then determine what data speed and granularity it requires.

Accuracy matters just as much. A polished control tower cannot make contradictory part records, inconsistent supplier identifiers or stale inventory positions true. Nor can an advanced model reason about a pallet, product condition or machine event that the operation never captured.

The digital picture fails in two ways: the recorded data may be unreliable, or the necessary physical event may be absent. More sophisticated analytics can conceal the first problem and cannot repair the second.

This is why data work should not be dismissed as preliminary housekeeping. Establishing the source, ownership, quality, freshness and maintenance of a critical event is part of operational design. Without that discipline, visibility can become a more persuasive representation of conflicting information.

End to end is an agreement, not a screen

The phrase “end-to-end visibility” suggests a single vantage point over the entire supply chain. Real operations are less accommodating.

Orders, inventory, production, warehouse execution, yard movements, transport status, supplier capacity and risk data sit in different applications. They also sit in different companies, governed by different contracts, incentives and standards. One platform may describe its portion accurately while missing the dependency that determines the outcome.

The answer is not to pull every datum into one enormous repository. It is to identify the minimum reliable information that must cross each boundary for a critical decision to be made.

That requires explicit agreements. Who creates the event? Who assures it? How fresh must it be? Which organisation can use it? What happens when it is missing? Where does responsibility sit when a critical service has been outsourced to a provider whose own dependencies remain opaque?

This last question matters. A company can outsource software, logistics or data processing. It cannot outsource accountability for the disruption that follows when those services fail.

Visibility across organisational boundaries is therefore as much a commercial and governance challenge as a technical one. Integration architecture matters. So do data standards, partner obligations, portability, escalation rights and the willingness to engage suppliers rather than merely send them another questionnaire.

Better information does not settle a business trade-off

Even a shared, accurate view leaves a harder question: what should the organisation do?

Procurement may optimise purchase price. Finance protects cash and working capital. Operations prioritises throughput. Customer teams protect service. A visibility platform can expose the tension among those objectives, but it cannot decide how the enterprise should value them.

More information can even deepen fragmentation if each function uses it to optimise its own scorecard faster.

Resilience requires choices that may look inefficient through a narrow cost lens. Extra capacity, alternative suppliers or inventory flexibility carry a visible price; the disruption they prevent remains hypothetical until it occurs. Senior leadership must decide how cost, cash, service and risk will be balanced before a time-critical alert forces the debate.

That means every important visibility product needs more than a sponsor. It needs a decision owner, defined escalation thresholds, agreed trade-offs and a forum capable of resolving conflict. A metric without a required response is reporting. A warning without authority is noise.

The final mile is physical, and human

Suppose the data is reliable, the boundary has been crossed and the decision is clear. The response can still fail.

A yard may need another driver. A warehouse may lack space. Production may require a configuration change. A supplier switch may depend on contractual approval. A site may operate differently from the process designed at headquarters. The user receiving the recommendation may not trust it, understand it or possess the authority to act.

These are not downstream implementation details. They determine whether the investment can alter performance.

The interviews repeatedly returned to local variation, frontline behaviour and deployable capacity. Technology must fit the operation as it exists while helping that operation improve. That requires training, configuration, clear decision rights and safe fallback procedures. It also requires testing under real conditions, where labour is constrained, data can fail and disruption rarely follows the demonstration script.

“Change management” is too often treated as communication after the technical work is complete. In a resilient operating model, adoption is part of the product specification from the beginning.

Design backwards from the consequence

The usual technology discussion starts with capability: What can the platform display? Which systems can it connect? Where can AI generate a prediction or recommendation?

Leaders should reverse the sequence.

Start with the consequence. Which service, cost, safety or recovery outcome deteriorates because the business reacts too late?

Name the decision. What must be decided, by whom and within what window?

Design the action path. Which person, workflow, partner, contract, vehicle, labour pool or inventory position can change the outcome? If there is no credible intervention, more visibility will mostly document failure in greater detail.

Then specify the minimum data contract: required events, granularity, freshness, assurance, system of record, maintenance owner and acceptable failure rate. Align the trade-off among cost, cash, service and risk. Finally, test the complete loop in operational time.

The success measure is not the number of connected sources or deployed dashboards. It is whether decisions became earlier, interventions were completed and operational outcomes improved.

This approach also gives AI a more useful role. Models can detect patterns, rank options and remove administrative friction at remarkable speed. But they cannot compensate for a missing physical event, an unresolved commercial trade-off or an unavailable truck. If the operating loop is broken, AI may generate recommendations faster than the organisation can absorb them.

From visibility to resilience

The encouraging signal is that the supply-chain conversation is maturing. Leaders are asking less about whether they can see an exception and more about whether they can act before it becomes a consequence. Attention is moving towards decision latency, deeper-tier relationships, data ownership, human accountability and operational execution.

That does not reduce the importance of visibility. It completes its purpose.

Connect the signal to a decision. Connect the decision to an owner. Connect the owner to an available action. Then verify that the action changed the outcome.

That is resilience without excess: not endless data, inventory or technology, but enough dependable information, authority and capacity to respond while meaningful choices remain.

The red alert at 9.07 should be the beginning of the response, not the end of the achievement.

My new executive brief, Resilience Without Excess: Why Supply-Chain Visibility Still Fails to Create Resilience, is available to read and download free: read the full research brief.

Sources

• Tom Raftery, Resilience Without Excess: Why Supply-Chain Visibility Still Fails to Create Resilience (2026): https://tomraftery.com/research/resilience-without-excess/
• Allianz Commercial, Allianz Risk Barometer 2026: https://commercial.allianz.com/content/dam/onemarketing/commercial/commercial/reports/allianz-risk-barometer-2026.pdf
• McKinsey & Company, Supply chain risk pulse 2025: Tariffs reshuffle global trade priorities (2 December 2025): https://www.mckinsey.com/capabilities/operations/our-insights/supply-chain-risk-survey

When I started recording conversations with supply-chain executives, technologists and practitioners, the aim was straightforward: understand what people close to the work were actually seeing, building and learning.

One episode became ten. Ten became a hundred. Eventually there was an archive large enough to contain something I had never set out to create: a dataset.

Researchers Stefan Seuring, Jannik Neuberger, Sharfah Ahmad Qazi, Lara Schilling and Andrea S. Patrucco have now analysed 52 of those interviews for a peer-reviewed paper in the International Journal of Physical Distribution & Logistics Management. 

The interesting part is not that a podcast became research material.

It is what they found inside the conversations.

And the strongest finding is a useful corrective to much of the technology rhetoric currently washing through boardrooms: technology is not the transformation.

What the researchers actually analysed

At the cut-off point in June 2024, the podcast archive contained 404 episodes. The researchers used a purposeful selection process to identify 52 conversations dealing substantively with both digital technology and supply-chain sustainability. 

They examined four technology groups, artificial intelligence, Internet of Things, blockchain and cloud services, against established sustainable supply-chain management practices and economic, environmental and social outcomes.

This was not simply a word count. Keyword analysis surfaced patterns; researchers then manually coded the transcripts for context and meaning and used contingency analysis to examine which technologies, practices and outcomes tended to appear together. A small practitioner “resonance” survey was added afterwards as a supplementary plausibility check. 

Podcast interviews are a slightly odd research source. That is partly their value. They are comparatively unscripted and practitioners can wander into examples, barriers and explanations that a tightly structured survey might never invite.

But they are curated media. Guests know they are speaking publicly. Hosts select them. Success stories travel better than post-mortems.

That limitation becomes important later. 

Technology creates capability. Management converts it into outcomes.

This is the conceptual heart of the paper.

The researchers did not find a simple line running from “deploy technology” to “achieve sustainability”. Practitioner narratives rarely described digital tools as directly producing better environmental, economic or social performance.

Instead, technologies enabled management practices, particularly sustainable risk management, proactivity and collaboration, and those practices were associated with sustainability outcomes. 

Sustainable risk management accounted for 48% of the practice-related coded segments, appearing across 87% of the selected episodes. Proactivity represented another 26%, while collaboration accounted for 15%. Broader strategic ideas such as organisational orientation and continuity of supplier relationships appeared less often as explicit technology links; the researchers interpret them more as enabling context. 

In practitioner language:

Technology creates capability. Management converts capability into outcomes.

AI can detect a supplier anomaly. It cannot decide what risk tolerance your business should accept.

IoT can tell you a refrigerated shipment spent too long above specification. It cannot repair a procurement process that rewards the cheapest supplier regardless of spoilage, emissions or service failure.

A blockchain ledger can make provenance harder to dispute. It cannot make a weak due-diligence process strong.

The implication for executives is that buying technology is the visible part. Data architecture, governance, decision rights, supplier engagement and process redesign are where the actual transformation happens.

What the technologies are actually good at

The paper is useful because it does not throw AI, IoT, blockchain and cloud into one digital-transformation bucket.

AI: less automation, more anticipation

AI was the most frequently discussed technology by some distance: 178 coded segments, 52% of all digital-technology mentions, across 29 of the 52 episodes. 

The use cases include predictive analytics, supplier-risk assessment, digital twins, predictive maintenance, scenario analysis and automated risk detection. But the pattern behind them is more interesting.

AI shifts the supply chain from explaining yesterday to estimating tomorrow.

A predictive-maintenance model can flag degradation before a line stops. A supplier-risk engine can combine signals that a category manager could never process manually. A digital twin can test routing, production or capacity choices before the downside appears in the physical network.

The researchers associate AI most strongly with proactivity and sustainable risk management. My interpretation is that anticipation may become AI’s most valuable supply-chain capability: buying decision time.

And decision time is resilience.

IoT: giving the physical supply chain a nervous system

If AI is the prediction layer, IoT is the sensing layer.

The interviews linked IoT to real-time visibility, automated tracking and tracing, condition monitoring, lifecycle assessment and product-level carbon measurement. Sensors turn physical events, temperature, location, vibration, equipment use, into data that systems and people can act upon. 

Better sensing can reduce spoilage, disruption losses and waste while improving service and asset utilisation. More importantly, it closes part of the gap between what the digital supply chain says is happening and what is physically happening.

No data architecture can compensate for a blind physical network.

Blockchain: narrower, and therefore more credible

Blockchain gets a more restrained verdict. That is healthy.

The paper does not support the old claim that blockchain will remake supply chains wholesale. Its strongest role is narrower: traceability, certification, due diligence, proof of origin, compliance and verification. 

The practitioner resonance survey was notably more cautious about blockchain than IoT or cloud. That does not make blockchain a failure. Technologies often become useful after expectations collapse from “this changes everything” to “this solves a specific expensive problem”.

A verification technology does not need to transform the enterprise. It needs to make verification better.

Cloud: important because it disappears

Cloud services are the least glamorous part of the story and arguably one of the most important.

The interviews describe cloud as infrastructure for real-time data sharing, collaborative forecasting, scenario analysis, visibility, predictive maintenance and coordination across dispersed organisations. 

Cloud is rarely the sustainability intervention itself. It is the layer that lets other interventions operate across sites, partners and systems.

That is a recurring pattern in mature technology. The most consequential infrastructure eventually becomes boring.

The unexpected result: social sustainability moved to the centre

Environmental sustainability dominated the explicit discussion, accounting for 58% of sustainability-outcome coded segments. Economic outcomes represented 22%. Social outcomes represented 20%. 

If you stopped at frequency counts, environmental performance would look like the obvious centre of gravity.

The contingency analysis told a more interesting story.

Social outcomes sat at the centre of many relationships between digital technologies and sustainability. AI, IoT and cloud services showed comparatively strong positive associations with social outcomes in the coded material, appearing alongside working conditions, human rights, supplier compliance, safety and ethical sourcing. 

The researchers are careful here, and we should be too: co-occurrence is not causality. The study does not prove that installing IoT improves human rights.

What it does show is that practitioners repeatedly connect digital visibility with the ability to see social risk.

That matters. Supply-chain digitalisation is becoming part of the evidence infrastructure through which a company can establish whether it knows what is happening beyond Tier 1.

That regulatory pressure has not disappeared, despite considerable EU simplification. Under the amended Corporate Sustainability Due Diligence Directive, very large companies within scope must identify and address actual and potential adverse human-rights and environmental impacts in their operations, subsidiaries and chains of activities. Following the 2026 Omnibus amendments, member states are due to apply the revised rules from July 2029. European Commission

The management question is shifting from “Do we have a supplier code of conduct?” to:

Can we produce credible evidence that the standards are being followed?

The part of the research I found most uncomfortable

The paper also points a finger at the dataset itself.

Most conversations are positive. Many guests are technology providers, 64% of the organisations represented in the sample. Failures, internal resistance, unintended consequences and negative impacts are underrepresented. 

That matters most around AI.

The selected interviews discussed predictive power, optimisation, safety and efficiency far more often than bias, energy consumption, workforce displacement, excessive monitoring or bad organisational consequences. The researchers explicitly warn that the dataset cannot provide a balanced account of the “dark side” of digitalisation. 

That is not a reason to dismiss the findings.

It is a reason to improve the questions.

I need to start asking more questions like:

• Where did this fail?
• What did you underestimate?
• What appeared in the business case only after implementation started?
• Who carries the downside?
• What resistance emerged from employees or suppliers?
• Which promised benefit never materialised?
• What trade-off became visible only after deployment?
• What would the sceptic in the room say?

Better questions produce better practitioner intelligence. Success stories tell us what is possible. Failure stories tell us what can prevent success, and that is often more useful when trying to replicate it..

What supply-chain leaders should do differently

The paper’s practical lesson is almost unfashionably sensible: start with the management problem, not the technology.

“Where can we use AI?” is usually a poor opening question.

Ask instead: Which risks are invisible? Which decisions arrive too late? Where is supplier data unreliable? Where does latency create cost, emissions or vulnerability? Which compliance processes remain manual? Where would prediction materially change the decision?

Then choose the technology.

The research also points to prerequisites that receive far less conference-stage attention than generative AI: integrated ERP foundations, accessible data, coherent architectures, governance and people capable of interpreting what the systems produce. 

Implementation should be sequential.

Instrument a critical flow with IoT before instrumenting everything. Apply predictive maintenance where downtime matters most. Use supplier-risk analytics where visibility is weak and exposure high. Use blockchain where provenance, certification or multi-party verification is genuinely difficult.

Then tie each project to an outcome somebody can measure: fewer disruptions, lower waste, better working conditions, reduced emissions, faster due diligence, improved service, less unplanned downtime.

Digital transformation should accumulate evidence, not slogans.

A podcast archive is also a record of an industry thinking

This is where the research changes how I think about the archive.

I started these conversations to explore where supply-chain technology, sustainability and operations were going. I did not set out to build a longitudinal record of practitioner thinking.

But that is increasingly what the archive is.

It contains changing attitudes to resilience, AI, Scope 3, procurement, supplier risk, geopolitics, reshoring, traceability and digital transformation across years in which those subjects moved from specialist conversations into executive priorities.

That does not mean the podcast has been “academically validated”. It means researchers judged a defined slice of the archive rich enough to analyse as secondary qualitative evidence, with explicit caveats about selection, bias and interpretation. 

The more interesting possibility is that podcast archives can become records of how an industry explains change to itself while that change is still happening.

Conversations accumulate

A podcast episode feels temporary.

One guest. One conversation. One week in a publishing schedule.

Then the episodes accumulate, and patterns begin to appear. Some become visible only when somebody steps back, codes the language, compares the themes and asks a different question of the archive.

For me, the deepest lesson from this research is not about podcasts at all. It is about transformation.

Technology gives organisations new ways to see, predict, verify and coordinate. None of that guarantees a better supply chain.

The outcomes still depend on management: what gets measured, what gets acted on, whose interests count, which trade-offs are accepted, and whether leaders ask what went wrong as carefully as they ask what worked.

They begin with a well.

A field.

A failed crop.

A neighbour asking why strawberries, irrigation, tourism, politics, aquifers, and Europe’s most important wetlands now seem to be fighting over the same shrinking resource. Around Doñana, this is no abstraction. In 2021, the European Court of Justice ruled that Spain had breached EU law because excessive groundwater extraction was damaging the protected area, and WWF has estimated that around 1,000 illegal wells and 3,000 hectares of illegal farms have contributed to unsustainable water use.

That is what resource constraint looks like when it stops being theory.

It becomes local.

It becomes political.

It becomes permission.

And that is precisely where artificial intelligence is heading.

AI is still spoken about in strangely weightless language. Models. Agents. Workflows. Productivity. Acceleration. The vocabulary is digital, frictionless, almost airborne. Yet the reality is anything but. AI sits in data centres. Data centres sit on land. They draw electricity from grids, water from local systems, hardware from strained supply chains, and legitimacy from communities, customers, regulators, investors, and employees.

That was the most important thread in my Climate Confident conversation with Sophia Mendelsohn, who leads SAP’s Global Sustainability Platform. Her argument was clear: AI needs sustainability. A company cannot achieve its AI objectives – or the growth those objectives are meant to deliver – without the discipline, data, and stakeholder experience that sustainability leaders hold.

That is not a moral flourish.

It is a business argument.

Because in 2026, AI is no longer merely a technology decision. It is an infrastructure decision. A procurement decision. A governance decision. A social licence decision. And, increasingly, a board decision.

The data says AI is becoming an industrial load

The International Energy Agency’s 2025 Energy and AI analysis estimates that global data centre electricity consumption will more than double to around 945 TWh by 2030 – slightly more than Japan’s current total electricity use. The same analysis projects data centre electricity demand to grow by around 15% per year between 2024 and 2030, more than four times faster than electricity demand growth from all other sectors combined.

The 2026 update sharpened the picture. The IEA reported that global data centre electricity demand grew 17% in 2025, while AI-focused data centres grew even faster, with electricity use from that subset rising 50%.

This is not a rounding error inside an IT budget.

It is a new industrial load arriving at speed, often in places where grids are already being asked to electrify transport, heat, buildings, and industry. The bottlenecks are physical: transformers, substations, switchgear, circuit breakers, land, permitting, water, grid queues, and skilled labour. Reuters reported in July 2026 that surging AI data centre demand is worsening shortages of critical electrical equipment in the US, with some lead times reaching up to 160 weeks. Wood Mackenzie expects US data centre capacity to rise from roughly 24 GW today to 110 GW by 2030.

That is the point many boardrooms still miss.

AI does not scale because a strategy deck says it should. It scales where power, water, hardware, permits, public consent, and procurement discipline allow it to scale.

Water is the most visceral example. The Uptime Institute rightly cautions against lazy comparisons between data centres and cities. In its 2024 Cooling System survey, only 14% of respondents with water-cooled data centres used more than 16 million US gallons, or around 60,000 cubic metres, per year.

But water is not a global average.

Water is local.

A litre used in a wet, cool region is not the same as a litre drawn from a stressed aquifer in a Mediterranean heatwave. That distinction matters because data centre impacts are felt at the level of specific grids, specific watersheds, specific communities, and specific planning authorities.

The question, therefore, is not “Are data centres efficient on average?”

The sharper question is: whose constraint does this facility worsen?

The implications are bigger than carbon

The easy version of this conversation is to say AI may increase emissions. True. But too narrow.

The real issue is that AI can collide with energy security, customer commitments, water politics, community trust, employee acceptance, industrial competitiveness, and long-term affordability. That makes sustainability a strategic discipline, not a reporting function.

For CEOs, the risk is not simply that AI adds tonnes of carbon to a Scope 3 inventory. The risk is that poorly governed AI creates hidden exposure across the business.

An AI contract may look clean on paper while masking grid congestion, water scarcity, weak supplier disclosure, volatile power prices, hardware replacement cycles, and reputational risk. A workload may appear cheap because the vendor has absorbed some costs for now. A procurement decision may look efficient because the sustainability questions were never asked.

That is not innovation.

That is deferred due diligence.

Sophia’s point is powerful because it reverses the usual caricature. Sustainability teams are often portrayed as the people who slow things down. In the AI infrastructure buildout, they may be the people who prevent programmes from stalling later – under the weight of energy constraints, community opposition, regulatory scrutiny, or customer pushback.

AI scales only where it is permitted to scale.

That permission comes from three constituencies.

First, the communities asked to host the infrastructure.

Second, the customers whose own public commitments may be contradicted by the technology they buy.

Third, the employees asked to adopt AI systems in their daily work.

That is social licence to operate. Mining, energy, chemicals, agriculture, and manufacturing have had to earn it for decades. Technology is now learning the lesson at uncomfortable speed.

The C-suite question, then, is no longer “How fast can we deploy AI?”

It is: “Can we deploy AI in a way that strengthens the business rather than quietly loading it with new resource, reputational, and regulatory risk?”

The strategies start before the contract is signed

The first move is procedural, but vital: put sustainability into AI procurement before contracts harden.

Not after deployment.

Not when the annual report is being assembled.

Not when a journalist asks where the data centre gets its water.

Before.

Every significant AI contract should ask where workloads run, how electricity is sourced, what cooling systems are used, what water risks exist, what hardware lifecycle assumptions sit behind the service, how e-waste is managed, how emissions are calculated, and whether the provider can report at a level useful for Scope 3 accounting.

The direction of travel in Europe is already clear. The EU Energy Efficiency Directive introduced monitoring and reporting obligations for data centres, and the European Commission’s database collects data relevant to energy performance and water footprint for facilities with significant energy consumption.

Second, boards need to broaden the AI ROI model.

Most AI business cases still over-index on speed, labour productivity, customer response times, automation, and margin improvement. Those are legitimate metrics. But they are incomplete.

A serious AI business case should also include energy exposure, water exposure, regulatory risk, vendor concentration, Scope 3 implications, grid delay risk, community acceptance, and operational resilience.

A narrow ROI model can make a fragile decision look precise.

Third, leaders should rank AI workloads by value density.

Not every prompt deserves expensive compute. Not every internal experiment deserves scale. Not every “AI-powered” feature creates strategic value. Some will be transformative. Some will be useful. Some will be computational theatre dressed up as digital leadership.

Token discipline is becoming financial discipline.

It is also becoming climate discipline.

Fourth, sustainability teams should use AI to change their own operating model. This is where the opportunity becomes genuinely interesting.

For years, Scope 3 work has depended on supplier questionnaires, partial disclosures, inconsistent spreadsheets, and the corporate equivalent of politely asking into the void. AI can shift that posture. Companies can model baselines, estimate product carbon footprints, flag anomalies, pressure-test assumptions, and then ask suppliers to confirm, correct, or improve the data.

That changes the power dynamic.

Sustainability moves from passive data collection to decision intelligence.

Finally, AI infrastructure should be designed around clean power, flexibility, and storage. The good news is that the clean technology stack is improving quickly. IRENA’s 2026 24/7 Renewables report estimates that four-hour utility-scale battery costs fell to around $140/kWh in 2025, close to 95% below 2010 levels.

That matters because AI loads do not have to be dumb loads. With better software, storage, grid signals, and contractual design, some workloads can become more flexible. Some can shift. Some can pair with clean power. Some can support grid stability rather than merely compete for capacity.

That is the strategic prize.

Not AI versus sustainability.

AI with sustainability built into the operating system of the business.

The signal of change is already visible

The transition is not hypothetical.

According to Ember’s Global Electricity Review 2026, solar met 75% of global electricity demand growth in 2025, while renewables supplied 33.8% of global power generation. Solar generation rose by a record 636 TWh in a single year, equivalent to roughly twice the UK’s annual electricity demand.

Clean power is scaling.

Storage is scaling.

Electrification is scaling.

And AI is now arriving as a major new claimant on that same electricity system.

This creates a fork in the road. Poorly governed, AI demand could extend grid bottlenecks, raise costs, intensify local water conflicts, and slow decarbonisation. Governed well, it could accelerate investment in renewables, storage, grid flexibility, efficiency, and better digital energy management.

That is why the sustainability function belongs in the AI conversation now.

Not as a compliance afterthought.

As a strategic partner.

The CEO should want sustainability in the room because sustainability leaders understand resource constraints. The CFO should want them there because hidden infrastructure risk becomes financial risk. The CIO should want them there because systems without trust fail adoption. The procurement leader should want them there because the leverage is greatest before the contract is signed. And the board should want them there because AI is becoming too material to treat as a siloed technology programme.

The companies that get this right will not simply have cleaner AI.

They will have more durable AI.

More investable AI.

More credible AI.

More useful AI.

And that brings us back to water.

A dry aquifer does not care about anyone’s innovation narrative. Neither does an overloaded grid. Neither does a community facing heat, rising bills, water stress, and another large infrastructure project at the edge of town.

Physical systems do not negotiate with PowerPoint.

AI’s promise is real. In medicine, science, engineering, climate modelling, materials discovery, logistics, and energy optimisation, it can be extraordinary. But the promise will only turn into durable business value if leaders treat AI as physical infrastructure, governed inside the constraints of power, water, land, materials, climate, and trust.

That is why my Climate Confident conversation with Sophia Mendelsohn matters. It is not another generic discussion about AI and sustainability. It is a board-level warning and a business opportunity.

The warning is this: treat AI as weightless software, and you will miss the risks until they become expensive.

The opportunity is better: bring sustainability into AI strategy now, and resource discipline becomes competitive advantage.

The next phase of AI will not be won by the organisations that deploy the most tools.

It will be won by the organisations that deploy the right AI, in the right places, for the right reasons, with the right infrastructure beneath it.

Listen to the full Climate Confident episode with Sophia Mendelsohn for the deeper conversation.

Photo credit dklaughman on Flickr

The myth says the work is the interview.

It is not.

The interview is the raw material. The real work is the system around it: how guests are selected, prepared, recorded, edited, packaged, hosted, published, and improved over time without depending on luck, adrenaline, or Sunday evening panic.

I have learned this over hundreds of episodes.

My Resilient Supply Chain podcast now has more than 510 episodes behind it. Climate Confident has passed 280. Together, they have generated more than 550,000 audio downloads and more than 600,000 YouTube views. Consistency at that scale is not an accident. It is a production system.

This is my current workflow. Current is doing quite a lot of work in that sentence.

It has changed repeatedly. It will change again. Podcasting is shifting from audio-first to multi-format media, and Apple’s move to support a stronger native video podcast experience inside Apple Podcasts is one more signal that production workflows cannot stand still. [1]

I think of the workflow as a media supply chain.

There are inputs, quality gates, dependencies, production steps, packaging, hosting, publishing, feedback loops, and a constant search for the next bottleneck.

For me, consistency is the anchor. Resilient Supply Chain goes live every Monday at 7am in Spain. Climate Confident goes live every Wednesday at 7am. The time itself is not sacred. The rhythm is. Listeners know when to expect a new episode. Guests and partners understand the cadence. I know what has to happen before each episode ships.

That predictability matters because podcasting has expanded well beyond audio. Edison Research reported in 2025 that 73% of Americans aged 12+ had consumed a podcast in audio or video format, 51% had watched one, and YouTube was the service used most often by U.S. weekly podcast listeners. [2]

A modern podcast is audio, video, search asset, credibility engine, and library of reusable thinking. Treat the recording as the finished product and most value quietly leaks away.

The workflow starts before anyone presses record.

I use online scheduling tools such as Cal.com and Calendly for intro and vetting calls. The call asks three questions: can the guest explain their ideas clearly, does the topic serve the audience, and is there enough substance?

If the fit is good, we schedule the recording. I ask the guest to send a few bullet points on topics they would be happy to discuss. These are not a script. Scripted interviews tend to sound like two corporate statements trapped in a lift. The bullets are a safety net if the conversation drifts.

Once the recording is booked, I add the recording link, platform details, guest notes, context, and audio/video guidance to the calendar invite. I ask guests to use a proper microphone if possible, avoid noisy rooms, wear headphones, and sit somewhere with decent light.

Some follow the advice. Some do not. This is why the workflow needs repair mechanisms.

My own hardware is intentionally stable: MacBook Pro, RodeCaster Duo interface, Shure SM7B microphone on a Rode PSA1 boom arm, Elgato Facecam, two Elgato Key Light Air lights, and Ecamm Live as my virtual camera.

This setup is about reliability, clarity, and repeatability. Gear should reduce friction. If it creates fresh anxiety before every recording, it is a tax on attention.

The setup has evolved too. I used to use my iPhone as a webcam through Apple’s Continuity Camera feature. It worked well. I now use an Elgato Facecam because it gives me more predictability and control.

That is the broader pattern.

I used Trint for transcription. Then Otter. Now transcription is handled inside Descript, alongside recording and editing. I used Audacity, then GarageBand, then Hindenburg for editing. Now I use Descript. For now.

That qualifier matters. This is not a love letter to a tool stack. Tools are useful until a better process, integration, or outcome appears. The loyalty is to the work, not the logo.

On recording day, I always have a warm-up conversation before the formal interview begins. Guests may be senior executives, founders, scientists, engineers, analysts, or policy specialists. They know their subject. That does not mean they are relaxed on camera. A few minutes of normal conversation changes the energy and lets me hear how they naturally speak before the recording starts.

I record using Descript Rooms. It captures local audio and video and keeps the session inside the same environment I use for editing. Descript says Rooms supports local recording with high-quality audio and video, including separate tracks, which gives more control later in production. [3]

After recording, the episode moves into editing in Descript.

This is one of the largest workflow changes I have made. Text-based editing changes the feel of audio and video production. Descript lets you edit media by editing the transcript, with the underlying audio or video changing as the text changes. [4]

That is powerful. It is not magic. And Descript is updated frequently. This is a double-edged sword. New features are added which can be good, but also existing functionality can move, behaviours can change, and things can occasionally break. Its AI functionality is useful too, but potentially eye-wateringly expensive if used carelessly as I found out to my cost when I allowed my older son to use it recently for a college project . AI is a tool. A blank cheque to a cloud model is not a production strategy.

My edit includes several stages. I correct the transcription, and remove filler words where appropriate. Then I create clips. I identify a cold opening, usually a short moment that captures the stakes of the conversation. I record the intro. I apply automatic multicam. I add the bumper and outro. Then I export subtitles, transcription, and the video.

From there, I prepare episode artwork in Canva, both for the podcast feed and for YouTube. Packaging matters. A strong conversation can still underperform if the title, artwork, or thumbnail fails to make the value clear.

Each episode then enters what I think of as the metadata factory: title, show notes, hashtags, guest profile, chapter markers, subtitles, transcript, episode number, and supporting details. Chapter markers are especially useful for long-form interviews. Podcast hosting company Buzzsprout describes them as labelled sections that help listeners move through an episode, preview what is ahead, or skip parts. [5]

Before publication, I email the guest to confirm the publication date and request a profile photo and bio if needed. This is ordinary admin, but ordinary admin is where many workflows become clogged. A missing bio, a late headshot, or a last-minute correction can slow everything else.

Once the video is exported, I run it through Auphonic to clean it up.

Auphonic has become hugely important because remote guest audio is inherently variable. I can control my microphone, room, and levels. I cannot control the guest’s laptop fan, kitchen acoustics, internet connection, keyboard noise, or the dog who has apparently chosen that exact moment to protest late capitalism.

Auphonic helps smooth some of that variation. Its published feature set includes noise and reverb reduction, intelligent levelling, filtering, AutoEQ, loudness processing, video support, metadata, and chapters. [6] For an interview-led podcast, that kind of post-production support is not cosmetic. It protects listenability.

After Auphonic finishes processing, I download the video and move into hosting and publishing.

Buzzsprout is the host for both of my podcasts. I have used other hosts in the past, including Libsyn and Podbean, and both have their place. Buzzsprout has become my preferred home because it is clean, practical, reliable, and backed by excellent support.

Support is easy to undervalue until something breaks.

When you publish every week, support is part of the resilience architecture. If an upload fails, a feed behaves strangely, or a metadata issue appears just before publication, you do not want to shout into a ticketing void. You want people who understand podcasting and respond with clarity.

Buzzsprout’s own customer-support podcast, Happy to Help, is a useful signal here. It is hosted by Priscilla Brooke, Buzzsprout’s Head of Podcaster Success, and focuses on making customer support better. [7] A company willing to talk publicly about support usually understands that support is part of the product.

For the podcast feed, I add the title, show notes, episode art, episode number, creator details, hashtags, processed video file, guest page, chapters, and custom transcription to Buzzsprout. Buzzsprout’s support for submitting video podcasts to Apple Podcasts also matters now that video is becoming a more central part of podcast distribution. [8]

Then I prepare YouTube separately: title, description, hashtags, thumbnail text, subtitles, playlist selection, and end screen. YouTube has its own discovery logic and viewer behaviour. A title that works for a podcast app may be too flat for YouTube. A podcast artwork tile may be too quiet as a video thumbnail.

Once the episode is packaged for both the podcast feed and YouTube, I schedule it for publication. In practice, I typically finish the edit the day before go-live, then schedule the episode to publish at the usual 7am slot.

Then, at 7am, it goes live. The waiting multitudes presumably abandon breakfast, sprint to their podcast apps, and download immediately. Or, in the slightly less theatrical version, the episode enters the world at the expected time, on the expected day, in the expected places.

That matters. Consistency builds trust quietly. No fireworks. No drama. Just showing up when people expect you to show up.

After that, the promotion process begins.

This post, though, is deliberately focused on production and publication. I am not covering promotion, monetisation, sponsorship, paid amplification, newsletter strategy, social distribution, or the broader business model here. Those are important, but they deserve separate posts.

For now, the lesson from hundreds of episodes is this: creativity scales only when operations carry it.

The warm-up conversation helps the guest. The bullet points protect the flow. The calendar invite reduces friction. The hardware stabilises quality. Descript consolidates recording, transcription, and editing. Auphonic reduces audio risk. Canva supports packaging. Buzzsprout hosts and supports the shows. YouTube requires its own publishing logic. Chapters, subtitles, transcripts, titles, artwork, descriptions, tags, and end screens turn one conversation into a structured media asset.

None of this is heroic. That is the point.

Heroics are a poor operating model. If a publishing system depends on repeated last-minute rescue missions, it is not a system. It is theatre with a deadline.

Podcasting is an operating system for trust.

And like any operating system, it needs maintenance, updates, guardrails, responsive partners, and a willingness to keep improving.

The surprise, for me, is that podcasting has taught me as much about supply chains as supply chains have taught me about podcasting. Flow matters. Bottlenecks matter. Quality control matters. Consistency matters. Support matters. Resilience is built before the disruption arrives.

This workflow will change again. It should. If you have suggestions, better tools, sharper practices, or lessons from your own production process, I would love to hear them. Continuous improvement is not a slogan. It is how the shows keep getting better.

Citations

[1] Apple announced a new HLS-enabled video podcast experience for Apple Podcasts in February 2026, including switching between watching and listening, horizontal display, and offline video downloads. 
[2] Edison Research’s Infinite Dial 2025 reported that 70% of Americans aged 12+ had listened to a podcast, 51% had watched one, 73% had consumed a podcast in audio or video format, and YouTube was the service used most often by U.S. weekly podcast listeners. 
[3] Descript describes Rooms as supporting local recording with high-quality audio and video, including separate tracks and up to 4K capture. 
[4] Descript describes its product as letting users record, transcribe, edit, and publish in one tool, with audio and video editing built around text. 
[5] Buzzsprout describes chapter markers as labelled sections that help listeners navigate, preview, or skip through podcast episodes. 
[6] Auphonic lists noise and reverb reduction, intelligent levelling, filtering, AutoEQ, loudness processing, video support, metadata, and chapters among its features. 
[7] Happy to Help is Buzzsprout’s customer-support podcast, hosted by Priscilla Brooke, Buzzsprout’s Head of Podcaster Success. 
[8] Buzzsprout provides guidance for submitting video podcasts to Apple Podcasts, and coverage of its launch notes that its video plans include transcripts. 

A weak ESG report.
A few awkward investor questions.
Maybe a raised eyebrow from procurement.
A consultant hired. A dashboard built. A target announced. Everyone went home feeling faintly virtuous.

That world is ending.

Increasingly, poor climate data will not just make a company look bad. It may make products harder to sell, harder to insure, harder to finance, harder to import, and harder to defend in front of regulators, customers, and investors.

This was the line that stayed with me from my recent Climate Confident conversation with Stephen Jamieson, Chief Marketing Officer for SAP Sustainability. Stephen made a deceptively simple point: if companies cannot meet emerging carbon data and product compliance conditions, they may simply not be able to put certain products on the market. He was talking about digital product passports, product carbon footprints, CBAM, metals, fashion, and the rising granularity of carbon data needed just to do business.

That is the shift.

Carbon data is moving from reporting obligation to commercial operating condition.

And for senior leaders, that changes everything.

The data says…

Let’s start with the policy stack, because this is not about one regulation, one disclosure framework, or one enthusiastic sustainability team trying to get procurement to return its calls.

The EU Carbon Border Adjustment Mechanism, or CBAM, entered its definitive regime on 1 January 2026. EU importers of covered goods now need to declare embedded emissions and surrender the corresponding number of CBAM certificates each year, with authorised declarant status becoming central to compliance.

Translation: carbon is becoming part of the cost of crossing a border.

Then there is the Ecodesign for Sustainable Products Regulation, or ESPR. The European Commission’s 2025-2030 working plan prioritises steel and aluminium, textiles, furniture, tyres, mattresses, and energy-related products for future ecodesign and energy labelling measures. Digital Product Passports sit inside this agenda, turning product sustainability from a nice brochure into structured, exchangeable product data. The QR code is not the strategy. The data behind it is.

Batteries show where this is heading. The EU Batteries Regulation takes a full life-cycle approach, covering sourcing, manufacturing, use, and recycling. From 2025, it gradually introduces carbon footprint declarations, performance classes, and maximum carbon footprint limits for electric vehicle batteries, light means of transport batteries, and rechargeable industrial batteries. It also provides for QR-code access to a digital battery passport with detailed product information.

Packaging is joining the queue too. The Packaging and Packaging Waste Regulation entered into force in February 2025 and will generally apply from August 2026. It covers all packaging and packaging waste, regardless of material or origin, and aims to make all packaging on the EU market recyclable in an economically viable way by 2030.

And this is broader than carbon. The EU Deforestation Regulation applies from 30 December 2026 for large and medium operators, requiring covered products to be deforestation-free. The EU Forced Labour Regulation will ban products made with forced labour from being sold in the EU market from 14 December 2027, applying to imports, EU-made products, and exports.

Different rules. Same direction.

Prove it.

At the same time, finance is tightening the lens. IFRS S2, effective for annual reporting periods beginning on or after 1 January 2024, requires companies to disclose climate-related risks and opportunities that could affect cash flows, access to finance, or cost of capital over the short, medium, or long term.

This is where the neat old boundary between “sustainability” and “business” collapses. Bad climate data is no longer just a disclosure gap. It is a pricing gap. A risk gap. A market-access gap.

The same pressure is visible in the voluntary and industry-led space. WBCSD’s Partnership for Carbon Transparency, or PACT, says supply chains cannot decarbonise without accurate and comparable product-level data, and its methodology is built around supplier-specific primary data for product carbon footprints.

Averages got us started.

They will not get us through this next phase.

The implications…

The implications are enormous, and not in the abstract “ESG is important” way that makes executives reach for their phones.

This is about product viability.

A company may still be able to manufacture a product. It may still have demand. It may still have a strong brand, loyal customers, and a procurement team that thinks it has things under control. But if the company cannot provide credible data on embedded carbon, recycled content, deforestation risk, packaging compliance, forced labour exposure, or product-level sustainability attributes, its commercial freedom narrows.

That is a different conversation.

It means sustainability data becomes part of revenue protection.

Procurement will need to know it.
Finance will need to trust it.
Operations will need to act on it.
Product teams will need to design with it.
AI systems will need to optimise around it.

This last point is crucial.

Stephen made a point in the episode that should make every AI-happy boardroom pause. Businesses are very good at measuring cost, revenue, utilisation, and finance. So AI will optimise for those high-quality inputs. If carbon, water, recycled content, resilience, and wider sustainability factors are not in the same decision environment, they risk becoming secondary, or worse, optimised against.

That is the danger.

AI will not wake up one morning with a conscience. It will optimise for what the system tells it to value.

So if sustainability data lives in a side platform, updated annually, disconnected from ERP, procurement, product lifecycle management, finance, and supply chain planning, then we should not be surprised when automated decisions accelerate the wrong outcomes.

Efficient nonsense is still nonsense.

There is an affordability angle here too. Clean technology is already pulling capital at scale. According to the IEA’s World Energy Investment 2025 report, global energy investment was set to reach USD 3.3 trillion in 2025, with around USD 2.2 trillion going to renewables, nuclear, grids, storage, low-emissions fuels, efficiency, and electrification, twice the USD 1.1 trillion going to oil, gas, and coal.

Capital is moving.

But capital follows confidence. And confidence follows data.

If a company can prove that its product has lower embedded emissions, lower exposure to carbon-intensive inputs, stronger traceability, and better compliance readiness, that is no longer just a sustainability claim. It is commercial differentiation.

The strategies…

So what should leaders do?

First, stop treating climate data as an annual reporting project. That model is too slow, too blunt, and too disconnected from the decisions that matter.

Start with a product exposure map. Which products rely on CBAM-covered materials? Which products fall into ESPR priority sectors? Which use packaging exposed to PPWR? Which contain batteries? Which touch commodities covered by deforestation rules? Which depend on suppliers in high-risk labour regions?

This is not box-ticking. It is revenue risk analysis.

Second, build what I would call a climate-data bill of materials. For priority products, companies need to know the materials, suppliers, production sites, energy inputs, packaging, recycled content, logistics, emissions factors, primary data availability, and evidence gaps.

Boring? Maybe.

Vital? Absolutely.

Third, put procurement in the middle of this. Supplier climate data cannot remain a favour requested politely by sustainability teams once a year. It needs to be built into supplier onboarding, contracts, scorecards, renewal criteria, and preferred supplier decisions.

Fourth, connect the systems.

This is where many organisations will struggle. Product carbon data needs to move across ERP, procurement, finance, product lifecycle management, compliance, supplier platforms, and digital product passport infrastructure. If the data cannot move, it cannot matter.

Fifth, be careful with AI. AI agents can help scale product carbon footprinting, compliance checks, emissions factor mapping, and documentation. Stephen described how AI capabilities could move companies from manually managing handfuls of emissions factors to scaling across tens of thousands of products. That is powerful.

But humans remain accountable. Stephen was clear on this too: AI agents can assist, but they cannot be accountable for decisions.

The strategy is not “let AI solve sustainability.”

The strategy is: give AI decision-grade sustainability data, strong governance, clear constraints, and human accountability.

The signal of change…

The transition is already happening.

CBAM is live. Product passports are being built. Battery rules are moving from aspiration to evidence. Packaging is becoming a compliance system. Deforestation-free proof is becoming a market requirement. Forced labour rules will put human rights evidence into product eligibility.

This is not a far-off future. It is a staggered rollout of commercial reality.

The signal of change is also visible in how business leaders now talk about climate. Stephen noted that climate has moved from a niche sustainability-team topic, through an era of ambition, into a phase focused on the economics of carbon, risk exposure, and investor relevance.

That tracks with what I am seeing too.

The serious conversations are no longer about whether sustainability matters. They are about how to make it operational. How to measure it properly. How to use it in procurement. How to price risk. How to make product-level carbon data trustworthy enough for investors, customers, regulators, and AI systems.

This is the real story.

Sustainability is leaving the PDF.

It is entering the transaction.

And that is good news, even if it feels uncomfortable. Because once climate data becomes part of how products are designed, sourced, financed, priced, and sold, it stops being a communications exercise and starts becoming a lever for emissions reduction.

Back to where we started.

A few years ago, bad carbon data meant a weak report.

Increasingly, bad carbon data may mean a weaker product, a weaker supplier position, a weaker investment case, or a blocked market opportunity.

That should concentrate minds.

But it should also energise us. Because the companies that get this right will not just comply better. They will compete better. They will build cleaner supply chains, more resilient products, more credible claims, and smarter operating systems.

And that is exactly the kind of climate action we need now.

Practical.
Measurable.
Commercially real.

If you want to understand where this is heading, listen to my Climate Confident conversation with Stephen Jamieson of SAP Sustainability. It is one of those episodes that reframes the issue: carbon data is no longer just about reporting what happened.

It is becoming part of whether you get to sell what comes next.

If this resonates, it makes a strong keynote. I speak on sustainability, AI, and the future of supply chains – details here.

For years, it was just there. A kitchen staple. Drizzled on toast, poured over tomatoes, deployed with generous Mediterranean confidence. Then drought and heat hit production. Prices surged. Sunflower oil overtook olive oil in Spanish kitchens in the first half of 2024, with Reuters reporting 179 million litres of sunflower oil bought versus 107 million litres of olive oil, after olive oil prices rose sharply on the back of climate-driven production shocks.

That is climate risk in plain clothes.

Not abstract. Not theoretical. Not waiting politely for a board agenda item called “ESG update”. It is on the supermarket shelf. In household budgets. In food manufacturing costs. In procurement conversations. In margins.

And this is where emissions data enters the story.

Once climate risk turns into food inflation, supply disruption, insurance exposure, financing cost, procurement risk, customer churn, employee disengagement, and investor scrutiny, a company’s carbon data stops being decorative. It becomes infrastructure.

Bad data is no longer harmless.
Average data is no longer good enough.
Missing data is not neutral.

It is a risk signal.

In a recent episode of my Resilient Supply Chain podcast, Cynthia Lai made the point bluntly. If a company cannot provide credible emissions figures, banks and insurers may use proxy data or industry averages instead. Those proxies will likely push the company into a higher-risk bucket, increasing borrowing costs and insurance premiums. The market fills in the blanks. Rarely with generosity. 

That should make every board sit up.

Because emissions data is no longer just about reporting. It is about access to capital. Insurance availability. Tender eligibility. Customer trust. Employee recruitment. Retention. Brand credibility. Strategic resilience.

It is about whether an organisation can prove what it says.

The data says…

The emissions problem is mostly hiding outside the walls of the organisation.

According to CDP and Boston Consulting Group’s 2024 analysis, based on 2023 disclosures, corporate supply chain Scope 3 emissions were, on average, 26 times greater than emissions from direct operations.

A quick translation. Scope 1 covers emissions from sources a company directly owns or controls, such as boilers, furnaces, or vehicles. Scope 2 covers purchased electricity, heat, steam, or cooling. Scope 3 covers the rest of the value chain: purchased goods, transport, product use, waste, business travel, investments, and more.

In other words, the messy bit. So, naturally, the important bit.

The same CDP/BCG work found that upstream emissions from manufacturing, retail, and materials alone had a footprint 1.4 times the total CO₂ emitted in the EU in 2022. Yet only 15% of companies disclosing to CDP had set a Scope 3 target.

That is not a data gap.
That is a strategic blindfold.

CDP’s 2024 supply chain report also found that failure to address climate-related risks in supply chains costs nearly three times more than the actions required to mitigate them. It estimated that companies could unlock US$165 billion in potential financial gains from upstream climate-related opportunities.

The World Economic Forum and Boston Consulting Group have previously shown that eight supply chains, including food, construction, fashion, electronics, automotive, fast-moving consumer goods, professional services, and freight, account for more than half of global emissions. They also estimated that decarbonising many end-to-end supply chains would add as little as 1–4% to end-consumer costs in the medium term.

That matters.

For decades, the fossil economy has presented low-carbon transition as uniquely expensive, while quietly offloading the cost of drought, floods, heat, volatility, health damage, conflict exposure, stranded assets, and price shocks onto everyone else. Very tidy accounting, provided one ignores the planet, the balance sheet, and basic decency.

Regulation is catching up too.

In Europe, the Corporate Sustainability Reporting Directive, or CSRD, requires companies in scope to publish sustainability information using common European reporting standards. The first companies had to apply the rules for financial year 2024, with reports published in 2025.

In California, the Climate Corporate Data Accountability Act, known as SB 253, requires US-based companies doing business in California with more than US$1 billion in annual revenue to report Scope 1 and Scope 2 emissions beginning in 2026, and Scope 3 emissions beginning in 2027.

And then there is IFRS S2.

IFRS S2 is the climate disclosure standard issued by the International Sustainability Standards Board. Put simply, it asks companies to disclose climate-related risks and opportunities that could affect cash flows, access to finance, or cost of capital. It became effective for annual reporting periods beginning on or after 1 January 2024.

That last phrase is the one to underline.

Access to finance.
Cost of capital.

Not “nice sustainability story”. Not “PDF garnish”. Money.

The implications…

The first implication is financial.

Financed emissions are the greenhouse gas emissions linked to loans and investments. If a bank lends to a high-emitting company, those emissions become part of the bank’s own climate risk picture. Cynthia Lai explained this clearly on the podcast: banks are no longer looking only at their own operations. They are increasingly looking across their loan books and customer portfolios, adding emissions-related factors into risk assessment and credit scoring. Insurers are doing something similar across insured operations. 

Then there is PCAF, the Partnership for Carbon Accounting Financials. PCAF develops greenhouse gas accounting standards for financial institutions, helping banks, investors, and insurers measure emissions associated with their financial activities. In 2025, PCAF updated its standards, including clearer guidance on financed emissions and insurance-associated emissions.

Insurance-associated emissions are the emissions linked to what insurers cover. That sounds technical until a company finds its insurance renewal becoming more expensive, more conditional, or harder to secure.

As Cynthia put it, if you cannot get your operations insured, getting financing becomes much harder because banks like protection. There’s a sentence that should be stuck beside every procurement dashboard in Europe. 

The second implication is commercial trust.

Customers increasingly want evidence, not slogans. PwC’s 2024 Voice of the Consumer Survey found that consumers said they were willing to pay an average of 9.7% more for sustainably produced or sourced goods, even with cost-of-living pressures.

Now, stated willingness is not the same as actual checkout behaviour. Anyone who has ever watched a procurement process knows the gap between intention and purchase can be wide enough to drive a diesel HGV through.

But the direction is clear. Customers are asking sharper questions. B2B buyers are asking sharper ones again. Weak emissions data makes those conversations harder.

That has a cost.

Customer acquisition is expensive. Losing a tender because your emissions data is vague, unverifiable, or built on lazy averages does not just mean lost revenue. It means higher cost of sales. More time defending claims. More procurement friction. More legal review. More reputational drag. More work to win the next customer.

Retention matters too. Existing customers do not want to carry your climate uncertainty inside their Scope 3 inventory. If you cannot give them credible data, a competitor who can may suddenly look far more attractive.

The third implication is talent.

Deloitte’s sustainability research, reported in 2025, found that 63% of employees surveyed globally did not think their employers were doing enough on climate and sustainability, and 21% had considered changing jobs to work for a more sustainable company.

Again, intention does not always become action. Mortgages exist. Children eat. Careers are complicated.

But employer brand is now part of the climate credibility equation. Skilled people are expensive to recruit. Expensive to replace. Expensive to train. If a company says climate matters but cannot produce credible emissions data, that gap can become a recruitment problem, a retention problem, and eventually a cost problem.

The fourth implication is investor confidence.

EY’s 2024 Global Institutional Investor Survey found that 36% of investors were dissatisfied with company progress on nonfinancial reporting. Investors were especially disappointed in the materiality, comparability, and accuracy of sustainability data.

Accuracy.
Comparability.
Materiality.

These are not soft words. They are capital allocation words.

The strategies…

The first strategy is simple: move from averages to primary data.

In another Resilient Supply Chain conversation, John Beath defined primary data as data from the supplier actually making the product or material, rather than generic database averages or published studies. That means asking basic questions. What are the materials? How much do they weigh? Where did they come from? How were they transported? What energy was used? What waste was generated? What process created them? 

This is not glamorous work. It will not make the keynote reel.

But it is where the truth lives.

The second strategy is to look where the carbon is, not where the optics are.

John gave a striking example of a company that had worked hard to remove styrofoam cups from a huge office site, while a tiny thermostat change would have had vastly greater impact. The point was not that visible actions are useless. The point was that visibility is a terrible proxy for materiality. 

Corporate sustainability has a weakness for symbolic gestures. Cups. Posters. Reusable tote bags. Bamboo cutlery. The theatre is seductive. The carbon often sits elsewhere.

Raw materials. Process heat. Refrigerants. Freight. Product use. Waste. Supplier electricity. Aluminium. Cement. Steel. Plastics. Packaging. Returns. End-of-life treatment.

John also described a solar panel manufacturer that assumed silicon was the main footprint problem, only to discover the aluminium frame was the real hotspot. Switching frame material cut the product footprint in half. 

Measure first. Moralise later. Ideally, much later.

The third strategy is supplier segmentation.

Start with the top 20% of suppliers likely driving 80% of emissions. Use spend data if that is all you have. Use industry averages as a first screen, not the final answer. Build a heat map. Identify hotspots. Engage the critical suppliers. Ask for primary data. Run pilots. Document improvement plans. Bring banks and insurers into the conversation before they bring you into theirs.

Cynthia Lai recommended exactly this pragmatic 80/20 approach, because perfect data delayed for years is just another form of inaction wearing a consultant’s lanyard. 

The fourth strategy is to embed carbon into decision systems.

Procurement. Product design. Enterprise resource planning systems. Supplier onboarding. Freight routing. Contract renewal. Capital approvals. Product lifecycle management. Board dashboards.

Carbon data cannot live in a sustainability side-file, updated annually, with a prayer and a pivot table. It has to show up where decisions are made.

Interestingly, the purchase order may become one of the most important climate tools in the enterprise. Not the glossy sustainability report. Not the annual video with drone footage of trees. The purchase order. If it carries product carbon data, supplier energy mix, recycled content, logistics mode, and assurance status, it becomes a quiet machine for cutting emissions at scale.

The fifth strategy is disciplined use of AI.

AI can help classify supplier risk, detect anomalies, summarise regulation, spot missing data, prioritise supplier engagement, and map emissions hotspots. But it cannot magically convert poor data into truth. Bad data plus AI is still bad data, only now it arrives faster and with more confidence. A marvellous achievement, if the goal was industrialised self-deception.

The signal of change…

Capital is already moving.

The International Energy Agency’s World Energy Investment 2025 report estimated that global energy investment would reach US$3.3 trillion in 2025. Around US$2.2 trillion was set to go to renewables, nuclear, grids, storage, low-emissions fuels, efficiency, and electrification, twice the US$1.1 trillion going to oil, gas, and coal.

That tells us something important. The clean technology transition is not waiting for perfect consensus. It is being driven by economics, energy security, industrial strategy, customer demand, policy, and climate necessity.

Emissions data is becoming the accounting layer beneath that shift.

And the next frontier is already visible: financed emissions, insurance-associated emissions, facilitated emissions, and, very likely, enabled emissions.

Enabled emissions are the emissions made possible by a company’s products, services, technology, or expertise. Think of an AI system, software or cloud platform, consultancy, engineering service, or advertising campaign that helps expand fossil fuel production or high-carbon consumption. The concept is still developing, but the logic is clear. If your business helps others emit more, eventually someone will ask you to account for that.

That question may come first from regulators.

Or investors.

Or customers.

Or employees.

Or insurers.

Or banks.

Or all of them.

Back to the olive oil

A bottle of olive oil does not care about your emissions factor.

It does not care whether your Scope 3 model is elegant, approximate, outsourced, delayed, or trapped in a procurement portal designed by someone who appears to dislike suppliers personally. It responds to heat, rain, soil, water, labour, logistics, energy, and markets.

So does business.

That is the point.

Accurate emissions data is not about pleasing regulators. It is about seeing the system clearly enough to act before the system acts on you.

Customers want credible claims because their own supply chain data depends on yours. Employees and potential employees want evidence because they are deciding where to spend their time, skills, and credibility. Boards want risk clarity. Investors want comparable information. Banks want credit signals. Insurers want exposure data. Policymakers want accountability. Suppliers need direction and support.

The organisations that win will not be the ones with the prettiest averages. They will be the ones with the best primary data, the clearest hotspots, the strongest supplier engagement, and the courage to move capital from carbon-heavy inertia into cleaner, cheaper, more resilient systems.

Measure first.
Then act.
Then prove it.

That is how we cut emissions without cutting through credibility. And it is how supply chains become not just cleaner, but stronger.

For more on this, listen to the full Resilient Supply Chain conversations with Cynthia Lai and John Beath, where we unpack financed emissions, insurance-associated emissions, primary data, lifecycle assessment, and why carbon accounting only matters if it changes real decisions.

Photo credit Esencia Andalusí on Flickr

Not taking them. Just knowing they were there.

In drawers. In presses. In Fridges. In kitchen cupboards around the country. Small packets of official anxiety, sent to Irish households in 2002 in case of a major nuclear accident abroad. The Department of Health later confirmed that Ireland was the only country to issue iodine tablets to every household at the time. Nothing says “energy policy” quite like posting thyroid medication to the nation.

I write this as an Irishman, but not as someone living in Ireland. I have lived in Spain since 2008. So this is not a domestic “we should” argument. It is the view of an interested outsider looking in. Irish by birth. Energy analyst by trade. Slightly exasperated observer by long experience.

And now nuclear is back in the Irish conversation.

Recent commentary has argued that, with electricity bills high and renewable delivery lagging, the case for reopening Ireland’s nuclear power debate has never been stronger. It is a fair debate to have. Energy costs are painful. Grid constraints are real. Data-centre demand is politically and technically unavoidable. Ireland needs clean power, security, affordability, and resilience.

But reopening a debate is not the same as reaching the right conclusion.

And in the Irish context, domestic nuclear power still makes very little sense.

Ireland’s grid is the wrong shape for nuclear

The strongest argument against nuclear in Ireland is not peak demand.

It is minimum demand.

EirGrid’s 2024 All-Island Transmission System Performance Report recorded a winter peak of 7,148 MW on 27 November 2024. That is the number nuclear advocates tend to point at. Big number. Serious face. Possibly a chart with a dramatic arrow.

But the more important number is the minimum summer night valley. In 2024, that was just 3,095 MW, recorded at 05:41 on 9 June. In 2020, it was even lower: 2,395 MW. That is the actual grid a nuclear plant would have to fit into. Not the political fantasy grid. The real one.

A conventional 1 GW reactor would represent roughly one-third of that low-demand period. Even smaller units would still be chunky in a small island system. Yes, nuclear plants can technically load-follow. But technical possibility is not the same as economic sense.

Nuclear economics depend on high utilisation. Build an expensive asset, then run it flat out for decades. That is the model. Turn it down because demand is low or wind is strong, and the capital cost does not politely disappear. It just gets spread over fewer megawatt-hours.

Ireland’s electricity challenge is not a lack of theoretical baseload.

It is flexibility.

Peaks. Troughs. Ramps. Wind surges. Wind lulls. Solar midday output. Evening demand. Interconnector flows. Battery cycles. Demand response. Dispatchable backup.

A large nuclear unit is not naturally suited to that system.

It is a fridge magnet in a clock mechanism.

The outage problem does not go away

There is another awkward question nuclear advocates need to answer.

What happens when the plant goes offline?

Nuclear reactors require planned outages for refuelling and maintenance. The US Energy Information Administration says nuclear plants typically refuel every 18 to 24 months, and that outages are usually longer than the refuelling itself because maintenance, upgrades, and repairs are carried out at the same time.

So if Ireland had a 1 GW reactor, the system would still need enough dispatchable capacity, storage, demand flexibility, and interconnection to cover the loss of that reactor when it was unavailable.

That is not a footnote.

That is the system cost.

In a large continental grid, losing a gigawatt is inconvenient. In a small island system, it is a major event. Nuclear does not remove the need for backup. It creates a large single contingency that the rest of the system must be built around.

So the real question is not simply: can nuclear supply clean power?

It is: can Ireland justify building a nuclear plant and then also building the parallel system needed for the days or weeks when that plant is offline?

Because that is what reliability requires.

Physics, tragically, does not care about op-eds.

The cost and timeline comparison is brutal

New nuclear is slow.

Really slow.

Hinkley Point C in the UK was once expected much earlier and is now delayed into the 2030s, with EDF’s 2024 update putting expected cost at up to £35bn in 2015 prices. That is before translating the lesson into Irish political, regulatory, and planning conditions, where optimism often goes to be mugged by a judicial review.

Lazard’s 2025 Levelized Cost of Energy analysis puts new nuclear in the US at roughly $141–220/MWh. Utility-scale solar sits around $38–78/MWh, onshore wind around $37–86/MWh, utility solar plus storage around $50–131/MWh, and onshore wind plus storage around $44–123/MWh. These are not Ireland-specific project bids, but they are a useful benchmark from a widely cited financial analysis.

Now add time.

A nuclear plant in a country with no civil nuclear generation sector, no domestic nuclear regulator, no supply chain, no operating skills base, no waste pathway, and no site would not be a 2030 solution. It would be a 2040s bet, at best.

By contrast, solar, wind, storage, interconnection, demand response, and grid upgrades can be delivered in increments. Utility-scale solar can often be built in 6–12 months once consented and grid-connected. Battery projects are modular and can also be delivered far faster than nuclear. Onshore wind is slower, especially through planning and grid connection, but the construction phase is typically measured in months to a couple of years, not decades.

So the practical comparison is not nuclear versus candles.

It is nuclear versus portfolios: solar, wind, storage, interconnection, demand response, grid reinforcement, flexible backup, and smarter large-load management.

That portfolio can learn. It can adapt. It can fail partially without bankrupting the national strategy.

Nuclear is more binary.

And more expensive when it goes wrong.

Data centres could help, if they behave like grid assets

Ireland’s data-centre electricity demand is astonishing. The Central Statistics Office reported that data centres consumed 22% of metered electricity in 2024, up from 5% in 2015. Consumption rose from 6,335 GWh in 2023 to 6,969 GWh in 2024.

But the data-centre debate is too often framed lazily.

Data centres are not automatically villains. I say that as a former developer of an Irish data centre, CIX.ie, in Cork. The issue is not whether data centres exist. They do. They are part of modern digital infrastructure. The issue is whether they behave like passive loads or active grid assets.

A data centre that demands power 24/7, ignores grid stress, and points to corporate PPAs as a moral fig leaf is part of the problem.

A data centre that can shift load, reduce demand when requested, support local constraints, co-locate with renewables, contract storage, provide flexibility, and participate properly in demand-side response can be part of the solution.

Ireland’s Large Energy Users policy is at least moving in that direction. The CRU’s 2025 decision assumes data centres procuring renewable energy equivalent to 80% of annual demand in its illustrative analysis, and the broader policy direction is clear: large loads need to contribute to the system, not simply lean on it.

That is the right framing.

Not “ban data centres”.

Not “build nuclear for data centres”.

But: if large loads want grid capacity, they should help the grid.

That is not anti-business. It is basic engineering discipline.

The SMR objection is coming. It is weak.

Someone will now mention small modular reactors.

They always do.

SMRs are the favoured escape hatch in modern nuclear debates because they sound like nuclear without the inconvenient bits: smaller, repeatable, factory-built, safer, cheaper, faster. A reactor you can order like a prefab kitchen, presumably with a tasteful backsplash and a twenty-year licensing process.

The problem is that commercial SMRs are still unproven.

The IEA says global nuclear capacity stayed flat in 2025: 3 GW of new nuclear came online, but that was offset by 3 GW of retirements, leaving global nuclear capacity at 420 GW. Construction started on 12.2 GW of nuclear in 2025, mostly in China and Russia. Interesting, yes. A bankable Irish electricity strategy? No.

Ireland would still need legislation, regulation, siting, emergency planning, waste management, security, skills, finance, and public consent. The “small” in SMR refers to reactor size. It does not magically shrink institutional complexity.

At this point, building Irish energy policy around SMRs is not pragmatism. It is delay when urgency is what is required.

Ireland might as well wait for other myths like fusion, cheap green hydrogen, or unicorn farts with grid-forming inverter capability.

The politics are as hard as the engineering

Ireland has a legal barrier too. The Electricity Regulation Act 1999 states that authorisation criteria “shall not provide for the use of nuclear fission for the generation of electricity.” Changing that is possible. But changing the law would be step one of about fifty.

Ireland would then need institutions.

A regulator. A waste policy. A decommissioning framework. Emergency planning. Site selection. Security. Public consultation. Finance. Skills. Long-term political consensus.

And then there is social licence.

Chernobyl is not ancient history for Irish people of a certain age. Sellafield is not abstract. The EPA says low levels of artificial radioactivity can still be detected in sediments, seawater, seaweeds, fish, and shellfish from the Irish Sea, although discharges have fallen significantly since their peak in the 1970s and 1980s.

Technically, that does not prove Ireland should never consider nuclear.

Politically, it matters enormously.

Infrastructure does not get built in an Excel file. It gets built in places. Near people. Through planning systems. Under scrutiny. With memories attached.

Ireland already struggles to build enough homes, grid lines, wind farms, water infrastructure, and transport projects. A nuclear site would not simply face NIMBYism. It would face national-scale resistance with historical depth.

Ignoring that would not be brave.

It would be naïve.

The better Irish strategy is already visible

The stronger Irish pathway is not mysterious.

Build the grid. Faster. More lines. More substations. More digital control. More connection capacity. More political honesty about the infrastructure required for electrification.

Accelerate renewables already in the pipeline. Ireland reached 8 GW of renewable electricity connected to the network in March 2026, according to ESB Networks and the Department of Climate, Energy and the Environment. That is not enough, but it is a real platform to build from.

Treat storage as core infrastructure. Batteries for short duration. Longer-duration storage for multi-hour and multi-day balancing. Thermal storage. Pumped hydro where feasible. Flexible demand. Clean dispatchable backup for rare events.

Use interconnection intelligently. Ireland does not need to own every generation technology domestically to benefit from system diversity. Interconnection with France gives access to a broader European electricity mix, including French nuclear, without Ireland taking on domestic nuclear siting, waste, construction, and political risk.

And make large loads earn their place. Data centres, hydrogen electrolysers, industrial heat, EV fleets, and other flexible demand should become grid partners, not just grid customers.

This is not anti-nuclear ideology.

It is pro-system realism.

The world is voting with deployment

Globally, the transition is moving.

Ember’s Global Electricity Review 2026 found that clean electricity sources grew fast enough to meet all new global electricity demand in 2025, preventing an increase in fossil generation. Solar alone met 75% of the net increase in electricity demand, while solar and wind together met almost all of it.

Capacity additions tell the same story. IRENA’s Renewable Capacity Statistics 2026 reported that renewable power capacity increased by 692 GW in 2025, including 511 GW of solar and 159 GW of wind.

One set of technologies is scaling by the hundreds of gigawatts per year.

Nuclear is fighting to add single digits net.

That does not mean nuclear is useless globally. It does mean it is a strange place for Ireland to put scarce political, financial, regulatory, and planning capacity.

Ireland’s energy problem is urgent. High prices hurt households. High prices hurt businesses. Fossil fuel exposure hurts energy security, and resilience. Slow grid buildout hurts everything. But urgency is not an excuse to choose the slowest available tool.

Nuclear in Ireland sounds serious because it is large, expensive, centralised, and wrapped in engineering mystique.

But seriousness is not measured in concrete volume.

It is measured in fit.

And domestic nuclear does not fit Ireland’s grid, demand shape, political history, planning reality, delivery timeline, outage risk, or near-term climate needs.

Back to those iodine tablets.

They were a symbol of fear. But also a reminder that energy choices carry long shadows. Ireland has enough experience with imported risk, fossil volatility, and infrastructure delay to know better than to chase a technology whose best-case timeline belongs to the 2040s.

The better future is already visible.

Wind. Solar. Storage. Interconnection. Flexible demand. Smarter data centres. Electrified heat and transport. A stronger grid. More local generation. Less exposure to imported fuels.

Ireland does not need nuclear to be serious about energy.

It needs to build the clean, flexible, resilient electricity system that actually matches the island it is.

Urgently.

And with a little less faith in magic reactors.

Photo credit IAEA Imagebank

A conflict thousands of kilometres away. Tankers delayed. Insurance costs rising. Traders getting jumpy. And suddenly that ripples into household bills, freight costs, food prices, and boardroom planning across Europe and far beyond. That is the basic indignity of fossil fuel dependence. It hands immense power to events you cannot control, in places you do not govern, through systems you did not design.

That is the real lesson from the US-Israel war on Iran.

Reuters reported that IEA Executive Director Fatih Birol believes it could take around two years overall to restore the energy output lost in the Middle East conflict, while warning that markets may be underestimating the consequences of a prolonged closure of the Strait of Hormuz. In the UK, Ed Miliband’s response has not been to pretend that more North Sea drilling will somehow magic away global price volatility, but to argue for faster solar deployment, stronger EV uptake, and reforms to reduce the grip of gas on electricity prices. That is not ideological theatre. It is the beginning of a more realistic security doctrine. 

The important point, though, is this: the war did not create the clean energy transition. Solar was already scaling. EVs were already getting cheaper. Batteries were already moving down the cost curve. Electrification was already spreading into buildings, transport and industry. What the war has done is expose, once again, how brittle the fossil system remains, and why the alternatives now look less like moral preference and more like strategic common sense. 

The data says…

The IEA’s Global Energy Review 2026  released this morning, is striking for what it confirms and for what it does not say. Global energy demand rose by 1.3% in 2025, slower than in 2024. Electricity demand, however, grew by around 3%, more than twice as fast. Solar PV was the single biggest contributor to the increase in global energy demand, accounting for more than 25% of the growth, the first time a modern renewable source has led global energy demand growth. Low-emissions sources together accounted for nearly 60% of demand growth. That is not a symbolic shift. That is system-level movement. 

On the power side, the numbers are stronger still. Renewables and nuclear together added more generation than the entire increase in global electricity supply in 2025. Solar alone posted a record 600 TWh increase in generation, the largest annual increase ever recorded for any source outside post-crisis rebound periods. Renewable capacity additions hit 800 GW, while battery storage additions reached 108 GW, up 40% year on year. The direction of travel is unmistakable. But so is the caveat: fossil fuels still generated 57% of global electricity in 2025, with coal alone still the largest single source at 34%. This is acceleration, not completion. 

Electric vehicles are following a similar arc. The IEA says electric car sales rose more than 20% in 2025 to 21 million vehicles, meaning roughly one in four new cars sold globally was electric. Its Global EV Outlook 2025 also found that first-quarter 2025 sales were up 35% year on year, with China projected to reach around 60% EV share of new car sales in 2025. In Europe, Reuters reported that BEV sales in the main markets surged 29.4% in the first quarter of 2026, helped by rising petrol prices after the war on Iran. That matters because it shows two forces working together: a structural cost and technology shift, and a geopolitical shock that makes oil dependence look even less attractive. 

The affordability story is becoming harder to ignore. In the UK, Autotrader says new EVs are now cheaper to buy than petrol cars on average for the first time, at £42,620 versus £43,405, based on advertised prices after discounts and grants. That qualifier matters. This is not universal unsubsidised parity across every model and market. But it is still a meaningful milestone, particularly when paired with rising consumer interest. Autotrader says visits to its new-car platform were up 21% year on year in April. SMMT adds that March 2026 was the best month ever for UK BEV registrations, at 86,120. That is not a fringe market behaving politely. That is scale starting to show up in registration data. 

The heavy-duty side of transport is also beginning to move, though this is where precision matters. Electrive reported that new energy heavy-duty trucks in China reached 54% of monthly new heavy-duty truck sales in December 2025, and 29% across the full year. But its source uses China’s NEV classification, which includes fully electric and certain plug-in drivetrains, and the article itself notes that subsidy phase-outs and anticipated tax changes distorted year-end buying. So this is not evidence that diesel is finished in trucking. It is evidence that even the difficult segments are starting to electrify faster than many assumed, once economics and policy begin to align. 

The emissions story remains uncomfortable. Global energy-related CO2 emissions rose by around 0.4% in 2025 to a record 38.4 Gt. That is the part nobody serious should duck. Emissions did not fall globally. But growth slowed again, China’s emissions fell by around 0.5%, India’s were essentially flat, and the IEA estimates that clean technologies deployed since 2019 avoided around 3 Gt of CO2 in 2025 alone. So the more defensible conclusion is not “we’ve peaked”. It is that clean deployment is now materially suppressing fossil fuel demand and slowing emissions growth, and in several major markets the peak is either near or already behind them. 

The implications…

First, climate. The climate case remains brutal in its simplicity: the transition is happening, but not yet fast enough. Record solar growth is good. Record EV sales are good. Slower emissions growth is better than faster emissions growth. None of that changes the fact that 38.4 Gt is still 38.4 Gt. A critic is right to insist on that. But there is a difference between realism and fatalism. Realism says the transition must accelerate. Fatalism says the current progress does not matter because it is incomplete. That second argument is analytically lazy and politically dangerous. 

Second, security. Fossil fuel security has always been overstated because it confuses access with sovereignty. If your country imports fuels priced on global markets and shipped through chokepoints vulnerable to war, blockade, sabotage or insurance shocks, you are exposed. Domestic clean power is not invulnerable, but it is much harder to embargo the wind, sanction the sun, or panic a battery with a tanker incident. That is why Miliband’s line about “clean energy security” resonates. It is not perfect. It is directionally right. 

Third, affordability. IRENA reports that 91% of newly commissioned utility-scale renewable projects in 2024 delivered lower-cost electricity than the cheapest new fossil-fuel alternative, and that renewables avoided USD 467 billion in fossil fuel costs that year. BloombergNEF says average lithium-ion battery pack prices fell to USD 108 per kWh in 2025, while stationary storage pack prices fell to USD 70 per kWh. This does not mean every clean technology is cheap in every context. It does mean the cost trend is not moving in fossil fuels’ favour. And when wars shove oil and gas prices upwards, that advantage becomes more obvious, faster. 

Fourth, resilience. This is where the critique bites hardest, and where leaders need to pay attention. Electrification is not a magic wand. It only works at scale with stronger grids, faster permitting, more storage, more flexible demand, better interconnection and more serious planning. The IEA’s Energy and AI work says data centres are set to account for nearly half of US electricity demand growth to 2030. Add EVs, heat pumps and industrial electrification, and the message is plain: the prize is huge, but the engineering challenge is real. The answer is not to retreat. It is to build the system that this new load profile requires. 

The strategies…

For business leaders, the first strategy is to stop treating electrification as a CSR line item. It is a risk-management play. Fleets that electrify reduce exposure to oil-price swings. Buildings that shift to heat pumps and on-site solar reduce exposure to gas shocks. Companies that sign long-term clean power contracts are not merely polishing their brand. They are buying more predictable energy economics in a disorderly world. 

For policymakers, the priority is system design, not slogan inflation. Faster grid build-out. Planning reform. Transmission investment. Storage support. Market rules that reward flexibility. Tariff structures that make electrification cheaper to use, not just cleaner to discuss. If governments want households and businesses to switch, the economics must be visible and the infrastructure must be there. Otherwise, the transition gets stuck between aspiration and irritation, which is where a lot of good ideas go to die. 

For industry, the second strategy is to be honest about supply chains. Clean tech reduces one kind of dependency, but it can increase another if countries fail to diversify manufacturing, processing and grid equipment supply. So yes, push harder on domestic renewables, EVs, batteries and electrified industry. But also push on local and allied-country manufacturing, transformer supply, recycling, grid equipment, permitting capacity, and workforce development. Resilience means not swapping one bottleneck for another with a congratulatory press release attached. 

The signal of change…

The best evidence that something deeper is shifting is not rhetoric. It is investment and behaviour.

The IEA’s World Energy Investment 2025 says global energy investment is set to reach USD 3.3 trillion in 2025, with USD 2.2 trillion going to clean energy technologies and infrastructure, twice the USD 1.1 trillion going to fossil fuels. Consumers are moving too. The Guardian reports that Octopus has seen a 50% rise in solar sales and a 50% rise in heat pump sales since the latest Middle East conflict began, while March was the best month ever for EV sales in the UK. Again, that war did not create these trends. But it has made the old system’s weaknesses visible in a way that no white paper ever could. 

That brings me back to the fuel-price board. To the family budget. To the logistics operator. To the minister trying to explain why violence abroad still whips through bills at home.

We should not have needed this many warnings. We should have moved earlier and faster. But the clean energy transition is no longer resting on climate ethics alone. It is now being pulled forward by the hard logic of security, affordability and resilience as well. That does not make the current moment good. It makes it clarifying.

So the strongest version of the argument is not that the war on Iran is somehow good news for energy. It plainly is not. It is that every fossil fuel shock now makes the case for electrification harder to evade. Solar is booming. EVs are scaling. Batteries are getting cheaper. Investment is shifting. Emissions are still too high, but the forces that can bend them down are becoming bigger, cheaper and more strategic with every passing year.

That is not victory. But it is movement. Real movement. Movement in the right direction. And in energy, that matters.